Recent scientific publications by research field

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that predominantly affects the elderly population and currently has no cure. Magnetic Resonance Imaging (MRI), as a non-invasive imaging technique, is essential for the early diagnosis of AD. MRI inherently contains both spatial and frequency information, as raw signals are acquired in the frequency domain and reconstructed into spatial images via the Fourier transform. However, most existing AD diagnostic models extract features from a single domain, limiting their capacity to fully capture the complex neuroimaging characteristics of the disease. While some studies have combined spatial and frequency information, they are mostly confined to 2D MRI, leaving the potential of dual-domain analysis in 3D MRI unexplored. To overcome this limitation, we propose Spatio-Frequency Network (SFNet), the first end-to-end deep learning framework that simultaneously leverages spatial and frequency domain information to enhance 3D MRI-based AD diagnosis. SFNet integrates an enhanced dense convolutional network to extract local spatial features and a global frequency module to capture global frequency-domain representations. Additionally, a novel multi-scale attention module is proposed to further refine spatial feature extraction. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that SFNet outperforms existing baselines and reduces computational overhead in classifying cognitively normal (CN) and AD, achieving an accuracy of 95.1%.

Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup

Abstract The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step‐by‐step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. Highlights We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early‐changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later‐changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

A bibliometric and systematic review of the global impact of air pollution on Alzheimer's disease: Insights from cohort studies

BACKGROUND: Emerging evidence highlights the potential role of environmental factors, particularly air pollution, in the development and progression of Alzheimer's disease (AD). Air pollutants may contribute to neurodegenerative processes through mechanisms such as oxidative stress, neuroinflammation, and disruption of the blood–brain barrier. OBJECTIVE: This review aims to systematically evaluate global cohort studies investigating the association between long-term exposure to key air pollutants—specifically particulate matter (PM2.5), nitrogen dioxide (NO(2)), and ozone (O(3))—and the risk of AD. METHODS: A total of 31 peer-reviewed cohort studies were included based on a structured search strategy. We analyzed epidemiological outcomes, exposure assessment methodologies, and geographic trends. Additionally, a bibliometric analysis was conducted using VOSviewer to identify major contributors and emerging research themes. RESULTS: Findings indicate a consistent association between PM2.5 exposure and increased risk of AD, independent of genetic predisposition and lifestyle factors. Proposed biological mechanisms include oxidative stress, microglial activation, blood–brain barrier disruption, and amyloid-β accumulation. Bibliometric mapping revealed regional concentration of research in North America, Europe, and East Asia, with increasing global interest over the past decade. CONCLUSIONS: There is mounting evidence linking air pollution to Alzheimer's disease. Advances in exposure assessment have improved the accuracy of epidemiological findings. Public health policies targeting air quality control and further studies on molecular and early-life exposures are essential to mitigate the neurotoxic effects of environmental pollutants.

Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Classification

Magnetic Resonance Imaging (MRI) plays a pivotal role in the early diagnosis and monitoring of Alzheimer's disease (AD). However, the subtle structural variations in brain MRI scans often pose challenges for conventional deep learning models to extract discriminative features effectively. In this work, we propose PseudoColorViT-Alz, a colormap-enhanced Vision Transformer framework designed to leverage pseudo-color representations of MRI images for improved Alzheimer's disease classification. By combining colormap transformations with the global feature learning capabilities of Vision Transformers, our method amplifies anatomical texture and contrast cues that are otherwise subdued in standard grayscale MRI scans. We evaluate PseudoColorViT-Alz on the OASIS-1 dataset using a four-class classification setup (non-demented, moderate dementia, mild dementia, and very mild dementia). Our model achieves a state-of-the-art accuracy of 99.79% with an AUC of 100%, surpassing the performance of recent 2024--2025 methods, including CNN-based and Siamese-network approaches, which reported accuracies ranging from 96.1% to 99.68%. These results demonstrate that pseudo-color augmentation combined with Vision Transformers can significantly enhance MRI-based Alzheimer's disease classification. PseudoColorViT-Alz offers a robust and interpretable framework that outperforms current methods, providing a promising tool to support clinical decision-making and early detection of Alzheimer's disease. ;12 pages, 4 figures

Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder

Generative AI framework-based modeling and prediction of longitudinal human brain images offer an efficient mechanism to track neurodegenerative progression, essential for the assessment of diseases like Alzheimer's. Among the existing generative approaches, recent diffusion-based models have emerged as an effective alternative to generate disease progression images. Incorporating multi-modal and non-imaging attributes as conditional information into diffusion frameworks has been shown to improve controllability during such generations. However, existing methods do not explicitly ensure that information from non-imaging conditioning modalities is meaningfully aligned with image features to introduce desirable changes in the generated images, such as modulation of progression-specific regions. Further, more precise control over the generation process can be achieved by introducing progression-relevant structure into the internal representations of the model, lacking in the existing approaches. To address these limitations, we propose a diffusion autoencoder-based framework for disease progression modeling that explicitly enforces alignment between different modalities. The alignment is enforced by introducing an explicit objective function that enables the model to focus on the regions exhibiting progression-related changes. Further, we devise a mechanism to better structure the latent representational space of the diffusion auto-encoding framework. Specifically, we assign separate latent subspaces for integrating progression-related conditions and retaining subject-specific identity information, allowing better-controlled image generation. These results demonstrate that enforcing alignment and better structuring of the latent representational space of diffusion auto-encoding framework leads to more anatomically precise modeling of Alzheimer's disease progression.

Neuroprotection by chronic administration of Fluoroethylnormemantine (FENM) in mouse models of Alzheimer's disease

International audience; Background: Fluoroethylnormemantine (FENM), a new Memantine (MEM) derivative, prevented amyloid-β[25-35] peptide (Aβ25-35)-induced neurotoxicity in mice, a pharmacological model of Alzheimer's disease (AD) with high predictive value for drug discovery. Here, as drug infusion is likely to better reflect drug bioavailability due to the interspecies pharmacokinetics variation, we analyzed the efficacy of FENM after chronic subcutaneous (SC) infusion, in comparison with IP injections in two AD mouse models, Aβ25-35-injected mice and the transgenic APPswe/PSEN1∂E9 (APP/PS1) line.Methods: In Aβ25-35-treated mice, FENM was infused at 0.03-0.3 mg/kg/day during one week after Aβ25-35 injection. For comparison, FENM and MEM were administered IP daily at 0.03-0.3 mg/kg. In 10-month-old APP/PS1 mice, FENM was administered during four weeks by daily IP injections at 0.3 mg/kg or chronic SC infusion at 0.1 mg/kg/day. Memory deficits, spatial working memory and recognition memory, were analysed. Markers of neuroinflammation, apoptosis, oxidative stress, and amyloid burden in APP/PS1 mice, were quantified. Markers of synaptic plasticity such as PSD-95 and GluN2A/B/D subunits expression in hippocampus homogenates or synaptosomes were quantified in Aβ25-35-treated mice and synaptic long-term potentiation (LTP) in hippocampal slices was analysed in APP/PS1 mice.Results: Deficits in spontaneous alternation and object recognition in Aβ25-35 mice were prevented by infused FENM at all doses tested. Similar effects were observed with the daily FENM or MEM treatments. Animals infused with 0.1 mg/kg/day FENM showed prevention of Aβ25-35-induced neuroinflammation, oxidative stress and apoptosis. FENM infusion restored Aβ25-35-induced alterations in synaptosomal PSD-95, GluN2A and P-GluN2B levels. GluN2D levels were unchanged whatever the treatment. In APP/PS1 mice, FENM infused or administered IP alleviated spontaneous alternation deficits, neuroinflammation, increases in Aβ1-40/Aβ1-42 and hippocampal LTP alteration.Conclusion: These data confirmed the neuroprotective potential of FENM in the pharmacological Aβ25-35 and transgenic APP/PS1 mouse models of AD, with a superiority to MEM, and showed that the drug can be efficiently infused chronically.

MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility

Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasoning, and various external analytical tools. Current AI systems often rely on either task-specific models, which offer limited adaptability, or general language models without grounding with specialized external knowledge and tools. We introduce MedOrch, a novel framework that orchestrates multiple specialized tools and reasoning agents to provide comprehensive medical decision support. MedOrch employs a modular, agent-based architecture that facilitates the flexible integration of domain-specific tools without altering the core system. Furthermore, it ensures transparent and traceable reasoning processes, enabling clinicians to meticulously verify each intermediate step underlying the system's recommendations. We evaluate MedOrch across three distinct medical applications: Alzheimer's disease diagnosis, chest X-ray interpretation, and medical visual question answering, using authentic clinical datasets. The results demonstrate MedOrch's competitive performance across these diverse medical tasks. Notably, in Alzheimer's disease diagnosis, MedOrch achieves an accuracy of 93.26%, surpassing the state-of-the-art baseline by over four percentage points. For predicting Alzheimer's disease progression, it attains a 50.35% accuracy, marking a significant improvement. In chest X-ray analysis, MedOrch exhibits superior performance with a Macro AUC of 61.2% and a Macro F1-score of 25.5%. Moreover, in complex multimodal visual question answering (Image+Table), MedOrch achieves an accuracy of 54.47%. These findings underscore MedOrch's potential to advance healthcare AI by enabling reasoning-driven tool utilization for multimodal medical data processing and supporting intricate cognitive tasks in clinical decision-making.

3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification. ;5 tables, 6 figures, Submitted to International Conference on Power, Electronics, Communications, Computing, and Intelligent Infrastructure 2026

A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosis

Deep learning methods have significantly advanced medical image segmentation, yet their success hinges on large volumes of manually annotated data, which require specialized expertise for accurate labeling. Additionally, these methods often demand substantial computational resources, particularly for three-dimensional medical imaging tasks. Consequently, applying deep learning techniques for medical image segmentation with limited annotated data and computational resources remains a critical challenge. In this paper, we propose a novel parameter-efficient fine-tuning strategy, termed HyPS, which employs a hybrid parallel and serial architecture. HyPS updates a minimal subset of model parameters, thereby retaining the pre-trained model's original knowledge tructure while enhancing its ability to learn specific features relevant to downstream tasks. We apply this strategy to the state-of-the-art SwinUNETR model for medical image segmentation. Initially, the model is pre-trained on the BraTs2021 dataset, after which the HyPS method is employed to transfer it to three distinct hippocampus datasets.Extensive experiments demonstrate that HyPS outperforms baseline methods, especially in scenarios with limited training samples. Furthermore, based on the segmentation results, we calculated the hippocampal volumes of subjects from the ADNI dataset and combined these with metadata to classify disease types. In distinguishing Alzheimer's disease (AD) from cognitively normal (CN) individuals, as well as early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI), HyPS achieved classification accuracies of 83.78% and 64.29%, respectively. These findings indicate that the HyPS method not only facilitates effective hippocampal segmentation using pre-trained models but also holds potential for aiding Alzheimer's disease detection. Our code is publicly available.

Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech

Background: Alzheimer's disease and related dementias (ADRD) are progressive neurodegenerative conditions where early detection is vital for timely intervention and care. Spontaneous speech contains rich acoustic and linguistic markers that may serve as non-invasive biomarkers for cognitive decline. Foundation models, pre-trained on large-scale audio or text data, produce high-dimensional embeddings encoding contextual and acoustic features. Methods: We used the PREPARE Challenge dataset, which includes audio recordings from over 1,600 participants with three cognitive statuses: healthy control (HC), mild cognitive impairment (MCI), and Alzheimer's Disease (AD). We excluded non-English, non-spontaneous, or poor-quality recordings. The final dataset included 703 (59.13%) HC, 81 (6.81%) MCI, and 405 (34.06%) AD cases. We benchmarked a range of open-source foundation speech and language models to classify cognitive status into the three categories. Results: The Whisper-medium model achieved the highest performance among speech models (accuracy = 0.731, AUC = 0.802). Among language models, BERT with pause annotation performed best (accuracy = 0.662, AUC = 0.744). ADRD detection using state-of-the-art automatic speech recognition (ASR) model-generated audio embeddings outperformed others. Including non-semantic features like pause patterns consistently improved text-based classification. Conclusion: This study introduces a benchmarking framework using foundation models and a clinically relevant dataset. Acoustic-based approaches -- particularly ASR-derived embeddings -- demonstrate strong potential for scalable, non-invasive, and cost-effective early detection of ADRD.

AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the aging population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework that integrates both 2D Convolutional Neural Networks (2D-CNN) and 3D Convolutional Neural Networks (3D-CNN), along with a custom loss function and volumetric data augmentation, to enhance feature extraction and improve classification performance in AD diagnosis. According to extensive experiments, AlzhiNet outperforms standalone 2D and 3D models, highlighting the importance of combining these complementary representations of data. The depth and quality of 3D volumes derived from the augmented 2D slices also significantly influence the model's performance. The results indicate that carefully selecting weighting factors in hybrid predictions is imperative for achieving optimal results. Our framework has been validated on the Magnetic Resonance Imaging (MRI) from Kaggle and MIRIAD datasets, obtaining accuracies of 98.9% and 99.99%, respectively, with an AUC of 100%. Furthermore, AlzhiNet was studied under a variety of perturbation scenarios on the Alzheimer's Kaggle dataset, including Gaussian noise, brightness, contrast, salt and pepper noise, color jitter, and occlusion. The results obtained show that AlzhiNet is more robust to perturbations than ResNet-18, making it an excellent choice for real-world applications. This approach represents a promising advancement in the early diagnosis and treatment planning for Alzheimer's disease.

AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans

This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. At the same time, the importance of each slice in decision-making is learned, allowing the generation of a voxel-level attention map to produce an explainable MRI. To test our method and ensure the reproducibility of our results, we chose a standardized collection of MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). On this dataset, our method significantly outperforms state-of-the-art methods in (i) distinguishing AD from cognitive normal (CN) with an accuracy of 0.856 and Matthew's correlation coefficient (MCC) of 0.712, representing improvements of 2.4% and 5.3% respectively over the second-best, and (ii) in the prognostic task of discerning stable from progressive mild cognitive impairment (MCI) with an accuracy of 0.725 and MCC of 0.443, showing improvements of 10.2% and 20.5% respectively over the second-best. We achieved this prognostic result by adopting a double transfer learning strategy, which enhanced sensitivity to morphological changes and facilitated early-stage AD detection. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions: the hippocampus, the amygdala, the parahippocampal, and the inferior lateral ventricles. All these areas are clinically associated with AD development. Furthermore, our approach consistently found the same AD-related areas across different cross-validation folds, proving its robustness and precision in highlighting areas that align closely with known pathological markers of the disease. ;Comment: 21 pages, 9 figures, 9 tables

Potential link of high fat diet and mRNA expression of Alzheimer's disease-related genes in the enteric mucosa of a rat model of Alzheimer's disease

BACKGROUND: High-fat diet (HFD) consumption is linked to Alzheimer's disease (AD). Identifying changes in the mRNA expression due to the ingestion of HFD in the intestine—often called the second brain due to its dense enteric neurons—could offer insights into AD development and progression. OBJECTIVE: This study assesses whether the introduction of HFD at adult-age influence expression of AD-related genes in the intestines of Wild-type (WT) or the amyloid precursor protein/presenilin1 (APP/PS1)-overexpressing Transgenic (TG) rats. METHODS: Twelve-month-old WT and TG rats (male and female) were fed a control diet (CD; 8% energy from fat), or HFD (45% energy from fat) for six months. Ileal tissues were assessed for the mRNA expression of genes responsible for development/progression of AD. RESULTS: The WT HFD-fed rats (compared to CD-fed rats) showed increased mRNA expression of genes involved in the development of AD. In contrast, the TG HFD-fed female group, showed a higher number of upregulated genes compared to their respective CD-fed TG group. In TG HFD-fed rats there was higher mRNA expression of genes crucial for synaptic transmission such as Brain-derived neurotrophic factor in females and Choline acetyltransferase in males. Expression of Plasminogen was higher in HFD-fed TG female rats and HFD-fed WT male rats. Overall, the HFD-fed WT male showed mRNA expression of genes involved in the development of AD. However, HFD-fed TG females were more vulnerable for the progression of AD. It is likely that the enteric Plasminogen plays a major role in gut-brain axis for the development of AD in WT male, and progression of AD in TG female during the consumption of HFD. CONCLUSIONS: The consumption of HFD perturbed the expression of enteric genes known to be involved in amyloid-β generation, clearance, and degradation, in a sex-dependent manner.

Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models

Brain imaging has allowed neuroscientists to analyze brain morphology in genetic and neurodevelopmental disorders, such as Down syndrome, pinpointing regions of interest to unravel the neuroanatomical underpinnings of cognitive impairment and memory deficits. However, the connections between brain anatomy, cognitive performance and comorbidities like Alzheimer's disease are still poorly understood in the Down syndrome population. The latest advances in artificial intelligence constitute an opportunity for developing automatic tools to analyze large volumes of brain magnetic resonance imaging scans, overcoming the bottleneck of manual analysis. In this study, we propose the use of generative models for detecting brain alterations in people with Down syndrome affected by various degrees of neurodegeneration caused by Alzheimer's disease. To that end, we evaluate state-of-the-art brain anomaly detection models based on Variational Autoencoders and Diffusion Models, leveraging a proprietary dataset of brain magnetic resonance imaging scans. Following a comprehensive evaluation process, our study includes several key analyses. First, we conducted a qualitative evaluation by expert neuroradiologists. Second, we performed both quantitative and qualitative reconstruction fidelity studies for the generative models. Third, we carried out an ablation study to examine how the incorporation of histogram post-processing can enhance model performance. Finally, we executed a quantitative volumetric analysis of subcortical structures. Our findings indicate that some models effectively detect the primary alterations characterizing Down syndrome's brain anatomy, including a smaller cerebellum, enlarged ventricles, and cerebral cortex reduction, as well as the parietal lobe alterations caused by Alzheimer's disease.

AlzheimerRAG: Multimodal Retrieval Augmented Generation for Clinical Use Cases using PubMed articles

Recent advancements in generative AI have fostered the development of highly adept Large Language Models (LLMs) that integrate diverse data types to empower decision-making. Among these, multimodal retrieval-augmented generation (RAG) applications are promising because they combine the strengths of information retrieval and generative models, enhancing their utility across various domains, including clinical use cases. This paper introduces AlzheimerRAG, a Multimodal RAG application for clinical use cases, primarily focusing on Alzheimer's Disease case studies from PubMed articles. This application incorporates cross-modal attention fusion techniques to integrate textual and visual data processing by efficiently indexing and accessing vast amounts of biomedical literature. Our experimental results, compared to benchmarks such as BioASQ and PubMedQA, have yielded improved performance in the retrieval and synthesis of domain-specific information. We also present a case study using our multimodal RAG in various Alzheimer's clinical scenarios. We infer that AlzheimerRAG can generate responses with accuracy non-inferior to humans and with low rates of hallucination.

Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification

Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia. It leads to a decline in memory, reasoning ability, and behavior, especially in older people. The cause of Alzheimer's Disease is still under exploration and there is no all-inclusive theory that can explain the pathologies in each individual patient. Nevertheless, early intervention has been found to be effective in managing symptoms and slowing down the disease's progression. Recent research has utilized electroencephalography (EEG) data to identify biomarkers that distinguish Alzheimer's Disease patients from healthy individuals. Prior studies have used various machine learning methods, including deep learning and graph neural networks, to examine electroencephalography-based signals for identifying Alzheimer's Disease patients. In our research, we proposed a Flexible and Explainable Gated Graph Convolutional Network (GGCN) with Multi-Objective Tree-Structured Parzen Estimator (MOTPE) hyperparameter tuning. This provides a flexible solution that efficiently identifies the optimal number of GGCN blocks to achieve the optimized precision, specificity, and recall outcomes, as well as the optimized area under the Receiver Operating Characteristic (AUC). Our findings demonstrated a high efficacy with an over 0.9 Receiver Operating Characteristic score, alongside precision, specificity, and recall scores in distinguishing health control with Alzheimer's Disease patients in Moderate to Severe Dementia using the power spectrum density (PSD) of electroencephalography signals across various frequency bands. Moreover, our research enhanced the interpretability of the embedded adjacency matrices, revealing connectivity differences in frontal and parietal brain regions between Alzheimer's patients and healthy individuals.

Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines

MEG are non invasive neuroimaging techniques with excellent temporal and spatial resolution, crucial for studying brain function in dementia and Alzheimer Disease. They identify changes in brain activity at various Alzheimer stages, including preclinical and prodromal phases. MEG may detect pathological changes before clinical symptoms, offering potential biomarkers for intervention. This study evaluates classification techniques using MEG features to distinguish between healthy controls and mild cognitive impairment participants from the BioFIND study. We compare MEG based biomarkers with MRI based anatomical features, both independently and combined. We used 3 Tesla MRI and MEG data from 324 BioFIND participants;158 MCI and 166 HC. Analyses were performed using MATLAB with SPM12 and OSL toolboxes. Machine learning analyses, including 100 Monte Carlo replications of 10 fold cross validation, were conducted on sensor and source spaces. Combining MRI with MEG features achieved the best performance; 0.76 accuracy and AUC of 0.82 for GLMNET using LCMV source based MEG. MEG only analyses using LCMV and eLORETA also performed well, suggesting that combining uncorrected MEG with z-score-corrected MRI features is optimal. ;Comment: 28 pages, 9 figures, 3 tables, 19 supplimetary material

Four-Stage Alzheimer's Disease Classification from MRI Using Topological Feature Extraction, Feature Selection, and Ensemble Learning

Accurate and efficient classification of Alzheimer's disease (AD) severity from brain magnetic resonance imaging (MRI) remains a critical challenge, particularly when limited data and model interpretability are of concern. In this work, we propose TDA-Alz, a novel framework for four-stage Alzheimer's disease severity classification (non-demented, moderate dementia, mild, and very mild) using topological data analysis (TDA) and ensemble learning. Instead of relying on deep convolutional architectures or extensive data augmentation, our approach extracts topological descriptors that capture intrinsic structural patterns of brain MRI, followed by feature selection to retain the most discriminative topological features. These features are then classified using an ensemble learning strategy to achieve robust multiclass discrimination. Experiments conducted on the OASIS-1 MRI dataset demonstrate that the proposed method achieves an accuracy of 98.19% and an AUC of 99.75%, outperforming or matching state-of-the-art deep learning--based methods reported on OASIS and OASIS-derived datasets. Notably, the proposed framework does not require data augmentation, pretrained networks, or large-scale computational resources, making it computationally efficient and fast compared to deep neural network approaches. Furthermore, the use of topological descriptors provides greater interpretability, as the extracted features are directly linked to the underlying structural characteristics of brain MRI rather than opaque latent representations. These results indicate that TDA-Alz offers a powerful, lightweight, and interpretable alternative to deep learning models for MRI-based Alzheimer's disease severity classification, with strong potential for real-world clinical decision-support systems. ;15 pages, 7 figures

Multimodal Visual Surrogate Compression for Alzheimer's Disease Classification

High-dimensional structural MRI (sMRI) images are widely used for Alzheimer's Disease (AD) diagnosis. Most existing methods for sMRI representation learning rely on 3D architectures (e.g., 3D CNNs), slice-wise feature extraction with late aggregation, or apply training-free feature extractions using 2D foundation models (e.g., DINO). However, these three paradigms suffer from high computational cost, loss of cross-slice relations, and limited ability to extract discriminative features, respectively. To address these challenges, we propose Multimodal Visual Surrogate Compression (MVSC). It learns to compress and adapt large 3D sMRI volumes into compact 2D features, termed as visual surrogates, which are better aligned with frozen 2D foundation models to extract powerful representations for final AD classification. MVSC has two key components: a Volume Context Encoder that captures global cross-slice context under textual guidance, and an Adaptive Slice Fusion module that aggregates slice-level information in a text-enhanced, patch-wise manner. Extensive experiments on three large-scale Alzheimer's disease benchmarks demonstrate our MVSC performs favourably on both binary and multi-class classification tasks compared against state-of-the-art methods.

From allegory to conceptualization, hypothesis and finally evidences: Alzheimer’s dementia, Parkinson's disease "gut–brain axis" and their preclinical phenotype

Researchers are constantly trying to develop therapeutic targets in neurodegenerative disorders like Alzheimer's dementia and Parkinson's disease. Despite enormous endeavors, there are several unmet needs. Several contradictory pathophysiological basis of neurodegenerative disorders are considered to be one of the most important cause underpinning. "Gut–brain dysbiosis" has been considered as one of the most crucial link to explore. Contemporary researches have suggested similar pathophysiological mechanisms underpinning Alzheimer's dementia and Parkinson's disease. "Gut–brain dysbiosis" may be the missing thread connecting Alzheimer's dementia and Parkinson's disease prior to the expression of their overt clinical phenotype. Recognition of preclinical phenotype of Alzheimer's dementia and Parkinson's disease have much broader perspective as it will help in building robust therapeutics at the earliest. Authors herein critically analyze the pathophysiological basis of Alzheimer's dementia and Parkinson's disease in relationship with "Gut–brain dysbiosis" and also try to search the preclinical phenotype/s of Alzheimer's dementia and Parkinson's disease pivoting around the Freudian hypothesis.

Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification

Objectives: High classification accuracy of Alzheimer's disease (AD) from structural MRI has been achieved using deep neural networks, yet the specific image features contributing to these decisions remain unclear. In this study, the contributions of T1-weighted (T1w) gray-white matter texture, volumetric information, and preprocessing -- particularly skull-stripping -- were systematically assessed. Methods: A dataset of 990 matched T1w MRIs from AD patients and cognitively normal controls from the ADNI database were used. Preprocessing was varied through skull-stripping and intensity binarization to isolate texture and shape contributions. A 3D convolutional neural network was trained on each configuration, and classification performance was compared using exact McNemar tests with discrete Bonferroni-Holm correction. Feature relevance was analyzed using Layer-wise Relevance Propagation, image similarity metrics, and spectral clustering of relevance maps. Results: Despite substantial differences in image content, classification accuracy, sensitivity, and specificity remained stable across preprocessing conditions. Models trained on binarized images preserved performance, indicating minimal reliance on gray-white matter texture. Instead, volumetric features -- particularly brain contours introduced through skull-stripping -- were consistently used by the models. Conclusions: This behavior reflects a shortcut learning phenomenon, where preprocessing artifacts act as potentially unintended cues. The resulting Clever Hans effect emphasizes the critical importance of interpretability tools to reveal hidden biases and to ensure robust and trustworthy deep learning in medical imaging.

Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls

Alzheimer's disease (AD) is a neurodegenerative disorder marked by memory loss and cognitive decline, making early detection vital for timely intervention. However, early diagnosis is challenging due to the heterogeneous presentation of symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous brain activity and functional connectivity, which are known to be disrupted in AD and mild cognitive impairment (MCI). Traditional methods, such as Pearson's correlation, have been used to calculate association matrices, but these approaches often overlook the dynamic and non-stationary nature of brain activity. In this study, we introduce a novel method that integrates discrete wavelet transform (DWT) and graph theory to model the dynamic behavior of brain networks. Our approach captures the time-frequency representation of brain activity, allowing for a more nuanced analysis of the underlying network dynamics. Machine learning was employed to automate the discrimination of different stages of AD based on learned patterns from brain network at different frequency bands. We applied our method to a dataset of rs-fMRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, demonstrating its potential as an early diagnostic tool for AD and for monitoring disease progression. Our statistical analysis identifies specific brain regions and connections that are affected in AD and MCI, at different frequency bands, offering deeper insights into the disease's impact on brain function.

Plasma proteomics links brain and immune system aging with healthspan and longevity

Plasma proteins derived from specific organs can estimate organ age and mortality, but their sensitivity to environmental factors and their robustness in forecasting onset of organ diseases and mortality remain unclear. To address this gap, we estimate the biological age of 11 organs using plasma proteomics data (2,916 proteins) from 44,498 individuals in the UK Biobank. Organ age estimates were sensitive to lifestyle factors and medications and were associated with future onset (within 17 years' follow-up) of a range of diseases, including heart failure, chronic obstructive pulmonary disease, type 2 diabetes and Alzheimer's disease. Notably, having an especially aged brain posed a risk of Alzheimer's disease (hazard ratio (HR) = 3.1) that was similar to carrying one copy of APOE4, the strongest genetic risk factor for sporadic Alzheimer's disease, whereas a youthful brain (HR = 0.26) provided protection that was similar to carrying two copies of APOE2, independent of APOE genotype. Accrual of aged organs progressively increased mortality risk (2-4 aged organs, HR = 2.3; 5-7 aged organs, HR = 4.5; 8+ aged organs, HR = 8.3), whereas youthful brains and immune systems were uniquely associated with longevity (youthful brain, HR = 0.60 for mortality risk; youthful immune system, HR = 0.58; youthful both, HR = 0.44). Altogether, these findings support the use of plasma proteins for monitoring of organ health and point to the brain and immune systems as key targets for longevity interventions.

AD-CDO: A Lightweight Ontology for Representing Eligibility Criteria in Alzheimer's Disease Clinical Trials

Objective This study introduces the Alzheimer's Disease Common Data Element Ontology for Clinical Trials (AD-CDO), a lightweight, semantically enriched ontology designed to represent and standardize key eligibility criteria concepts in Alzheimer's disease (AD) clinical trials. Materials and Methods We extracted high-frequency concepts from more than 1,500 AD clinical trials on ClinicalTrials.gov and organized them into seven semantic categories: Disease, Medication, Diagnostic Test, Procedure, Social Determinants of Health, Rating Criteria, and Fertility. Each concept was annotated with standard biomedical vocabularies, including the UMLS, OMOP Standardized Vocabularies, DrugBank, NDC, and NLM VSAC value sets. To balance coverage and manageability, we applied the Jenks Natural Breaks method to identify an optimal set of representative concepts. Results The optimized AD-CDO achieved over 63% coverage of extracted trial concepts while maintaining interpretability and compactness. The ontology effectively captured the most frequent and clinically meaningful entities used in AD eligibility criteria. We demonstrated AD-CDO's practical utility through two use cases: (a) an ontology-driven trial simulation system for formal modeling and virtual execution of clinical trials, and (b) an entity normalization task mapping raw clinical text to ontology-aligned terms, enabling consistency and integration with EHR data. Discussion AD-CDO bridges the gap between broad biomedical ontologies and task-specific trial modeling needs. It supports multiple downstream applications, including phenotyping algorithm development, cohort identification, and structured data integration. Conclusion By harmonizing essential eligibility entities and aligning them with standardized vocabularies, AD-CDO provides a versatile foundation for ontology-driven AD clinical trial research.

A Multimodal Approach to Alzheimer's Diagnosis: Geometric Insights from Cube Copying and Cognitive Assessments

Early and accessible detection of Alzheimer's disease (AD) remains a critical clinical challenge, and cube-copying tasks offer a simple yet informative assessment of visuospatial function. This work proposes a multimodal framework that converts hand-drawn cube sketches into graph-structured representations capturing geometric and topological properties, and integrates these features with demographic information and neuropsychological test (NPT) scores for AD classification. Cube drawings are modeled as graphs with node features encoding spatial coordinates, local graphlet-based topology, and angular geometry, which are processed using graph neural networks and fused with age, education, and NPT features in a late-fusion model. Experimental results show that graph-based representations provide a strong unimodal baseline and substantially outperform pixel-based convolutional models, while multimodal integration further improves performance and robustness to class imbalance. SHAP-based interpretability analysis identifies specific graphlet motifs and geometric distortions as key predictors, closely aligning with clinical observations of disorganized cube drawings in AD. Together, these results establish graph-based analysis of cube copying as an interpretable, non-invasive, and scalable approach for Alzheimer's disease screening.

High serum uric acid levels are protective against cognitive impairment in amyotrophic lateral sclerosis

Background Uric acid (UA) has emerged as a factor that can modify cognitive function both in the general population and in people with neurodegenerative disorders. Since very few data are available concerning amyotrophic lateral sclerosis (ALS), we explored the correlation of UA levels and cognitive impairment in a large cohort of ALS patients. Methods We enrolled ALS patients consecutively seen at the Turin ALS expert center in the 2007–2018 period who underwent both cognitive/behavioral and UA evaluation at diagnosis. Patients were classified in 5 categories: normal cognition (ALS-CN), isolated cognitive impairment (ALSci), isolated behavioural impairment (ALSbi), cognitive and behavioural impairment (ALScbi), frontotemporal dementia (ALS-FTD). For this study, ALSci, ALSbi and ALScbi were merged as ALS with intermediate cognitive impairment (ALS-INT). Results Out of the 841 ALS patients, 422 had ALS-CN, 271 ALS-INT and 148 ALS-FTD. The mean values of UA were significantly different among the cognitive subgroups of patients, with the lowest values in the ALS-FTD (ALS-CN, 288.5 ± 78.0 (μmol/L; ALS-INT, 289.7 ± 75.5 μmol/L; ALS-FTD, 271.8 ± 74.9 μmol/L; p  = 0.046). The frequency of ALS-FTD was significantly higher in the 1st tertile of UA. Lower UA levels were independently associated with FTD (OR 1.32, 95% c.i. 1.01–1.43; p  = 0.038) in binary logistic regression. Conclusions We found that in ALS lower UA serum levels are correlated with reduced frequency of co-morbid FTD. Patients with intermediate cognitive impairment showed UA levels similar to ALS-CN but higher than ALS-FTD, implying that higher UA levels can prevent or delay cognitive function deterioration.

Inhibition of colony stimulating factor-1 receptor (CSF-1R) as a potential therapeutic strategy for neurodegenerative diseases: opportunities and challenges

Microglia are specialized dynamic immune cells in the central nervous system (CNS) that plays a crucial role in brain homeostasis and in disease states. Persistent neuroinflammation is considered a hallmark of many neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson's disease (PD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS) and primary progressive multiple sclerosis (MS). Colony stimulating factor 1-receptor (CSF-1R) is predominantly expressed on microglia and its expression is significantly increased in neurodegenerative diseases. Cumulative findings have indicated that CSF-1R inhibitors can have beneficial effects in preclinical neurodegenerative disease models. Research using CSF-1R inhibitors has now been extended into non-human primates and humans. This review article summarizes the most recent advances using CSF-1R inhibitors in different neurodegenerative conditions including AD, PD, HD, ALS and MS. Potential challenges for translating these findings into clinical practice are presented.

Cerebrospinal fluid and blood exosomes as biomarkers for amyotrophic lateral sclerosis; a systematic review

Background Amyotrophic lateral sclerosis (ALS) is a progressive and fatal motor neuron disease. Due to the limited knowledge about potential biomarkers that help in early diagnosis and monitoring disease progression, today’s diagnoses are based on ruling out other diseases, neurography, and electromyography examination, which takes a time-consuming procedure. Methods PubMed, ScienceDirect, and Web of Science were explored to extract articles published from January 2015 to June 2023. In the searching strategy following keywords were included; amyotrophic lateral sclerosis, biomarkers, cerebrospinal fluid, serum, and plama. Results A total number of 6 studies describing fluid-based exosomal biomarkers were included in this study. Aggregated proteins including SOD1, TDP-43, pTDP-43, and FUS could be detected in the microvesicles (MVs). Moreover, TDP-43 and NFL extracted from plasma exosomes could be used as prognostic biomarkers. Also, downregulated miR-27a-3p detected through exoEasy Maxi and exoQuick Kit in the plasma could be measured as a diagnostic biomarker. Eventually, the upregulated level of CORO1A could be used to monitor disease progression. Conclusion Based on the results, each biomarker alone is insufficient to evaluate ALS. CNS-derived exosomes contain multiple ALS-related biomarkers (SOD1, TDP-43, pTDP-43, FUS, and miRNAs) that are detectable in cerebrospinal fluid and blood is a proper alternation. Exosome detecting kits listed as exoEasy, ExoQuick, Exo-spin, ME kit, ExoQuick Plus, and Exo-Flow, are helpful to reach this purpose.

Increasing prevalence 2015–2019 of amyotrophic lateral sclerosis in Sardinia, Italy

Background While amyotrophic lateral sclerosis (ALS) incidence has increased during the last decades, structured evidence on increased prevalence is lacking. After reporting a significant yearly increase of ALS incidence over a 10-year period, we checked for increased prevalence in Southern Sardinia over a quinquennium. Methods ALS patients (El Escorial Criteria) recruited from the study area and followed at ALS Centre, University of Cagliari, were included. Prevalence was computed for January 1, 2015 and January 1, 2019 and was calculated for the overall ALS population as well as for tracheostomized and non-tracheostomized patients. Results We observed a non-significant trend for greater ALS prevalence in 2019 than in 2015 (18.31 per 100,000 vs. 15.26 per 100,000; rate ratio: 1.83, p  = 0.01). By contrast, a significantly raising 2015 to 2019 ALS prevalence was observed in tracheostomized patients. No significant difference could be detected in non-tracheostomized. Conclusions We provided the highest prevalence rate to date reported in the worldwide literature, and also showed a non-significant raising ALS prevalence in the Sardinian population over a quinquennium. The trend in raising ALS prevalence was likely due to extended survival due to invasive interventions.

Home-monitoring of vital capacity in people with a motor neuron disease

Background Home-monitoring of spirometry has the potential to improve care for patients with a motor neuron disease (MND) by enabling early detection of respiratory dysfunction and reducing travel burden. Our aim was to evaluate the validity and feasibility of home-monitoring vital capacity (VC) in patients with MND. Methods We included 33 patients with amyotrophic lateral sclerosis, progressive muscular atrophy or primary lateral sclerosis who completed a 12-week home-monitoring protocol, consisting of 4-weekly unsupervised home assessments of VC and a functional rating scale. At baseline, during a home visit, patients/caregivers were trained in performing a VC test, and the investigator performed a supervised VC test, which was repeated at final follow-up during a second home visit. Validity of the unsupervised VC tests was evaluated by the differences between supervised and unsupervised VC tests, and through Bland–Altman 95% limits-of-agreement. Feasibility was assessed by means of a survey of user-experiences. Results The 95% limits-of-agreement were [− 14.3; 11.7] %predicted VC, and 88% of unsupervised VC tests fell within 10%predicted of supervised VC. 88% of patients experienced VC testing as easy and not burdensome, however, 15% patients did not think their VC test was performed as well as in the clinic. 94% of patients would like home-monitoring of VC in MND care. Discussion Unsupervised VC testing at home, with prior face-to-face training, is a valid and time-efficient method for the remote monitoring of respiratory function, and well-accepted by patients with MND and their caregivers.

Covid-19 threat and coping: application of protection motivation theory to the pandemic experiences of people affected by amyotrophic lateral sclerosis

Background People with amyotrophic lateral sclerosis (ALS) are at high risk for severe outcomes from Covid-19 infection. Researchers exploring ALS and Covid-19 have focused primarily on system response and adaptation. Using Protection Motivation Theory, we investigated how people with ALS and family caregivers appraised and responded to Covid-19 threat, the ‘costs’ associated with pandemic response, and how health professionals and systems can better support people affected by ALS who are facing public health emergencies. Methods Data were drawn from the ‘ALS Talk Project,’ an asynchronous, moderated focus group study. Participants were recruited from regions across Canada. Seven groups met online over 14 weeks between January and July 2020. Fifty-three participants contributed to Covid-19 discussions. Data were qualitatively analyzed using directed content analysis and the constant-comparative approach. Results Participants learned about the Covid-19 pandemic from the media. They rapidly assessed their vulnerability and responded to Covid-19 threat by following recommendations from health authorities, information monitoring, and preparing for worst-case scenarios. Adopting protective behaviors had substantial response costs, including adaptations for medical care and home support workers, threatened access to advance care, and increased caregiver burden. Participants expressed need for ALS-specific, pandemic information from trusted health professionals and/or ALS health charities. Telemedicine introduced both conveniences and costs. Prior experience with ALS provided tools for coping with Covid-19. Threat and coping appraisal was a dynamic process involving ongoing vigilance and adaptation. Findings draw attention to the lack of emergency preparedness among participants and within health systems. Conclusions Clinicians should engage ALS patients and families in ongoing discussions about pandemic coping, strategies to mitigate response costs, care pathways in the event of Covid-19 infection, and changing information about Covid-19 variants and vaccines. Healthcare systems should incorporate flexible approaches for medical care, leveraging the benefits of telemedicine and facilitating in-person interaction as needed and where possible. Research is needed to identify strategies to mitigate response costs and to further explore the interaction between prior experience and coping. Further study is also needed to determine how communication about emergency preparedness might be effectively incorporated into clinical care for those with ALS and other medically vulnerable populations.

Impairment of oculomotor functions in patients with early to advanced amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) can result into an incomplete locked in state (iLIS), in which communication depends on eye tracking computer devices. Oculomotor function impairments in ALS have been reported, but there is little research, particularly with respect to patients in iLIS. In the present study, we compared reflexive and executive oculomotor function by means of an eye tracking test battery between three groups: advanced ALS patients in iLIS (n = 22), patients in early to middle ALS stages (n = 44) and healthy subjects (n = 32). Patients with ALS showed significant deteriorations in oculomotor functions, with stronger impairments in iLIS. More specifically, ALS patients produced visually guided prosaccades with longer latencies and more frequent hypometria compared to healthy subjects. Longest latencies were obtained in iLIS patients, with a stronger prolongation for vertical than for horizontal prosaccades. ALS patients made more antisaccade errors and generated antisaccades with longer latencies. Smooth pursuit was also impaired in ALS. In the earlier ALS stages, bulbar onset patients presented stronger antisaccade and smooth pursuit deficits than spinal onset patients. Our findings reveal a relevant deterioration of important oculomotor functions in ALS, which increases in iLIS. It includes impairments of reflexive eye movements to loss of executive inhibitory control, indicating a progressing pathological involvement of prefrontal, midbrain and brainstem areas. The assessment of oculomotor functions may therefore provide clinically relevant bio- and progression marker, particularly in advanced ALS.

Role of the nigrosome 1 absence as a biomarker in amyotrophic lateral sclerosis

Introduction The absence of nigrosome 1 on brain MRI and the hyperechogenicity of substantia nigra (SNh) by transcranial sonography are two useful biomarkers in the diagnosis of parkinsonisms. We aimed to evaluate the absence of nigrosome 1 in amyotrophic lateral sclerosis (ALS) and to address its meaning. Methods 136 ALS patients were recruited, including 16 progressive muscular atrophy (PMA) and 22 primary lateral sclerosis (PLS) patients. The SNh area was measured planimetrically by standard protocols. The nigrosome 1 status was qualitatively assessed by two blind evaluators in susceptibility weight images of 3T MRI. Demographic and clinical data were collected and the C9ORF72 expansion was tested in all patients. Results Nigrosome 1 was absent in 30% of ALS patients (36% of PLS, 29% of classical ALS and 19% of PMA patients). There was no relationship between radiological and clinical laterality, nor between nigrosome 1 and SNh area. Male sex (OR = 3.63 [1.51, 9.38], p  = 0.005) and a higher upper motor neuron (UMN) score (OR = 1.10 [1.02, 1.2], p  = 0.022) were independently associated to nigrosome 1 absence, which also was an independent marker of poor survival (HR = 1.79 [1.3, 2.8], p  = 0.013). Conclusion In ALS patients, the absence of nigrosome 1 is associated with male sex, UMN impairment and shorter survival. This suggests that constitutional factors and the degree of pyramidal involvement are related to the substantia nigra involvement in ALS. Thus, nigrosome 1 could be a marker of a multisystem degeneration, which in turn associates to poor prognosis.

Translation and reliability and validity of the Chinese version of Amyotrophic Lateral Sclerosis-Specific Quality of Life-Short Form

Objective To translate Amyotrophic Lateral Sclerosis-Specific Quality of Life-Short Form (ALSSQOL-SF) and test its reliability and validity, so that explore feasibility in Chinese mainland and make up the gap of specific tools for measuring quality of life of patients with ALS. Methods This was a cross-sectional design. The Brislin translation model was used to translate ALSSQOL-SF, and the Chinese version of ALSSQOL-SF (C-ALSSQOL-SF) was revised through cultural adaptation and pre-test. The convenience sampling method was used to investigate 138 patients with ALS in Tianjin to test the reliability and validity of the C-ALSSQOL-SF. Results The C-ALSSQOL-SF included 20 items, covering 6 dimensions: physical symptoms, bulbar function, negative emotion, interaction with people and the environment, religiosity and intimacy. The scale-level content validity index (S-CVI) of C-ALSSQOL-SF was 0.964, and the item-level content validity index (I-CVI) was between 0.857 to 1.000. The results of Confirmatory Factor Analysis (CFA) showed that CMIN/DF = 1.161, RMSEA = 0.034, GFI = 0.892, IFI = 0.976, TLI = 0.969, CFI = 0.975, and the 6-factor model fitted well. The scores of C-ALSSQOL-SF and WHOQOL-BREF were positively correlated (r = 0.745). The Cronbach’s α coefficient of the scale was 0.85, the Cronbach’s α coefficient of each dimension was between 0.59 to 0.86, and the split-half reliability was 0.78. Conclusion The Chinese version of ALSSQOL-SF has good reliability and validity, and can be used as a tool to evaluate the quality of life of patients with ALS in Chinese mainland.

SUMOylation Regulates TDP-43 Splicing Activity and Nucleocytoplasmic Distribution

The nuclear RNA-binding protein TDP-43 forms abnormal cytoplasmic aggregates in the brains of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) patients and several molecular mechanisms promoting TDP-43 cytoplasmic mislocalization and aggregation have been proposed, including defects in nucleocytoplasmic transport, stress granules (SG) disassembly and post-translational modifications (PTM). SUMOylation is a PTM which regulates a variety of cellular processes and, similarly to ubiquitination, targets lysine residues. To investigate the possible regulatory effects of SUMOylation on TDP-43 activity and trafficking, we first assessed that TDP-43 is SUMO-conjugated in the nuclear compartment both covalently and non-covalently in the RRM1 domain at the predicted lysine 136 and SUMO-interacting motif (SIM, 106–110 residues), respectively. By using the SUMO-mutant TDP-43 K136R protein, we demonstrated that SUMOylation modifies TDP-43 splicing activity, specifically exon skipping, and influences its sub-cellular localization and recruitment to SG after oxidative stress. When promoting deSUMOylation by SENP1 enzyme over-expression or by treatment with the cell-permeable SENP1 peptide TS-1, the cytoplasmic localization of TDP-43 increased, depending on its SUMOylation. Moreover, deSUMOylation by TS-1 peptide favoured the formation of small cytoplasmic aggregates of the C-terminal TDP-43 fragment p35, still containing the SUMO lysine target 136, but had no effect on the already formed p25 aggregates. Our data suggest that TDP-43 can be post-translationally modified by SUMOylation which may regulate its splicing function and trafficking, indicating a novel and druggable mechanism to explore as its dysregulation may lead to TDP-43 pathological aggregation in ALS and FTD.

Racial Disparities in the Diagnosis and Prognosis of ALS Patients in the United States

Background Amyotrophic lateral sclerosis (ALS) is a progressive, fatal disease with largely unknown etiology. This study compares racial differences in clinical characteristics of ALS patients enrolled in the National ALS Registry (Registry). Methods Data from ALS patients who completed the Registry’s online clinical survey during 2013–2022 were analyzed to determine characteristics such as site of onset, associated symptoms, time of symptom onset to diagnosis, and pharmacological and non-pharmacological interventions for White, Black, and other race patients. Results Surveys were completed by 4242 participants. Findings revealed that Black ALS patients were more likely to be diagnosed at a younger age, to have arm or hand initial site of onset, and to experience pneumonia than were White ALS patients. ALS patients of other races were more likely than White ALS patients to be diagnosed at a younger age and to experience twitching. The mean interval between the first sign of weakness and an ALS diagnosis for Black patients was almost 24 months, statistically greater than that of White ( p  = 0.0374; 16 months) and other race patients ( p  = 0.0518; 15.8 months). The mean interval between problems with speech until diagnosis was shorter for White patients (6.3 months) than for Black patients (17.7 months) and other race patients (14.8 months). Conclusions and Relevance Registry data shows racial disparities still exist in the diagnosis and clinical characteristics of ALS patients. Increased recruitment of non-White ALS patients and better characterization of symptom onset between races might aid clinicians in diagnosing ALS sooner, leading to earlier therapeutic interventions.

Estrogen Enhances Dendrite Spine Function and Recovers Deficits in Neuroplasticity in the prpTDP-43^A315T Mouse Model of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) attacks the corticomotor system, with motor cortex function affected early in disease. Younger females have a lower relative risk of succumbing to ALS than males and older females, implicating a role for female sex hormones in disease progression. However, the mechanisms driving this dimorphic incidence are still largely unknown. We endeavoured to determine if estrogen mitigates disease progression and pathogenesis, focussing upon the dendritic spine as a site of action. Using two-photon live imaging we identify, in the prp TDP-43^A315T mouse model of ALS, that dendritic spines in the male motor cortex have a reduced capacity for remodelling than their wild-type controls. In contrast, females show higher capacity for remodelling, with peak plasticity corresponding to highest estrogen levels during the estrous cycle. Estrogen manipulation through ovariectomies and estrogen replacement with 17β estradiol in vivo was found to significantly alter spine density and mitigate disease severity. Collectively, these findings reveal that synpatic plasticity is reduced in ALS, which can be amelioriated with estrogen, in conjuction with improved disease outcomes.

Analysis and occurrence of biallelic pathogenic repeat expansions in RFC1 in a German cohort of patients with a main clinical phenotype of motor neuron disease

Biallelic pathogenic repeat expansions in RFC1 were recently identified as molecular origin of cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS) as well as of one of the most common causes of adult-onset ataxia. In the meantime, the phenotypic spectrum has expanded massively and now includes mimics of multiple system atrophy or parkinsonism. After identifying a patient with a clinical diagnosis of amyotrophic lateral sclerosis (ALS) as a carrier of biallelic pathogenic repeat expansions in RFC1 , we studied a cohort of 106 additional patients with a clinical main phenotype of motor neuron disease (MND) to analyze whether such repeat expansions are more common in MND patients. Indeed, two additional MND patients (one also with ALS and one with primary lateral sclerosis/PLS) have been identified as carrier of biallelic pathogenic repeat expansions in RFC1 in the absence of another genetic alteration explaining the phenotype, suggesting motor neuron disease as another extreme phenotype of RFC1 spectrum disorder. Therefore, MND might belong to the expanding phenotypic spectrum of pathogenic RFC1 repeat expansions, particularly in those MND patients with additional features such as sensory and/or autonomic neuropathy, vestibular deficits, or cerebellar signs. By systematically analyzing the RFC1 repeat array using Oxford nanopore technology long-read sequencing, our study highlights the high intra- and interallelic heterogeneity of this locus and allows the identification of the novel repeat motif ‘ACAAG’.

Safety and efficacy of edaravone in patients with amyotrophic lateral sclerosis: a systematic review and meta-analysis

Aim The study aims to increase understanding of edaravone’s efficacy and safety as an amyotrophic lateral sclerosis (ALS) treatment and provide significant insights regarding this field’s future research. Methods We conducted a comprehensive search of the Embase, PubMed, Cochrane Library, Web of Science, and Scopus databases for randomized controlled trials and observational studies up until September 2022. We evaluated the studies’ quality using the Cochrane risk of bias tool and the National Institutes of Health tool. Results We included 11 studies with 2845 ALS patients. We found that edaravone improved the survival rate at 18, 24, and 30 months (risk ratio (RR) = 1.03, 95% confidence interval (CI) [1.02 to 1.24], P  = 0.02), (RR = 1.22, 95% CI [1.06 to 1.41], P  = 0.007), and (RR = 1.17, 95% CI [1.01 to 1.34], P  = 0.03), respectively. However, the administration of edaravone did not result in any significant difference in adverse effects or efficacy outcomes between the two groups, as indicated by a P value greater than 0.05. Conclusion Edaravone improves survival rates of ALS patients at 18, 24, and 30 months with no adverse effects. However, edaravone does not affect functional outcomes. In order to ensure the validity of our findings and assess the results in accordance with the disease stage, it is essential to carry out additional prospective, rigorous, and high-quality clinical trials. The current study offers preliminary indications regarding the effectiveness and safety of edaravone. However, further comprehensive research is required to establish the generalizability and sustainability of the findings.

Assessment of bidirectional relationships between 98 genera of the human gut microbiota and amyotrophic lateral sclerosis: a 2-sample Mendelian randomization study

Background Growing evidence suggests a mutual interaction between gut microbiome alterations and ALS pathogenesis. However, previous studies were susceptible to potential confounding factors and reverse causation bias, likely leading to inconsistent and biased results. Objectives To decipher the potentially mutual relationship between gut microbiota and ALS, we used a bidirectional two-sample MR approach to examine the associations between the gut microbiome and ALS. Results Using the inverse variance-weighted method, OTU10032 unclassified Enterobacteriaceae species-level OTU and unclassified Acidaminococcaceae were associated with a higher risk of ALS (per relative abundance: OR, 1.04; 95% CI, 1.01–1.07; P  = 0.011 and OR, 1.02; 95% CI, 1.01–1.04; P  = 0.009, respectively). Importantly, Gamma-Glu-Phe was showed potential deleterious effects on the risk of ALS (genetically predicted per a 1-standard deviation increase in the level of Gamma-Glu-Phe: OR, 1.96; 95% CI, 1.50–2.55; P  = 0.012). Sensitivity analysis of the two candidate genera and metabolites using the MR-Egger and weighted-median methods produced similar estimates, and no horizontal pleiotropy or outliers were observed. Intriguingly, genetically predicted ALS was associated with an increase in the relative abundance of OTU4607_Sutterella (per 1-unit higher log odds: β, 2.23; 95% CI, 1.27–3.18; P  = 0.020) and Lactobacillales_ORDER (per 1-unit higher log odds: β, 0.51; 95% CI, 0.09–0.94; P  = 0.019). Conclusions Our findings provide novel evidence supporting the bidirectional relationship between the gut microbiota and ALS. These results may contribute to designing microbiome- and microbiome-dependent metabolite interventions in future ALS clinical trials.

Sensory neuropathy in amyotrophic lateral sclerosis: a systematic review

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the degeneration of both upper and lower motoneurons, leading to motor and non-motor symptoms. Recent evidence suggests that ALS is indeed a multisystem disorder, associated with cognitive impairment, dysautonomia, pain and fatigue, excess of secretions, and sensory symptoms. To evaluate whether sensory neuropathy could broaden its spectrum, we systematically reviewed its presence and characteristics in ALS, extracting data on epidemiological, clinical, neurophysiological, neuropathological, and genetic features. Sensory neuropathy can be found in up to 20% of ALS patients, affecting both large and small fibers, although there is a great heterogeneity related to different techniques used for its detection (electromyography vs skin biopsy vs nerve biopsy). Moreover, the association between CIDP-like neuropathy and ALS needs to be better explored, although it could be interpreted as part of the neuroinflammatory process in the latter disease. Sensory neuropathy in ALS may be associated with a spinal onset and might be more frequent in SOD1 patients. Moreover, it seems mutually exclusive with cognitive impairment. No associations with sex and other genetic mutation were observed. All these data in the literature reveal the importance of actively looking for sensory neuropathy in ALS patients, and suggest including sensory neuropathy among ALS non-motor features, as it may explain sensory symptoms frequently reported throughout the course of the disease. Its early identification could help avoid diagnostic delays and improve patients’ treatment and quality of life.

Health-related quality of life across disease stages in patients with amyotrophic lateral sclerosis: results from a real-world survey

Background Amyotrophic lateral sclerosis (ALS) is characterized by a rapid disease course, with disease severity being associated with declining health-related quality of life (HRQoL) in persons living with ALS (pALS). The main objective of this study was to assess the impact of disease progression on HRQoL across King’s, Milano-Torino Staging (MiToS), and physician-judgement clinical staging. Additionally, we evaluated the impact of the disease on the HRQoL of care partners (cALS). Methods Data were sourced from the Adelphi ALS Disease Specific Programme (DSP)™, a cross-sectional survey of neurologists, pALS and cALS presenting in a real-world clinical setting between July 2020 and March 2021 in Europe and the United States. Results Neurologists ( n  = 142) provided data for 880 pALS. There were significant negative correlations between all three clinical staging systems and EuroQol (European Quality of Life) Five Dimension Five Level Scale (EQ-5D-5L) utility scores and visual analogue scale (VAS) ratings. Although not all differences were significant, 5-item Amyotrophic Lateral Sclerosis Assessment Questionnaire (ALSAQ-5) scores showed a stepwise increase in HRQoL impairment at each stage of the disease regardless of the staging system. At later stages, high levels of fatigue and substantial activity impairment were reported. As pALS disease states progressed, cALS also experienced a decline in HRQoL and increased burden. Conclusions Across outcomes, pALS and cALS generally reported worse outcomes at later stages of the disease, highlighting an unmet need in this population for strategies to maximise QoL despite disease progression. Recognition and treatment of symptoms such as pain and fatigue may lead to improved outcomes for pALS and cALS.

Clinical and epidemiological characteristics of amyotrophic lateral sclerosis in an Egyptian cohort

Objective Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder associated with progressive loss of motor neurons. It is a growing and underestimated disease, prompting this epidemiological study to describe the characteristics of ALS in Egyptian patients. Methods This is a prospective hospital based study. ALS patients were recruited consecutively from Neuromuscular Unit in Ain Shams university Hospital from December 2018 to June 2023. Demographic data and disease related parameters were recorded. Results 203 ALS patients had a mean age of onset equal 39 years and an inter quartile range IQR of (28.00–51.00). 76% of the cases were spinal onset ALS. Median disease duration was 2 years with IQR of (1–4 years); male to female ratio was 2.5:1; 18% of patients were familial ALS (FALS), while 19% were Juvenile ALS (JALS). Median diagnostic delay was 12 ± (6–36) months. Median Amyotrophic Lateral Sclerosis Functional Rating Scale Revised scores (ALSFRS-R) at presentation was 34.5 IQR of (26.00–40.00). Also, the mean rate of disease progression ALSFRS-R decline [points/month] was 0.76 ± 0.51. Conclusion Our cohort was characterized by a younger age of onset, male predominance, more familial cases, within average Initial ALSFRS-R scores as well as diagnostic delay. Juvenile ALS patients were much more common in our population. These findings suggest an influential presence of genetic and epigenetic factors affecting the clinical phenotype of Egyptian ALS patients.

Nonpharmacological intervention therapies for dementia: potential break-even intervention price and savings for selected risk factors in the European healthcare system

Background New effective treatments for dementia are lacking, and early prevention focusing on risk factors of dementia is important. Non-pharmacological intervention therapies aimed at these factors may provide a valuable tool for reducing the incidence of dementia. This study focused on the development of a mathematical model to predict the number of individuals with neurodegenerative diseases, specifically Alzheimer’s disease, Parkinson’s disease, vascular dementia, and amyotrophic lateral sclerosis. Scenarios for non-pharmacological intervention therapies based on risk factor reduction were also assessed. The estimated total costs and potential cost savings from societal were included. Methods Based on demographic and financial data from the EU, a mathematical model was developed to predict the prevalence and resulting care costs of neurodegenerative diseases in the population. Each disease (Alzheimer’s disease, Parkinson’s disease, vascular dementia, and amyotrophic lateral sclerosis) used parameters that included prevalence, incidence, and death risk ratio, and the simulation is related to the age of the cohort and the disease stage. Results A replicable simulation for predicting the prevalence and resulting cost of care for neurodegenerative diseases in the population exhibited an increase in treatment costs from 267 billion EUR in 2021 to 528 billion EUR by 2050 in the EU alone. Scenarios related to the reduction of the prevalence of dementia by up to 20% per decade led to total discounted treatment cost savings of up to 558 billion EUR. Conclusion The model indicates the magnitude of the financial burden placed on EU healthcare systems due to the growth in the population prevalence of neurodegenerative diseases in the coming decades. Lifestyle interventions based on reducing the most common risk factors could serve as a prevention strategy to reduce the incidence of dementia with substantial cost-savings potential. These findings could support the implementation of public health approaches throughout life to ultimately prevent premature mortality and promote a healthier and more active lifestyle in older individuals.

Use of Pharyngeal High-Resolution Manometry to Evaluate Dysphagia in Adults with Motor Neurone Disease: A Scoping Review

There has been a recent shift towards proactive dysphagia intervention in motor neurone disease (MND) to maintain physiological reserve. Pharyngeal high-resolution manometry (PHRM) can quantify swallowing pathophysiology to inform and evaluate proactive dysphagia intervention. This study aims to explore the current use of PHRM as a dysphagia evaluation in adults with MND. A scoping review based on the Joanna Briggs Framework was completed. Four electronic databases (PubMed, EMBASE, CINAHL and Web of Science core) were searched (inception to March 2021) by two independent researchers. Data were analysed according to (i) PHRM protocol and analysis methods and the feasibility of same, (ii) swallow biomechanics data and (iii) dysphagia intervention effects as measured by PHRM. Six studies with 78 people with MND (PwMND) were included. There was considerable variation in PHRM protocol and analysis methods. Five studies reported a 100% completion rate and three studies reported no adverse events. Swallow biomechanics data were reported across all studies. The effects of sensory stimulation, increased bolus consistency, effortful swallow and cricopharyngeal myotomy were evaluated using PHRM with 20 PwMND across four studies with varying effects. Literature on the use of PHRM in PwMND is limited. Variability in PHRM methods restricts comparison of metrics. PHRM appears to be a feasible tool for PwMND. PHRM can provide novel swallow physiology data in PwMND and quantify discrete effects of compensatory and surgical dysphagia interventions not detectable by videofluoroscopy or FEES. Further research on the effects of proactive dysphagia intervention as measured by PHRM is required.

Efficacy and safety of Lenzumestrocel (Neuronata-R® inj.) in patients with amyotrophic lateral sclerosis (ALSUMMIT study): study protocol for a multicentre, randomized, double-blind, parallel-group, sham procedure-controlled, phase III trial

Background A single cycle (two repeated treatments) with intrathecal autologous bone marrow-derived mesenchymal stem cells (BM-MSCs, 26-day interval) showed safety and provided therapeutic benefit lasting 6 months in patients with ALS but did not demonstrate long-term efficacy. This phase III clinical trial (ALSUMMIT) protocol was developed to evaluate the long-term efficacy and safety of the combined protocol of single-cycle intrathecal therapy and three additional booster injections of BM-MSC (Lenzumestrocel) treatment in patients with ALS. Methods ALSUMMIT is a multicentre, randomized, double-blind, parallel-group, sham procedure-controlled, phase III trial for ALS. The 115 subjects will be randomized (1:2:2) into three groups: (1) study Group 1 (single-cycle, two repeated injections with 26-day interval), (2) study Group 2 (single-cycle + three additional booster injections at 4, 7, and 10 months), and (3) the control group. Participants who have an intermediate rate of disease progression will be included in this trial to reduce clinical heterogeneity. The primary endpoint will be evaluated by combined assessment of function and survival (CAFS), also known as joint rank scores (JRS), at 6 months (study Group 1 vs. control) and 12 months (study Group 2 vs. control) after the first Lenzumestrocel or placebo administration. Safety assessment will be performed throughout the study period. Additionally, after the 56-week main study, a long-term follow-up observational study will be conducted to evaluate the long-term efficacy and safety up to 36 months. Discussion Lenzumestrocel is the orphan cell therapy product for ALS conditionally approved by the South Korea Ministry of Food and Drug Safety (MFDS). This ALSUMMIT protocol was developed for the adoption of enrichment enrolment, add-on design, and consideration of ethical issues for the placebo group. Trial registration ClinicalTrials.gov NCT04745299 . Registered on Feb 9, 2021. Clinical Research Information Service (CRIS) KCT0005954 . Registered on Mar 4, 2021.

Mutation spectrum of chinese amyotrophic lateral sclerosis patients with frontotemporal dementia

Background Studies have reported that a noncoding hexanucleotide repeat in C9ORF72 , is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) among Caucasian population, nevertheless it is rare in Chinese population. Therefore, we aimed to investigate the mutation spectrum of Chinese ALS patients with FTD (ALS-FTD). Methods ALS patients with and without cognitive impairments were enrolled. Clinical features were collected including age, sex, disease duration, ALSFRS-r, family history and cognitive evaluation. Thirty-six ALS genes were screened by whole exome sequencing (WES) and repeat-primed polymerase chain reaction (PCR) were used for detection of and abnormal repeat expansions of C9ORF72 . Results A total of 1208 patients, including 66 familial ALS (FALS) and 1142 sporadic ALS (SALS) patients were included. Twenty-three patients with sporadic ALS and one familial ALS index had concomitant FTD, which accounts for 1.99% (24/1208) of patients with ALS. In sporadic ALS-FTD, one case harboring C9ORF72 expansion variant, two cases harboring ANXA11 variants and one individual carrying CCNF variant were identified. A recurrent UBQLN2 variant was detected in a familial ALS-FTD patient. All of the ALS-FTD patients carrying variants in known causative genes manifested motor symptom onset (two bulbar onset and three limb onset) and developed cognitive impairment thereafter. It is not easy to draw a conclusion of the genotype-phenotype association in ALS-FTD with certain variants, limited by the small number of patients. Conclusion Our findings provide an overview of spectrum of genetic variants in Chinese ALS-FTD patients. Variants of uncertain significance in UBQLN2, ANXA11 and CCNF were identified and further studies are required for causal relations of these variants with ALS-FTD.

Towards clinical application of implantable brain–computer interfaces for people with late-stage ALS: medical and ethical considerations

Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain–computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.

Seeding activity of human superoxide dismutase 1 aggregates in familial and sporadic amyotrophic lateral sclerosis postmortem neural tissues by real-time quaking-induced conversion

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease with average lifespan of 2–5 years after diagnosis. The identification of novel prognostic and pharmacodynamic biomarkers are needed to facilitate therapeutic development. Metalloprotein human superoxide dismutase 1 (SOD1) is known to accumulate and form aggregates in patient neural tissue with familial ALS linked to mutations in their SOD1 gene. Aggregates of SOD1 have also been detected in other forms of ALS, including the sporadic form and the most common familial form linked to abnormal hexanucleotide repeat expansions in the Chromosome 9 open reading frame 72 ( C9ORF72 ) gene. Here, we report the development of a real-time quaking-induced conversion (RT-QuIC) seed amplification assay using a recombinant human SOD1 substrate to measure SOD1 seeding activity in postmortem spinal cord and motor cortex tissue from persons with different ALS etiologies. Our SOD1 RT-QuIC assay detected SOD1 seeds in motor cortex and spinal cord dilutions down to 10^–5. Importantly, we detected SOD1 seeding activity in specimens from both sporadic and familial ALS cases, with the latter having mutations in either their SOD1 or C9ORF72 genes. Analyses of RT-QuIC parameters indicated similar lag phases in spinal cords of sporadic and familial ALS patients, but higher ThT fluorescence maxima by SOD1 familial ALS specimens and sporadic ALS thoracic cord specimens. For a subset of sporadic ALS patients, motor cortex and spinal cords were examined, with seeding activity in both anatomical regions. Our results suggest SOD1 seeds are in ALS patient neural tissues not linked to SOD1 mutation, suggesting that SOD1 seeding activity may be a promising biomarker, particularly in sporadic ALS cases for whom genetic testing is uninformative.

Bridging knowledge gaps: a bibliometric analysis of non-invasive ventilation in palliative care studies

Background Despite being a useful strategy for providing respiratory support to patients with advanced or terminal illnesses, non-invasive ventilation (NIV) requires in-depth investigation in several key aspects. Objectives This bibliometric analysis seeks to comprehensively examine the existing research on the subject. Its goal is to uncover valuable insights that can inform the prediction trajectory of studies, guide the implementation of corrective measures, and contribute to the improvement of research networks. Methods A comprehensive review of literature on NIV in the context of palliative care was conducted using the Web of Science core collection online database. The search utilized the key terms “non-invasive ventilation” and “palliative care” to identify the most relevant articles. All data were gathered on November 7, 2023. Relevant information from documents meeting the specified criteria was extracted, and Journal Citation Reports™ 2022 (Clarivate Analytics) served as the data source. The analysis employed literature analysis and knowledge visualization tools, specifically CiteScope (version 6.2.R4) and VOSviewer (version 1.6.20). Results A dataset with bibliometric findings from 192 items was analyzed. We found a consistent upward of the scientific output trend over time. Guidelines on amyotrophic lateral sclerosis management received the highest number of citations. Most documents were published in top-ranked journals. Less than one-third of the documents pertain to clinical studies, especially retrospective analyses (25%). Key topics such as “decision making”, and “communication” were less addressed. Conclusions Given the substantial clinical implications, further high-quality studies on this subject are recommended. Encouraging international collaborations is needed. Despite the growing volume of documents in the field, this bibliometric analysis indicates a decline in collaborative networks.

Super-enhancer profiling reveals ThPOK/ZBTB7B, a CD4 (+) cell lineage commitment factor, as a master regulator that restricts breast cancer cells to a luminal non-migratory phenotype

Despite efforts to understand breast cancer biology, metastatic disease remains a clinical challenge. Identifying suppressors of breast cancer progression and mechanisms of transition to more invasive phenotypes could provide game changing therapeutic opportunities. Transcriptional deregulation is central to all malignancies, highlighted by the extensive reprogramming of regulatory elements that underlie oncogenic programs. Among these, super-enhancers (SEs) stand out due to their enrichment in genes controlling cancer hallmarks. To reveal novel breast cancer dependencies, we integrated the analysis of the SE landscape with master regulator activity inference for a series of breast cancer cell lines. As a result, we identified T - h elper-inducing Poxviruses and Zinc-finger ( PO Z)/ K rüppel-like factor (ThPOK, ZBTB7B ), a CD4 (+) cell lineage commitment factor, as a breast cancer master regulator that is recurrently associated with a SE. ThPOK expression is highest in luminal breast cancer but is significantly reduced in the basal subtype. Manipulation of ThPOK levels in cell lines shows that its repressive function restricts breast cancer cells to an epithelial phenotype by suppressing the expression of genes involved in the epithelial-mesenchymal transition (EMT), WNT/β-catenin target genes, and the pro-metastatic TGFβ pathway. Our study reveals ThPOK as a master transcription factor that restricts the acquisition of metastatic features in breast cancer cells.

Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework

Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all cohorts. To address this, we propose a personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for each patient by combining cohort-specific knowledge with modern retrieval and reasoning techniques. Given a patient's CT scan and structured metadata -- including demographic, clinical, and nodule-level features -- the agent first performs cohort retrieval using FAISS-based similarity search across nine diverse real-world cohorts to identify the most relevant patient population from a multi-institutional database. Second, a Large Language Model (LLM) is prompted with the retrieved cohort and its associated performance metrics to recommend the optimal prediction algorithm from a pool of eight representative models, including classical linear risk models (e.g., Mayo, Brock), temporally-aware models (e.g., TD-VIT, DLSTM), and multi-modal computer vision-based approaches (e.g., Liao, Sybil, DLS, DLI). This two-stage agent pipeline -- retrieval via FAISS and reasoning via LLM -- enables dynamic, cohort-aware risk prediction personalized to each patient's profile. Building on this architecture, the agent supports flexible and cohort-driven model selection across diverse clinical populations, offering a practical path toward individualized risk assessment in real-world lung cancer screening.

LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction

Second primary cancer (SPC), a new cancer in patients different from previously diagnosed, is a growing concern due to improved cancer survival rates. Early prediction of SPC is essential to enable timely clinical interventions. This study focuses on lung cancer survivors treated in Taiwanese hospitals, where the limited size and geographic scope of local datasets restrict the effectiveness and generalizability of traditional machine learning approaches. To address this, we incorporate external data from the publicly available US-based Surveillance, Epidemiology, and End Results (SEER) program, significantly increasing data diversity and scale. However, the integration of multi-source datasets presents challenges such as feature inconsistency and privacy constraints. Rather than naively merging data, we proposed a loss fusion horizontal federated learning (LF2L) framework that can enable effective cross-institutional collaboration while preserving institutional privacy by avoiding data sharing. Using both common and unique features and balancing their contributions through a shared loss mechanism, our method demonstrates substantial improvements in the prediction performance of SPC. Experiment results show statistically significant improvements in AUROC and AUPRC when compared to localized, horizontal federated, and centralized learning baselines. This highlights the importance of not only acquiring external data but also leveraging it effectively to enhance model performance in real-world clinical model development.

Survival outcomes and healthcare utilization between immigrant patients and Danish-born patients with hematological cancers: a Danish population-based study

Overall survival (OS) for patients with a hematological cancer may differ between immigrant and Danish-born patients due to disparities in socioeconomic status, health literacy, and language proficiency. This cohort study aimed to investigate survival and hospitalization according to immigrant status while controlling for confounders. Patients with newly diagnosed hematological cancer in 2000–2020 were identified in the Danish nationwide hematological registers and stratified into Danish-born, Western, and non-Western patients. Patients were followed from diagnosis until death, 31st December 2021, or emigration, whichever came first. Crude OS, standardized OS, and 5-years OS differences were computed using flexible parametric models and hazard ratios using Cox regression. Number of hospitalization days in the year before and after diagnosis, respectively, were calculated using Poisson regression. A total of 2,241 immigrants and 41,519 Danish-born patients with a hematological cancer were included. Standardized 5-years OS was similar between groups with 58% (95% confidence interval 57–58%) for Danish-born patients, 57% (55–60%) for Western, and 56% (53–58%) for non-Western immigrant patients. Subgroup analyses identified OS differences in selected subgroups. Non-Western immigrant patients had 1.3 (0.5–2.1) more hospitalization days in the year before diagnosis and an adjusted incidence rate ratio of hospitalization days of 1.14 (1.13–1.15) in the year after diagnosis compared with Danish-born patients. In conclusion, there were no overall differences in survival when comparing immigrant patients to Danish-born patients after controlling for relevant confounders. Healthcare utilization was slightly higher among non-Western immigrant patients before and after diagnosis, but differences were small on an individual patient level.

Modeling the role of urokinase plasminogen activator, uPA, and circulating Cancer-Associated Fibroblasts (cCAFS) in breast cancer cell extravasation

Circulating Cancer-Associated Fibroblasts (cCAFs) have been discovered in circulating tumor cell clusters from all stages of disease progression in breast cancer patients. We have shown that CAFs promote lung metastases in the mouse tail vein model when they are clustered with triple negative breast cancer (TNBC) MDA-MB231 cells. Following on this observation, we saw that MDA-MB231-luciferase labeled cells persist at higher levels when present in CAF23/MDA-MB231 co-clusters compared to MDA-MB231 mono-clusters within the first 3 days after tail vein injection. This prompted us to investigate whether CAFs aid cancer cell extravasation from capillary venules into the lung parenchyma, which would impart better survival and faster seeding of metastases. Ex vivo lung extravasation assays showed that within the first 8-24 hrs after tail vein injection, more cells from CAF23/MDA-MB231 co-clusters extravasated than cells from MDA-MB231 mono-clusters. Using in vitro endothelial binding assays, we determined that CAF/TNBC co-clusters bind to HUVEC endothelial cells better than TNBC mono-clusters. Single Cell RNA-seq identified several genes in the fibrinolysis pathway whose expression increases in TNBC cells when they are clustered with CAFs. One of these genes is PLAU , which encodes the urokinase-type plasminogen activator, uPA. siRNA knockdown of PLAU decreased in vitro TNBC-endothelial cell interactions and ex vivo extravasation of MDA-MB231 mono-clusters, revealing a role for uPA/PLAU in breast cancer cell extravasation. Our data helps to define the role of CAFs in breast cancer extravasation and highlights the importance of our previous work showing that CAFs promote tumor cell dissemination and metastasis.

The Path to Cancer Cure

This work proposes a unifying theoretical framework based on the principle of Dynamic and Creative Persistence (DCP), which reconceptualizes life, and cancer, as an adaptive interplay of bioenergy, bioinformation, and biostructure. Within this lens, cancer is not merely a condition caused by genetic alterations, but a cyclical, self-organizing system that co-evolves with its environment. Its survival depends on internal coherence and the ability to reshape its ecological niche. The breakdown of organismal homeodynamics, particularly the disruption of the internal body-climate, is viewed as a critical precursor to oncogenesis. This model advocates for early interception, focusing on preclinical metabolomic shifts, systemic vulnerabilities, and niche formation. Integration of multi-omics, liquid biopsy, and AI-guided analysis enables highly individualized, anticipatory diagnostics. Preventive interventions, such as exercise, circadian rhythm optimization, diet modulation, and psychophysiological stress reduction, are proposed as systemic recalibrators, aiming to restore homeodynamic balance and enhance resilience before malignancy becomes entrenched. Therapeutically, the homeodynamic model calls for a shift from a paradigm of cellular destruction to one of dynamic rebalancing, viewing cancer treatment not as eradication but as modulation of the disease’s generative processes. Beyond molecular targets, the framework expands to include metabolic nodes (e.g., acetyl-CoA), phenotypic transitions, exosomal communication, splicing regulation, and proteostasis control. As precision oncology reaches limitations, particularly in metastatic disease, combining systemic interventions, AI-orchestrated personalization, and organism-level therapies represent a promising frontier. Cancer treatment becomes not a war, but an intelligent, adaptive stewardship of life’s homeodynamic balance.

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Supporting cardiomyopathy screening behavior in adult survivors of childhood cancer: an eHealth motivational interviewing-framed pilot intervention

Purpose The Healthy Hearts pilot study evaluated the effect of an eHealth motivational interviewing-framed intervention on cardiomyopathy screening-related knowledge, health beliefs, intrinsic motivation, and behavioral action steps among adult survivors of childhood cancer. Methods We consented N  = 73 survivors to participate in a single-arm pilot study. Participants completed an online baseline survey ( n  = 68) assessing knowledge, health beliefs, and intrinsic motivation related to cancer therapy-induced cardiomyopathy and screening echocardiograms. Then, they engaged in two sessions with a fully automated web-based health intervention platform based on motivational interviewing techniques to build motivation. Participants completed an online post-intervention survey ( n  = 61) re-assessing knowledge, health beliefs, and intrinsic motivation and a 1-month follow-up survey ( n  = 59) assessing actions taken toward obtaining an echocardiogram and intervention satisfaction. Results Participant age averaged 40.5 years ( SD  = 7.2). Most self-identified as women ( n  = 42) and non-Hispanic white ( n  = 62). Paired t -tests revealed statistically significant pre-post changes in several health beliefs and elements of intrinsic motivation. Specifically, we observed decreases in perceived barriers ( p  < .001) and increases in knowledge ( p  < .001), perceived benefits ( p  < .001), self-efficacy ( p  < .001), perceived importance ( p  = .001), confidence ( p  = .02), and intentions ( p  < .001). By 1-month follow-up, 38.6% of participants had engaged in behavioral action steps (e.g., set echocardiogram appointment). Intervention satisfaction was moderately high (M = 3.9, SD = 0.9, median = 4, min = 2, max = 5). Conclusions Results provide preliminary indications of efficacy of the eHealth motivational interviewing-framed intervention on uptake of screening echocardiograms among adult survivors of childhood cancer. Implications for Cancer Survivors An eHealth motivational interviewing-framed intervention could increase childhood cancer survivors’ ability to receive high-quality support for engaging in cardiomyopathy screening.

Risk prediction models for depression in older adults with cancer

Background Depression in people with cancer is common and debilitating, but few instruments exist for early identification. We aimed at developing streamlined Risk Prediction Models (Arturo RPMs) for identifying individuals at higher risk for depression. Methods Predictors of depression were identified from a review of available literature. Then, we used data from the Survey of Health, Ageing and Retirement in Europe (SHARE) prospective study. SHARE recruited community residing adults aged 55 years or older, from which we selected those who reported having received a diagnosis of cancer. The outcome was the presence of depression (EURO-D score ≥ 4) in the Classification Approach (CA), and the severity of depression at a two-year follow up evaluation (EURO-D sum-score) in the Regression Approach (RA). Multiple RPMs were developed using combinations of sample balancing techniques (no balancing, under- or oversampling), learning methods (Generalized Linear Models (GLM), Decision Trees (DT), Random Forest (RF)) and variable selection methods (none, Backward (BW) and Forward (FW) sequential, Genetic Algorithm (GA)). Results We identified 90 predictors of depression that were measured in the SHARE dataset and, combining waves 4 to 8, selected a sample of 4057 participants with cancer. Of these, 33.5% were depressed at 2 years follow-up. In the classification approach the RPM based on undersampling, GLM and GA variable selection used 34 predictors and reached satisfying accuracy (74.4%, AUC-ROC: 0.80; PPV: 84.7%; NPV: 60.1%). In the regression approach, the GLM model with Genetic Algorithm reached the best accuracy (75.1%, AUC-ROC: 0.81; PPV: 70.5%; NPV: 76.2%). The calibration curve suggested a satisfactory level of prediction, homogeneous at all levels of risk thresholds. Using a threshold of 50% risk, the model yields a PPV of 80% and an NPV of 75%. Conclusions The Arturo RPMs can identify older adults with cancer who are at higher risk of developing depression over the following two years. The model is freely available as a web-based calculator for use by individuals, clinicians, and policy makers. Arturo might help to target preventive interventions. Clinical trial number Not applicable.

ALKBH4 impedes 5-FU Sensitivity through suppressing GSDME induced pyroptosis in gastric cancer

5-Fluorouracil (5-FU) is the primary treatment option for advanced gastric cancer. However, the current challenge lies in the absence of validated biomarkers to accurately predict the efficacy and sensitivity of 5-FU in individual patients. It has been confirmed that 5-FU can regulate tumor progression by promoting gasdermin E (GSDME, encoded by DFNA5) cleavage to induce pyroptosis. Lysine demethylase ALKBH4 has been shown to be upregulated in a variety of tumors to promote tumor progression. However, its role in gastric cancer is not clear. In this study, we observed a significant upregulation of ALKBH4 expression in gastric cancer tissues compared to adjacent normal tissues, indicating its potential as a predictor for the poor prognosis of gastric cancer patients. On the contrary, GSDME exhibits low expression levels in gastric cancer and demonstrates a negative correlation with poor prognosis among patients diagnosed with gastric cancer. In addition, we also found that high expression of ALKBH4 can inhibit pyroptosis and promote the proliferation of gastric cancer cells. Mechanistically, ALKBH4 inhibits GSDME activation at the transcriptional level by inhibiting H3K4me3 histone modification in the GSDME promoter region, thereby reducing the sensitivity of gastric cancer cells to 5-FU treatment. These findings provide further insight into the regulatory mechanisms of ALKBH4 in the progression of gastric cancer and underscore its potential as a prognostic marker for predicting the sensitivity of gastric cancer cells to 5-FU treatment.

Knowledge Graph Modulated Deep Learning for Limited-Sample Clinical Data Analysis

Biological systems are governed by structured molecular interactions, where pathways, regulatory circuits, and functional gene relationships shape cellular behavior and disease progression. Much of this knowledge is naturally represented as graphs. However, most biomedical AI models cannot directly use graph-encoded biological knowledge and instead require compressed low-dimensional representations, which can lose important structure and reduce performance, especially in limited-sample clinical studies. Here, we introduce Graph-in-Graph (GiG), a knowledge graph-modulated deep learning framework for data-efficient clinical prediction. GiG represents each patient as a standalone modular graph, in which curated biological knowledge graphs define edges and patient-specific measurements, such as gene expression, define node features. This design allows multiple biological knowledge graphs to be integrated while preserving gene-gene interactions and pathway topology during patient-level representation learning. Across cohorts comprising nearly 9,700 patients and five clinical tasks, including liquid biopsy cancer detection, prostate cancer diagnosis, and 32-class pan-cancer classification, GiG consistently outperforms traditional and state-of-the-art methods, with the largest gains in limited-sample settings. On the challenging prostate cancer diagnosis task, GiG improves macro-F1 by up to 49 percentage points relative to competing methods. Control experiments replacing real pathway graphs with random topologies confirm that these gains arise from biologically grounded knowledge graph structure rather than graph modeling alone. These findings show that knowledge graph-modulated deep learning can improve robustness, interpretability, and sample efficiency in clinical data analysis, and provide a principled framework for integrating biological knowledge graphs into predictive modeling. ;17 pages, 4 figures, 12 supplementary figures

Epidemiological and clinicopathological characteristics of ovarian cancer in Syria during the war years: a national multicenter retrospective study (2017–2021)

Background Despite the high burden and regional variability of ovarian cancer (OC), data on its status in Syria, a country impacted by long-standing conflict, remain scarce. This study aimed to provide the first nationwide analysis of OC epidemiological and clinicopathological characteristics. Methods In this national, multicenter, retrospective cross-sectional study, we collected data from three tertiary referral university hospitals in Syria: Tishreen University Hospital, Al-Bairouni University Hospital, and Ibn Rushd Hospital. The inclusion period spanned from 2017 to 2021. All included patients were 18 years or older and had a newly diagnosed, confirmed primary OC. Data collected included sociodemographic information, presenting symptoms, CA-125 levels, and treatment details. Results A total of 531 patients with OC with a mean age of 53.1 years (range: 18–91) were included in the study, representing 1.2% (95% confidence interval: 1.1%-1.3%) of the 44,248 patients diagnosed with all types of cancer. Serous carcinoma (55.4%) was the most common OC subtype, followed by mucinous (13.8%) and endometrioid carcinoma (9.7%). Among epithelial OCs, having high levels of CA-125 was associated with higher odds of serous carcinoma (adjusted OR: 2.30, 95% CI: 1.08–4.91) and lower odds of endometrioid cancer adjusted OR: 0.21, 95% CI: 0.08–0.54). Abdominal bloating was the most frequently reported symptom, affecting 194 patients (36.5%). Conclusions Our study revealed a lower proportion of OCs diagnoses among all cancer cases compared to global and regional data which may be attributed to underreporting, influenced by the disease’s specific characteristics and presence of more barriers for women to healthcare access in Syria’s conflict-affected setting. Given the study’s limitations, further research is essential to provide a more comprehensive assessment of the OC burden in Syria.

Neuronal transfer of mitochondria to tumour cells promotes cancer spread

Neurons often receive organelles called mitochondria from other cells. It emerges that neurons donate mitochondria that support cancer-cell spread. Neurons often receive organelles called mitochondria from other cells. It emerges that neurons donate mitochondria that support cancer-cell spread.

Rat Models of Hormone Receptor-Positive Breast Cancer

Hormone receptor-positive (HR^+) breast cancer (BC) is the most common type of breast cancer among women worldwide, accounting for 70–80% of all invasive cases. Patients with HR^+ BC are commonly treated with endocrine therapy, but intrinsic or acquired resistance is a frequent problem, making HR^+ BC a focal point of intense research. Despite this, the malignancy still lacks adequate in vitro and in vivo models for the study of its initiation and progression as well as response and resistance to endocrine therapy. No mouse models that fully mimic the human disease are available, however rat mammary tumor models pose a promising alternative to overcome this limitation. Compared to mice, rats are more similar to humans in terms of mammary gland architecture, ductal origin of neoplastic lesions and hormone dependency status. Moreover, rats can develop spontaneous or induced mammary tumors that resemble human HR^+ BC. To date, six different types of rat models of HR^+ BC have been established. These include the spontaneous, carcinogen-induced, transplantation, hormone-induced, radiation-induced and genetically engineered rat mammary tumor models. Each model has distinct advantages, disadvantages and utility for studying HR^+ BC. This review provides a comprehensive overview of all published models to date.

Proteasome Inhibition Enhances Lysosome-mediated Targeted Protein Degradation

Proteasome inhibitor drugs are currently used in the clinic to treat multiple myeloma and mantle cell lymphoma. These inhibitors cause accumulation of undegraded proteins, thus inducing proteotoxic stress and consequent cell death. However, cancer cells counteract this effect by activating an adaptive response through the transcription factor Nuclear factor erythroid 2-related factor 1 (NRF1, also known as NFE2L1). NRF1 induces transcriptional upregulation of proteasome and autophagy/lysosomal genes, thereby reducing proteotoxic stress and diminishing the effectiveness of proteasome inhibition. While suppressing this protective autophagy response is one potential strategy, here we investigated whether this heightened autophagy could instead be leveraged therapeutically. To this end, we designed an autophagy-targeting chimera (AUTAC) compound to selectively degrade the anti-apoptotic protein Mcl1 via the lysosome. Our results show that this lysosome-mediated targeted degradation is significantly amplified in the presence of proteasome inhibition, in a NRF1-dependent manner. The combination of the proteasome inhibitor carfilzomib and Mcl1 AUTAC synergistically promoted cell death in both wild-type and proteasome inhibitor-resistant multiple myeloma and lung cancer cells. Thus, our work offers a novel strategy for enhancing proteasome inhibitor efficacy by exploiting the adaptive autophagy response. More broadly, our study establishes a framework for amplifying lysosome-mediated targeted protein degradation, with potential applications in cancer therapeutics and beyond.

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

BACKGROUND: Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. METHODS: We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. FINDINGS: Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. INTERPRETATION: This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. FUNDING: 10.13039/501100012166National Key Research and Development Program of China (2021YFA1300601). 10.13039/501100001809National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

VMRA-MaR: An Asymmetry-Aware Temporal Framework for Longitudinal Breast Cancer Risk Prediction

Breast cancer remains a leading cause of mortality worldwide and is typically detected via screening programs where healthy people are invited in regular intervals. Automated risk prediction approaches have the potential to improve this process by facilitating dynamically screening of high-risk groups. While most models focus solely on the most recent screening, there is growing interest in exploiting temporal information to capture evolving trends in breast tissue, as inspired by clinical practice. Early methods typically relied on two time steps, and although recent efforts have extended this to multiple time steps using Transformer architectures, challenges remain in fully harnessing the rich temporal dynamics inherent in longitudinal imaging data. In this work, we propose to instead leverage Vision Mamba RNN (VMRNN) with a state-space model (SSM) and LSTM-like memory mechanisms to effectively capture nuanced trends in breast tissue evolution. To further enhance our approach, we incorporate an asymmetry module that utilizes a Spatial Asymmetry Detector (SAD) and Longitudinal Asymmetry Tracker (LAT) to identify clinically relevant bilateral differences. This integrated framework demonstrates notable improvements in predicting cancer onset, especially for the more challenging high-density breast cases and achieves superior performance at extended time points (years four and five), highlighting its potential to advance early breast cancer recognition and enable more personalized screening strategies. Our code is available at https://github.com/Mortal-Suen/VMRA-MaR.git. ;MICCAI 2025, Provisional Accept

Robust statistical assessment of Oncogenotype to Organotropism translation in xenografted zebrafish

Organotropism results from the functional versatility of metastatic cancer cells to survive and proliferate in diverse microenvironments. This adaptivity can originate in clonal variation of the spreading tumor and is often empowered by epigenetic and molecular reprogramming of cell regulatory circuits. Related to organotropic colonization of metastatic sites are environmentally-sensitive, differential responses of cancer cells to therapeutic attack. Accordingly, understanding the organotropic profile of a cancer and probing the underlying driver mechanisms are of high clinical importance. However, determining systematically the organotropism of one cancer versus the organotropism of another cancer, potentially with the granularity of comparing the same cancer type between patients or tracking the evolution of a cancer in a single patient for the purpose of personalized treatment, has remained very challenging. It requires a host organism that allows observation of the spreading pattern over relatively short experimental times. Moreover, organotropic patterns often tend to be statistically weak and superimposed by experimental variation. Thus, an assay for organotropism must give access to statistical powers that can separate ‘meaningful heterogeneity’, i.e., heterogeneity that determines organotropism, from ‘meaningless heterogeneity’, i.e., heterogeneity that causes experimental noise. Here we describe an experimental workflow that leverages the physiological properties of zebrafish larvae for an imaging-based assessment of organotropic patterns over a time-frame of 3 days. The workflow incorporates computer vision pipelines to automatically integrate the stochastic spreading behavior of a particular cancer xenograft in tens to hundreds of larvae allowing subtle trends in the colonization of particular organs to emerge above random cell depositions throughout the host organism. We validate our approach with positive control experiments comparing the spreading patterns of a metastatic sarcoma against non-transformed fibroblasts and the spreading patterns of two melanoma cell lines with previously established differences in metastatic propensity. We then show that integration of the spreading pattern of xenografts in 40 – 50 larvae is necessary and sufficient to generate a Fish Metastatic Atlas page that is representative of the organotropism of a particular oncogenotype and experimental condition. Finally, we apply the power of this assay to determine the function of the EWSR1::FLI1 fusion oncogene and its transcriptional target SOX6 as plasticity factors that enhance the adaptive capacity of metastatic Ewing sarcoma.

Short- and long-term outcomes of patients with active cancer presenting with an acute coronary syndrome

Background Management of cancer patients presenting with an acute coronary syndrome (ACS) may be challenging. In this study, we sought to examine whether and how a concomitant diagnosis of active cancer affects patients’ management and outcomes following an event of ACS. Methods We used a retrospective cohort data analysis of patients from the Acute Coronary Syndrome Israeli Survey (ACSIS) carried out between the years 2016–2021 to compare patients with and without a concomitant diagnosis of active cancer. Results Of 4913 patients who presented with an ACS, 90 (1.8%) patients had a concomitant active cancer. Cancer patients were older, with a higher prevalence of hypertension and chronic renal failure. The rate of ST-elevation myocardial infarction (STEMI) was similar (40%) between both groups. Cancer patients were less likely to undergo coronary angiography during hospitalization; but once it was performed, the rate of percutaneous coronary intervention was similar. The presence of cancer during an ACS was associated with an increased short- and long-term mortality. In a multivariate analysis, the risk for 1-year mortality remained significantly higher in cancer patient (HR 2.72, 95% CI 1.74–4.24, p  < 0.001), and was most prominent in patients presenting with STEMI (HR 5.00, 95% CI 2.40–10.39, p  < 0.001). Short- and long-term death rates were also higher in cancer patients after a propensity score matching and adjustment for comorbidities other than cancer. Conclusion Despite significant advances in oncologic and cardiac care, the presence of active cancer in patients with an ACS is still associated with significantly increased risk for 1-year mortality. Graphical Abstract

Screening Young Adult Cancer Patients for Vocational Rehabilitation Services: A Vocational Rehabilitation Readiness Screening Tool

Purpose We aimed to develop an online vocational rehabilitation (VR) readiness screening (VRRS) tool for young adults diagnosed with cancer. VR readiness was defined as being physically and cognitively ready to enter or return to work or school. Methods We developed an initial VRRS tool informed by previous studies, a scoping review to determine such a tool had not already been developed, and consultation with subject matter experts. We iteratively refined the tool on the basis of four rounds of interviews with young adults with cancer. The refined VRRS was then administered online to patients before they attended scheduled clinic appointments. We evaluated the VRRS completion rate; the ability of the online VRRS tool to assess patients’ readiness for VR; and the concordance between clinical interview and that of the online VRRS tool, which was determined by using Cohen’s κ or a fitting alternative; κ  ≥ .61 suggested clinical utility. Results The proportion of study participants completing the VRRS was 72.3% (136/188, 95% CI [65.4%, 78.6%]) and 69.2% (81/117, 95% CI [60.0%, 77.4%]) of participants who completed a clinical interview also completed the online VRRS tool. All clinical interview determinations and 93.8% (76/81) of online VRRS tool indicated participants’ VR readiness. The VRRS-clinical interview agreement rate was 93.8% (95% CI [86.2%, 98.0%]). The prevalence index, bias index, and prevalence- and bias-adjusted κ statistic with 95% CIs were 0.938 [− 0.991, − 0.886], 0.062 [0.009, 0.114], and 0.877 [0.724, 0.959], respectively. Conclusion The VRRS tool is thus both feasible and effective in this sample of young adults with cancer.

Harnessing artificial intelligence for cancer rehabilitation: A call to action

Recent evidence from the CHALLENGE trial confirms that structured exercise can significantly improve survival outcomes in colon cancer survivors, reinforcing the principle that exercise is medicine. However, implementing personalized and scalable rehabilitation programs remains challenging. This paper explores how artificial intelligence (AI), when integrated with the International Classification of Functioning, Disability and Health (ICF) framework, can transform cancer rehabilitation through personalized exercise prescriptions, real-time monitoring, virtual coaching, and data-driven decision-making. We also discuss the role of emerging technologies such as virtual reality, wearables, and telehealth in expanding access to care. While limitations regarding data bias, equity, and regulation persist, strategic research and implementation efforts are essential. A multidisciplinary, patient-centered approach is needed to harness AI’s potential in the era of digital health.

A Transformative Decade: An Evaluation of the Francophone African Group of Pediatric Oncology’s Training Program (2014–2024)

Childhood cancer in Africa faces significant challenges due to workforce shortages and limited training opportunities. The French African Group for Pediatric Oncology (GFAOP) established the African School of Pediatric Oncology and introduced a pediatric oncology teaching called the “Diplome Universitaire de Cancérologie Pédiatrique” (DUCP) training program. This report evaluates the contributions of the DUCP program to pediatric oncology in Africa and discusses the sustainability of the program. The DUCP program trained six cohorts of healthcare professionals from French-speaking African countries since 2014. An evaluation was done on the participant demographics and regional contributions. Data were collected from trainee records and DUCP records. The DUCP program was evaluated based on the domains developed by the Education Program Assessment Tool (EPAT). Over the 10-year period, the DUCP program trained 107 healthcare professionals from 20 Francophone countries of which 99% were retained in Africa. Of the 83 graduates, 55 (66%) actively practice in pediatric oncology. Of the 18 francophone countries, 17 countries increased the number of pediatric oncologists and 16 improved the ratio of pediatric oncologists to children under 15 years. Nine new pediatric oncology services were established by the graduates thus far. Despite challenges, such as the COVID−19 pandemic, the program remains sustainable because of continued financial support, collaborations with the international pediatric oncology community, and adapting the program content to participant and local setting needs. Retention of graduates in childhood cancer services remains a challenge that necessitates governmental involvement. The DUCP program is impactful and sustainable and improves access for children to cancer services in Africa. By fostering continued collaboration with governments, addressing the needs of an increasing African population, and expanding support for similar initiatives, the program’s longevity and positive impact can be further ensured.

Ex vivo model of breast cancer cell invasion in live lymph node tissue

Lymph nodes (LNs) are common sites of metastatic invasion in breast cancer, often preceding spread to distant organs and serving as key indicators of clinical disease progression. However, the mechanisms of cancer cell invasion into LNs are not well understood. Existing in vivo models struggle to isolate the specific impacts of the tumor-draining lymph node (TDLN) milieu on cancer cell invasion due to the co-evolving relationship between TDLNs and the upstream tumor. To address these limitations, we used live ex vivo LN tissue slices with intact chemotactic function to model cancer cell spread within a spatially organized microenvironment. After showing that BRPKp110 breast cancer cells were chemoattracted to factors secreted by naïve LN tissue in a 3D migration assay, we demonstrated that ex vivo LN slices could support cancer cell seeding, invasion, and spread. This novel approach revealed dynamic, preferential cancer cell invasion within specific anatomical regions of LNs, particularly the subcapsular sinus (SCS) and cortex, as well as chemokine-rich domains of immobilized CXCL13 and CCL1. While CXCR5 was necessary for a portion of BRPKp110 invasion into naïve LNs, disruption of CXCR5/CXCL13 signaling alone was insufficient to prevent invasion towards CXCL13-rich domains. Finally, we extended this system to pre-metastatic TDLNs, where the ex vivo model predicted a lower invasion of cancer cells. The reduced invasion was not due to diminished chemokine secretion, but it correlated with elevated intranodal IL-21. In summary, this innovative ex vivo model of cancer cell spread in live LN slices provides a platform to investigate cancer invasion within the intricate tissue microenvironment, supporting time-course analysis and parallel read-outs. We anticipate that this system will enable further research into cancer-immune interactions and allow isolation of specific factors that make TDLNs resistant to cancer cell invasion, which are challenging to dissect in vivo.

A Hybrid Deep Learning Framework with Explainable AI for Lung Cancer Classification with DenseNet169 and SVM

Lung cancer is a very deadly disease worldwide, and its early diagnosis is crucial for increasing patient survival rates. Computed tomography (CT) scans are widely used for lung cancer diagnosis as they can give detailed lung structures. However, manual interpretation is time-consuming and prone to human error. To surmount this challenge, the study proposes a deep learning-based automatic lung cancer classification system to enhance detection accuracy and interpretability. The IQOTHNCCD lung cancer dataset is utilized, which is a public CT scan dataset consisting of cases categorized into Normal, Benign, and Malignant and used DenseNet169, which includes Squeezeand-Excitation blocks for attention-based feature extraction, Focal Loss for handling class imbalance, and a Feature Pyramid Network (FPN) for multi-scale feature fusion. In addition, an SVM model was developed using MobileNetV2 for feature extraction, improving its classification performance. For model interpretability enhancement, the study integrated Grad-CAM for the visualization of decision-making regions in CT scans and SHAP (Shapley Additive Explanations) for explanation of feature contributions within the SVM model. Intensive evaluation was performed, and it was found that both DenseNet169 and SVM models achieved 98% accuracy, suggesting their robustness for real-world medical practice. These results open up the potential for deep learning to improve the diagnosis of lung cancer by a higher level of accuracy, transparency, and robustness.

Survival rates of cervical cancer patients in Sarawak: a single-centre referral study

Background Cervical cancer represents a significant health challenge in Malaysia, especially in the state of Sarawak which records some of the highest incidence rates across the country. This study evaluates the survival rates of cervical cancer patients in Sarawak, focusing on demographic characteristics, disease stage and survival outcomes to inform healthcare strategies. Methods A retrospective case notes review of disease stage, patterns of care and survival outcomes of patients diagnosed with cervical cancer at Sarawak General Hospital between January 2018 to December 2022 was conducted. Kaplan-Meier survival analysis and Cox Regression analysis were performed to assess survival outcomes and factors influencing survival. Results A total of 555 patients were included in this review. The majority of patients were diagnosed between the ages of 40 and 59, with a mean age of 53 years. Ibans comprised the largest subgroup by ethnicity. Only 11.2% of patients were diagnosed with Stage I disease. The majority of patients were diagnosed at advanced stages III and IV. The overall 5-year survival rate was 59.4%. Factors significantly affecting survival included FIGO cancer stage and ethnicity. Conclusions Diagnosis at advanced stages of disease lead to poorer clinical outcomes in cervical cancer patients in Sarawak. This study highlights the critical need for enhanced screening and early diagnosis to improve survival rates amongst cervical cancer patients Sarawak. Efforts should focus on improving cervical health literacy, expanding access to healthcare services and improving the uptake of HPV vaccination and cervical screening particularly in rural communities.

The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis

Continual learning is often motivated by the idea, known as the big world hypothesis, that "the world is bigger" than the agent. Recent problem formulations capture this idea by explicitly constraining an agent relative to the environment. These constraints lead to solutions in which the agent continually adapts to best use its limited capacity, rather than converging to a fixed solution. However, explicit constraints can be ad hoc, difficult to incorporate, and may limit the effectiveness of scaling up the agent's capacity. In this paper, we characterize a problem setting in which an agent, regardless of its capacity, is constrained by being embedded in the environment. In particular, we introduce a computationally-embedded perspective that represents an embedded agent as an automaton simulated within a universal (formal) computer. Such an automaton is always constrained; we prove that it is equivalent to an agent that interacts with a partially observable Markov decision process over a countably infinite state-space. We propose an objective for this setting, which we call interactivity, that measures an agent's ability to continually adapt its behaviour by learning new predictions. We then develop a model-based reinforcement learning algorithm for interactivity-seeking, and use it to construct a synthetic problem to evaluate continual learning capability. Our results show that deep nonlinear networks struggle to sustain interactivity, whereas deep linear networks sustain higher interactivity as capacity increases. ;NeurIPS 2025 (spotlight)

An energy- and helicity-conserving enriched galerkin method for the incompressible Navier-Stokes equations

We develop an enriched Galerkin (EG) method for the incompressible Navier-Stokes equations that conserves both kinetic energy and helicity in the inviscid limit without introducing any additional projection variables. The method employs an EG velocity space, which is the first-order continuous Galerkin space enriched with piecewise constants defined on mesh faces, together with piecewise-constant pressure. Two numerical schemes based on the rotational form of the convective term are proposed: a nonlinear scheme and a linear variant. Both schemes exactly preserve the discrete helicity and kinetic energy, and the Picard iteration maintains the conservation properties of the nonlinear scheme. We prove the conservation properties of both the methods, and establish stability and rigorous error estimates for the nonlinear scheme. Numerical examples demonstrate the accuracy and conservation of the proposed linear scheme.

Robust LLM-based Column Type Annotation via Prompt Augmentation with LoRA Tuning

Column Type Annotation (CTA) is a fundamental step towards enabling schema alignment and semantic understanding of tabular data. Existing encoder-only language models achieve high accuracy when fine-tuned on labeled columns, but their applicability is limited to in-domain settings, as distribution shifts in tables or label spaces require costly re-training from scratch. Recent work has explored prompting generative large language models (LLMs) by framing CTA as a multiple-choice task, but these approaches face two key challenges: (1) model performance is highly sensitive to subtle changes in prompt wording and structure, and (2) annotation F1 scores remain modest. A natural extension is to fine-tune large language models. However, fully fine-tuning these models incurs prohibitive computational costs due to their scale, and the sensitivity to prompts is not eliminated. In this paper, we present a parameter-efficient framework for CTA that trains models over prompt-augmented data via Low-Rank Adaptation (LoRA). Our approach mitigates sensitivity to prompt variations while drastically reducing the number of necessary trainable parameters, achieving robust performance across datasets and templates. Experimental results on recent benchmarks demonstrate that models fine-tuned with our prompt augmentation strategy maintain stable performance across diverse prompt patterns during inference and yield higher weighted F1 scores than those fine-tuned on a single prompt template. These results highlight the effectiveness of parameter-efficient training and augmentation strategies in developing practical and adaptable CTA systems. ;13 pages, 8 figures

Timely Parameter Updating in Over-the-Air Federated Learning

Incorporating over-the-air computations (OAC) into the model training process of federated learning (FL) is an effective approach to alleviating the communication bottleneck in FL systems. Under OAC-FL, every client modulates its intermediate parameters, such as gradient, onto the same set of orthogonal waveforms and simultaneously transmits the radio signal to the edge server. By exploiting the superposition property of multiple-access channels, the edge server can obtain an automatically aggregated global gradient from the received signal. However, the limited number of orthogonal waveforms available in practical systems is fundamentally mismatched with the high dimensionality of modern deep learning models. To address this issue, we propose Freshness Freshness-mAgnItude awaRe top-k (FAIR-k), an algorithm that selects, in each communication round, the most impactful subset of gradients to be updated over the air. In essence, FAIR-k combines the complementary strengths of the Round-Robin and Top-k algorithms, striking a delicate balance between timeliness (freshness of parameter updates) and importance (gradient magnitude). Leveraging tools from Markov analysis, we characterize the distribution of parameter staleness under FAIR-k. Building on this, we establish the convergence rate of OAC-FL with FAIR-k, which discloses the joint effect of data heterogeneity, channel noise, and parameter staleness on the training efficiency. Notably, as opposed to conventional analyses that assume a universal Lipschitz constant across all the clients, our framework adopts a finer-grained model of the data heterogeneity. The analysis demonstrates that since FAIR-k promotes fresh (and fair) parameter updates, it not only accelerates convergence but also enhances communication efficiency by enabling an extended period of local training without significantly affecting overall training efficiency.

Discovering Sparse Recovery Algorithms Using Neural Architecture Search

The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the idea of automated algorithm discovery in the signal processing context through meta-learning tools such as Neural Architecture Search (NAS). Specifically, we examine the Iterative Shrinkage Thresholding Algorithm (ISTA) and its accelerated Fast ISTA (FISTA) variant as candidates for algorithm rediscovery. We develop a meta-learning framework which is capable of rediscovering (several key elements of) the two aforementioned algorithms when given a search space of over 50,000 variables. We then show how our framework can apply to various data distributions and algorithms besides ISTA/FISTA. ;Presented at the 59th Asilomar Conference on Signals, Systems, and Computers

A Tri-Dynamic Preprocessing Framework for UGC Video Compression

In recent years, user generated content (UGC) has become the dominant force in internet traffic. However, UGC videos exhibit a higher degree of variability and diverse characteristics compared to traditional encoding test videos. This variance challenges the effectiveness of data-driven machine learning algorithms for optimizing encoding in the broader context of UGC scenarios. To address this issue, we propose a Tri-Dynamic Preprocessing framework for UGC. Firstly, we employ an adaptive factor to regulate preprocessing intensity. Secondly, an adaptive quantization level is employed to fine-tune the codec simulator. Thirdly, we utilize an adaptive lambda tradeoff to adjust the rate-distortion loss function. Experimental results on large-scale test sets demonstrate that our method attains exceptional performance. ;Accepted as a POSTER and for publication in the ICASSP 2024 proceedings

MoonBot: Modular and On-Demand Reconfigurable Robot Toward Moon Base Construction

The allure of lunar surface exploration and development has recently captured widespread global attention. Robots have proved to be indispensable for exploring uncharted terrains, uncovering and leveraging local resources, and facilitating the construction of future human habitats. In this article, we introduce the modular and on-demand reconfigurable robot (MoonBot), a modular and reconfigurable robotic system engineered to maximize functionality while operating within the stringent mass constraints of lunar payloads and adapting to varying environmental conditions and task requirements. This article details the design and development of MoonBot and presents a preliminary field demonstration that validates the proof of concept through the execution of milestone tasks simulating the establishment of lunar infrastructure. These tasks include essential civil engineering operations, infrastructural component transportation and deployment, and assistive operations with inflatable modules. Furthermore, we systematically summarize the lessons learned during testing, focusing on the connector design and providing valuable insights for the advancement of modular robotic systems in future lunar missions. ;This is the authors' version of a paper accepted for publication in IEEE Transactions on Field Robotics, (c) IEEE. The final published version is available at https://doi.org/10.1109/TFR.2025.3624346

SlicerOrbitSurgerySim: An Open-Source Platform for Virtual Registration and Quantitative Comparison of Preformed Orbital Plates

Poor adaptation of orbital implants remains a major contributor to postoperative complications and revision surgery. Although preformed orbital plates are widely used to reduce cost and operative time compared with customized implants, surgeons currently lack publicly available tools and standardized metrics to quantitatively compare plate fit across vendors, sizes, and patient anatomy. We developed SlicerOrbitSurgerySim, an open-source extension for the 3D Slicer platform that enables interactive virtual registration, evaluation, and comparison of multiple preformed orbital plates in a patient-specific virtual planning environment. The software generates reproducible quantitative plate-to-orbit distance metrics and visualization tools that support both patient-specific planning and population-level statistical analysis of plate adaptability. By facilitating objective comparison of implant designs and placement strategies, this tool aims to improve preoperative decision-making, reduce intraoperative plate modification, and promote collaborative research and surgical education. Pilot studies, sample datasets, and detailed tutorials are provided to support testing, transparency, and reproducibility. ;12 pages, 8 figures. Submitted to Journal of Oral and Maxillofacial Surgery. Code: https://github.com/chz31/SlicerOrbitSurgerySim/tree/main

RecurGS: Interactive Scene Modeling via Discrete-State Recurrent Gaussian Fusion

Recent advances in 3D scene representations have enabled high-fidelity novel view synthesis, yet adapting to discrete scene changes and constructing interactive 3D environments remain open challenges in vision and robotics. Existing approaches focus solely on updating a single scene without supporting novel-state synthesis. Others rely on diffusion-based object-background decoupling that works on one state at a time and cannot fuse information across multiple observations. To address these limitations, we introduce RecurGS, a recurrent fusion framework that incrementally integrates discrete Gaussian scene states into a single evolving representation capable of interaction. RecurGS detects object-level changes across consecutive states, aligns their geometric motion using semantic correspondence and Lie-algebra based SE(3) refinement, and performs recurrent updates that preserve historical structures through replay supervision. A voxelized, visibility-aware fusion module selectively incorporates newly observed regions while keeping stable areas fixed, mitigating catastrophic forgetting and enabling efficient long-horizon updates. RecurGS supports object-level manipulation, synthesizes novel scene states without requiring additional scans, and maintains photorealistic fidelity across evolving environments. Extensive experiments across synthetic and real-world datasets demonstrate that our framework delivers high-quality reconstructions with substantially improved update efficiency, providing a scalable step toward continuously interactive Gaussian worlds.

BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis

Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their "black-box" nature hinders interpretability. Furthermore, restricted by binary classification paradigms, they struggle to provide root cause analysis and maintenance recommendations. To address these limitations, this paper proposes BatteryAgent, a hierarchical framework that integrates physical knowledge features with the reasoning capabilities of Large Language Models (LLMs). The framework comprises three core modules: (1) A Physical Perception Layer that utilizes 10 mechanism-based features derived from electrochemical principles, balancing dimensionality reduction with physical fidelity; (2) A Detection and Attribution Layer that employs Gradient Boosting Decision Trees and SHAP to quantify feature contributions; and (3) A Reasoning and Diagnosis Layer that leverages an LLM as the agent core. This layer constructs a "numerical-semantic" bridge, combining SHAP attributions with a mechanism knowledge base to generate comprehensive reports containing fault types, root cause analysis, and maintenance suggestions. Experimental results demonstrate that BatteryAgent effectively corrects misclassifications on hard boundary samples, achieving an AUROC of 0.986, which significantly outperforms current state-of-the-art methods. Moreover, the framework extends traditional binary detection to multi-type interpretable diagnosis, offering a new paradigm shift from "passive detection" to "intelligent diagnosis" for battery safety management.

Comparing Approaches to Automatic Summarization in Less-Resourced Languages

Automatic text summarization has achieved high performance in high-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of different approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with and without three data augmentation approaches and multilingual transfer. We also explore an LLM translation pipeline approach, translating from the source language to English, summarizing and translating back. Evaluating with five different metrics, we find that there is variation across LLMs in their performance across similar parameter sizes, that our multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics, and that LLM as judge may be less reliable on less-resourced languages. ;Under review

Finite element exterior calculus for time-dependent Hamiltonian partial differential equations

The success of symplectic integrators for Hamiltonian ODEs has led to a decades-long program of research seeking analogously structure-preserving numerical methods for Hamiltonian PDEs. In this paper, we construct a large class of such methods by combining finite element exterior calculus (FEEC) for spatial semidiscretization with symplectic integrators for time discretization. The resulting methods satisfy a local multisymplectic conservation law in space and time, which generalizes the symplectic conservation law of Hamiltonian ODEs, and which carries finer information about Hamiltonian structure than other approaches based on global function spaces. We give particular attention to conforming FEEC methods and hybridizable discontinuous Galerkin (HDG) methods. The theory and methods are illustrated by application to the semilinear Hodge wave equation. ;42 pages, 4 figures

Adaptable Teastore with Energy Consumption Awareness: A Case Study

[Context and Motivation] Global energy consumption has been steadily increasing in recent years, with data centers emerging as major contributors. This growth is largely driven by the widespread migration of applications to the Cloud, alongside a rising number of users consuming digital content. Dynamic adaptation (or self-adaptive) approaches appear as a way to reduce, at runtime and under certain constraints, the energy consumption of software applications. [Question/Problem] Despite efforts to make energy-efficiency a primary goal in the dynamic adaptation of software applications, there is still a gap in understanding how to equip these self-adaptive software systems (SAS), which are dynamically adapted at runtime, with effective energy consumption monitoring tools that enable energy-awareness. Furthermore, the extent to which such an energy consumption monitoring tool impacts the overall energy consumption of the SAS ecosystem has not yet been thoroughly explored. [Methodology] To address this gap, we introduce the EnCoMSAS (Energy Consumption Monitoring for Self-Adaptive Systems) tool that allows to gather the energy consumed by distributed software applications deployed, for instance, in the Cloud. EnCoMSAS enables the evaluation of energy consumption of SAS variants at runtime. It allows to integrate energy-efficiency as a main goal in the analysis and execution of new adaptation plans for the SAS. In order to evaluate the effectiveness of EnCoMSAS and investigate its impact on the overall energy consumption of the SAS ecosystem, we conduct an empirical study by using the Adaptable TeaStore case study. Adaptable TeaStore is a self-adaptive extension of the TeaStore application, a microservice benchmarking application. For this study, we focus on the recommender service of Adaptable TeaStore. Regarding the experiments, we first equip Adaptable TeaStore with EnCoMSAS. Next, we execute Adaptable TeaStore by varying workload conditions that simulate users interactions. Finally, we use EnCoMSAS for gathering and assessing the energy consumption of the recommender algorithms of Adaptable TeaStore. To run these experiments, we use nodes of the Grid5000 testbed. [Results] The results show that EnCoMSAS is effective in collecting energy consumption of software applications for enabling dynamic adaptation at runtime. The observed correlation between CPU usage and energy consumption collected by EnCoMSAS provides evidence supporting the validity of the collected energy measurements. Moreover, we point out, through EnCoMSAS, that energy consumption is influenced not only by the algorithmic complexity but also by the characteristics of the deployment environment. Finally, the results show that the impact of EnCoMSAS on the overall energy consumption of the SAS ecosystem is comparatively modest with respect to the entire set of the TeaStore applications microservices. ;In Proceedings WACA 2025, arXiv:2512.22054

Robust Uncertainty Quantification for Factual Generation of Large Language Models

The rapid advancement of large language model(LLM) technology has facilitated its integration into various domains of professional and daily life. However, the persistent challenge of LLM hallucination has emerged as a critical limitation, significantly compromising the reliability and trustworthiness of AI-generated content. This challenge has garnered significant attention within the scientific community, prompting extensive research efforts in hallucination detection and mitigation strategies. Current methodological frameworks reveal a critical limitation: traditional uncertainty quantification approaches demonstrate effectiveness primarily within conventional question-answering paradigms, yet exhibit notable deficiencies when confronted with non-canonical or adversarial questioning strategies. This performance gap raises substantial concerns regarding the dependability of LLM responses in real-world applications requiring robust critical thinking capabilities. This study aims to fill this gap by proposing an uncertainty quantification scenario in the task of generating with multiple facts. We have meticulously constructed a set of trap questions contained with fake names. Based on this scenario, we innovatively propose a novel and robust uncertainty quantification method(RU). A series of experiments have been conducted to verify its effectiveness. The results show that the constructed set of trap questions performs excellently. Moreover, when compared with the baseline methods on four different models, our proposed method has demonstrated great performance, with an average increase of 0.1-0.2 in ROCAUC values compared to the best performing baseline method, providing new sights and methods for addressing the hallucination issue of LLMs. ;9 pages, 5 tables, 5 figures, accepted to IJCNN 2025

Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model

Understanding the dietary preferences of ancient societies and their evolution across periods and regions is crucial for revealing human-environment interactions. Seeds, as important archaeological artifacts, represent a fundamental subject of archaeobotanical research. However, traditional studies rely heavily on expert knowledge, which is often time-consuming and inefficient. Intelligent analysis methods have made progress in various fields of archaeology, but there remains a research gap in data and methods in archaeobotany, especially in the classification task of ancient plant seeds. To address this, we construct the first Ancient Plant Seed Image Classification (APS) dataset. It contains 8,340 images from 17 genus- or species-level seed categories excavated from 18 archaeological sites across China. In addition, we design a framework specifically for the ancient plant seed classification task (APSNet), which introduces the scale feature (size) of seeds based on learning fine-grained information to guide the network in discovering key "evidence" for sufficient classification. Specifically, we design a Size Perception and Embedding (SPE) module in the encoder part to explicitly extract size information for the purpose of complementing fine-grained information. We propose an Asynchronous Decoupled Decoding (ADD) architecture based on traditional progressive learning to decode features from both channel and spatial perspectives, enabling efficient learning of discriminative features. In both quantitative and qualitative analyses, our approach surpasses existing state-of-the-art image classification methods, achieving an accuracy of 90.5%. This demonstrates that our work provides an effective tool for large-scale, systematic archaeological research.

Training Report of TeleChat3-MoE

TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one trillion,trained end-to-end on Ascend NPU cluster. This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes. We detail systematic methodologies for operator-level and end-to-end numerical accuracy verification, ensuring consistency across hardware platforms and distributed parallelism strategies. Furthermore, we introduce a suite of performance optimizations, including interleaved pipeline scheduling, attention-aware data scheduling for long-sequence training,hierarchical and overlapped communication for expert parallelism, and DVM-based operator fusion. A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations. Additionally, we present methodological approaches to cluster-level optimizations, addressing host- and device-bound bottlenecks during large-scale training tasks. These infrastructure advancements yield significant throughput improvements and near-linear scaling on clusters comprising thousands of devices, providing a robust foundation for large-scale language model development on hardware ecosystems.

"Even GPT Can Reject Me": Conceptualizing Abrupt Refusal Secondary Harm (ARSH) and Reimagining Psychological AI Safety with Compassionate Completion Standard (CCS)

Large Language Models (LLMs) and AI chatbots are increasingly used for emotional and mental health support due to their low cost, immediacy, and accessibility. However, when safety guardrails are triggered, conversations may be abruptly terminated, introducing a distinct form of emotional disruption that can exacerbate distress and elevate risk among already vulnerable users. As this phenomenon gains attention, this viewpoint introduces Abrupt Refusal Secondary Harm (ARSH) as a conceptual framework to describe the psychological impacts of sudden conversational discontinuation caused by AI safety protocols. Drawing on counseling psychology and communication science as conceptual heuristics, we argue that abrupt refusals can rupture perceived relational continuity, evoke feelings of rejection or shame, and discourage future help seeking. To mitigate these risks, we propose a design hypothesis, the Compassionate Completion Standard (CCS), a refusal protocol grounded in Human Centered Design (HCD) that maintains safety constraints while preserving relational coherence. CCS emphasizes empathetic acknowledgment, transparent boundary articulation, graded conversational transition, and guided redirection, replacing abrupt disengagement with psychologically attuned closure. By integrating awareness of ARSH into AI safety design, developers and policymakers can reduce preventable iatrogenic harm and advance a more psychologically informed approach to AI governance. Rather than presenting incremental empirical findings, this viewpoint contributes a timely conceptual framework, articulates a testable design hypothesis, and outlines a coordinated research agenda for improving psychological safety in human AI interaction.

MuS-Polar3D: A Benchmark Dataset for Computational Polarimetric 3D Imaging under Multi-Scattering Conditions

Polarization-based underwater 3D imaging exploits polarization cues to suppress background scattering, exhibiting distinct advantages in turbid water. Although data-driven polarization-based underwater 3D reconstruction methods show great potential, existing public datasets lack sufficient diversity in scattering and observation conditions, hindering fair comparisons among different approaches, including single-view and multi-view polarization imaging methods. To address this limitation, we construct MuS-Polar3D, a benchmark dataset comprising polarization images of 42 objects captured under seven quantitatively controlled scattering conditions and five viewpoints, together with high-precision 3D models (+/- 0.05 mm accuracy), normal maps, and foreground masks. The dataset supports multiple vision tasks, including normal estimation, object segmentation, descattering, and 3D reconstruction. Inspired by computational imaging, we further decouple underwater 3D reconstruction under scattering into a two-stage pipeline, namely descattering followed by 3D reconstruction, from an imaging-chain perspective. Extensive evaluations using multiple baseline methods under complex scattering conditions demonstrate the effectiveness of the proposed benchmark, achieving a best mean angular error of 15.49 degrees. To the best of our knowledge, MuS-Polar3D is the first publicly available benchmark dataset for quantitative turbidity underwater polarization-based 3D imaging, enabling accurate reconstruction and fair algorithm evaluation under controllable scattering conditions. The dataset and code are publicly available at https://github.com/WangPuyun/MuS-Polar3D.

Adapting, Fast and Slow: On Few-Shot Transportability of Compositions

Generalization across domains requires stable structure that links the source and target distributions. Building on causal transportability theory, we study a sequential prediction setting in which the target predictor can be represented as a circuit composed of causal mechanisms that are learnable from source data. We introduce two classes of transportability. Module transportability captures the atomic case, where the target predictor is given by a mechanism learnable from a single source domain. Circuit transportability generalizes this idea to target predictors obtained by composing several modules learned from source data, enabling zero-shot prediction even when no source mechanism directly predicts the target label. We study these classes of circuits under increasingly relaxed assumptions. First, we provide conditions under which the relevant circuits can be learned from source data alone, given causal knowledge about the source and target domains. We then relax these structural assumptions by allowing limited data from the target domain. In particular, we develop a supervised domain adaptation scheme that learns circuits without requiring explicit causal structure. The resulting few-shot guarantees tie the achievable error to the size of the smallest target circuit composable from modules learned from source data. Finally, we propose a gradient-based relaxation of the symbolic circuit search and evaluate it empirically, showing that it qualitatively tracks the predicted regimes of fast adaptation -- with and without process supervision over intermediate positions -- and slow adaptation when no source mechanism matches.

Asia Cup 2025: A Structured T20 Match-Level Dataset and Exploratory Analysis for Cricket Analytics

This paper presents a structured and comprehensive dataset corresponding to the 2025 Asia Cup T20 cricket tournament, designed to facilitate data-driven research in sports analytics. The dataset comprises records from all 19 matches of the tournament and includes 61 variables covering team scores, wickets, powerplay statistics, boundary counts, toss decisions, venues, and player-specific highlights. To demonstrate its analytical value, we conduct an exploratory data analysis focusing on team performance indicators, boundary distributions, and scoring patterns. The dataset is publicly released through Zenodo under a CC-BY 4.0 license to support reproducibility and further research in cricket analytics, predictive modeling, and strategic decision-making. This work contributes an open, machine-readable benchmark dataset for advancing cricket analytics research. ;Dataset available via Zenodo:{https://doi.org/10.5281/zenodo.17228056}. Source code and analysis scripts are publicly available at : https://github.com/kousarraza/AsiaCup2025

Navigating Taxonomic Expansions of Entity Sets Driven by Knowledge Bases

Recognizing similarities among entities is central to both human cognition and computational intelligence. Within this broader landscape, Entity Set Expansion is one prominent task aimed at taking an initial set of (tuples of) entities and identifying additional ones that share relevant semantic properties with the former -- potentially repeating the process to form increasingly broader sets. However, this ``linear'' approach does not unveil the richer ``taxonomic'' structures present in knowledge resources. A recent logic-based framework introduces the notion of an expansion graph: a rooted directed acyclic graph where each node represents a semantic generalization labeled by a logical formula, and edges encode strict semantic inclusion. This structure supports taxonomic expansions of entity sets driven by knowledge bases. Yet, the potentially large size of such graphs may make full materialization impractical in real-world scenarios. To overcome this, we formalize reasoning tasks that check whether two tuples belong to comparable, incomparable, or the same nodes in the graph. Our results show that, under realistic assumptions -- such as bounding the input or limiting entity descriptions -- these tasks can be implemented efficiently. This enables local, incremental navigation of expansion graphs, supporting practical applications without requiring full graph construction.

CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction

Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration. ;11 pages, 4 figures

Automatic Reward Shaping from Multi-Objective Human Heuristics

Designing effective reward functions remains a central challenge in reinforcement learning, especially in multi-objective environments. In this work, we propose Multi-Objective Reward Shaping with Exploration (MORSE), a general framework that automatically combines multiple human-designed heuristic rewards into a unified reward function. MORSE formulates the shaping process as a bi-level optimization problem: the inner loop trains a policy to maximize the current shaped reward, while the outer loop updates the reward function to optimize task performance. To encourage exploration in the reward space and avoid suboptimal local minima, MORSE introduces stochasticity into the shaping process, injecting noise guided by task performance and the prediction error of a fixed, randomly initialized neural network. Experimental results in MuJoCo and Isaac Sim environments show that MORSE effectively balances multiple objectives across various robotic tasks, achieving task performance comparable to those obtained with manually tuned reward functions.

Marco-ASR: A Principled and Metric-Driven Framework for Fine-Tuning Large-Scale ASR Models for Domain Adaptation

Automatic Speech Recognition (ASR) models have achieved remarkable accuracy in general settings, yet their performance often degrades in domain-specific applications due to data mismatch and linguistic variability. This challenge is amplified for modern Large Language Model (LLM)-based ASR systems, whose massive scale and complex training dynamics make effective fine-tuning non-trivial. To address this gap, this paper proposes a principled and metric-driven fine-tuning framework for adapting both traditional and LLM-based ASR models to specialized domains. The framework emphasizes learning rate optimization based on performance metrics, combined with domain-specific data transformation and augmentation. We empirically evaluate our framework on state-of-the-art models, including Whisper, Whisper-Turbo, and Qwen2-Audio, across multi-domain, multilingual, and multi-length datasets. Our results not only validate the proposed framework but also establish practical protocols for improving domain-specific ASR performance while preventing overfitting. ;Technical Report

Impact of Volatility on Time-Based Transaction Ordering Policies

We study Arbitrum's Express Lane Auction (ELA), an ahead-of-time second-price auction that grants the winner an exclusive latency advantage for one minute. Building on a single-round model with risk-averse bidders, we propose a hypothesis that the value of priority access is discounted relative to risk-neutral valuation due to the difficulty of forecasting short-horizon volatility and bidders' risk aversion. We test these predictions using ELA bid records matched to high-frequency ETH prices and find that the result is consistent with the model.

Early physical rehabilitation dosage in the intensive care unit associates with hospital outcomes after critical COVID-19

Objective To examine the relationship between physical rehabilitation parameters including an approach to quantifying dosage with hospital outcomes for patients with critical COVID-19. Design Retrospective practice analysis from March 5, 2020, to April 15, 2021. Setting Intensive care units (ICU) at four medical institutions. Patients n = 3780 adults with ICU admission and diagnosis of COVID-19. Interventions We measured the physical rehabilitation treatment delivered in ICU and patient outcomes: (1) mortality; (2) discharge disposition; and (3) physical function at hospital discharge measured by the Activity Measure-Post Acute Care (AM-PAC) “6-Clicks” (6–24, 24 = greater functional independence). Physical rehabilitation dosage was defined as the average mobility level scores in the first three sessions (a surrogate measure of intensity) multiplied by the rehabilitation frequency (PT + OT frequency in hospital). Measurements and main results The cohort was a mean 64 ± 16 years old, 41% female, mean BMI of 32 ± 9 kg/m^2 and 46% (n = 1739) required mechanical ventilation. For 2191 patients who received rehabilitation, the dosage and AM-PAC at discharge were moderately, positively associated (Spearman’s rho [r] = 0.484, p  < 0.001). Multivariate linear regression (model adjusted R^2 = 0.68, p  < 0.001) demonstrates mechanical ventilation (β = − 0.86, p  = 0.001), average mobility score in first three sessions (β = 2.6, p  < 0.001) and physical rehabilitation dosage (β = 0.22, p  = 0.001) were predictive of AM-PAC scores at discharge when controlling for age, sex, BMI, and ICU LOS. Conclusions Greater physical rehabilitation exposure early in the ICU is associated with better physical function at hospital discharge.

Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program

Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients’ clinical histories to then identify groups of patients with similar presentations. The results indicate that cardiorespiratory presentations are most common (present in 54% of patients) followed by subphenotypes marked (in decreasing order of frequency) by musculoskeletal pain, neuropsychiatric conditions, gastrointestinal symptoms, headache, and fatigue.

An Environmental Health Early Warning System: When Resilient Climates and Windblown Dusts Risk Public Health

Arid regions, the source of most airborne mineral dusts, comprise a third of the Earth’s land surface, where around two billion people are exposed daily to the fine particles raised by wind. Crossing political borders and traveling on air currents around the world, these particles affect the health of their local communities as well as distant populations—putting all at risk for cardiovascular and respiratory illnesses that are overlain with emerging health issues, including potential clinical misdiagnoses of tuberculosis, influenza, and SARS COVID that present similar initial symptoms. Risks of exposure are affected by local and regional weather characteristics and climatic conditions that are in a state of flux. Advances in science and technology promise to reduce the health problems, but windblown dusts and what travels with them can change the fundamental problem and appropriate response. This chapter uses examples of meningitis, asthma, and Valley fever to illustrate how risks may be lowered through an (environmental) dust-health early warning system. A half century of dedicated measurements of particulate air quality and of environmental science enhanced by Earth-orbiting satellites reveal the truth of airborne dust extent, and much of its variability in time and space. These truths have been essential in advancing numerical, dynamic models of the atmosphere which mimic and predict weather systems that loft and transport the airborne dusts that medical sciences and epidemiology prove harmful. The union of many scientific disciplines and services makes possible improved public health around the world.

Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss

Long COVID is characterized by persistent symptoms, particularly pulmonary impairment, which necessitates advanced imaging for accurate diagnosis. Hyperpolarised Xenon-129 MRI (XeMRI) offers a promising avenue by visualising lung ventilation, perfusion, as well as gas transfer. Integrating functional data from XeMRI with structural data from Computed Tomography (CT) is crucial for comprehensive analysis and effective treatment strategies in long COVID, requiring precise data alignment from those complementary imaging modalities. To this end, CT-MRI registration is an essential intermediate step, given the significant challenges posed by the direct alignment of CT and Xe-MRI. Therefore, we proposed an end-to-end multimodal deformable image registration method that achieves superior performance for aligning long-COVID lung CT and proton density MRI (pMRI) data. Moreover, our method incorporates a novel Multi-perspective Loss (MPL) function, enhancing state-of-the-art deep learning methods for monomodal registration by making them adaptable for multimodal tasks. The registration results achieve a Dice coefficient score of 0.913, indicating a substantial improvement over the state-of-the-art multimodal image registration techniques. Since the XeMRI and pMRI images are acquired in the same sessions and can be roughly aligned, our results facilitate subsequent registration between XeMRI and CT, thereby potentially enhancing clinical decision-making for long COVID management.

Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts

Zang et al. use RECOVER EHR data to study Long COVID risk factors and apply mathematical modeling to predict the development of long COVID conditions. They find that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities are likely associated with increased risk of having Long COVID. Most people who develop COVID-19 make a full recovery, but some go on to develop post-acute sequelae of SARS-CoV-2 infection, commonly known as Long COVID. Up to now, we did not know why some people are affected by Long COVID whilst others are not. We conducted a study to identify risk factors for Long COVID and developed a mathematical modeling approach to predict those at risk. We find that Long COVID is associated with some factors such as experiencing severe acute COVID-19, being underweight, and having conditions including cancer or cirrhosis. Due to the wide variety of symptoms defined as Long COVID, it may be challenging to come up with a set of risk factors that can predict the whole spectrum of Long COVID. However, our approach could be used to predict a variety of Long COVID conditions. Background SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors. Methods In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests. Results We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7–0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.

Impact of Preexisting Rare Diseases on COVID-19 Severity, Reinfection, and Long COVID, and the Modifying Effects of Vaccination and Antiviral Therapy: A Retrospective Study from the N3C Data Enclave

BACKGROUND: Over 10,000 rare diseases (RDs) affect more than 300 million people globally, yet their influence on COVID-19 severity, reinfection risk, and long COVID remains poorly understood. This study evaluates the impact of RDs on these outcomes and examines the effectiveness of vaccination and antiviral treatments among individuals with and without RDs. METHODS: We conducted a retrospective cohort study using harmonized electronic health records (EHRs) from the National COVID Cohort Collaborative (N3C), encompassing 21,704,702 individuals, including 4,825,605 with confirmed SARS-CoV-2 infection between Jan 1, 2020, and Jan 4, 2024. RDs were defined using 12,003 conditions curated from GARD and Orphanet, mapped to OMOP concepts, and classified into 18 RD classes based on medical specialty involvement. Primary outcomes included: (1) COVID-19 severity (hospitalization and life-threatening disease), (2) long COVID, and (3) SARS-CoV-2 reinfection. We applied multivariable logistic regression with inverse probability of treatment weighting and reported adjusted odds ratios with 95% confidence intervals and associated p-values. Models were controlled for demographics, comorbidities, and exposure to vaccination and antiviral treatments. FINDINGS: Of 21,704,702 individuals, we identify 4,825,605 COVID-19 positive individuals, 6.36% had RDs, with markedly higher rates of rare disease (RD) patients that have life-threatening illness (16% vs. 6.1% without life-threatening illness) and that are hospitalized (13% vs. 6.0% without hospitalization). Otorhinolaryngologic diseases showed the highest risk of life-threatening outcomes (OR 4.51; 95% CI 3.81-5.33), followed by developmental defect during embryogenesis (OR 1.84; 95% CI 1.72-1.98) and cardiac conditions (OR 1.79; 95% CI 1.51-2.11). Hospitalization risk was highest for otorhinolaryngologic (OR 2.90; 95% CI 2.61-3.23), developmental defect during embryogenesis (OR 2.06; 95% CI 1.97-2.16), and hematologic and endocrine diseases (OR 1.81; 95% CI 1.75-1.87 and OR 1.81; 95% CI 1.64-1.99, respectively). In patients with RDs, vaccination alone or antiviral treatment alone was associated with reduced odds of life-threatening COVID-19 disease compared to non-vaccinated individuals (OR 0.71; 95% CI 0.66-0.77 and OR 0.33; 95% CI 0.26-0.42, respectively). The combination of both vaccination and antiviral treatment showed the greatest reduction in odds ratio (OR 0.24; 95% CI 0.20-0.27). Similar results were observed in patients without RDs. In contrast, vaccination or antiviral therapy alone, compared to no intervention, did not significantly reduce long COVID risk in RD patients, although these interventions alone did result in a lower odds ratio in patients without RD. However, their combination was protective in both groups. Vaccination alone, compared to no vaccination, also reduced the risk of reinfection across RD and non-RD populations. INTERPRETATION: RD patients face elevated risks of severe COVID-19 outcomes. While vaccination and antivirals significantly reduce the acute severity of illness, their impact on long COVID appears limited in this population. Notably, vaccination was protective against COVID-19 reinfection in both RD and non-RD populations. These findings highlight the need for targeted strategies to protect RD patients beyond current interventions, particularly in preventing long-term complications. FUNDING: This work was supported in part by the intramural and extramural programs at NCATS (ZIA ZICTR000410).

Developing survey weights to ensure representativeness in a national, matched cohort study: results from the children and young people with Long Covid (CLoCk) study

Background Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP. Methods Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall ‘envisioned population’ survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census. Results Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings. Conclusions Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

Long-term effects of the COVID-19 pandemic for patients with cancer

Since the outbreak in Wuhan (China) at the end of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has caused instability at various levels of society. While most patients completely recover from their SARS-CoV-2 infection, 10–20% of infected persons and up to 60% of infected patients with cancer develop long COVID. Long COVID is defined as the continuation of symptoms, which cannot be explained by alternative causes, that last longer than four weeks after initial infection. Even though it is generally accepted that patients with cancer are at increased risk of developing severe COVID-19, it is still unclear how long COVID manifests and whether long COVID impacts quality of life in this cohort. Hence, this study observed that patients with cancer reported a negative impact of the enforced COVID-19 restrictions on the emotional and psychological wellbeing. While patients with cancer experience similar long COVID symptoms as healthy controls, the prevalence is remarkably higher possibly due to their compromised immune system and weakened physiological reserve. Introduction: Long COVID is defined as the continuation of symptoms, unexplainable by alternative diagnosis, longer than four weeks after SARS-CoV-2 infection. These symptoms might hinder daily activities and overall well-being, ultimately impacting quality of life (QoL). Several studies have reported fatigue as the most common symptom, followed by dyspnoea, headache and myalgia. Although it is assumed that long COVID affects 10–20% of SARS-CoV-2 infected individuals, recently numbers up to 60% were described for patients with cancer. This study uncovers the impact of the COVID-19 pandemic on QoL of patients with cancer and how long COVID manifests in this cohort. Methods: A group of 96 patients with cancer was followed from March 2022 till March 2023. Online questionnaires assessing symptoms associated with long COVID, anxiety and depression (HADS), quality of life (EORTC-QLQ-C30) and cognitive functioning (CFQ) were sent every three months during this period. Furthermore, a semi-structured focus group was organised for qualitative data collection. Results: Overall, these patients reported a negative impact of the enforced COVID-19 restrictions on the emotional and psychological wellbeing. Forty nine patients with cancer (51.0%) were infected with SARS-CoV-2 over the course of the study, of which 39 (79.6%) reported long COVID symptoms. The most commonly reported symptoms were myalgia (46.2%), fatigue (38.5%) and disturbed sleep (35.9%) and it was observed that male sex is associated with poor long COVID outcomes. Conclusion: While patients with cancer experience similar long COVID symptoms as healthy controls, the prevalence is remarkably higher possibly due to their compromised immune system and weakened physiological reserve.

Post-acute COVID-19 outcomes including participant-reported long COVID: amubarvimab/romlusevimab versus placebo in the ACTIV-2 trial

BACKGROUND: It is unknown if early COVID-19 monoclonal antibody (mAb) therapy can reduce risk of Long COVID. The mAbs amubarvimab/romlusevimab were previously demonstrated to reduce risk of hospitalization/death by 79%. This study assessed the impact of amubarvimab/romlusevimab on late outcomes, including Long COVID. METHODS: Non-hospitalized high-risk adults within 10 days of COVID-19 symptom onset enrolled in a randomized, double-blind, placebo-controlled phase 2/3 trial of amubarvimab/romlusevimab for COVID-19 treatment. Late symptoms, assessed using a participant-completed symptom diary, were a pre-specified exploratory endpoint. The primary outcome for this analysis was the composite of Long COVID by participant self-report (presence of COVID-19 symptoms as recorded in the diary at week 36) or hospitalization or death by week 36. Inverse probability weighting (IPW) was used to address incomplete outcome ascertainment, giving weighted risk ratios (wRR) comparing amubarvimab/romlusevimab to placebo. FINDINGS: Participants received amubarvimab/romlusevimab (n = 390) or placebo (n = 390) between January and July 2021. Median age was 49 years, 52% were female, 18% Black/African American, 49% Hispanic/Latino, and 9% COVID-19-vaccinated at entry. At week 36, 103 (13%) had incomplete outcome ascertainment, and 66 (17%) on amubarvimab/romlusevimab and 92 (24%) on placebo met the primary outcome (wRR = 0.70, 95% confidence interval (CI) 0.53–0.93). The difference was driven by fewer hospitalizations/deaths with amubarvimab/romlusevimab (4%) than placebo (13%). Among 652 participants with available diary responses, 53 (16%) on amubarvimab/romlusevimab and 44 (14%) on placebo reported presence of Long COVID. INTERPRETATION: Amubarvimab/romlusevimab treatment, while highly effective in preventing hospitalizations/deaths, did not reduce risk of Long COVID. Additional interventions are needed to prevent Long COVID. FUNDING: 10.13039/100000060National Institute of Allergy and Infectious Diseases of the 10.13039/100000002National Institutes of Health. Amubarvimab and romlusevimab supplied by Brii Biosciences.

Laryngo pharyngeal affection of COVID-19 during Delta and Omicron variant (Comparative Study)

Background Since the declaration of COVID-19 as a pandemic in 2020, its main symptoms have primarily affected the respiratory system. However, it also presents other systemic manifestations, including symptoms related to the larynx and pharynx. COVID-19 has evolved into subsequent variants, starting from the alpha variant and currently dominated by the Omicron variant, with the Delta variant being the most severe. The study aimed to elucidate the laryngo pharyngeal manifestations related to Delta and Omicron variants of COVID-19 as well as the associated risk factors. Results This study adopted a case–control design. The data were collected from patients who attended the phoniatric outpatient clinic at Menoufia University Hospital from January to December 2022. Patients were categorized into three groups (50 patients each). Group I consisted of patients who exhibited COVID-19 Laryngo pharyngeal symptoms during the Delta wave (Group 1) and the Omicron wave (Group 2). The control group included non-COVID-19 participants. The symptoms related to the larynx and pharynx were documented. In addition, laryngoscopic and stroboscopic examinations were done. The age of individuals affected by the Delta variant was higher. Delta cases exhibited a higher rate of smoking, diabetes, hypertension, and COPD compared to Omicron cases. Additionally, Delta cases displayed greater severity. Dysphagia, dysphonia, choking attacks related to swallowing, and stridor had significant higher rate in the Delta variant compared to the Omicron variant. The Delta variant primarily exhibited unilateral vocal fold paralysis in 38% of cases, as well as bilateral vocal fold paresis and paralysis in 20% and 22% of cases, respectively. In contrast, Omicron cases predominantly showed vocal fold congestion (80% of cases). Delta cases were more prone to experiencing abnormalities in amplitude, symmetry, and periodicity. Conclusions The Delta variant is more prone to neurologic affection of the vocal folds manifesting as paresis and paralysis, whereas the Omicron variant, which has maintained its dominance thus far, experiences milder affection, primarily manifesting as congestion. Consequently, laryngeal affection with various degrees of severity is still suspected.

Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers

We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time, how changes vary across demographic groups, the impact of the COVID-19 pandemic, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing behaviors and trends beyond those available from aggregate metrics. For example, in addition to analyzing spending behavior, we develop new metrics to quantify changes in consumers' online purchase frequency and the diversity of products purchased, to better reflect the growing ubiquity and dominance of online retail. Between 2018 and 2022 these consumer-level metrics grew on average by more than 85%, peaking in 2021. We find a steady upward trend in individuals' online purchasing prior to COVID-19, with a significant increase in the first year of COVID, but without a lasting effect. Purchasing behaviors in 2022 were no greater than the result of the pre-pandemic trend. We also find changes in purchasing significantly differ by demographics, with different responses to the pandemic. We further use the consumer-level data to show substitution effects between online and offline retail in sectors where Amazon heavily invested: books, shoes, and grocery. Prior to COVID we find year-to-year changes in the number of consumers making online purchases for books and shoes negatively correlated with changes in employment at local bookstores and shoe stores. During COVID we find online grocery purchasing negatively correlated with in-store grocery visits. This work demonstrates how crowdsourced, open purchases data can enable economic insights that may otherwise only be available to private firms.

Long COVID Brain Fog Treatment: Findings from a Pilot Randomized Controlled Trial of Constraint-Induced Cognitive Therapy

PURPOSE: Long COVID brain fog is often disabling. Yet, no empirically-supported treatments exist. This study’s objectives were to evaluate feasibility and efficacy, provisionally, of a new rehabilitation approach, Constraint-Induced Cognitive Therapy (CICT), for post-COVID-19 cognitive sequelae. DESIGN: Sixteen community-residents ≥ 3-months post-COVID-19 infection with mild cognitive impairment and dysfunction in instrumental activities of daily living (IADL) were enrolled. Participants were randomized to Immediate-CICT or treatment-as-usual (TAU) with crossover to CICT. CICT combined behavior change techniques modified from Constraint-Induced Movement Therapy with Speed of Processing Training, a computerized cognitive-training program. CICT was deemed feasible if (a) ≥80% of participants completed treatment, (b) the same found treatment highly satisfying and at most moderately difficult, and (c) <2 study-related, serious adverse-events occurred. The primary outcome was IADL performance in daily life (Canadian Occupational Performance Measure). Employment status and brain fog (Mental Clutter Scale) were also assessed. RESULTS: Fourteen completed Immediate-CICT ( n =7) or TAU ( n =7); two withdrew from TAU before their second testing session. Completers were [ M (S D )]: 10 (7) months post-COVID; 51 (13) years old; 10 females, 4 males; 1 African American, 13 European American. All the feasibility benchmarks were met. Immediate-CICT, relative to TAU, produced very large improvements in IADL performance ( M =3.7 points, p<.001, d =2.6) and brain fog ( M =-4 points, p<.001, d =-2.9). Four of five non-retired Immediate-CICT participants returned-to-work post-treatment; no TAU participants did, p =.048. CONCLUSIONS: CICT has promise for reducing brain fog, improving IADL, and promoting returning-to-work in adults with Long COVID. Findings warrant a large-scale RCT with an active-comparison group. IMPACT: Brain fog in adults with Long COVID is often associated with dysfunction in everyday activities and unemployment. Yet, there are no empirically supported treatments targeting cognition in this population. Findings from this small-scale, pilot randomized controlled trial (RCT) suggest that a novel intervention, i.e., Constraint-Induced Cognitive Therapy, is a feasible cognitive rehabilitation method in adults with Long COVID cognitive sequelae with promise of (a) improving performance of cognition-based tasks in daily life and (b) promoting return-to-work. Further studies with larger sample sizes are warranted. Speed of Processing Training (SOPT) has been shown to increase processing speed in older adults without neurological disorders but has not been applied to adults with brain fog due to Long COVID, in whom slowing of cognitive processing speed is common. The results of this pilot RCT suggest that SOPT, in conjunction with behavior change techniques, may increase cognitive processing speed in this brain-injured population.

Assessing Neuropsychiatric Symptoms in Long COVID: A Retrospective Cohort Study from a South Texas Long COVID Clinic

Long COVID, previously known as Post-Acute Sequelae of SARS-CoV-2 (PASC), refers to prolonged symptoms or diagnosable conditions following COVID-19 infection. The neuropsychiatric profile of Long COVID patients remains ambiguous. This study aimed to assess neuropsychiatric symptoms in a retrospective cohort of Long COVID patients (N = 162) at a Rehabilitation Medicine clinic in South Texas. Clinical data from patient records were used to calculate a Symptom Score, and screening tools for stress/PTSD (PCL-5), depression (PHQ-9), anxiety (GAD-7), and quality of life (SWL) were employed to evaluate if Long COVID duration and severity could predict neuropsychiatric outcomes. The majority were female (71%) and Hispanics (53%) who presented for treatment of Long COVID symptoms during the study period, including fatigue (93%), coughing/shortness of breath (81%), fever (67%), anosmia (58%), ageusia (54%), and weight loss (56%). A minority of participants were hospitalized (N = 49) or required ventilator support (N = 5) during acute infection. There was a high burden of neuropsychiatric symptoms, including subjective cognitive impairment (79%), headache (74%), and insomnia (58%). Symptom Score (median = 9, IQR [8,11]) was significantly correlated with increased depression (PHQ-9; p < 0.05), anxiety (GAD-7; p < 0.05) and elevated stress/PTSD (PCL-5; p < 0.05) symptoms. Long COVID patients taking stimulants or mood stabilizers had higher GAD-7 (p < 0.031, p < 0.035) and PHQ-9 (p < 0.034, p < 0.009) scores but not PCL-5 scores. Importantly, duration of Long COVID symptomatology also did not predict PCL-5 scores. No patient factors (e.g., sex, age, BMI, ethnicity) mediated Symptom Score. Nonetheless, historically marginalized groups, such as women and Hispanics, have been disproportionately affected by COVID-19. This study is the first to utilize validated screening tools to determine the presence and severity of neuropsychiatric symptoms in Long COVID patients. These findings may guide clinical management and future research on Long COVID, especially in historically excluded populations. SCOPE STATEMENT: We enthusiastically submit our Original Research article, entitled “ Assessing Neuropsychiatric Symptoms in Long COVID: A Retrospective Cohort Study from a South Texas Long COVID Clinic ” for consideration for publication in the journal Frontiers in Neurology. We believe the scope of our article aligns well with the scope and aim of the journal’s Neurorehabilitation Section. Long COVID is a debilitating neurological disorder with prominent and enduring cognitive and psychological impact. This study sought to characterize Long COVID symptoms from a cohort of patients at a Rehabilitation Medicine/Long COVID clinic in Southwest Texas. We stratified symptoms using validated psychiatric evaluation tools (e.g., PCL-5, GAD-7, PHQ-9, SWL) to determine if and to what extent psychiatric comorbidity exacerbated Long COVID symptoms. Our findings suggest that a Long COVID patient’s depression, anxiety, and stress/post traumatic stress scores are highly correlated with other neurological symptoms. We advance the implementation of a Long COVID “Symptom Score”, as well as the use of validated screening instruments to identify psychiatric features of Long COVID with the goal of maximizing life satisfaction and function over the course of treatment.

The Covid Long patient, highlighting a process for legitimising the patient experience;Le patient Covid Long, mise en évidence d'un processus de légitimation de l'expérience patient

The patient experience is a variable that has been neglected or even ignored for a long time in the health field, even if it is an important resource for improving and adapting patient care. The patient empowerment initiated in the 1970s is the main manifestation of what can be described as a new paradigm: a more and more active patient, actor of health and his care and finally with a specific form of expertise. This evolution is founded on a process of legitimisation in order that the lived experience of illness can be transformed into an accepted and recognised expertise. The health crisis caused by the coronavirus 19 offers a particularly relevant field for studying this process of legitimation. Indeed, many patients infected with covid 19 report persistent and acute symptoms that are not explained at first and therefore not recognised by society and institutions. Based on the literature on legitimacy and by studying the action of the French association #ApresJ20 we identify the main stages of the process of recognition of Covid Long patient expertise. ; L'expérience patient constitue une variable longtemps délaissée voire ignorée dans le champ sanitaire, pourtant elle est une ressource importante pour faire évoluer et adapter les prises en charge des patients. L'empowerment du patient initié dans les années 1970 est la principale manifestation de ce qui peut être qualifié de nouveau paradigme : un patient de plus en plus actif, acteur de la santé et de ses soins et finalement doté d'une forme particulière d'expertise. Cette évolution repose sur un processus de légitimation afin que l'expérience vécue de la maladie puisse se transformer en expertise reconnue et admise de tous. La crise sanitaire provoquée par le coronavirus 19 offre un terrain d'étude de ce processus de légitimation particulièrement pertinent. En effet, de nombreux patients infectés pas la covid 19 rapportent des symptômes persistants et aigus non expliqués dans les premiers temps et donc non reconnus par la société et les institutions. En nous appuyant sur les travaux portant sur la légitimité et en étudiant l'action de l'association française #ApresJ20 nous dégageons les grandes étapes du processus de reconnaissance de l'expertise patient Covid Long.

COVID-19 pandemic effects on neonatal inpatient admissions and mortality: interrupted time series analysis of facilities implementing NEST360 in Kenya, Malawi, Nigeria, and Tanzania

Background The emergence of COVID-19 precipitated containment policies (e.g., lockdowns, school closures, etc.). These policies disrupted healthcare, potentially eroding gains for Sustainable Development Goals including for neonatal mortality. Our analysis aimed to evaluate indirect effects of COVID-19 containment policies on neonatal admissions and mortality in 67 neonatal units across Kenya, Malawi, Nigeria, and Tanzania between January 2019 and December 2021. Methods The Oxford Stringency Index was applied to quantify COVID-19 policy stringency over time for Kenya, Malawi, Nigeria, and Tanzania. Stringency increased markedly between March and April 2020 for these four countries (although less so in Tanzania), therefore defining the point of interruption. We used March as the primary interruption month, with April for sensitivity analysis. Additional sensitivity analysis excluded data for March and April 2020, modelled the index as a continuous exposure, and examined models for each country. To evaluate changes in neonatal admissions and mortality based on this interruption period, a mixed effects segmented regression was applied. The unit of analysis was the neonatal unit ( n  = 67), with a total of 266,741 neonatal admissions (January 2019 to December 2021). Results Admission to neonatal units decreased by 15% overall from February to March 2020, with half of the 67 neonatal units showing a decline in admissions. Of the 34 neonatal units with a decline in admissions, 19 (28%) had a significant decrease of ≥ 20%. The month-to-month decrease in admissions was approximately 2% on average from March 2020 to December 2021. Despite the decline in admissions, we found no significant changes in overall inpatient neonatal mortality. The three sensitivity analyses provided consistent findings. Conclusion COVID-19 containment measures had an impact on neonatal admissions, but no significant change in overall inpatient neonatal mortality was detected. Additional qualitative research in these facilities has explored possible reasons. Strengthening healthcare systems to endure unexpected events, such as pandemics, is critical in continuing progress towards achieving Sustainable Development Goals, including reducing neonatal deaths to less than 12 per 1000 live births by 2030.

Patient-Made Knowledge Networks: Long COVID Discourse, Epistemic Injustice, and Online Community Formation

Long COVID represents an unprecedented case of patient-led illness definition, emerging through Twitter in May 2020 when patients began collectively naming, documenting, and legitimizing their condition before medical institutions recognized it. This study examines 2.8 million tweets containing #LongCOVID to understand how contested illness communities construct knowledge networks and respond to epistemic injustice. Through topic modeling, reflexive thematic analysis, and exponential random graph modeling (ERGM), we identify seven discourse themes spanning symptom documentation, medical dismissal, cross-illness solidarity, and policy advocacy. Our analysis reveals a differentiated ecosystem of user roles -- including patient advocates, research coordinators, and citizen scientists -- who collectively challenge medical gatekeeping while building connections to established ME/CFS advocacy networks. ERGM results demonstrate that tie formation centers on epistemic practices: users discussing knowledge sharing and community building formed significantly more network connections than those focused on policy debates, supporting characterization of this space as an epistemic community. Long COVID patients experienced medical gaslighting patterns documented across contested illnesses, yet achieved WHO recognition within months -- contrasting sharply with decades-long struggles of similar conditions. These findings illuminate how social media affordances enable marginalized patient populations to rapidly construct alternative knowledge systems, form cross-illness coalitions, and contest traditional medical authority structures.

Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life

BACKGROUND: Post-acute sequelae of SARS-CoV-2, referred to as “long COVID”, are a globally pervasive threat. While their many clinical determinants are commonly considered, their plausible social correlates are often overlooked. METHODS: Here, we use data from a multinational prospective cohort study to compare social and clinical predictors of differences in quality of life with long COVID. We further measure the extent to which clinical intermediates may explain relationships between social variables and quality of life with long COVID. FINDINGS: Beyond age, neuropsychological and rheumatological comorbidities, educational attainment, employment status, and female sex were important predictors of long COVID-associated quality of life days (long COVID QALDs). Furthermore, most of their associations could not be attributed to key long COVID-predicting comorbidities. In Norway, 90% (95% CI: 77%, 100%) of the adjusted association between belonging to the top two quintiles of educational attainment and long COVID QALDs was not explained by these clinical intermediates. The same was true for 86% (73%, 100%) and 93% (80%,100%) of the adjusted association between full-time employment and long COVID QALDs in the United Kingdom (UK) and Russia. Additionally, 77% (46%,100%) and 73% (52%, 94%) of the adjusted associations between female sex and long COVID QALDs in Norway and the UK were unexplained by the clinical mediators. INTERPRETATION: Our findings highlight that socio-economic proxies and sex are key predictors of long COVID QALDs and that other (non-clinical) mechanisms drive their observed relationships. Importantly, we outline a multi-method, adaptable causal approach for evaluating the isolated contributions of social disparities to experiences with long COVID. FUNDING: UK Foreign, Commonwealth and Development Office; Wellcome Trust; Bill & Melinda Gates Foundation; Oxford COVID-19 Research Response Funding; UK National Institute for Health and Care Research; UK Medical Research Council; Public Health England; Liverpool Experimental Cancer Medicine Centre; Research Council of Norway; Vivaldi Invest A/S; South Eastern Norway Health Authority

Immunological Density Shapes Recovery Trajectories in Long COVID

Post-acute sequelae of SARS-CoV-2 infection (Long COVID) frequently persists for months, yet drivers of clinical remission remain incompletely defined. Here we analyzed 97,564 longitudinal PASC assessments from 13,511 participants with linked vaccination histories to disentangle passive temporal progression from vaccine-associated change. Using a clinically validated threshold (PASC $\geq 12$), trajectories separated into three phenotypes: Protected (persistently sub-threshold), Refractory (persistently symptomatic), and Responders (transitioning from symptomatic to recovered). Across the full cohort, symptom severity increased modestly with elapsed time ($r=0.0521$, $P=1.26\times10^{-59}$), whereas cumulative vaccination showed an inverse association with severity ($r=-0.0434$, $P=5.95\times10^{-42}$). In summary, baseline Long COVID severity appears clinically deterministic. In the absence of intervention, symptoms typically persist without spontaneous resolution. Recovery is primarily associated with repeated immunization.

VEGFA sex-specific signature is associated to long COVID symptom persistence

Background Long COVID involves persistent symptoms after COVID-19 recovery, affecting multiple organ systems for months or years. Risk factors include female sex, prior chronic conditions, severe SARS-CoV-2 infection, reinfections, and lack of vaccination. As a major public health concern, ongoing research continues to investigate its causes, mechanisms, and long-term effects. Methods Proteomic expression analysis of 171 individuals, in two time points, with confirmed SARS-CoV-2 infection, including 133 long COVID patients from the deeply characterized COVICAT cohort, assessed 1395 protein biomarkers using Olink® technology. Statistical analyses with linear mixed models examined protein expression changes, long COVID status, and sex-specific differences. Functional analysis included gene set enrichment analysis and protein–protein interaction networks. Results Findings revealed VEGFA overexpression in long COVID patients (effect size 0.322, SE = 0.098, p  = 0.0013), along with sex-specific expression patterns and the influence of sex-hormonal status in females, with significant overexpression of circulating VEGFA levels specifically in postmenopausal women (Mann–Whitney U test p value = 8.55 × 10^−3). Network analysis identified 109 nodes and 274 edges, with VEGFA ranking highest in centrality. Dysregulated chemokine signaling, complement activation, and viral reactivation were also confirmed, consistent with prior studies. Conclusions Using high-throughput proteomic profiling in a population-based cohort, we observed that vascular dysfunction, particularly involving VEGFA, is a key feature of long COVID, especially in milder cases, with significant overexpression of VEGFA in postmenopausal women. Sex-specific proteomic patterns suggest distinct recovery mechanisms, highlighting the need to consider sex, vascular health, and disease severity in the pathogenesis and management of long COVID.

Predicting psychological distress in advanced ovarian cancer patients during the COVID-19 pandemic

Purpose This longitudinal study investigated distress rates in patients with advanced ovarian cancer during the COVID-19 pandemic and examined whether time, illness representations, and coping strategies predicted distress levels. Methods UK patients with stage 3 or 4 ovarian cancer were recruited between September 2020 and March 2021. Data were collected at baseline (T0), 2 months (T1), and 4 months (T2) post-enrolment. Validated questionnaires assessed distress (anxiety, depression, PTSD, fear of progression) and predictors (coping strategies and illness perceptions), analysed via multilevel modelling. Results Seventy-two participants returned a questionnaire at T0, decreasing to 49 by T2. High distress was observed, with over 50% of participants experiencing anxiety and depression consistently. Nearly 60% reported clinical levels of fear of progression at some point. PTSD rates resembled the general population. Although distress levels remained stable over time, some individual variability was observed. Time had minimal effect on distress. Coping strategies and illness perceptions remained stable. Threatening illness perceptions consistently predicted distress, while specific coping strategies such as active coping, acceptance, self-blame, and humour predicted various aspects of distress. Together, these factors explained up to half of the distress variance. Conclusion The findings have implications for routine screening for distress and the inclusion of psychological treatment pathways in advanced ovarian cancer care. Addressing illness representations is crucial, with attention to informational support. Future research should explore the long-term effects of heightened distress and the effectiveness of interventions targeting illness perceptions. This study informs current clinical practice and future pandemic preparedness in cancer care.

Primary health care utilisation and delivery in remote Australian clinics during the COVID-19 pandemic

Introduction The COVID-19 pandemic period (2020 to 2022) challenged and overstretched the capacity of primary health care services to deliver health care globally. The sector faced a highly uncertain and dynamic period that encompassed anticipation of a new, unknown, lethal and highly transmissible infection, the introduction of various travel restrictions, health workforce shortages, new government funding announcements and various policies to restrict the spread of the COVID-19 virus, then vaccination and treatments. This qualitative study aims to document and explore how the pandemic affected primary health care utilisation and delivery in remote and regional Aboriginal and Torres Strait Islander communities. Methods Semi-structured interviews were conducted with staff working in 11 Aboriginal Community-Controlled Health Services (ACCHSs) in outer regional, remote and very remote Australia. Interviews were transcribed, inductively coded and thematically analysed. Results 248 staff working in outer regional, remote and very remote primary health care clinics were interviewed between February 2020 and June 2021. Participants reported a decline in numbers of primary health care presentations in most communities during the initial COVID-19 lock down period. The reasons for the decline were attributed to community members apprehension to go to the clinics, change in work priorities of primary health care staff (e.g. more emphasis on preventing the virus entering the communities and stopping the spread) and limited outreach programs. Staff forecasted a future spike in acute presentations of various chronic diseases leading to increased medical retrieval requirements from remote communities to hospital. Information dissemination during the pre-vaccine roll-out stage was perceived to be well received by community members, while vaccine roll-out stage information was challenged by misinformation circulated through social media. Conclusions The ability of ACCHSs to be able to adapt service delivery in response to the changing COVID-19 strategies and policies are highlighted in this study. The study signifies the need to adequately fund ACCHSs with staff, resources, space and appropriate information to enable them to connect with their communities and continue their work especially in an era where the additional challenges created by pandemics are likely to become more frequent. While the PHC seeking behaviour of community members during the COVID-19 period were aligned to the trends observed across the world, some of the reasons underlying the trends were unique to outer regional, remote and very remote populations. Policy makers will need to give due consideration to the potential effects of newly developed policies on ACCHSs operating in remote and regional contexts that already battle under resourcing issues and high numbers of chronically ill populations.

COVID-19 induces persistent transcriptional changes in adipose tissue that are not associated with Long COVID

Long COVID is a heterogeneous condition characterized by a wide range of symptoms that persist for 90 days or more following SARS-CoV-2 infection. Now more than five years out from the onset of the SARS-CoV-2 pandemic, the mechanisms driving Long COVID are just beginning to be elucidated. Adipose tissue has been proposed as a potential reservoir for viral persistence and tissue dysfunction contributing to symptomology seen in Long COVID. To test this hypothesis, we analyzed subcutaneous adipose tissue (SAT) from two cohorts: participants with subacute COVID-19 (28–89 days post-infection) compared to pre-pandemic controls, and participants with Long COVID compared to those with those classified as “indeterminate” based on the RECOVER-Adult Long COVID Research Index (12-47 months post-infection). We found no evidence of persistent SARS-CoV-2 RNA in adipose tissue in any participant. SAT from participants with subacute COVID-19 displayed significant transcriptional remodeling, including depleted immune activation pathways and upregulated Hox genes and integrin interactions, suggesting resident immune cell exhaustion and perturbations in tissue function. However, no consistent changes in gene expression were observed between Long COVID samples and samples from indeterminant participants. Thus, SAT may contribute to inflammatory dysregulation following COVID-19, but does not appear to play a clear role in Long COVID pathophysiology. Further research is needed to clarify the role of adipose tissue in COVID-19 recovery.

"If it has an exclamation point, I step away from it, I need facts, not excited feelings": Technologically Mediated Parental COVID Uncertainty

As a novel virus, COVID introduced considerable uncertainty into the daily lives of people all over the globe since late 2019. Relying on twenty-three semi-structured interviews with parents whose children contracted COVID, we analyzed how the use of social media moderated parental uncertainty about the symptoms, prognosis, long-term potential health ramifications of infection, vaccination, and other issues. We framed our findings using Mishel's Uncertainty in Illness theory. We propose new components to the theory that account for technological mediation in uncertainty. We also propose design recommendations to help parents cope with health uncertainty using social media.

Statistical and Predictive Analysis to Identify Risk Factors and Effects of Post COVID-19 Syndrome

Based on recent studies, some COVID-19 symptoms can persist for months after infection, leading to what is termed long COVID. Factors such as vaccination timing, patient characteristics, and symptoms during the acute phase of infection may contribute to the prolonged effects and intensity of long COVID. Each patient, based on their unique combination of factors, develops a specific risk or intensity of long COVID. In this work, we aim to achieve two objectives: (1) conduct a statistical analysis to identify relationships between various factors and long COVID, and (2) perform predictive analysis of long COVID intensity using these factors. We benchmark and interpret various data-driven approaches, including linear models, random forests, gradient boosting, and neural networks, using data from the Lifelines COVID-19 cohort. Our results show that Neural Networks (NN) achieve the best performance in terms of MAPE, with predictions averaging 19\% error. Additionally, interpretability analysis reveals key factors such as loss of smell, headache, muscle pain, and vaccination timing as significant predictors, while chronic disease and gender are critical risk factors. These insights provide valuable guidance for understanding long COVID and developing targeted interventions. ;Comment: 8 pages, 9 figures, 2 tables, initially submitted in IJCNN 2025, but rejected because of the high number of contributions (requested to be presented as a poster in the conference without being published in conference proceedings)

The heightened impact of COVID-19 on emotional wellbeing and delays in care in patients with cutaneous T-cell lymphoma: a cross-sectional study

Diagnostic Accuracy of the Updated FujiLAM II Assay to Detect Tuberculosis in Outpatients With Advanced HIV Disease

BACKGROUND: An urgent need exists for point-of-care diagnostics to detect tuberculosis (TB) among people with advanced human immunodeficiency virus (HIV) disease. The Fujifilm SILVAMP TB LAM (FujiLAM II) is a novel point-of-care assay that detects mycobacterial lipoarabinomannan (LAM) antigen in the urine to identify TB. We present a validation of the FujiLAM II assay on prospectively collected urine samples from outpatient adults with advanced HIV disease in Uganda. METHODS: We performed a prospective diagnostic accuracy study of the FujiLAM II assay among outpatients with advanced HIV disease at 16 clinics Uganda. FujiLAM II was run on cryopreserved urine. We determined diagnostic performance for the FujiLAM II test against the cases of confirmed TB (Xpert or mycobacterial culture positive) versus cases without TB. We additionally assessed sensitivity and specificity of the FujiLAM II assay as compared with confirmed TB among pre-specified subgroups: CD4 <100 cells/µL versus 100–200 cells/µL, antiretroviral therapy (ART) naive versus experienced, and CRP <5 mg/L versus ≥5 mg/L. RESULTS: Among 583 participants who had FujiLAM II testing performed, the FujiLAM II assay demonstrated 54% (25/46) (95% confidence interval [CI]: 40%–69%) sensitivity to identify confirmed TB and 95% (326/342) (95% CI: 93% to 98%) specificity to identify the absence of TB. Among participants with CD4 cell counts <100 cells/µL, FujiLAM II sensitivity was 69% (95% CI: 53%–85%) and specificity was 94% (95% CI: 90%–97%). Among participants with serum C-reactive protein (CRP) ≥5 mg/L, the FujiLAM II assay exhibited a sensitivity of 62% (95% CI: 46%–77%) and specificity of 93% (95% CI: 89%–98%). CONCLUSIONS: FujiLAM II is a point-of-care, non-sputum based TB diagnostic with moderate sensitivity and high specificity in outpatients with advanced HIV disease.

Prevalence and risk factors of human papilloma virus infection among women living with HIV, Egypt, a cross sectional study

Background HPV is considered the most common sexually transmitted infection. It is responsible of 70% of cervical cancers worldwide. HIV infection is associated with increased rates of HPV infection. Women Living With HIV (WLWH) are 6 times at greater risk of developing cervical cancer. The current study aimed to estimate prevalence and identify genotypes of HPV infection among WLWH in Egypt compared to women with negative HIV status and determine associated risk factors. Methods The study conducted among 251 WLWH and 268 women with negative HIV status enrolled from gynecological clinics in primary health care centers from nine Egyptian governorates. Data was collected from participants using a structured interview questionnaire and cervical samples were collected for HPV DNA detection and genotyping. Results The overall prevalence of HPV infection was 13.5%, 3.4% among women with HIV negative status and 24.4% among WLWH. HR-HPVs other than genotype 16 and 18 were isolated from 71% of infected women. Woman’s age, age at first marriage, number of lifetime marriages and drug addiction are significant predictors for HPV infection (odds 0.96, 0.91, 2.06, 2.01 respectively). Conclusion HPV infection is more prevalent among WLWH. Infection with HR-HPV other than genotype 16 and 18 was the most prevalent among infected women in both groups. Young age, early life sexual activity, having more than one sexual partner during the life time, and drug addiction are independent predictors for HPV infection. Having a husband who has had other sexual partners is significantly associated with infection.

Sleep Disturbance Mediates the Associations Between HIV Stigma and Mental and Physical Health Among Black Adults with HIV

Objectives Black Americans have been disproportionally affected by the HIV epidemic, and experience significant disparities in sleep health, mental health, and physical health domains. Using longitudinal data from a sample of Black adults with HIV, the current study examined the associations between stigma and mental and physical health outcomes and how sleep disturbance may play a mediating role. Methods Data were drawn from a recent randomized controlled trial. Questionnaires were used to examine internalized and anticipated HIV stigma, perceived discrimination (enacted stigma) based on multiple social identities (i.e., HIV-serostatus, race, sexual orientation), sleep disturbance, mental health problems (depressive and posttraumatic stress disorder [PTSD] symptoms), and mental and physical health-related quality of life (HRQOL) at baseline, 7-month follow-up, and 13-month follow-up assessments. Linear mixed modeling was used to examine main effects of stigma on health outcomes; causal mediation analysis was used to estimate indirect paths through sleep disturbance. Results Internalized and anticipated HIV stigma and multiple discrimination were associated with more sleep disturbance, more depressive and PTSD symptoms, and poorer mental and physical HRQOL. Results also indicated significant indirect paths (i.e., mediation) through greater sleep disturbance between HIV-related stigma and discrimination and mental health and health-related quality of life. Conclusions Results support that sleep disturbance is a mediating pathway through which different forms of stigmas impact health outcomes. Sleep may be an intervention target to help improve mental and physical well-being and reduce health disparities among racial and ethnic minority people with HIV.

Clinical-epidemiological profile and factors associated with viral non-suppression in patients living with HIV/AIDS assisted at the Integrated Treatment Center at the Maputo Military Hospital (CITRA/MMH), 2019 to 2020

Background HIV remains a critical global public health challenge. In 2022, it was estimated that approximately 39.0 million people worldwide were living with HIV, and of these, around 29.8 million were receiving antiretroviral therapy (ART). The objective was to evaluate the clinical and epidemiological profile and factors associated with viral load (VL) non-suppression in people living with HIV/AIDS at the Maputo Military Hospital (CITRA/MMH). Methods A retrospective cross-sectional analytical study was conducted on 9105 people aged 15 years and over. We use secondary data from participants on ART for at least 2 years being followed up between the years 2019–2020 at CITRA/MMH. Those recently enrolled (on ART < 1 year) were excluded and data analysis was performed using STATA version 16. Pearson's chi-square test and logistic regression were used for statistical modeling of viral non-suppression with a 95%/CI confidence interval and p  < 0.05. Results Among a total of 9105 HIV participants included, 52.8% ( n  = 4808) were female and 13.6% ( n  = 1235) were military personnel. The average age was 47.9 years (standard deviation ± 12.1), with the most prevalent age group being individuals aged between 25 and 59, totalizing 7,297 (80.2%) participants. Only 5395 (100%) participants had VL results. Among these, 23.1% ( n  = 1247) had a result VL non-suppressed. Single marital status (Adjusted Odds Ratio [AOR] = 4.8, 95%CI: 3.93–5.76, p  < 0.001), with active tuberculosis (AOR = 4.6, 95%CI: 3.15–6.63, p  < 0.001) and current ART regimen in categories TDF + 3TC + EFV (AOR = 12.7, 95%CI: 9.74–16.63, p  < 0.001), AZT + 3TC + NVP (AOR = 21.8, 95% CI: 14.13–33.59, p  < 0.001) and “other” regimens (AOR = 25.8, 95%CI: 18.58–35.80, p  < 0.001), when compared to the TDF + 3TC + DTG regime, were statistically significant for viral non- suppression. Conclusion The study highlights the crucial role of ART adherence and ongoing monitoring to achieve viral suppression, particularly among adults aged 25 to 59. It underscores the need for transitioning eligible individuals to DTG-based regimens and addressing the implications of single marital status and comorbid conditions like active tuberculosis. The study emphasizes the importance of ARV adherence and continuous monitoring to meet the UNAIDS 95–95-95 targets and improve clinical outcomes for people living with HIV/AIDS.

Effectiveness and safety of darunavir and lopinavir for treating children and adolescents living with HIV: a systematic review

Introduction Darunavir and lopinavir solid formulations are recommended protease inhibitors for treating HIV. An evidence review of their effectiveness/efficacy and safety in young populations was needed to inform the 2025 World Health Organization guidelines. Methods We conducted a systematic review of publications reporting efficacy/effectiveness and/or safety of darunavir and/or lopinavir solid formulations in children and/or adolescents with HIV aged 0–19 years, with at least 6 months follow-up. Sources included MEDLINE, Embase, Cochrane Library, recent HIV conferences and trial registries. Data were synthesized narratively. Results We screened 4,196 abstracts and 421 papers. Thirteen studies (including two randomized controlled trials (RCTs)) on darunavir were identified in 1,895 children/adolescents (2% treatment-naïve). Among treatment-experienced participants on ritonavir-boosted darunavir, 86% (198/229) and 95% (143/150) had viral suppression (< 50 or < 400 copies/mL) at 48 weeks in the RCTs and 29% (4/14)-100% (12/12) across seven other studies ( N  = 736), respectively. Participants in the four studies ( N  = 184) with < 70% suppression were heavily treatment-experienced and unsuppressed at darunavir start. Grade 3/4 adverse events (clinical/laboratory) occurred in 0%-30% of participants on ritonavir-boosted darunavir (seven studies, N  = 515; 8–9% in two large RCTs), and adverse events leading to discontinuation or treatment modification in 0%-10% (10 studies, N  = 974; 2–3% in two RCTs). Three grade 3/4 adverse events in one study were drug related. Twenty-nine studies (including four randomized trials) on ritonavir-boosted lopinavir solid formulations were identified in 3,605 children/adolescents (17% treatment-naïve). Among treatment-experienced children/adolescents at 48–52 weeks, viral suppression was 80% (179/223) in one RCT and 53% (448/852)-100% (12/12) across five other studies (N = 1305). Across all populations, grade 3/4 adverse events were reported in 8%-60% of participants (four studies, N = 1405; 8–13% in two large RCTs), and events leading to discontinuation or treatment modification in 0%-9% (seven studies, N = 905; 2–3% in two RCTs). Five participants experienced drug-related grade 3/4 adverse events (five studies, N = 1107). Lipid levels were significantly greater with lopinavir versus other regimens (five studies). Conclusions Darunavir was safe in treatment-experienced children and adolescents, and viral suppression was high in RCTs; data were limited in treatment-naïve populations. Viral suppression with lopinavir solid formulations was variable. Lopinavir was generally well-tolerated but compared unfavourably with other drugs in terms of lipid outcomes. PROSPERO number CRD42020204432.

Condom Failure Among HIV-Negative Men in Serodiscordant Relationships in Australia, Brazil, and Thailand

Condoms continue to be used by many gay, bisexual, and other men who have sex with men (GBM) to reduce the risk of HIV transmission. However this is impacted by condom failure events, defined here as condom breakage and slippage. In a prospective, observational cohort study of 343 HIV serodiscordant male couples recruited through high HIV caseload clinics and hospitals between 2012 and 2016 in Australia, Brazil, and Thailand, condom failure rates and associated factors were analysed, including with the study partner versus other sexual partners. There were 717 reported instances of condom failure from an estimated total of 25,831 sex acts with condoms, from over 588.4 participant years of follow up. Of the HIV-negative partners (n = 343) in the study, more than a third (n = 117, 36.7%) reported at least one instance of condom failure with any partner type during study follow-up. Condom failure with their study partner was reported by 91/343 (26.5%) HIV-negative partners, compared with 43/343 (12.5%) who reported condom failure with other partners. In total, there were 86 events where the HIV-negative partner experienced ano-receptive condom failure with ejaculation, representing 12.0% of all failure events. In multivariable analysis, compared to Australia, HIV-negative men in Brazil reported a higher incidence risk rate of condom failure (IRR = 1.64, 95%CI 1.01–2.68, p = 0.046) and HIV-negative men who reported anal sex with other partners reported an increased risk of condom failure compared with men who only had sex with their study partner (IRR = 1.89, 95%CI 1.08–3.33, p = 0.025). Although at least one event of condom failure was reported by a significant proportion of participants, overall condom failure events represented a small proportion of the total condom protected sex acts.

The managers' perspectives on service providing in women's harm reduction centers during the COVID-19 pandemic: mixed method study

Background The COVID-19 pandemic posed significant challenges for managers overseeing women's harm reduction centers. This study seeks to capture managers' perspectives on the service providing in women's harm reduction centers during the COVID-19 pandemic. Methods This convergent mixed-method study conducted in three provinces of Iran: Tehran, Khuzestan, and Kermanshah. The study was carried out between January and May 2023. In the quantitative part, the researchers utilized reports from 10 center managers. A researcher-designed questionnaire was employed to collect data on a wide range of services and referrals provided by the centers. The qualitative part of the research involved conventional content analysis and included 12 individual interviews. Two directors from the Ministry of Health and ten managers of women’s harm reduction centers participated in the interviews. During the interpretation phase, the researchers compared the quantitative and qualitative findings to obtain a comprehensive understanding of the topic. Results During the quantitative stage of the study, it was observed that all the managers were women, with an average age of 40.7 ± 7 years. More than half of the managers had obtained a postgraduate education (n = 6, 60%), and a majority of them were married (n = 7, 70%). Additionally, 40% of the managers (n = 4) were working as contractors. During the non-COVID-19 period, there were higher coverage by centers and residents, more referrals of non-injecting drug users and sex workers, and a higher number of group counseling sessions in all three provinces compared to the COVID-19 period. The qualitative analysis revealed two primary themes: "challenges" and "capabilities." Conclusions During the COVID-19 pandemic, providing some services and client referrals decreased in the centers, and center managers faced increased challenges. Many of these challenges were in the communication, executive, management, structure, education, financial, civilization, facilities, and socio-cultural sectors. Managers used their skills to manage and control these challenges. It is important to focus on these challenges and managerial capabilities to effectively handle future crises.

Network-Based Interventions for HIV Prevention via Cascade-Aware Suppression of Transmission

Treating and preventing Human Immunodeficiency Virus (HIV) remains a critical global health challenge. While antiretroviral therapy provides a path toward viral suppression -- effectively eliminating an individual's transmission risk -- systemic resource constraints limit the reach of intervention efforts. This work addresses the strategic distribution of intensive resources among virally unsuppressed individuals to minimize the expected cascade of new infections within a transmission network. We formalize this challenge as a novel constrained optimization problem where we have resources to "treat" $k$ out of a set $\mathbf{P}$ of virally unsuppressed individuals, and establish its theoretical connections to existing computational literature. We then propose Cascade-Aware Suppression of Transmission (CAST), a polynomial-time $(δ, ε)$-approximation algorithm that achieves a $2\sqrt{|\mathbf{P}|}$ approximation ratio by leveraging connections to the Minimum-$k$-Union (MkU) problem and Hoeffding-style concentration bounds. Extensive evaluations on real-world HIV networks demonstrate that CAST outperforms standard public health and computer science baselines. Furthermore, we show that CAST is empirically robust across diverse infectious disease networks, varied edge probability initializations, and settings involving imperfect network data.

Pregnancy and neonatal outcomes of HIV infection in pregnant women in Iran: a systematic review and meta-analysis

Background HIV infection is a major public health concern among pregnant women globally. Beyond the risk of mother-to-child transmission, it can lead to serious complications and negative pregnancy and neonatal outcomes. In Iran, there is currently no comprehensive meta-analysis evidence measuring the adverse pregnancy and neonatal complications of HIV infection among pregnant women. The review objective is to provide pregnancy outcomes, the prevention of mother-to-child transmission (PMTCT), and antiviral therapy (ART) of pregnant women infected with HIV in Iran. Methods The review systematically searched PubMed, Scopus, ISI, Embase, and also the Iranian national databases up to 25th September 2024. Any records that reported pregnancy and neonatal outcomes among pregnant women in Iran were included in the inclusion criteria. The study outcomes included any pregnancy and neonatal implications related to HIV infection in pregnant women, as well as PMTCT, ART therapy, delivery type, and HIV morbidity in spouse. Meta-analysis was carried out to estimate pooled prevalence with 95% confidence intervals (CIs) for all the study outcomes. Results The study pooled data from 497 pregnant women infected with HIV across seven eligible studies. The majority of participants had no university education, lived in urban areas, and were infected by their spouses. The most frequent adverse pregnancy outcome was abortion. The pooled proportions and 95% CIs for each outcome were: HIV-positive spouses 78% (95% CI: 73–83%), wanted pregnancy 63% (95% CI: 46–80%), ART and/or PMTCT uptake 94% (95% CI: 89–98%), cesarean delivery 88% (95% CI: 77–98%), live birth rate 98% (95% CI: 96–100%), newborns who were HIV positive 1.0% (95% CI: 0.0–1.0%), and newborn prophylaxis 100% (95% CI: 99–100%). Conclusion Although most pregnant women with HIV are enrolled in PMTCT programs and adverse pregnancy and neonatal outcomes are rare, longitudinal, population-based studies are still needed to better understand the situation of pregnant women with HIV in Iran.

Humoral Immunity Against Orthopoxvirus in Vaccinated and Unvaccinated Individuals during 2022 Mpox Outbreak

Little is known about serological responses to MVA-BN (JYNNEOS) against mpox in elderly individuals with or without HIV. In this study, MVA-BN induced sustained IgG levels regardless of HIV status even up to one year. Birth before 1973 correlated with higher IgG. MVA-BN unvaccinated individuals with HIV had lower IgG than vaccinated.

Safety and immunogenicity of CD40.HIVRI.Env, a dendritic cell-based HIV vaccine, in healthy HIV-uninfected adults: a first-in-human randomized, placebo-controlled, dose-escalation study (ANRS VRI06)

BACKGROUND: Current HIV prophylactic vaccines evaluate HIV Env as purified proteins. CD40.HIVRI.Env is an innovative antigen delivery targeting gp140 Env from HIV Clade C 96ZM651 to CD40-expressing antigen-presenting cells, thus harnessing the intrinsic immune-stimulant properties. DNA-HIV-PT123 vaccine encodes 96ZM651 gp140/Gag and 97CN54 Pol/Nef. METHODS: Seventy-two HIV-negative volunteers were enrolled between 05/2021 and 10/2022 in a phase 1 placebo-controlled trial conducted in France and Switzerland (N° EudraCT: 2020-001814-40; NCT04842682). Volunteers were randomized (5:1 active versus placebo) in groups receiving either 0.3, 1.0, or 3.0 mg CD40.HIVRI.Env (Hiltonol® adjuvanted) alone or co-administered with DNA-HIV-PT123 at weeks (W) 0, 4, and 24. Safety and immunogenicity were monitored until W48. The primary safety endpoint was the proportion of participants per dose cohort and randomized arm without any grade 3 or 4 biological (abnormal laboratory values), or clinical local or systemic solicited, or unsolicited adverse events between W0 and W48 considered to be related or possibly related to the investigational products. FINDINGS: CD40.HIVRI.Env was well tolerated. Env-specific CD4(+) T-cells (IL-2(+) or IFN-γ(+) or TNF(+)) were detected in all vaccinees from W6 to W26 and persisted until W48 without a dose–response signal or an effect of DNA-HIV-PT123 co-administration. At W26, IgG response rates (RR) against autologous and nine heterologous gp120/gp140 were 89–100% across all groups and 56–100% at W48. RR against 96ZM651gp70V1V2 were high (90–100%) at W6 and W26 in all groups. Tier1A MW965.26 neutralizing antibody (nAb) titres were detectable in 50–100% of vaccinated individuals at W26, with a dose–response signal, while one volunteer developed nAbs against five Tier2 viruses. INTERPRETATION: CD40.HIVRI.Env alone or administered with DNA-HIV-PT123 was safe and induced early, and sustained anti-Env cellular and V1V2 IgG responses, identified as correlates of protection in the RV144 trial. CD40 targeting Env-based vaccines may be instrumental for inducing protective vaccine responses in prime-boost strategies. FUNDING: ANRS Emerging infectious diseases (ANRS MIE); Vaccine Research Institute (VRI).

Knowledge, and utilization of HIV self-testing, and its associated factors among women in sub–Saharan Africa: evidence from 21 countries demographic and health survey

Background HIV Self-Testing (HIVST) holds great significance in the fight against the HIV epidemic in Sub-Saharan Africa (SSA). It offers a convenient and confidential option for individuals to know their HIV status and seek appropriate care and support. For women in this region, where stigma, discrimination, and lack of access to healthcare services are prevalent, HIVST can empower them to take control of their health and make informed decisions. However, no study in the region has been conducted on this topic. Hence, this study aimed to fill the evidence, and population gaps by identifying women’s HIVST knowledge, and utilization, and its associated factors in SSA. Methods The data used were gathered from the most recent demographic and health surveys conducted in SSA nations between 2015 and 2022. We incorporated DHS data from 21 countries into our investigation. For our analysis, we used a weighted sample of 270,241 women overall was utilized. To handle both individual and community level factors, a multilevel logistic regression was used for the analysis. The adjusted odds ratio and its 95% confidence interval were then presented, and variables with univariate multilevel regression p -values of ≤ 0.25 and in multivariable multilevel logistic regression < 0.05 p value were considered significant factors of HIVST. Results The overall prevalence of knowledge, and utilization of HIVST among women was about 2.17 (95% CI: 2.12, 2.23) only. Women aged 25–34 years old (AOR = 1.78, 95% CI: 1.65,1.92), and 35–49 years old (AOR = 1.33, 95% CI: 1.22,1.46), primary education(AOR = 1.25, 95%CI: 1.12, 1.38), and secondary/higher education (AOR = 3.08, 95% CI: 2.79, 3.41), poorer (AOR = 1.22, 95% CI: 1.08, 1.38), middle (AOR = 1.19, 95% CI: 1.06, 1.37), richer (AOR = 1.45, 95% CI 1.45, 1.64), and richest (AOR = 1.81, 95% CI: 1.59, 2.05), employed (AOR = 1.73 05% CI: 1.62, 1.85), mass media exposure (AOR = 1.39, 95% CI: 1.31, 1.49), knew modern contraception (AOR = 2.75, 95% CI: 1.84, 4.13), health facility delivery (AOR = 1.17, 95% CI: 1.02, 1.37), being from urban (AOR = 1.53, 95% CI: 1.63, 1.73), divorced or widowed (AOR = 77, 95% CI:1.13, 1.34), have more than one sexual partners (AOR =, 95% CI: 1.24, 1.41), heard about STIs (AOR 7.47 =, 95% CI: 5.16, 10.81), high community ANC coverage (AOR = 1.46, 95% CI: 1.31, 1.63), high community mass media (AOR = 1.37 95% CI: 1.21, 1.56), Central/Southern Africa (AOR = 0.66 95% CI: 0.59,0.74), and East Africa regions (AOR = 0.87 95% CI: 0.81,0.94) were associated with the knowledge and utilization of HIVST. Conclusions The level of knowledge and utilization of HIVST among women in SSA was very low. To improve this situation, maternal health services can be enhanced. This can be achieved by facilitating institutional delivery, promoting access to modern contraception, increasing ANC coverage, empowering women’s associations, creating culturally respectful mass media content, and involving rural and economically disadvantaged women. By implementing these measures, we can enhance women’s knowledge and improve their use of HIVST.

The Association Between Different Levels of Suppressed Viral Load and the Risk of Sexual Transmission of HIV Among Serodiscordant Couples on Antiretroviral Therapy: A Systematic Review

The risk of sexual transmission of HIV in serodiscordant couples when the partner living with HIV (PLHIV) uses antiretroviral therapy (ART) and maintains a suppressed viral load (VL) (< 200 copies/mL) is estimated at 0.00 transmissions per 100 person-years (95% CI 0.00–0.10). A recent review reported an almost zero transmission risk for VL < 1000 copies/mL but lacked quantitative risk estimates. This systematic review aimed to quantify transmission risk across VL levels. A literature search (November 2023, updated January 2025) identified 152 studies, with 9 included in the analysis. Seven possible transmissions occurred when the PLHIV had a VL of < 1000 copies/mL at the last measurement before the estimated time of transmission. However, VL at the exact time of transmission was unknown and VL > 1000 copies/mL cannot be excluded. This review suggests sexual transmission is very rare with recent VL < 1000 copies/mL however, future research is required to establish precise risk estimates. PROSPERO Number: CRD42023476946.

Sexual orientation, gender identity and virologic failure among people with HIV: a cohort study in all of US research program

Background Sexual and gender minorities (SGMs) are at higher risk of HIV incidence compared to their heterosexual cisgender counterparts. Despite the high HIV disease burden among SGMs, there was limited data on whether they are at higher risk of virologic failure, which may lead to potential disease progression and increased transmission risk. The All of Us (AoU) Research Program, a national community-engaged program aiming to improve health and facilitate health equity in the United States by partnering with one million participants, provides a promising resource for identifying a diverse and large volunteer TGD cohort. Leveraging various data sources available through AoU, the current study aims to explore the association between sexual orientation and gender identity (SOGI) and longitudinal virologic failure among adult people with HIV (PWH) in the US. Methods This retrospective cohort study used integrated electronic health records (EHR) and self-reported survey data from the All of Us (AoU) controlled tier data, version 7, which includes participants enrolled in the AoU research program from May 31, 2017, to July 1, 2022. Based on participants’ sexual orientation, gender identity, and sex assigned at birth, their SOGI were categorized into six groups, including cisgender heterosexual women, cisgender heterosexual men, cisgender sexual minority women, cisgender sexual minority men, gender minority people assigned female at birth of any sexual orientation, and gender minority people assigned male at birth of any sexual orientation. Yearly virologic failure was defined yearly after one’s first viral load testing, and individuals with at least one viral load test > 50 copies/mL during a year were defined as having virologic failure at that year. Generalized linear mixed-effects models were used to explore the association between SOGI and longitudinal virologic failure while adjusting for potential confounders, including age, race, ethnicity, education attainment, income, and insurance type. Results A total of 1,546 eligible PWH were extracted from the AoU database, among whom 1,196 (77.36%) had at least one viral failure and 773 (50.00%) belonged to SGMs. Compared to cisgender heterosexual women, cisgender sexual minority women (adjusted Odds Ratio [aOR] = 1.85, 95% CI: 1.05–3.27) were at higher risk of HIV virologic failure. Additionally, PWH who were Black vs. White (aOR = 2.15, 95% CI: 1.52–3.04) and whose insurance type was Medicaid vs. Private insurance (aOR = 2.07, 95% CI: 1.33–3.21) were more likely to experience virologic failure. Conclusions Maintaining frequent viral load monitoring among sexual minority women with HIV is warranted because it allows early detection of virologic failure, which could provide opportunities for interventions to strengthen treatment adherence and prevent HIV transmission. To understand the specific needs of subgroups of SGMs, future research needs to examine the mechanisms for SOGI-based disparities in virologic failure and the combined effects of multi-level psychosocial and health behavior characteristics.

Determinants of virologic failure among adult HIV patients on first line antiretroviral treatment in Oromia, Central Ethiopia: 2022 a case-control study

Background Ethiopia’s viral suppression rate was less than 90% by 2020, and more than 10% of adult clients on ART in Woliso Town were unsuppressed at the end of March 2022. This study aims to identify determinants of virologic failure among adult clients on ART at health facilities in Oromia region of Ethiopia. Methods A facility-based unmatched case-control study was conducted at health facilities in Oromia region from August 1 to September 1, 2022. The study cases were clients with virologic-confirmed first-line ART failure, while controls were clients on first-line ART with a suppressed viral load. A total of 135 cases and 268 control participants were selected using simple random sampling techniques, and data were collected by reviewing the client’s document. Epi-Info7 was used for data entry and SPSS version 20 for data analysis. Variables having a P-value of less than 0.25 in the bi-variable analysis were included in multivariable logistic regression. Determinants of virologic failure were determined based on an adjusted odds ratio using 95% CI and a P-value of < 0.05. Result In this study, clients with an age ≥ 35 years (AOR = 3.4, 95% CI: 1.6, 7.0), clients with a baseline regimen of AZT + 3TC + NVP (AOR = 3.5, 95% CI: 1.4, 8.8), clients with a base-line CD4 count < 350 mm^3 (AOR = 2.3, 95% CI: 1.1, 4.5), being single marital status (AOR = 3.7, 95% CI: 1.4, 10.5), TB-HIV coinfection (AOR = 2.58, 95% CI: 1.3, 5.1), and having opportunistic infection other than TB in the last six months (AOR = 3.06, 95% CI: 1.5, 6.3) were factors significantly associated with virologic failure while clients within the appointment spacing model (AOR = 0.05, 95% CI: 0.03, 0.10) is inversely associated with virologic failure. Conclusion This study showed that age ≥ 35 years, being single, baseline ART regimen with (AZT + 3TC + NVP), baseline CD4 cell count < 350 mm^3, Tb-co infection, and opportunistic infection in the last 6 months were factors associated with virologic failure. Involvement in the appointment spacing model was found to be protective.

Management and outcome of intracranial fungal infections in children and adults in Africa: a scoping review

Introduction Intracranial fungal infections’ (IcFIs) varying clinical manifestations lead to difficulties in diagnosis and treatment. African populations are disproportionately affected by the high burden of the disease. There is a lack of clarity as to the diagnostic and treatment modalities employed across the continent. In this review, we aim to detail the management, and outcome of IcFIs across Africa. Methods This scoping review was conducted using the Arksey and O'Malley framework. MEDLINE, EMBASE, Cochrane Library, African Index Medicus, and African Journals Online were searched for relevant articles from database inception to August 10th, 2021. The Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews guidelines were used to report the findings of the review. Results Of the 5,779 records identified, 131 articles were included. The mean age was 35.6 years, and the majority (56.4%) were males. The majority ( n = 8,433/8,693, 97.0%) of IcFIs presented as a meningitis, the most common communicable predisposing factor of IcFIs was HIV/AIDS ( n = 7,815/8,693, 89.9%), and the most common non-communicable risk factor was diabetes mellitus ( n = 32/8,693, 0.4%). Cryptococcus species was the most common ( n = 8,428/8,693, 97.0%) causative organism. The most commonly used diagnostic modality was cerebrospinal (CSF) cultures ( n = 4,390/6,830, 64.3%) for diffuse IcFIs, and MRI imaging (n = 12/30, 40%) for focal IcFIs. The most common treatment modality was medical management with antifungals only ( n = 4,481/8,693, 51.6%). The most commonly used antifungal agent in paediatric, and adult patients was amphotericin B and fluconazole dual therapy (51.5% vs 44.9%). The overall mortality rate was high ( n = 3,475/7,493, 46.3%), and similar for both adult and paediatric patients (47.8% vs 42.1%). Conclusion Most IcFIs occurred in immunosuppressed individuals, and despite the new diagnostic techniques, CSF culture was mostly used in Africa. Antifungals regimens used was similar between children and adults. The outcome of IcFIs in Africa was poor for both paediatric and adult patients.

HIV phylogenetic clusters point to unmet hiv prevention, testing and treatment needs among men who have sex with men in kenya

Background The HIV epidemic in Kenya remains a significant public health concern, particularly among gay, bisexual, and other men who have sex with men (GBMSM), who continue to bear a disproportionate burden of the epidemic. This study’s objective is to describe HIV phylogenetic clusters among different subgroups of Kenyan GBMSM, including those who use physical hotspots, virtual spaces, or a combination of both to find male sexual partners. Methods Dried blood spots (DBS) were collected from GBMSM in Kisumu, Mombasa, and Kiambu counties, Kenya, in 2019 (baseline) and 2020 (endline). HIV pol sequencing was attempted on all seropositive DBS. HIV phylogenetic clusters were inferred using a patristic distance cutoff of ≤ 0.02 nucleotide substitutions per site. We used descriptive statistics to analyze sociodemographic characteristics and risk behaviors stratified by clustering status. Results Of the 2,450 participants (baseline and endline), 453 (18.5%) were living with HIV. Only a small proportion of seropositive DBS specimens were successfully sequenced ( n  = 36/453; 7.9%), likely due to most study participants being virally suppressed (87.4%). Among these sequences, 13 (36.1%) formed eight distinct clusters comprised of seven dyads and one triad. The clusters mainly consisted of GBMSM seeking partners online ( n  = 10/13; 76.9%) and who tested less frequently than recommended by Kenyan guidelines ( n  = 11/13; 84.6%). Conclusions Our study identified HIV phylogenetic clusters among Kenyan GBMSM who predominantly seek sexual partners online and test infrequently. These findings highlight potential unmet HIV prevention, testing, and treatment needs within this population. Furthermore, these results underscore the importance of tailoring HIV programs to address the diverse needs of GBMSM in Kenya across different venues, including both physical hotspots and online platforms, to ensure comprehensive prevention and care strategies.

Acceptability and feasibility of using a blended quality improvement strategy among health workers to monitor women engagement in Option B+ program in Lilongwe Malawi

Option B + provides lifelong ART to pregnant and breastfeeding women with HIV to reduce mother-to-child transmission of HIV (eMTCT) and improve maternal health. The effectiveness of Option B + relies on continuous engagement, but suboptimal monitoring of HIV care hinders our measurements of engagement. Process mapping and quality improvement (PROMAQI) is a quality improvement strategy for healthcare workers (HCWs) to optimize complex processes such as monitoring HIV care. We assessed the acceptability and feasibility of the PROMAQI among HCWs and identified barriers and facilitators for PROMAQI implementation. A cross-sectional study using a mixed method approach was conducted from August 2021 to March 2022 across five urban health facilities participating in PROMAQI implementation n the Lilongwe district, Malawi. We assessed PROMAQI acceptability and feasibility at the end of the study. A 5-point Likert (1 = worst to 5 = best) scale tool was administered to 110 HCWs ( n  = 15–33 per facility) involved in PROMAQI implementationThese data were analysed using descriptive statistics Among the 110 HCWs, twenty-two (QI team ( n  = 11) and QI implementers ( n  = 11)) were purposively selected for in-depth interviews. Thematic analysis was conducted using deducted and inductive approaches. The theoretical framework for acceptability (TFA) was used to identify reasons for acceptability. The Consolidated Framework for Implementation Research (CFIR) was used to characterize the barriers and facilitators of PROMAQI implementation. HCWs recruited had a median age of 37 (32–43) years, 82.0% of whom were female. Most (42%) had completed secondary education, and 84% were nurses and community health workers. The median (IQR) acceptability and feasibility scores for the PROMAQI were 5 (IQR 4–5) and 4 (IQR 4–5), respectively. Reasons for high PROMAQI acceptability included addressing a relevant gap and improving performance. Perceived implementation barriers included poor work attitudes, time constraints, resource limitations, knowledge gaps, and workbook difficulties. The facilitators included communication, mentorship, training, and financial incentives. PROMAQI is a highly acceptable and feasible tool for monitoring engagement of women in Option B + . Addressing these barriers may optimize the implementation of PROMAQI. Scaling up PROMAQI may enhance retention in the Option B + program and facilitate eMTCT.

Automated HIV Screening on Dutch Electronic Health Records with Large Language Models

Efficient screening and early diagnosis of HIV are critical for reducing onward transmission. Although large scale laboratory testing is not feasible, the widespread adoption of Electronic Health Records (EHRs) offers new opportunities to address this challenge. Existing research primarily focuses on applying machine learning methods to structured data, such as patient demographics, for improving HIV diagnosis. However, these approaches often overlook unstructured text data such as clinical notes, which potentially contain valuable information relevant to HIV risk. In this study, we propose a novel pipeline that leverages a Large Language Model (LLM) to analyze unstructured EHR text and determine a patient's eligibility for further HIV testing. Experimental results on clinical data from Erasmus University Medical Center Rotterdam demonstrate that our pipeline achieved high accuracy while maintaining a low false negative rate. ;28 pages, 6 figures

POC viral load testing in an antenatal clinic setting for Ugandan pregnant women living with HIV: a qualitative implementation process analysis

Introduction Point of care viral load (POC VL) testing improves viral suppression and retention in HIV care, and is increasingly being integrated into routine health services for African pregnant women living with HIV. We examined processes of implementing POC VL testing in antenatal care and at delivery for Ugandan mothers living with HIV as part of a pilot randomized trial (Clinical Trial Number : NCT05092997). Methods We conducted individual qualitative interviews with 12 clinical and research staff who implemented POC VL testing and 22 mothers who received POC VL testing using the Xpert® HIV-1 Viral Load Assay (Cepheid Inc., Sunnyvale, CA, USA). An inductive, content analytic approach was used to examine the interview transcripts. The analysis addressed the question: How did a group of Ugandan health care providers approach the process of implementing POC VL testing in antenatal care and at delivery for mothers living with HIV? Results The analysis yielded three themes. (1) Staff created an efficient system of communication and then relied on that system to coordinate testing procedures. (2) They also found ways of increasing the speed and efficiency of the testing process. (3) They adopted a “mother-centered” approach to implementation, prioritizing the needs, preferences, and well-being of women in planning and carrying out testing procedures. Conclusion As POC VL testing becomes more widely used across high HIV burden settings, understanding how implementers approach the implementation process and what they do to make an intervention successful will be an important part of evaluating feasibility. Clinical Trial Number: NCT05092997.

Epidemiological and virological characteristics of people living with HIV on antiretroviral treatment for more than 6 months in virological failure in Brazzaville, Republic of Congo

Introduction. Virological failure is one of the main causes of failing to treat, and better management of HIV infection requires understanding and controlling the factors that contribute to this phenomenon. The main objective was to characterize the patients of the active file of the Brazzaville Outpatient Treatment Center in virological failure to identify predictive factors leading to virological failure. Methods. Conducted between June and December 2020, this was a cross-sectional study. Patients enrolled were HIV-1-infected patients from the Brazzaville Outpatient Treatment Center receiving a potent combination therapy for at least 6 months but experiencing virological failure. Viral load was measured using the automated Abbott Real-time HIV-1 m2000rt System. Sociodemographic and clinical data were collected from a computerized patient record software called Santia. For the identification of the independent predictors of virological failure, statistical analysis was performed. Results. A total of 109 patients with virological failure were recruited. The median age of the patients was 45 years (interquartile range: 37–52 years) and women were more represented (74%). More than half of the patients had World Health Organization stage IV HIV and the median duration of antiretroviral treatment was 96 months. The most followed treatment regimen was AZT+3TC+EFV (or nevirapine) with 48%, while the median viral load was 12985 copies ml(−1). Conclusion. In our study, we did not identify any sociodemographic or clinical variables predictive of virological failure. However, we felt that it would be desirable to carry out a study with temporal follow-up and the possibility of sequencing in order to identify the different circulating genotypes and resistance mutations.

Virus-induced vesicular acidification enhances HIV immune evasion

To inhibit endocytic entry of some viruses, cells promote acidification of endosomes by expressing the short isoform of human nuclear receptor 7 (NCOA7) which increases activity of vacuolar ATPase (V-ATPase). While we found that HIV-1 infection of primary T cells led to acidification of endosomes, NCOA7 levels were only marginally affected. Contrastingly, levels of the 50 kDa form of the sodium/hydrogen exchanger 6 (NHE6) were greatly reduced. NHE6 overexpression and low concentrations of the V-ATPase inhibitor, concanamycin A, selectively reversed endosomal acidification. Endosomal neutralization by these interventions also reduced Nef-dependent MHC-I downmodulation by our wildtype HIV reporter virus. NHE6 overexpression disrupted MHC-I downmodulation by reducing recruitment of Nef to Rab11 (+) compartments and inhibiting interactions between Nef, β-COP, and ARF-1. In addition, we found that the HIV Vif protein was required for downmodulation of the 50 kDa form of NHE6 and for endosomal acidification but was dispensable for Nef-dependent MHC-I downmodulation.

Epidemiological characteristics of HIV and AIDS in mainland China: a 16-Year national surveillance analysis (2005–2020)

Background This study aimed to describe the epidemiological trends and regional/age distributions of HIV and AIDS in China from 2005 to 2020 and provide scientific reference for the government about HIV and AIDS prevention and control. Methods Data on HIV and AIDS cases reported encompassing various age groups and 31 provinces, autonomous regions, and municipalities in mainland China from 2005 to 2020 were sourced from the Data Center of China Public Health Science. Results A total of 570,576 AIDS cases were identified and reported in China from 2005 to 2020. The reported AIDS incidence, AIDS-related mortality, HIV incidence, and HIV mortality increased from 0.4324/100,000, 0.1012/100,000, 1.9437/100,000, and 0.0216/100,000 in 2005, respectively, to 4.4283/100,000, 1.3405/100,000, 6.3839/100,000, and 1.2884/100,000 in 2020, respectively. The corresponding average annual growth rates were 16.33%, 18.19%, 7.83%, and 29.40%. The reported HIV and AIDS stage-specific incidence and mortality significantly differed by geography and age. The reported HIV incidence peaked among young adults (20–34 years old), and the > 50 age group showed the highest HIV mortality, AIDS-related mortality and AIDS incidence. Conclusions The HIV and AIDS epidemic in China showed a gradual rising trend over the years and exhibited geographical and age distribution difference. Young adults and older populations were the major populations in the HIV and AIDS epidemic in China. Health education, interventions, and testing efforts targeting these key populations should be strengthened. Building a resilient HIV and AIDS treatment service system is essential for ensuring stable HIV and AIDS services during public health emergencies. Clinical trial number Not applicable.

Feeding modalities, HIV transmission and its predictors among HIV-exposed infants visited Gamo and Gofa zones public health facilities, Southern Ethiopia: a retrospective follow up study

Background Despite the highest (88%) Prevention of Mother-To-Child Transmission (PMTCT) of HIV coverage in Eastern Africa, 50% of new HIV infections in children aged 0–14 years occur in the region. Objective The aim of this study was to assess the feeding modalities, the rate of HIV transmission and its predictors among HIV exposed infants (HIV-EIs) visited Gamo and Gofa Zones public health facilities, Southern Ethiopia from January 2013 to February 2019. Method and materials Institution-based retrospective follow up study was employed among 450 HIV-EIs having DNA/PCR test results. All infant-mother pair records in selected health facilities were reviewed using a standard data extraction tool from March to July 2019. HIV transmission probabilities were assessed by Kaplan–Meier time-to-event analysis method and log-rank tests were used to compare the risk among different groups. The Cox-proportional hazards model, adjusted on infant feeding modalities and other co-variants was used to identify predictors of HIV transmission, and statistical significance was declared at a p -value of < 0.05. Results In total, 383 complete records were analyzed. In the study, 85.6% (95%CI: 81.6%, 89.1%) of HIV-EIs were exclusively breastfed in the first six months. The 18 months probability of infant HIV transmission was 64 (16.7%) (95%CI: 13.1%–20.8%). The risk of HIV-transmission was higher among infants who were delivered at the hospital than health centers/health posts (AHR = 3.07; 95%CI: 1.19, 7.95); discontinued Cotrimoxazole prophylaxis in at least one visit (AHR = 6.32; 95%CI: 3.35, 11.94); did not exclusively breastfeed (AHR = 3.07; 95%CI: 1.72, 5.47) and came from urban areas (AHR = 5.90; 95%CI: 1.40, 24.85). Conclusions The study showed that HIV-EIs had a greater rate of 18 months HIV transmission than the national pooled prevalence. The risk of transmission is higher among infants who do not breastfeed exclusively for the first 6 months, and the risk increases with the number of months spent by breastfeeding. Therefore, strengthening counselling on safer feeding options and Cotrimoxazole prophylaxis use; provision of quality PMTCT service with special focus in hospitals and urban residents were recommended.

Predictors of successful transition of adolescents and young adults living with HIV from pediatric to adult-oriented care in southern Ethiopia: a retrospective cohort study

Background The introduction of highly active antiretroviral therapy has significantly improved the life expectancies of children and adolescents living with HIV, leading to an increased number transitioning to adult care. However, there has been a lack of studies in Ethiopia focusing on factors influencing the success of this transition. Therefore, this study aimed to determine predictors of a successful transition from pediatric to adult HIV clinics among adolescents and young adults living with HIV in health facilities in southern Ethiopia. Methods A retrospective cohort study included 337 adolescents and young adults who transitioned to adult-oriented HIV care. Successful transition was defined as having a viral load of less than 1000 copies/ml and maintaining care during the first year post-transition. Patients’ antiretroviral therapy (ART) cards and monitoring charts were reviewed. Secondary data analysis was conducted using a multivariable binary logistic regression model to identify predictors of a successful transition. Using the variance inflation factor, we checked for multi-collinearity between variables and assessed model fitness with the Hosmer and Lemeshow goodness-of-fit test. Adjusted Odds Ratio (AOR) with 95% confidence intervals (CI) and P-value ≤ 0.05 measured the strength of association and statistical significance. Results Of 337 participants, 230 (68.25%) successfully transitioned (95% CI = 63.25, 73.25). Transitioning at age 18 or older (AOR = 4.25; 95% CI = 2.29, 7.87), residing in an urban area (AOR = 1.78; 95% CI = 1.04, 3.02), and being on antiretroviral therapy for more than two years (AOR = 4.25; 95% CI = 1.17, 4.94; P  < 0.017) were identified as positive predictors and opportunistic infection (AOR = 0.34; 95% CI = 0.15, 0.75; P  < 0.008) was identified as a negative predictor for a successful transition from pediatric to adult ART clinic. Conclusion This study sheds light on the challenges faced by HIV patients transitioning from pediatric to adult care, with less than 70% successfully navigating this critical phase. Factors such as age at transition, residence, duration of ART, and the presence of opportunistic infections were identified as key predictors of successful transition. The findings underscore the urgent need for tailored interventions, including standardized transition plans that address age and urban/rural disparities, to enhance transition outcomes for adolescents and young adults living with HIV in the region.

Immunity induced by vaccination with recombinant influenza B virus neuraminidase protein breaks viral transmission chains in guinea pigs in an exposure intensity-dependent manner

Mucosal vaccines and vaccines that block pathogen transmission are under-appreciated in vaccine development. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has shown that blocking viral transmission is an important attribute of efficient vaccines. Here, we investigated if recombinant influenza virus neuraminidase (NA) vaccines delivered at a mucosal site could protect from onward transmission of influenza B viruses in the guinea pig model. We tested four different scenarios in which sequential transmission was investigated in chains of four guinea pigs. The variables tested included a low and a high viral inoculum (10(4) vs 10(5) plaque forming units) in the initial donor guinea pig and variation of exposure/cohousing time (1 day vs 6 days). In three out of four scenarios – low inoculum-long exposure, low inoculum-short exposure and high inoculum-short exposure – transmission chains were efficiently blocked. Based on this data we believe an intranasal recombinant NA vaccine could be used to efficiently curtail influenza virus spread in the human population during influenza epidemics.

How drivers of seasonality in respiratory infections may impact vaccine strategy: a case study in how COVID-19 may help us solve one of influenza's biggest challenges

Vaccines against seasonal infections like influenza offer a recurring testbed, encompassing challenges in design, implementation, and uptake to combat a both familiar and ever-shifting threat. One of the pervading mysteries of influenza epidemiology is what causes the distinctive seasonal outbreak pattern. Proposed theories each suggesting different paths forwards in being able to tailor precision vaccines and/or deploy them most effectively. One of the greatest challenges in contrasting and supporting these theories is, of course, that there is no means by which to actually test them. In this communication we revisit theories and explore how the ongoing COVID-19 pandemic might provide a unique opportunity to better understand the global circulation of respiratory infections. We discuss how vaccine strategies may be targeted and improved by both isolating drivers and understanding the immunological consequences of seasonality, and how these insights about influenza vaccines may generalize to vaccines for other seasonal respiratory infections.

The impact of influenza on the ability to work, volunteer and provide care: results from an online survey of Canadian adults 50 years and older

Background Influenza is associated with a decline in functional abilities among Canadian older adults, although specific impacts on daily life have not been fully explored. Methods In August 2019 and May 2020, we conducted surveys of Canadian adults 50-64 years and 65 years and older through an online market research platform. The survey included questions about the impact of diagnosed influenza or self-reported influenza-like-illness (ILI) on working, volunteering and caregiving. Results We surveyed 1006 adults in the 50-64 year age group about the 2018/19 season and 1001 about the 2019/20 season. In the 65 years and older age group, we surveyed 3548 and 3500 individuals about the 2018/19 and 2019/20 influenza seasons, respectively. In each season, nearly two-thirds of individuals 50-64 years with influenza/ILI were employed; 51.7% reported absenteeism in 2018/19 and 53.6% in 2019/20. Of the 20% of individuals 65 years and older who were employed, 47.0% of those with influenza/ILI were absent while ill in 2018/19 (39.8% in 2019/20). In 2018/2019, 29.6% of respondents 50-64 years old with influenza/ILI identified as volunteers (29.3% in 2019/2020). In both seasons, nearly half were unable to do so while ill. Of the 164 (32.7%) individuals 65 years and older who volunteered during the 2018/19 season, 80 (48.8%) did not while ill; 224 (37.3%) respondents volunteered in the 2019/20 season, and half were absent while ill. Of those 50-64 years with influenza/ILI, 97 (42.2%) and 57 (22.2%) were caregivers in 2018/19 and 2019/20, respectively. In 2018/19 and 2019/20, 40 (41.2%) and 28 (49.1%) caregivers were unable to provide care when ill, respectively. Of those with influenza/ILI in the 65 years and older age group, 123 (24.6%) and 162 (27.0%) were caregivers in 2018/19 and 2019/20, respectively. In 2018/19, 18 (14.6%) caregivers with influenza/ILI did not provide care while ill (42 [25.9%] in 2019/20). Discussion In Canadian older adults, influenza and ILI had notable impacts on ability to volunteer and provide care across two recent seasons. Optimization of influenza prevention in this population may yield important societal benefits.

Comparison of Inconvenience Costs Between Influenza Antivirals for Japanese Pediatric Patients: A Conjoint Analysis of Parental Responses

Introduction Certain drug characteristics, including dosage and form, are associated with either convenience or inconvenience for the patients taking them, and any inconvenience can be considered as a “cost” in disease treatment. Multiple antivirals are available for influenza in Japan, with various dosages and forms. This study evaluated the inconvenience costs associated with influenza antivirals for pediatric patients by using conjoint analysis on responses from their parents. Methods An online survey (May 2021) was conducted for parents whose child took antivirals for influenza at 6–11 years during the 3 years until March 2021. Attributes of the conjoint analysis were administration routes and formulation (tablet, capsule, dry syrup, or inhalant), duration of administration, frequency of administration per day, and out-of-pocket expenses. We assumed the efficacy and safety to be equivalent among the antivirals. A logistic regression model was applied to the analysis. We also asked parents about their recent experiences with antiviral treatment for their child. Results We collected responses from 3161 eligible individuals. The mean age (standard deviation) of the children when taking the antivirals and percentage of female children were 8.27 (1.63) years old and 53.2%, respectively. The tablet was the most preferred formulation; the inconvenience costs for each administration route and formulation, relative to the tablet as zero, were Japanese yen (JPY) 515 (US dollar 4.61, as of October 2021) for the inhalant, JPY 775 for the capsule, and JPY 804 for the dry syrup. The inconvenience costs for 5 days relative to 1 day and for twice a day relative to once a day were JPY 2150 and JPY 399, respectively. Conclusion Based on the conjoint analysis, a single-dose tablet antiviral was suggested to have the lowest inconvenience cost for pediatric patients. Trial Registration UMIN000044243.

Epidemiological characteristics of influenza after COVID-19 pandemic in Zhejiang province, China

Background To analyse the epidemiological characteristics and variation trend of influenza in Zhejiang Province from 2018 to 2023, so as to provide reference for the prevention and control of influenza after COVID-19 pandemic. Methods Throat swab samples of influenza-like illness (ILI) cases from 16 local sentinel hospitals were collected every week, and real-time reverse transcription-polymerase chain reaction (RT-PCR) was used to detect and identify the specific types of influenza viruses. Descriptive epidemiological method was used to analyse the surveillance data from sentinel hospitals and network laboratories. Results The ILI% were fluctuated between 3.67% and 8.73% from 2018 to 2023 in Zhejiang Province, with a notable increase after COVID-19 pandemic ( P  < 0.05). The 0–4 years age group had the highest incidence of ILI cases (45.13%), whereas the 5–14 years age group exhibited the highest influenza virus positive rate (26.01%). There were significant differences in temporal distribution and age distribution in ILI% and influenza virus positive rates ( P  < 0.001). Apart from 2020, the influenza outbreaks in other years displayed a seasonal pattern, with varying predominant strains in different years. During 2020–2022, the correlation between ILI number and the influenza virus positive rate was not statistically significant among 0–4 years age group but it was statistically significant among age groups > 15 years ( P  < 0.001). Conclusions The COVID-19 pandemic has markedly influenced the epidemiological characteristics of influenza. Children under 15 years are the focus group for influenza prevention and control. The current definition of ILI may not be appropriate for influenza surveillance in children under 15 years during the COVID-19 pandemic. Continuous influenza surveillance, pay close attention to the changes of influenza virus strains, actively promote the influenza vaccination of key groups and the combination of COVID-19 surveillance in the influenza sentinel surveillance network will play an important role in realizing accurate influenza prevention and control.

Impact of COVID-19 control measures on influenza positivity among patients with acute respiratory infections, 2018–2023: an interrupted time series analysis

Background After experiencing the global COVID-19 pandemic, whether there have been new changes in the epidemiological characteristics of influenza has become a topic of great concern. This study aims to investigate the impact of implementation and lifting of COVID-19 control measures on influenza positivity among patients with acute respiratory infections (ARI) from 2018 to 2023. Methods The data were collected from January 2018 to December 2023 in two designated sentinel hospitals in Jinhua. We performed an interrupted time series analysis (ITSA) using a beta regression model and a generalized additive model (GAM), adopting a two-model cross-validation strategy to assess the effect of two major interventions on influenza positivity: the COVID-19 control measures implemented in early 2020 and lifted at the end of 2022. We also analyzed influenza epidemiological characteristics and seasonality before, during, and after the pandemic. Results A total of 98,244 cases were included in this study, and the overall influenza positivity rate was 39.34%. Females and the 6–17-year age group had higher positivity rates. Before the pandemic, influenza primarily showed a winter peak pattern, whereas during the pandemic, the positivity rate declined significantly with no distinct peak. After the pandemic ended, an unusual dual-peak pattern emerged. The interrupted time series analysis revealed that, following the implementation of non-pharmaceutical interventions (NPIs) in early 2020, influenza positivity immediately decreased significantly in the beta regression model ( β = -1.75, p  = 0.003). After the lifting of measures in late 2022, a marginally lagged increasing trend was observed in the beta regression model ( β  = 0.14, p  = 0.096) and a significant increasing trend was found in the GAM model (edf = 7.00, p  < 0.001). Seasonal effects differed between the models: the beta regression model exhibited significant annual seasonal fluctuations (sin12 = 0.67, p  < 0.001), while the GAM model did not exhibit a significant association independent of the time trend. Conclusion COVID-19 and its control measures substantially reduced influenza positivity rates; however, once these measures were lifted, influenza activity resurged, and its seasonal epidemic pattern changed. The intensity of influenza appeared to exceed pre-pandemic levels, underscoring the importance of NPIs in controlling respiratory infectious diseases. Strengthened surveillance and optimized strategies remain necessary to mitigate the threat of influenza in the post-pandemic era.

Comparison of Clinical Profiles and Mortality Outcomes Between Influenza and COVID-19 Patients Invasively Ventilated in the ICU: A Retrospective Study From All Paris Public Hospitals From 2016 to 2021

International audience; Studies comparing outcomes of ICU patients admitted for either COVID-19 or seasonal influenza are limited. Our objective was to describe baseline clinical profiles, care procedures, and mortality outcomes by infection status (influenza vs COVID-19) of patients who received invasive mechanical ventilation in the ICU. DESIGN: Retrospective observational study. SETTING: Data were extracted from the Assistance Publique-Hopitaux de Paris database from September 1, 2016, to April 20, 2021. It includes data from the 39 university hospitals. PATIENTS: A total of 752 influenza adult patients and 3,465 COVID-19 adult patients received invasive mechanical ventilation in one of the ICUs of the Paris area university hospitals, France. INTERVENTION: The characteristics and outcome by infection status were compared. Factors associated with mortality were assessed using Cox proportional hazard models after controlling for potential confounders, including infection status. MEASUREMENTS AND MAIN RESULTS: The median age at admission to the ICU was 67 (interquartile range [IQR], 57-77) and 63 yr (IQR, 54-71 yr) for influenza and COVID-19 patients, respectively. At ICU admission, COVID-19 patients were more frequently obese, more frequently had diabetes mellitus or high blood pressure, and were less likely to have chronic heart failure, chronic respiratory disease, chronic kidney failure, or active cancer than influenza patients. The overall survival at 90 days was 57% for COVID-19 patients and 66% for influenza patients (p < 0.001). In a multivariable Cox model, higher age, organ transplant, severe acute respiratory syndrome coronavirus 2 infection, and chronic kidney failure were associated with shorter survival, whereas obesity and high blood pressure were associated with longer survival after invasive ventilation. CONCLUSIONS: COVID-19 and influenza patients requiring mechanical ventilation in the ICU differed by many characteristics. COVID-19 patients showed lower survival independently of potential confounders.

RDN for the treatment of influenza in children: a randomized, double-blinded, parallel-controlled clinical trial

BACKGROUND: The morbidity of influenza in children increased rapidly in decade. Reduning injection (RDN), a small but fine Chinese herbal formula, has antipyretic, antiviral, anti-inflammatory effects. We intend to evaluate the efficacy and safety of RDN for the influenza in children versus Oseltamivir, explore the possible antiviral mechanism of RDN and provide evidence-based medical evidence for rational clinical drug usage. METHOD: We design a randomized, double-blind, double-dummy, parallel control of positive drug, multi-centre clinical study. According to the formula of mean superiority test, a total of 240 patients with influenza in children will be randomized 1:1 into the experimental group and control group. The experimental group will take RDN and Oseltamivir phosphate granule simulants and the control group will take Oseltamivir phosphate granule and RDN simulants. Each group will be treated for 5 days. The primary outcome measure is temperature recovery time, and the secondary outcome measures include time when the fever begins to subside, time and degree of disease to alleviate, disappearance rate of individual symptoms and so on. We will measure before enrollment and each 24 h after treatment for comparison. DISCUSSION: The study is launched to evaluate the efficacy and safety of RDN for the treatment of influenza in children and to provide an alternative option for influenza in children. TRIAL REGISTRATION: This study is registered in ClinicalTrials.gov as NCT04183725, registered on 3 December, 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12906-023-04037-1.

In Vitro and In Vivo Characterization of H5N8 High-Pathogenicity Avian Influenza Virus Neurotropism in Ducks and Chickens

Segment sequences can be found on GenBank (accession numbers MK859904 to MK859911 and MK859926 to MK859933).;International audience; ABSTRACT H5N8 high-pathogenicity avian influenza virus (HPAIV) of clade 2.3.4.4B, which circulated during the 2016 epizootics in Europe, was notable for causing different clinical signs in ducks and chickens. The clinical signs preceding death were predominantly neurological in ducks versus respiratory in chickens. To investigate the determinants for the predominant neurological signs observed in ducks, we infected duck and chicken primary cortical neurons. Viral replication was identical in neuronal cultures from both species. In addition, we did not detect any major difference in the immune and inflammatory responses. These results suggest that the predominant neurological involvement of H5N8 HPAIV infection in ducks could not be recapitulated in primary neuronal cultures. In vivo, H5N8 HPAIV replication in ducks peaked soon after infection and led to an early colonization of the central nervous system. In contrast, viral replication was delayed in chickens but ultimately burst in the lungs of chickens, and the chickens died of respiratory distress before brain damage became significant. Consequently, the immune and inflammatory responses in the brain were significantly higher in duck brains than those in chickens. Our study thus suggests that early colonization of the central nervous system associated with prolonged survival after the onset of virus replication is the likely primary cause of the sustained inflammatory response and subsequent neurological disorders observed in H5N8 HPAIV-infected ducks.IMPORTANCE The severity of high-pathogenicity avian influenza virus (HPAIV) infection has been linked to its ability to replicate systemically and cause lesions in a variety of tissues. However, the symptomatology depends on the host species. The H5N8 virus of clade 2.3.4.4B had a pronounced neurotropism in ducks, leading to severe neurological disorders. In contrast, neurological signs were rarely observed in chickens, which suffered mostly from respiratory distress. Here, we investigated the determinants of H5N8 HPAIV neurotropism. We provide evidence that the difference in clinical signs was not due to a difference in neurotropism. Our results rather indicate that chickens died of respiratory distress due to intense viral replication in the lungs before viral replication in the brain could produce significant lesions. In contrast, ducks better controlled virus replication in the lungs, thus allowing the virus to replicate for a sufficient duration in the brain, to reach high levels, and to cause significant lesions.

SRSF5‐Mediated Alternative Splicing of M Gene is Essential for Influenza A Virus Replication: A Host‐Directed Target Against Influenza Virus

Splicing of influenza A virus (IAV) RNA is an essential process in the viral life cycle that involves the co‐opting of host factors. Here, it is demonstrated that induction of host serine and arginine‐rich splicing factor 5 (SRSF5) by IAV facilitated viral replication by enhancing viral M mRNA splicing. Mechanistically, SRSF5 with its RRM2 domain directly bounds M mRNA at conserved sites (M mRNA position 163, 709, and 712), and interacts with U1 small nuclear ribonucleoprotein (snRNP) to promote M mRNA splicing and M2 production. Mutations introduced to the three binding sites, without changing amino acid code, significantly attenuates virus replication and pathogenesis in vivo. Likewise, SRSF5 conditional knockout in the lung protects mice against lethal IAV challenge. Furthermore, anidulafungin, an approved antifungal drug, is identified as an inhibitor of SRSF5 that effectively blocks IAV replication in vitro and in vivo. In conclusion, SRSF5 as an activator of M mRNA splicing promotes IAV replication and is a host‐derived antiviral target.

Household Influenza Transmission and Healthcare Resource Utilization Among Patients Treated with Baloxavir vs Oseltamivir: A United States Outpatient Prospective Survey

Introduction Influenza is a common, seasonal infectious disease with broad medical, economic, and social consequences. Real-world evidence on the effect of influenza treatment on household transmission and healthcare resource utilization is limited in outpatient settings in the USA. This study examined the real-world effectiveness of baloxavir vs oseltamivir in reducing influenza household transmission and healthcare resource utilization. Methods This prospective electronic survey on patient-reported outcomes was conducted between October 2022 and May 2023 via CVS Pharmacy in the USA. Adult participants (≥ 18 years old) were eligible if they filled a prescription for baloxavir or oseltamivir at a CVS Pharmacy within 2 days of influenza symptom onset. Participant demographics, household transmission, and all-cause healthcare resource utilization were collected. Transmission and utilization outcomes were assessed using χ ^2 and Fisher exact tests. Results Of 87,871 unique patients contacted, 1346 (1.5%) consented. Of 374 eligible patients, 286 (90 baloxavir- and 196 oseltamivir-treated patients) completed the survey and were included in the analysis. Mean age of participants was 45.4 years, 65.6% were female, and 86.7% were White. Lower household transmission was observed with baloxavir compared with oseltamivir therapy (17.8% vs 26.5%; relative risk = 0.67; 95% CI 0.41–1.11). Healthcare resource utilization, particularly emergency department visits (0.0% vs 4.6%), was also numerically lower in the baloxavir-treated group; no hospitalizations were reported in either cohort. Conclusions The findings from this real-world study suggest that antiviral treatment of influenza with baloxavir may decrease household transmission and reduce healthcare resource utilization compared with oseltamivir.

Patient Portal Reminders for Pediatric Influenza Vaccinations: A Randomized Clinical Trial

OBJECTIVES: In a large health system, we evaluated the effectiveness of electronic health record patient portal reminders in increasing pediatric influenza vaccination rates. METHODS: We conducted an intention-to-treat randomized clinical trial of 22 046 children from 6 months to <18 years of age in 53 primary care practices. Patients (or parent and/or proxies) who were active portal users were randomly assigned to receive reminder messages framed as gains or losses or no messages. They were separately randomly assigned to receive a precommitment message before the influenza season. The primary outcome was receipt of ≥1 seasonal influenza vaccinations. Additionally, children 6 months to <3 years of age due for a second influenza vaccine were randomly assigned to receive a reminder or no reminder for the second vaccination. RESULTS: First-dose influenza vaccination rates were 56.9% in the control group, 58.0% in the loss-frame reminders group (P = .07), and 58.0% in the gain-frame group (P = .47). Rates were 58.3% in the precommitment group versus 57.0% in the control group (P = .11). Adjusted risk ratios for first vaccination were 1.02 (95% confidence interval [CI]: 1.00–1.04) for loss-frame reminders, 1.01 (95% CI: 0.98–1.05) for gain-frame reminders, and 1.02 (95% CI: 1.00–1.04) for precommitment messages versus controls. Second-dose vaccination rates were 44.1% in the control group and 55.0% in the reminder group, with an adjusted risk ratio of 1.25 (95% CI: 1.07–1.45). CONCLUSIONS: Patient portal reminders for influenza vaccines in children, whether framed as gains or losses, did not increase first-dose influenza vaccination rates but were highly effective for the second dose of the vaccine.

Risk factors associated with influenza A (H1N1)pdm09: a nested case control study of TB patients with ILI in Lahore District, Pakistan

Background Co-morbidity with respiratory viruses including influenza A, cause varying degree of morbidity especially in TB patients compared to general population. This study estimates the risk factors associated with influenza A (H1N1)pdm09 in TB patients with ILI. Methods A cohort of tuberculosis (TB) patients who were admitted to and enrolled in a TB Directly Observed Therapy Program (DOTs) in tertiary care hospitals of Lahore (Mayo Hospital and Infectious Disease Hospital) were followed for 12 weeks. At the start of study period, to record influenza-like illness (ILI), a symptom card was provided to all the participants. Every participant was contacted once a week, in person. When the symptoms were reported by the participant, a throat swab was taken for the detection of influenza A (H1N1)pdm09. A nested case control study was conducted and TB patients with ILI diagnosed with influenza A (H1N1)pdm09 by conventional RT-PCR were selected as cases, while those who tested negative by conventional RT-PCR were enrolled as controls. All cases and controls in the study were interviewed face-to-face in the local language. Epidemiological data about potential risk factors were collected on a predesigned questionnaire. Logistic analysis was conducted to identify associated risk factors in TB patients with ILI. Results From the main cohort of TB patients ( n  = 152) who were followed during the study period, 59 (39%) developed ILI symptoms; of them, 39 tested positive for influenza A (H1N1)pdm09, while 20 were detected negative for influenza A (H1N1)pdm09. In univariable analysis, four factors were identified as risk factors ( p  < 0.05). The final multivariable model identified one risk factor (sharing of towels, P  = 0.008)) and one protective factor (wearing a face mask, p = < 0.001)) for influenza A (H1N1)pdm09 infection. Conclusion The current study identified the risk factors of influenza A (H1N1)pdm09 infection among TB patients with ILI.

A case–control study of the causes of acute respiratory infection among hospitalized patients in Northeastern Laos

With the advent of highly sensitive real-time PCR, multiple pathogens have been identified from nasopharyngeal swabs of patients with acute respiratory infections (ARIs). However, the detection of microorganisms in the upper respiratory tract does not necessarily indicate disease causation. We conducted a matched case–control study, nested within a broader fever aetiology project, to facilitate determination of the aetiology of ARIs in hospitalised patients in Northeastern Laos. Consenting febrile patients of any age admitted to Xiengkhuang Provincial Hospital were included if they met the inclusion criteria for ARI presentation (at least one of the following: cough, rhinorrhoea, nasal congestion, sore throat, difficulty breathing, and/or abnormal chest auscultation). One healthy control for each patient, matched by sex, age, and village of residence, was recruited for the study. Nasopharyngeal swabs were collected from participants and tested for 33 pathogens by probe-based multiplex real-time RT-PCR (FastTrack Diagnostics Respiratory pathogen 33 kit). Attributable fraction of illness for a given microorganism was calculated by comparing results between patients and controls (= 100 * [OR − 1]/OR) (OR = odds ratio). Between 24th June 2019 and 24th June 2020, 205 consenting ARI patients and 205 matching controls were recruited. After excluding eight pairs due to age mismatch, 197 pairs were included in the analysis. Males were predominant with sex ratio 1.2:1 and children < 5 years old accounted for 59% of participants. At least one potential pathogen was detected in 173 (88%) patients and 175 (89%) controls. ARI in admitted patients were attributed to influenza B virus, influenza A virus, human metapneumovirus (HMPV), and respiratory syncytial virus (RSV) in 17.8%, 17.2%, 7.5%, and 6.5% of participants, respectively. SARS-CoV-2 was not detected in any cases or controls. Determining ARI aetiology in individual patients remains challenging. Among hospitalised patients with ARI symptoms presenting to a provincial hospital in Northeastern Laos, half were determined to be caused by one of several respiratory viruses, in particular influenza A virus, influenza B virus, HMPV, and RSV.

A Systematic Review of Influenza Epidemiology and Surveillance in the Eastern Mediterranean and North African Region

Seasonal influenza represents a huge health burden, resulting in significant mortality and morbidity. Following the 2009 H1N1 pandemic, focus has been directed on the burden of influenza globally. Country and regional disease burden estimates play important roles in helping inform decisions on national influenza intervention programmes. Despite improvements in influenza surveillance following the 2009 pandemic, many opportunities remain unexplored in the Eastern Mediterranean and North African (EMNA) region, which has a high prevalence of patients with chronic disease and thus a population at high risk of influenza complications. We conducted a systematic literature review of Embase, Medline, Scopus and the Cochrane Database of Systematic Reviews from 1 January 1998 to 31 January 2020 covering the EMNA region with the aim to describe the epidemiology of influenza in the region and assess the influenza epidemiological surveillance research landscape. Relevant data on study characteristics, population, clinical/virology characteristics and epidemiology were extracted and summarised descriptively. Of the 112 studies identified for inclusion, 90 were conducted in the Eastern Mediterranean region, 19 in North Africa and three across the EMNA region. Data were reported on 314,058 laboratory-confirmed influenza cases, 96 of which were derived from surveillance systems. Amongst the surveillance studies, the percentage of positive cases reported ranged from 1% to 100%. The predominantly identified influenza strain was strain A; H1N1 was the most prominent circulating subtype. Typing was performed in approximately 75% and subtyping in 50% of studies, respectively. Data on those considered most at risk for influenza complications were collected in 21% of studies, highlighting a regional gap for these data. Our review reveals existing gaps in regional estimates of influenza health and economic burden, hospitalisation rates and duration, and highlights the need for robust and high-quality epidemiology data to help inform public health interventions.

Evaluation of the added value of viral genomic information for predicting severity of influenza infection

BACKGROUND: The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management. METHODS: 160 A(H3N2) influenza positive samples from the 2016–2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models. RESULTS: The model including viral mutations in addition to the host characteristics had an improved fit ([Formula: see text] =12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732. CONCLUSIONS: Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06510-z.

Comparison of PB1-F2 Proximity Interactomes Reveals Functional Differences between a Human and an Avian Influenza Virus

International audience; Most influenza viruses express the PB1-F2 protein which is regarded as a virulence factor. However, PB1-F2 behaves differently in avian and mammalian hosts, suggesting that this protein may be involved in the species barrier crossings regularly observed in influenza viruses. To better understand the functions associated with this viral protein, we decided to compare the BioID2-derived proximity interactome of a human PB1-F2 from an H3N2 virus with that of an avian PB1-F2 from an H7N1 strain. The results obtained reveal that the two proteins share only a few interactors and thus common functions. The human virus protein is mainly involved in signaling by Rho GTPases while the avian virus protein is mainly involved in ribonucleoprotein complex biogenesis. PB1-F2 H3N2 interactors include several members of the 14-3-3 protein family, a family of regulatory proteins involved in many signaling pathways. We then validated the interaction with 14-3-3 proteins and were able to show that the association of H3N2-PB1-F2 with YWHAH increased the activity of the antiviral sensor MDA5, while H7N1-PB1-F2 had no effect. Collectively, these results show that PB1-F2 can associate with a large range of protein complexes and exert a wide variety of functions. Furthermore, PB1-F2 interactome differs according to the avian or human origin of the protein.

Ongoing Symptoms After Acute SARS-CoV-2 or Influenza Infection in a Case-Ascertained Household Transmission Study: 7 US Sites, 2021–2023

BACKGROUND: The prevalence and risk factors for ongoing symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [SCV2]) or influenza infection are not well characterized. We conducted a prospective cohort study of households wherein ≥1 individual was infected with SCV2 or influenza to evaluate prevalence of and factors associated with ongoing symptoms at 90 days. METHODS: Index cases and their household contacts provided baseline health and sociodemographic information and collected daily respiratory specimens for 10 days following enrollment. Participants completed a follow-up survey 90 days after enrollment to characterize ongoing symptoms. RESULTS: We analyzed 1967 participants enrolled between December 2021 and May 2023. The risk of ongoing symptoms did not differ by infection status in SCV2 (SCV2-positive: 15.6%; SCV2-negative: 13.9%; odds ratio [OR]: 1.14; 95% CI: .7–1.69) or influenza (influenza-positive: 8.8%; influenza-negative: 10.0%; OR: .87; 95% CI: .45–1.72) households. However, among study participants with a documented infection, SCV2-positive participants had nearly twice the odds of ongoing symptoms as influenza-positive participants (OR: 1.92; 95% CI: 1.27–2.97). CONCLUSIONS: These results suggest that SCV2 households have a significantly higher prevalence of ongoing symptoms compared with influenza households (OR: 1.78; 95% CI: 1.28–2.47). Among participants with SCV2 infection, underlying conditions (adjusted OR [aOR]: 2.65; 95% CI: 1.80–3.90) and coronavirus disease 2019 (COVID-19)–like symptoms (aOR: 2.92; 95% CI: 1.15–7.43) during acute infection increased odds of ongoing symptoms at 90 days, whereas hybrid immunity reduced the odds of ongoing symptoms (aOR: 0.44; 95% CI: .22–.90).

Evaluation of early single dose vaccination on swine influenza A virus transmission in piglets: From experimental data to mechanistic modelling

International audience; Swine influenza A virus (swIAV) is a major pathogen affecting pigs with a huge economic impact and potentially zoonotic. Epidemiological studies in endemically infected farms permitted to identify critical factors favoring on-farm persistence, among which maternally-derived antibodies (MDAs). Vaccination is commonly practiced in breeding herds and might be used for immunization of growing pigs at weaning. Althoughinterference between MDAs and vaccination was reported in young piglets, its impact on swIAV transmission was not yet quantified. To this aim, this study reports on a transmission experiment in piglets with or without MDAs, vaccinated with a single dose injection at four weeks of age, and challenged 17 days post-vaccination. To transpose small-scale experiments to real-life situation, estimated parameters were used in a simulation tool to assess their influence at the herd level. Based on a thorough follow-up of the infection chain during the experiment, the transmission of the swIAV challenge strain was highly dependent on the MDA status of the pigs when vaccinated. MDA-positive vaccinated animals showed a direct transmission rate 3.6-fold higher than the one obtained in vaccinated animals without MDAs, estimated to 1.2. Vaccination nevertheless reduced significantly the contribution of airborne transmission when compared with previous estimates obtained in unvaccinated animals. The integration of parameter estimates in a large-scale simulation model, representing a typical farrow-to-finish pig herd, evidenced an extended persistence of viral spread when vaccination of sows and single dose vaccination of piglets was hypothesized. When extinction was quasi-systematic at year 5 post-introduction in the absence of sow vaccination but with single dose early vaccination of piglets, the extinction probability fell down to 33% when batch-to-batch vaccination was implemented both in breeding herd and weaned piglets. These results shed light on a potential adverse effect of single dose vaccination in MDA-positive piglets, which might lead to longer persistence of the SwIAV at the herd level.

Influenza virus infections in cats

In the past, cats were considered resistant to influenza. Today, we know that they are susceptible to some influenza A viruses (IAVs) originating in other species. Usually, the outcome is only subclinical infection or a mild fever. However, outbreaks of feline disease caused by canine H3N2 IAV with fever, tachypnoea, sneezing, coughing, dyspnoea and lethargy are occasionally noted in shelters. In one such outbreak, the morbidity rate was 100% and the mortality rate was 40%. Recently, avian H7N2 IAV infection occurred in cats in some shelters in the USA, inducing mostly mild respiratory disease. Furthermore, cats are susceptible to experimental infection with the human H3N2 IAV that caused the pandemic in 1968. Several studies indicated that cats worldwide could be infected by H1N1 IAV during the subsequent human pandemic in 2009. In one shelter, severe cases with fatalities were noted. Finally, the highly pathogenic avian H5N1 IAV can induce a severe, fatal disease in cats, and can spread via cat-to-cat contact. In this review, the Advisory Board on Cat Diseases (ABCD), a scientifically independent board of experts in feline medicine from 11 European countries, summarises current data regarding the aetiology, epidemiology, pathogenesis, clinical picture, diagnostics, and control of feline IAV infections, as well as the zoonotic risks.

Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction

Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, accurate multi-step-ahead influenza predictions using ILI series is of great importance, especially, to the potential coming influenza outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning (CLPSO-ML) framework incorporating support vector regression (SVR) and multilayer perceptron (MLP) for multi-step-ahead influenza prediction. A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China. The results show that: (1) The MIMO strategy achieves the best multi-step-ahead prediction, and is potentially more adaptive for longer horizon; (2) The iterated strategy demonstrates special potentials for deriving the least time difference between the occurrence of the predicted peak value and the true peak value of an influenza outbreak; (3) For ILI in the Northern China, SVR model implemented with MIMO strategy performs best, and SVR with iterated strategy also shows remarkable performance especially during outbreak periods; while for ILI in the Southern China, both SVR and MLP models with MIMO strategy have competitive prediction performance

Optical Biosensor Platforms Display Varying Sensitivity for the Direct Detection of Influenza RNA

Detection methods that do not require nucleic acid amplification are advantageous for viral diagnostics due to their rapid results. These platforms could provide information for both accurate diagnoses and pandemic surveillance. Influenza virus is prone to pandemic-inducing genetic mutations, so there is a need to apply these detection platforms to influenza diagnostics. Here, we analyzed the Fast Evaluation of Viral Emerging Risks (FEVER) pipeline on ultrasensitive detection platforms, including a waveguide-based optical biosensor and a flow cytometry bead-based assay. The pipeline was also evaluated in silico for sequence coverage in comparison to the U.S. Centers for Disease Control and Prevention’s (CDC) influenza A and B diagnostic assays. The influenza FEVER probe design had a higher tolerance for mismatched bases than the CDC’s probes, and the FEVER probes altogether had a higher detection rate for influenza isolate sequences from GenBank. When formatted for use as molecular beacons, the FEVER probes detected influenza RNA as low as 50 nM on the waveguide-based optical biosensor and 1 nM on the flow cytometer. In addition to molecular beacons, which have an inherently high background signal we also developed an exonuclease selection method that could detect 500 pM of RNA. The combination of high-coverage probes developed using the FEVER pipeline coupled with ultrasensitive optical biosensors is a promising approach for future influenza diagnostic and biosurveillance applications.

Molecular epidemiology of avian influenza viruses and risk mapping in Mali and West Africa;Epidémiologie moléculaire des virus influenza aviaires et cartographie du risque au Mali et en Afrique de l'Ouest

Avian influenza viruses represent a major threat to human and animal health. These viruses are responsible for high morbidity and mortality, especially in the poultry industry, resulting in huge economic losses. West Africa is a region with little information on the circulation and genetic characteristics of avian influenza viruses, despite the occurrence of several outbreaks in birds. In this thesis, we studied the circulation and genetic characteristics of avian influenza viruses of subtypes H5N1 and H9N2 circulating in West Africa. We also developed a model to map the risk of the spread of these viruses. The genetic and antigenic characteristics of the influenza viruses were determined by molecular characterization and phylogenetic and phylogeographic analysis. The H5N1 virus found in Benin in 2021 was highly pathogenic, belonged to the GS/GD lineage - clade 2.3.4.4.B, and was closely related to contemporary H5N1 viruses detected in West Africa and Europe. This virus had a mammalian adaptation mutation (on the PB1 protein) and an antigenic profile that differed from the vaccine strains recommended by the World Health Organization as part of pre-pandemic preparedness. The H9N2 viruses detected in poultry in Mali belonged to the G1 lineage and also possessed several molecular markers of virulence and adaptation to mammals. In particular, we have found a motif (RSNR) at the cleavage site of the haemagglutinin of these viruses that had not previously been detected in Africa. Analysis of the spatiotemporal dynamics of H9N2 viruses revealed that they were probably introduced into West Africa in 2015 from Morocco. Finally, we implemented the risk mapping model, based on the multicriteria decision analysis method associated with geographic information systems, to produce risk maps for the introduction and spread of avian influenza in Mali. This model can be used as a basis for implementing risk-based surveillance of avian influenza viruses.In conclusion, this thesis provides essential information on the circulation and genetic characteristics of avian influenza viruses in West Africa and proposes a risk mapping model for more effective surveillance in the region. ; Les virus influenza aviaires constituent une menace importante pour la santé humaine et animale. Ces virus sont responsables d’une morbidité et d’une mortalité très élevées surtout dans les filières avicoles occasionnant d’énormes pertes économiques. L’Afrique de l’Ouest constitue une région où l'on manque d’information sur la circulation et les propriétés génétiques des virus influenza, malgré l’apparition de plusieurs foyers chez les oiseaux. Dans cette thèse, nous nous sommes intéressés à la circulation et aux caractéristiques génétiques des virus influenza aviaires de sous-types H5N1 et H9N2 circulant en Afrique de l’Ouest. Nous avons également mis en place un modèle de cartographie du risque de propagation de ces virus. Les caractéristiques génétiques et antigéniques des virus influenza ont été déterminées à travers une caractérisation moléculaire et des analyses phylogénétiques et phylogéographiques. Le virus H5N1 retrouvé au Bénin en 2021 était hautement pathogène, appartenait à la lignée GS/GD - clade 2.3.4.4.B, et était associé aux virus H5N1 contemporains détectés en Afrique de l’Ouest et en Europe. Ce virus possédait une mutation d’adaptation aux mammifères (sur la protéine PB1) et présentait un profil antigéniquement différent des souches vaccinales recommandées par l’Organisation Mondiale de la Santé dans le cadre de la préparation pré-pandémique. Les virus H9N2 détectés chez la volaille au Mali appartenaient à la lignée G1 et possédaient aussi plusieurs marqueurs moléculaires de virulence et d’adaptation aux mammifères. Nous avons notamment trouvé un motif (RSNR) au niveau du site de clivage de l’Hémagglutinine de ces virus qui n’avait jusqu’à présent pas été détecté en Afrique. L’analyse des dynamiques spatio-temporelles des virus H9N2 a révélé que leur introduction est probablement survenue en Afrique de l’Ouest en 2015 en provenance du Maroc. Enfin, le modèle de cartographie sur la base de la méthode d’analyse multicritère d’aide à la décision associée aux systèmes d’information géographique a permis de produire des cartes de risque d’introduction et de propagation de foyers d’influenza aviaire au Mali. Ce modèle pourra servir de base pour la mise en place d’une surveillance basée sur le risque des virus influenza aviaire. En conclusion, cette thèse fournit des informations essentielles sur la circulation et les caractéristiques génétiques des virus influenza tout en proposant un modèle de cartographie du risque pouvant améliorer l’efficacité de leur surveillance en Afrique de l’Ouest.

Acute hemorrhagic pancreatitis following influenza infection: a case report 

Background Acute hemorrhagic pancreatitis is a life-threatening condition leading to shock and multiorgan failure. Although prevalent in the general population, the incidence during pregnancy is low, with a high maternal and fetal mortality rate. The highest incidence is in the third trimester/early postpartum period. Infectious etiology for acute hemorrhagic pancreatitis is rare with only a handful of cases following influenza infection being documented in the literature. Case presentation A 29-year-old Sinhalese pregnant lady in the third trimester presented with an upper respiratory tract infection and abdominal pain, for which she was managed with oral antibiotics. An elective caesarean section was done at 37 weeks gestation due to a past section. On postoperative day 3 she developed a fever with difficulty in breathing. Despite treatment, she succumbed to death on the sixth postoperative day. The autopsy revealed extensive fat necrosis with saponification. The pancreas was necrosed and hemorrhagic. The lungs showed features of adult respiratory distress syndrome and necrosis was observed in the liver and kidneys. Polymerase chain reaction of lungs detected influenza A virus (subtype H3). Conclusion Although rare, acute hemorrhagic pancreatitis from an infectious etiology carries risk of morbidity and mortality. Therefore, a high level of clinical suspicion must be upheld among clinicians to minimize adverse outcomes.

Cost-effectiveness of seasonal influenza vaccination of children in China: a modeling analysis

Background China has a high burden of influenza-associated illness among children. We aimed to evaluate the cost-effectiveness of introducing government-funded influenza vaccination to children in China (fully-funded policy) compared with the status quo (self-paid policy). Methods A decision tree model was developed to calculate the economic and health outcomes, from a societal perspective, using national- and provincial-level data. The incremental cost-effectiveness ratio (ICER) [incremental costs per quality-adjusted life year (QALY) gained] was used to compare the fully-funded policy with the self-paid policy under the willingness-to-pay threshold equivalent to national and provincial GDP per capita. Sensitivity analyses were performed and various scenarios were explored based on real-world conditions, including incorporating indirect effect into the analysis. Results Compared to the self-paid policy, implementation of a fully-funded policy could prevent 1,444,768 [95% uncertainty range (UR): 1,203,446–1,719,761] symptomatic cases, 92,110 (95% UR: 66,953–122,226) influenza-related hospitalizations, and 6494 (95% UR: 4590–8962) influenza-related death per season. The fully-funded policy was cost-effective nationally (7964 USD per QALY gained) and provincially for 13 of 31 provincial-level administrative divisions (PLADs). The probability of a funded vaccination policy being cost-effective was 56.5% nationally, and the probability in 9 of 31 PLADs was above 75%. The result was most sensitive to the symptomatic influenza rate among children under 5 years [ICER ranging from − 25,612 (cost-saving) to 14,532 USD per QALY gained]. The ICER of the fully-funded policy was substantially lower (becoming cost-saving) if the indirect effects of vaccination were considered. Conclusions Introducing a government-funded influenza policy for children is cost-effective in China nationally and in many PLADs. PLADs with high symptomatic influenza rate and influenza-associated mortality would benefit the most from a government-funded influenza vaccination program.

First report of Fusarium triseptatum and Fusarium sacchari causing vascular wilt on banana plants in Brazil

An altered uterine microbiota with endometrial hyperplasia

Background Endometrial hyperplasia (EH) is a precursor to endometrial cancer, and the role of the microbiome in its development is unclear. Results The present study investigated the uterine microbiome in patients with benign uterine conditions and endometrial hyperplasia. A significant structural shift in the uterine microbiome of patients with endometrial hyperplasia compared to those with benign conditions was found. Delftia , Serratia and Stenotrophomonas were significantly enriched in endometrial hyperplasia samples and associated with the presence of endometrial hyperplasia. Conclusions The novel finding suggested that increased abundance of Delftia , Serratia and Stenotrophomonas is associated with the presence of endometrial hyperplasia. Further investigation is needed to determine the value of these microbes as biomarkers for endometrial hyperplasia.

Characterization and genetic analysis of extensively drug-resistant hospital acquired Pseudomonas aeruginosa isolates

Background The incidence of hospital-acquired infections in extensively drug-resistant Pseudomonas aeruginosa (XDR-PA) has been increasing worldwide and is frequently associated with an increase in mortality and morbidity rates. The aim of this study was to characterize clinical XDR-PA isolates recovered during six months at three different hospitals in Egypt. Results Seventy hospital-acquired clinical isolates of P. aeruginosa were classified into multidrug-resistant (MDR), extensively drug-resistant (XDR) and pandrug-resistant (PDR), according to their antimicrobial resistance profile. In addition, the possession of genes associated with mobile genetic elements and genes encoding antimicrobial resistance determinants among isolates were detected using polymerase chain reaction. As a result, a significant percentage of the isolates (75.7%) were XDR, while 18.5% were MDR, however only 5.7% of the isolates were non-MDR. The phenotypic detection of carbapenemases, extended-spectrum β-lactamases (ESBLs) and metallo β-lactamase (MBL) enzymes showed that 73.6% of XDR-PA isolates were carbapenemases producers, whereas 75.5% and 88.7% of XDR-PA isolates produced ESBLs and MBL respectively. In addition, PCR screening showed that oxa gene was the most frequently detected gene of carbapenemases (91.4%), while aac(6ʹ)-lb gene was mostly detected (84.3%) among the screened aminoglycosides-resistance genes. Furthermore, the molecular detection of the colistin resistance gene showed that 12.9% of isolates harbored mcr-1 gene. Concerning mobile genetic element markers ( intI, traA, tnp513, and merA), intI was the highest detected gene as it was amplified in 67 isolates (95.7%). Finally , phylogenetic and molecular typing of the isolates via ERIC-PCR analysis revealed 10 different ERIC fingerprints. Conclusion The present study revealed a high prevalence of XDR-PA in hospital settings which were resistant to a variety of antibiotics due to several mechanisms. In addition, 98% of the XDR-PA clinical isolates contained at least one gene associated with movable genetic elements, which could have aided the evolution of these XDR-PA strains. To reduce spread of drug resistance, judicious use of antimicrobial agents and strict infection control measures are therefore essential.

Microbes, smile for the picture: protocol and guide for capturing images and processing of microbial cultures with smartphones

Photography has revolutionized science by enabling the documentation and analysis of phenomena, including microbial cultures, where high-quality images are essential for research. However, certain inherent qualities of objects, such as the reflective surfaces of Petri dishes and the characteristics of fungal colonies, often compromise image quality. This highlights the need for accessible, low-cost solutions. Here, we present “Microbes, smile for the picture” (MSFP), a practical, portable, and affordable protocol using smartphones and a handmade photo studio equipped with a ring light, enabling standardized, high-quality images of microbial colonies for scientific and outreach purposes. The aim of this work is to describe the procedure for constructing the photograph studio and a protocol for obtaining high-quality photos. We believe that images captured using our studio and protocol can enhance scientific reproducibility and improve image quality for research, illustration, and science communication. Additionally, this protocol has the potential to be applied in other fields, generating high-quality images for educational and science popularization purposes.

Living in mangroves: a syntrophic scenario unveiling a resourceful microbiome

Background Mangroves are complex and dynamic coastal ecosystems under frequent fluctuations in physicochemical conditions related to the tidal regime. The frequent variation in organic matter concentration, nutrients, and oxygen availability, among other factors, drives the microbial community composition, favoring syntrophic populations harboring a rich and diverse, stress-driven metabolism. Mangroves are known for their carbon sequestration capability, and their complex and integrated metabolic activity is essential to global biogeochemical cycling. Here, we present a metabolic reconstruction based on the genomic functional capability and flux profile between sympatric MAGs co-assembled from a tropical restored mangrove. Results Eleven MAGs were assigned to six Bacteria phyla, all distantly related to the available reference genomes. The metabolic reconstruction showed several potential coupling points and shortcuts between complementary routes and predicted syntrophic interactions. Two metabolic scenarios were drawn: a heterotrophic scenario with plenty of carbon sources and an autotrophic scenario with limited carbon sources or under inhibitory conditions. The sulfur cycle was dominant over methane and the major pathways identified were acetate oxidation coupled to sulfate reduction, heterotrophic acetogenesis coupled to carbohydrate catabolism, ethanol production and carbon fixation. Interestingly, several gene sets and metabolic routes similar to those described for wastewater and organic effluent treatment processes were identified. Conclusion The mangrove microbial community metabolic reconstruction reflected the flexibility required to survive in fluctuating environments as the microhabitats created by the tidal regime in mangrove sediments. The metabolic components related to wastewater and organic effluent treatment processes identified strongly suggest that mangrove microbial communities could represent a resourceful microbial model for biotechnological applications that occur naturally in the environment.

Horizontal gene transfer of the Mer operon is associated with large effects on the transcriptome and increased tolerance to mercury in nitrogen-fixing bacteria

Background Mercury (Hg) is highly toxic and has the potential to cause severe health problems for humans and foraging animals when transported into edible plant parts. Soil rhizobia that form symbiosis with legumes may possess mechanisms to prevent heavy metal translocation from roots to shoots in plants by exporting metals from nodules or compartmentalizing metal ions inside nodules. Horizontal gene transfer has potential to confer immediate de novo adaptations to stress. We used comparative genomics of high quality de novo assemblies to identify structural differences in the genomes of nitrogen-fixing rhizobia that were isolated from a mercury (Hg) mine site that show high variation in their tolerance to Hg. Results Our analyses identified multiple structurally conserved merA homologs in the genomes of Sinorhizobium medicae and Rhizobium leguminosarum but only the strains that possessed a Mer operon exhibited 10-fold increased tolerance to Hg. RNAseq analysis revealed nearly all genes in the Mer operon were significantly up-regulated in response to Hg stress in free-living conditions and in nodules. In both free-living and nodule environments, we found the Hg-tolerant strains with a Mer operon exhibited the fewest number of differentially expressed genes (DEGs) in the genome, indicating a rapid and efficient detoxification of Hg from the cells that reduced general stress responses to the Hg-treatment. Expression changes in S. medicae while in bacteroids showed that both rhizobia strain and host-plant tolerance affected the number of DEGs. Aside from Mer operon genes, nif genes which are involved in nitrogenase activity in S. medicae showed significant up-regulation in the most Hg-tolerant strain while inside the most Hg-accumulating host-plant. Transfer of a plasmid containing the Mer operon from the most tolerant strain to low-tolerant strains resulted in an immediate increase in Hg tolerance, indicating that the Mer operon is able to confer hyper tolerance to Hg. Conclusions Mer operons have not been previously reported in nitrogen-fixing rhizobia. This study demonstrates a pivotal role of the Mer operon in effective mercury detoxification and hypertolerance in nitrogen-fixing rhizobia. This finding has major implications not only for soil bioremediation, but also host plants growing in mercury contaminated soils.

Modeling of a Fragment of the Common Digital Space of Scientific Knowledge by the Example of Museum Collections

Abstract A model of a fragment of the Common Digital Space of Scientific Knowledge (CDSSK) is constructed. CDSSK contains virtual museum collections (VCs), which are structured sets of digital objects (images, texts, audio, video, 3D models) related by certain features and are an effective tool for popularizing science and culture. A special case of VCs is virtual exhibitions (VEs), some of which are presented on the platform of the electronic library “Scientific Heritage of Russia.” General information about VCs and VEs and their classification by types is provided. The construction of CDSSK elements characterizing the links of VC objects with objects of other classes—organizations, multimedia, encyclopedias, etc.—is described. The example of a virtual mycological collection was used to demonstrate the main stages of uploading of external VCs into the CDSSK using, among other things, the KAMIS Integrated Automated Museum Information System, which is most widely used in Russian museums, as well as the State Information System “Great Russian Encyclopedia.” The possibilities of navigation through the related elements of the generated model of the fragment of the CDSSK are shown.

The Systematics and Phylogeny of Myxomycetes: Yesterday, Today, and Tomorrow

Abstract —Myxomycetes are amoeboid fungus-like organisms ( Amoebozoa ) with a unique life cycle characterized by a great morphological diversity of fruiting bodies. Due to the similarity of these structures to the fruiting bodies of some representatives of Ascomycota and Basidiomycota, myxomycetes have been classified as fungi since the first known scientific description in 1654. Only in the 19th century, when their life cycle was studied, did the difference of this group from fungi become clear. During the same period, microscopic structures of fruiting bodies, as well as ornamentation of the spore surface, began to be considered as diagnostic features. Due to this, in the period from the end of the 19th to the middle of the 20th century, a rather stable system was formed. However, as further studies have shown, both macro- and micromorphological characters are often quite variable, depend on environmental conditions, and often result from convergent evolution, which causes difficulties in defining species and taxonomic units of higher ranks. Since the first decade of the 21st century, due to the development of molecular genetic methods and the accumulation of data on nucleotide sequences of marker genes together with the improvement of microscopic studies, it has been possible to obtain data on the evolutionary relationships of different groups of myxomycetes. A milestone in this process was the publication of the first phylogenetic system of myxomycetes in 2019. This work was the starting point for a number of studies on the relationships between different groups of myxomycetes at a lower taxonomic level. Thus, there has been a surge in the number of studies that bring us closer to constructing a natural system.

Fungi

Fungi still represent a largely unexplored biological frontier with extraordinary potential for the bioeconomy. This chapter reveals fungi as decomposers of matter, biochemical agents, or even manufacturing platforms whose unique capabilities are increasingly central to sustainable development. Through their exceptional enzymatic systems, mycelial networks, and metabolic versatility, fungi transform agricultural residues, forestry byproducts, and industrial waste streams into valuable new products, ranging from novel biomaterials and pharmaceuticals to biofuels. As we deepen our understanding of fungal biology and cultivation techniques, these organisms emerge as indispensable allies in creating truly circular systems that mimic nature’s waste-free paradigm.

The Changing Face of Candida in Southeast Asia

The phenotypic and genotypic plasticity of Candida species has been extensively characterized in Western literature, while countries in the Eastern Hemisphere are increasingly incorporating these findings into both research and clinical strategies. In Southeast Asia, however, the landscape is more complex. Some nations have taken a more limited role, focusing primarily on clinical identification, with research and surveillance receiving less emphasis. Conversely, other countries in the region actively contribute to global efforts to monitor Candida, including the surveillance of multidrug-resistant strains such as Candidozyma auris. The historical context, distribution patterns, and regional insights into Candida across Southeast Asian countries have been explored in various individual publications; however, a more comprehensive and comparative analysis remains limited. This chapter addresses the gap by presenting an up-to-date, region-wide synthesis of Candida -related data throughout Southeast Asia.

Lipopeptides from Bacillus velezensis ZLP-101 and their mode of action against bean aphids Acyrthosiphon pisum Harris

Background Natural products are important sources for the discovery of new biopesticides to control the worldwide destructive pests Acyrthosiphon pisum Harris . Here, insecticidal substances were discovered and characterized from the secondary metabolites of the bio-control microorganism Bacillus velezensis strain ZLP-101, as informed by whole-genome sequencing and analysis. Results The genome was annotated, revealing the presence of four potentially novel gene clusters and eight known secondary metabolite synthetic gene clusters. Crude extracts, prepared through ammonium sulfate precipitation, were used to evaluate the effects of strain ZLP-101 on Acyrthosiphon pisum Harris aphid pests via exposure experiments. The half lethal concentration (LC50) of the crude extract from strain ZLP-101 against aphids was 411.535 mg/L. Preliminary exploration of the insecticidal mechanism revealed that the crude extract affected aphids to a greater extent through gastric poisoning than through contact. Further, the extracts affected enzymatic activities, causing holes to form in internal organs along with deformation, such that normal physiological activities could not be maintained, eventually leading to death. Isolation and purification of extracellular secondary metabolites were conducted in combination with mass spectrometry analysis to further identify the insecticidal components of the crude extracts. A total of 15 insecticidal active compounds were identified including iturins, fengycins, surfactins, and spergualins. Further insecticidal experimentation revealed that surfactin, iturin, and fengycin all exhibited certain aphidicidal activities, and the three exerted synergistic lethal effects. Conclusions This study improved the available genomic resources for B. velezensis and serves as a foundation for comprehensive studies of the insecticidal mechanism by Bacillus velezensis ZLP-101 in addition to the active components within biological control strains.

Recent Advance: Fluorescein Stain

Fluorescein stain can be used efficiently and cost effective way for examining the raw ophthalmic specimens visualizing tissue details under microscopic examinations. Author(s) developed this staining method in various raw ophthalmic specimens to detect micro-organisms including parasites, corneal and conjunctival pathologies, lid, orbital pathologies and in enucleated eyeballs. The staining process is very simple and can be performed quickly spending minimum time. The staining also gave important informations about the retinal pigment epithelium, retina, intraocular cancer seedings etc. The novelty of the stain had wider applications in detecting fungus, viral inclusion bodies, parasites, immunological and tumor cells covering anatomy, pathology, onco-pathology, microbiology, parasitology, virology, immunology, entomology and so on.

Biofilm-producing ability of methicillin-resistant Staphylococcus aureus clinically isolated in China

Background Staphylococcus aureus , a commensal bacterium, colonizes the skin and mucous membranes of approximately 30% of the human population. Apart from conventional resistance mechanisms, one of the pathogenic features of S. aureus is its ability to survive in a biofilm state on both biotic and abiotic surfaces. Due to this characteristic, S. aureus is a major cause of human infections, with Methicillin-Resistant Staphylococcus aureus (MRSA) being a significant contributor to both community-acquired and hospital-acquired infections. Results Analyzing non-repetitive clinical isolates of MRSA collected from seven provinces and cities in China between 2014 and 2020, it was observed that 53.2% of the MRSA isolates exhibited varying degrees of ability to produce biofilm. The biofilm positivity rate was notably high in MRSA isolates from Guangdong, Jiangxi, and Hubei. The predominant MRSA strains collected in this study were of sequence types ST59, ST5, and ST239, with the biofilm-producing capability mainly distributed among moderate and weak biofilm producers within these ST types. Notably, certain sequence types, such as ST88, exhibited a high prevalence of strong biofilm-producing strains. The study found that SCC mec IV was the predominant type among biofilm-positive MRSA, followed by SCC mec II. Comparing strains with weak and strong biofilm production capabilities, the positive rates of the sdrD and sdrE were higher in strong biofilm producers. The genetic determinants ebp , icaA , icaB , icaC , icaD , icaR , and sdrE were associated with strong biofilm production in MRSA. Additionally, biofilm-negative MRSA isolates showed higher sensitivity rates to cefalotin (94.8%), daptomycin (94.5%), mupirocin (86.5%), teicoplanin (94.5%), fusidic acid (81.0%), and dalbavancin (94.5%) compared to biofilm-positive MRSA isolates. The biofilm positivity rate was consistently above 50% in all collected specimen types. Conclusions MRSA strains with biofilm production capability warrant increased vigilance.

Molecular characterization and epidemiological investigation of colistin resistance in carbapenem-resistant Klebsiella pneumoniae in a tertiary care hospital in Tehran, Iran

Background Carbapenemase-producing Klebsiella pneumoniae (CRKP) presents a significant challenge to antimicrobial therapy, especially when compounded by resistance to colistin. The objective of this study was to explore molecular epidemiological insights into strains of clinical K. pneumoniae that produce carbapenemases and exhibit resistance to colistin. Eighty clinical isolates of CRKP were obtained from Milad Hospital in Tehran, Iran. Antimicrobial susceptibility and colistin broth disk elution were determined. PCR assays were conducted to examine the prevalence of resistance-associated genes, including bla _ KPC , bla _ IMP , bla _ VIM , bla _ OXA−48 , bla _ NDM and mcr- 1 to -10. Molecular typing (PFGE) was used to assess their spread. Results Colistin resistance was observed in 27 isolates (33.7%) using the Broth Disk Elution method. Among positive isolates for carbapenemase genes, the most frequent gene was bla _ OXA−48 , identified in 36 strains (45%). The mcr-1 gene was detected in 3.7% of the obtained isolates, with none of the other of the other mcr genes detected in the studied isolates. Conclusion To stop the spread of resistant K. pneumoniae and prevent the evolution of mcr genes, it is imperative to enhance surveillance, adhere rigorously to infection prevention protocols, and implement antibiotic stewardship practices.

Tumour-associated and non-tumour-associated bacteria co-abundance groups in colorectal cancer

Background & aims Gut microbiota is closely related to the occurrence and development of colorectal cancer (CRC). However, the differences in bacterial co-abundance groups (CAGs) between tumor tissue (TT) and normal tissue (NT), as well as their associations with clinical features, are needed to be clarified. Methods Bacterial 16 S rRNA sequencing was performed by using TT samples and NT samples of 251 patients with colorectal cancer. Microbial diversity, taxonomic characteristics, microbial composition, and functional pathways were compared between TT and NT. Hierarchical clustering was used to construct CAGs. Results Four CAGs were grouped in the hierarchical cluster analysis. CAG 2, which was mainly comprised of pathogenic bacteria, was significantly enriched in TT samples (2.27% in TT vs. 0.78% in NT, p  < 0.0001). CAG 4, which was mainly comprised of non-pathogenic bacteria, was significantly enriched in NT samples (0.62% in TT vs. 0.79% in NT, p  = 0.0004). In addition, CAG 2 was also significantly associated with tumor microsatellite instability (13.2% in unstable vs. 2.0% in stable, p  = 0.016), and CAG 4 was positively correlated with the level of CA199 ( r  = 0.17, p  = 0.009). Conclusions Our research will deepen our understanding of the interactions among multiple bacteria and offer insights into the potential mechanism of NT to TT transition.

Dermatophytic Dynamics: A Holistic Approach to Superficial Mycosis Research

Human fungus infections are a serious public health issue that needs a prompt and correct diagnosis to cure effectively. Human fungal infections have historically been treated with in-person consultations and exams by trained laboratory mycologists. Lately, deep learning models-based computer-aided diagnosis systems have become more potent instruments, especially for late-stage mycological diagnoses. This research presents experimental results using a deep learning technique to classify different species of fungi. The work is noteworthy for using a vision transformer, demonstrating its effectiveness in classifying fungi. This work contributes to the ongoing advancement of diagnostic techniques and provides a feasible route for artificial intelligence integration in mycology. The results show a remarkable 97.3% accuracy rate, underscoring the potential of deep learning models—especially vision transformers—to enhance the efficacy and accuracy of fungal infection diagnosis. This study contributes to the growing body of research on artificial intelligence and medical mycology. The vision transformer’s efficacy in classifying fungi confirms the transformative potential of deep learning in diagnostic methods.

Natural antibiotics against antimicrobial resistance: sources and bioinspired delivery systems

The current burden associated to multidrug resistance, and the emerging superbugs, result in a decreased and even loss of antibiotic efficacy, which poses significant challenges in the treatment of infectious diseases. This situation has created a high demand for the discovery of novel antibiotics that are both effective and safe. However, while antibiotics play a crucial role in preventing and treating diseases, they are also associated with adverse effects. The emergence of multidrug-resistant and the extensive appearance of drug-resistant microorganisms, has become one of the major hurdles in healthcare. Addressing this problem will require the development of at least 20 new antibiotics by 2060. However, the process of designing new antibiotics is time-consuming. To overcome the spread of drug-resistant microbes and infections, constant evaluation of innovative methods and new molecules is essential. Research is actively exploring alternative strategies, such as combination therapies, new drug delivery systems, and the repurposing of existing drugs. In addition, advancements in genomic and proteomic technologies are aiding in the identification of potential new drug targets and the discovery of new antibiotic compounds. In this review, we explore new sources of natural antibiotics from plants, algae other sources, and propose innovative bioinspired delivery systems for their use as an approach to promoting responsible antibiotic use and mitigate the spread of drug-resistant microbes and infections.

Fact-finding with fungi: A scoping review on recent advancements in the role of fungi as evidence in forensic science

Background Fungi are important decomposers aiding in recycling of organic matter. The use of fungi in forensic settings has grown in popularity in the recent years, due to its diverse applications. Objective This scoping review seeks to compile the advances in using fungi as evidence and identify current trends in the workflow of fungal applications in forensic science. Design Web of Science, Scopus and PubMed databases were used to find relevant literature published during the years of 2005-2025. Eighty-one articles were identified as they fit the eligibility criteria of the review. Result Fungi growing on a cadaver can aid in identifying the stage of decomposition and approximate estimation of time since death, while alteration of soil fungal community due to decomposition can help in post-burial interval assessment. Fungal spores are effective as trace evidences to locate primary and secondary crime scenes, using either dust or soil, by integrating DNA metabarcoding and statistical approaches. However, fungi can also alter evidence, such as in hair, body fluids and drugs. Conclusion Research has established fungi as one of the most robust pieces of evidence. Research should be conducted on refining the methodologies and considering the various factors which can affect fungal growth.

Identification and antimicrobial susceptibility profiles of Staphylococcus species isolated from raw cow milk, and swabs in smallholder dairy farms in Meta district, Eastern Ethiopia

Background The safety of milk production in terms of foodborne infections is a worldwide issue, particularly in developing countries where production is often unhygienic. A cross-sectional study was conducted from December 2018 to August 2019 in the Meta District of Eastern Hararghe Zone, Oromia Regional State, Ethiopia. We aim to assess milk hygiene practices among smallholder dairy farmers, estimate the prevalence of Staphylococcus aureus in raw cow milk and swabs, assess associated risk factors, and the antimicrobial susceptibility test of S. aureus isolates. Face-to-face interviews with 30 respondents randomly selected from smallholder dairy farmers were used to assess the potential risk factors for S. aureus contaminations in milk. A total of 177 samples were examined using standard microbiological testing. The disc diffusion technique was also employed to assess the antibiotic susceptibility of the isolates. The data was analyzed using STATA^® version 14.0 statistical software. Results According to the milk hygiene assessment, 80% of respondents did not wash cow udder before milking, did not use detergent to clean milk containers, and did not keep milk refrigerated before consumption or sale, while 63.3% of milk consumers ingested raw milk. They had never heard of staphylococci foodborne disease. Likewise, the overall prevalence of S. aureus was 12.42% (95%CI: 8.32–18.98). The prevalence of S. aureus in udder milk, equipment swabs, and milkers’ hands was 18.8%, 26.7%, and 30%, respectively. The prevalence of S. aureus in milk is significantly associated with age, and mastitis history ( p  < 0.05). Moreover, old and mastitis positive animals were eight (OR: 8.40; 95%CI: 1.68–41.89) and four (OR: 4.33; 95%CI: 1.37–13.66) times more likely to be infected by S. aureus than adult, and mastitis negative animal. The isolates were resistant to penicillin G (97.4%) and tetracycline (69.2%) whereas susceptible to kanamycin, streptomycin, vancomycin, and cefotaxime, at 84.6%, 71.8%, 64%, and 58.8%, respectively. Conclusion This study revealed the presence of antimicrobial-resistant patterns of S. aureus on commonly used antibiotics, as well as inadequate milk handling practices in the study area. Thus, awareness should be created on proper milk handling and hygiene as well as appropriate uses of antibiotics should be encouraged.

Assessing the Validity and Impact of Remote Digital Image Reading in Fungal Diagnostics

Mycological diagnostics play a crucial role in patient management and treatment of invasive fungal infections. Despite the significant global burden of fungal diseases, awareness and diagnostic capabilities in mycology laboratories lag behind other microbiological disciplines. Mycological diagnostics often require microscopic analysis of clinical samples and culture. The interpretation of microscopy requires extensive expertise in clinical mycology. This study aimed to explore the feasibility of remote digital reading for preliminary identification of fungi. In this study, five mycology-trained participants were asked to analyze a total of 474 images divided into three main groups of yeasts (73 images), filamentous fungi (341 images), and direct fluorescent microscopy from clinical samples (60 images). The accuracy of the assessments varied, with an average correct decision rate between 78 and 93% across the three image groups. Individual participant’s performance showed a mean accuracy rate ranging between 76 and 92%. A significant difference was observed in the assessment accuracy across specimen groups and among individual participants (p < 0.05). However, there was no significant interaction effect between participants and image group (p = 0.118). In conclusion, telemycology offers a promising alternative to standard microscopy diagnostics of fungal infections, especially in settings where skilled mycologists are lacking, including low- and middle-income countries.

A Rare Case of Non-Invasive Fungal Sinusitis Caused by Scedosporium Apiospermum in an Immunocompetent Patient

Fungal sinusitis are very often caused by Aspergillus spp. Dematiaceae spp. and mucomycoses. Sinusitis with Scedosporium spp is very rare, especially in immunocompetent humans. A 42-year-old female presented with long history of facial pain and headache. Isolated opacification of maxillary sinus with double density sign was noted on left side in the CT scan. Diagnosed as fungal rhino sinusitis and endoscopic sinus surgery was carried out to clear the disease of the sinus and further mycological evaluation proved the organism to be Scedosporium apiospermum. We report the rare case of a non-invasive isolated fungal sinusitis caused by fungi Scedosporium apiospermum.

Fungal endophytes of Taxus species and regulatory effect of two strains on taxol synthesis

Background Taxol, derived from Taxus trees, is a valuable natural resource for the development of anticancer drugs. Endophytic fungi from Taxus trees are a promising alternative source of Taxol. However, the impact of plant-endophytic microbial interaction on the host’s Taxol biosynthesis is largely unknown. Results In the current study, the diversity of endophytic fungi in three different Taxus species was analyzed using Internal Transcribed Spacer sequencing. A total of 271 Operational Taxonomic Units (OTUs) were identified, grouping into 2 phyla, 8 classes, 16 orders, 19 families, and 19 genera. Alpha and beta diversity analysis indicated significant differences in endophytic fungal communities among the various Taxus trees. At the genus level, Alternaria and Davidiella were predominantly found in T. mairei and T. media , respectively. By utilizing a previously published dataset, a Pearson correlation analysis was conducted to predict the taxol biosynthesis-related fungal genera. Following screening, two isolates of Alternaria (L7 and M14) were obtained. Effect of inoculation with Alternaria isolates on the gene expression and metabolite accumulation of T. mairei was determined by transcriptomic and untargeted metabolomic studies. The co-inoculation assay suggests that the two Alternaria isolates may have a negative regulatory effect on taxol biosynthesis by influencing hormone signaling pathways. Conclusion Our findings will serve as a foundation for advancing the production and utilization of Taxus and will also aid in screening endophytic fungi related to taxol production.

Genetic characteristics of chromosomally integrated carbapenemase gene (bla_NDM−1) in isolates of Proteus mirabilis

Objective This study aims to conduct an in-depth genomic analysis of a carbapenem-resistant Proteus mirabilis strain to uncover the distribution and mechanisms of its resistance genes. Methods The research primarily utilized whole-genome sequencing to analyze the genome of the Proteus mirabilis strain. Additionally, antibiotic susceptibility tests were conducted to evaluate the strain’s sensitivity to various antibiotics, and related case information was collected to analyze the clinical distribution characteristics of the resistant strain. Results Study on bacterial strain WF3430 from a tetanus and pneumonia patient reveals resistance to multiple antibiotics due to extensive use. Whole-genome sequencing exposes a 4,045,480 bp chromosome carrying 29 antibiotic resistance genes. Two multidrug-resistant (MDR) gene regions, resembling Tn 6577 and Tn 6589 , were identified (MDR Region 1: 64.83 Kb, MDR Region 2: 85.64 Kbp). These regions, consist of integrative and conjugative elements (ICE) structures, highlight the intricate multidrug resistance in clinical settings. Conclusion This study found that a CR-PMI strain exhibits a unique mechanism for acquiring antimicrobial resistance genes, such as bla _NDM−1, located on the chromosome instead of plasmids. According to the results, there is increasing complexity in the mechanisms of horizontal transmission of resistance, necessitating a comprehensive understanding and implementation of targeted control measures in both hospital and community settings.

A review on the cultivation, bioactive compounds, health-promoting factors and clinical trials of medicinal mushrooms Taiwanofungus camphoratus, Inonotus obliquus and Tropicoporus linteus

Medicinal mushrooms, such as Taiwanofungus camphoratus , Inonotus obliquus , and Tropicoporus linteus , have been used in traditional medicine for therapeutic purposes and promotion of overall health in China and many East Asian countries for centuries. Modern pharmacological studies have demonstrated the large amounts of bioactive constituents (such as polysaccharides, triterpenoids, and phenolic compounds) available in these medicinal mushrooms and their potential therapeutic properties. Due to the rising demand for the health-promoting medicinal mushrooms, various cultivation methods have been explored to combat over-harvesting of the fungi. Evidence of the robust pharmacological properties, including their anticancer, hypoglycemic, hypolipidemic, antioxidant, and antiviral activities, have been provided in various studies, where the health-benefiting properties of the medicinal fungi have been further proven through numerous clinical trials. In this review, the cultivation methods, available bioactive constituents, therapeutic properties, and potential uses of T. camphoratus , I. obliquus and T. linteus are explored.

The role of gut microbiota in chronic restraint stress-induced cognitive deficits in mice

Background Chronic stress induces cognitive deficits. There is a well-established connection between the enteric and central nervous systems through the microbiota-gut-brain (MGB) axis. However, the effects of the gut microbiota on cognitive deficits remain unclear. The present study aimed to elucidate the microbiota composition in cognitive deficits and explore its potential in predicting chronic stress-induced cognitive deficits. Methods Mice were randomly divided into control and chronic restraint stress (CRS) groups. The mice subjected to CRS were further divided into cognitive deficit (CRS-CD) and non-cognitive deficit (CRS-NCD) groups using hierarchical cluster analysis of novel object recognition test results. The composition and diversity of the gut microbiota were analyzed. Results After being subjected to chronic restraint distress, the CRS-CD mice travelled shorter movement distances ( p  = 0.034 vs. CRS-NCD; p  < 0.001 vs. control) and had a lower recognition index than the CRS-NCD ( p  < 0.0001 vs. CRS-NCD; p  < 0.0001 vs. control) and control mice. The results revealed that 5 gut bacteria at genus levels were significantly different in the fecal samples of mice in the three groups. Further analyses demonstrated that Muricomes were not only significantly enriched in the CRS-CD group but also correlated with a decreased cognitive index. The area under the receiver operating curve of Muricomes for CRS-induced cognitive deficits was 0.96. Conclusions Our study indicates that the composition of the gut microbiota is involved in the development of cognitive deficits induced by chronic restraint stress. Further analysis revealed that Muricomes have the potential to predict the development of chronic stress-induced cognitive deficits in mice.

Psychotherapies and Impact on Borderline Personality Disorder and Ensuing Biological Markers: A New Narrative

Borderline personality disorder is characterized by challenges in interpersonal relationships and self-image, impulsivity, and affective instability. Putative neurobiological mechanisms have deepened understanding of the pathophysiology of the disorder, yet challenges remain in the provision of effective treatment. Psychotherapy is the first-line treatment for BPD and though a range of evidence-based modalities are supported, rates of nonresponse are approximately 50%. One approach to improving treatment outcomes concerns research at the cross-disciplinary intersection of neurobiology and psychotherapy. A comprehensive overview of psychotherapy studies that have incorporated biological markers will be provided in this chapter, including neuroimaging, genetics, and neuroendocrine studies. Most studies have investigated task-related brain activation, mainly using affective paradigms that have implicated primarily frontotemporal and limbic regions. High between-study heterogeneity and an overall small number of studies, particularly genetics and neuroendocrine studies, preclude firm conclusions. Recent developments in the field and recommendations for future progress will be discussed.

Systems Genetics Analyses Reveals Key Genes Related to Behavioral Traits in the Striatum of CFW Mice

The striatum plays a central role in directing many complex behaviors ranging from motor control to action choice and reward learning. In our study, we used 55 male CFW mice with rapid decay linkage disequilibrium to systematically mine the striatum-related behavioral functional genes by analyzing their striatal transcriptomes and 79 measured behavioral phenotypic data. By constructing a gene coexpression network, we clustered the genes into 13 modules, with most of them being positively correlated with motor traits. Based on functional annotations as well as Fisher's exact and hypergeometric distribution tests, brown and magenta modules were identified as core modules. They were significantly enriched for striatal-related functional genes. Subsequent Mendelian randomization analysis verified the causal relationship between the core modules and dyskinesia. Through the intramodular gene connectivity analysis, Adcy5 and Kcnma1 were identified as brown and magenta module hub genes, respectively. Knock outs of both Adcy5 and Kcnma1 lead to motor dysfunction in mice, and KCNMA1 acts as a risk gene for schizophrenia and smoking addiction in humans. We also evaluated the cellular composition of each module and identified oligodendrocytes in the striatum to have a positive role in motor regulation.

Mesoscale Calcium (Ca^2+) Imaging in Freely Behaving Mice

Mesoscale imaging of calcium dynamics has emerged as a powerful technique to study the coordination of activity of multiple cortical regions simultaneously during awake behavior. While much of the work in the field has focused on studying cortical dynamics during head-fixed preparations, we have recently developed the mini-mScope—a head mounted widefield imaging microscope capable of imaging large swathes of the dorsal cortex of freely behaving mice. In this chapter, we guide the reader with methodological details to build and use the mini-mScope for widefield Ca^2+ imaging in freely behaving mice performing complex behaviors such as spatial navigation.

Inhibition of the cGAS‑STING Pathway Reduces Cisplatin-Induced Inner Ear Hair Cell Damage

Although cisplatin is a widely used chemotherapeutic agent, it is severely toxic and causes irreversible hearing loss, restricting its application in clinical settings. This study aimed to determine the molecular mechanism underlying cisplatin-induced ototoxicity. Here, we established in vitro and in vivo ototoxicity models of cisplatin-induced hair cell loss, and our results showed that reducing STING levels decreased inflammatory factor expression and hair cell death. In addition, we found that cisplatin-induced mitochondrial dysfunction was accompanied by cytosolic DNA, which may act as a critical linker between the cyclic GMP-AMP synthesis−stimulator of interferon genes (cGAS-STING) pathway and the pathogenesis of cisplatin-induced hearing loss. H-151, a specific inhibitor of STING, reduced hair cell damage and ameliorated the hearing loss caused by cisplatin in vivo. This study underscores the role of cGAS-STING in cisplatin ototoxicity and presents H-151 as a promising therapeutic for hearing loss. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-024-01334-8.

The Vestibulospinal Nucleus Is a Locus of Balance Development

Mature vertebrates maintain posture using vestibulospinal neurons that transform sensed instability into reflexive commands to spinal motor circuits. Postural stability improves across development. However, due to the complexity of terrestrial locomotion, vestibulospinal contributions to postural refinement in early life remain unexplored. Here we leveraged the relative simplicity of underwater locomotion to quantify the postural consequences of losing vestibulospinal neurons during development in larval zebrafish of undifferentiated sex. By comparing posture at two timepoints, we discovered that later lesions of vestibulospinal neurons led to greater instability. Analysis of thousands of individual swim bouts revealed that lesions disrupted movement timing and corrective reflexes without impacting swim kinematics, and that this effect was particularly strong in older larvae. Using a generative model of swimming, we showed how these disruptions could account for the increased postural variability at both timepoints. Finally, late lesions disrupted the fin/trunk coordination observed in older larvae, linking vestibulospinal neurons to postural control schemes used to navigate in depth. Since later lesions were considerably more disruptive to postural stability, we conclude that vestibulospinal contributions to balance increase as larvae mature. Vestibulospinal neurons are highly conserved across vertebrates; we therefore propose that they are a substrate for developmental improvements to postural control.

The Impact of Educational Neuroscience on Understanding and Teaching English Learners with Dyscalculia

Advancements in neuroscience and educational research have provided new opportunities to understand and explore dyscalculia, a specific learning disability that impairs students’ abilities in math and numerical operations. Neuroscientific studies have pinpointed critical brain regions, such as the intraparietal sulcus and left angular gyrus, as central to numerical magnitude processing—a fundamental skill for math competence. Furthermore, the frontal, temporal, and occipital lobes are implicated in math tasks, indicating a comprehensive neural network involved in math cognition. Neuroplasticity underscores the brain’s adaptive responses to new learning experiences, which holds significant implications for educational practices, particularly in the instruction of math. Despite expanding research on dyscalculia, there remains a scarcity of empirical data on the effectiveness of interventions and math support programs specifically for students who are English learners (ELs) in U.S. schools. Educational strategies, such as Dynamic Strategic Math (DSM), integrate culturally relevant materials, multisensory learning, scaffolding, and ample practice to enhance math achievement and retention. By synthesizing empirical studies and meta-analyses, this chapter aims to (a) provide an overview of educational neuroscience research on dyscalculia and (b) establish connections between educational neuroscience research and classroom interventions for ELs with dyscalculia.

Distinctive Neurophysiological Signatures of Analgesia after Inflammatory Pain in the ACC of Freely Moving Mice

Preclinical assessments of pain have often relied upon behavioral measurements and anesthetized neurophysiological recordings. Current technologies enabling large-scale neural recordings, however, have the potential to unveil quantifiable pain signals in conscious animals for preclinical studies. Although pain processing is distributed across many brain regions, the anterior cingulate cortex (ACC) is of particular interest in isolating these signals given its suggested role in the affective (“unpleasant”) component of pain. Here, we explored the utility of the ACC toward preclinical pain research using head-mounted miniaturized microscopes to record calcium transients in freely moving male mice expressing genetically encoded calcium indicator 6f (GCaMP6f) under the Thy1 promoter. We verified the expression of GCaMP6f in excitatory neurons and found no intrinsic behavioral differences in this model. Using a multimodal stimulation paradigm across naive, pain, and analgesic conditions, we found that while ACC population activity roughly scaled with stimulus intensity, single-cell representations were highly flexible. We found only low-magnitude increases in population activity after complete Freund's adjuvant (CFA) and insufficient evidence for the existence of a robust nociceptive ensemble in the ACC. However, we found a temporal sharpening of response durations and generalized increases in pairwise neural correlations in the presence of the mechanistically distinct analgesics gabapentin or ibuprofen after (but not before) CFA-induced inflammatory pain. This increase was not explainable by changes in locomotion alone. Taken together, these results highlight challenges in isolating distinct pain signals among flexible representations in the ACC but suggest a neurophysiological hallmark of analgesia after pain that generalizes to at least two analgesics.

Enhanced Emotion Recognition from fMRI Using a Multi-model Neural Network Framework

Functional magnetic resonance imaging (fMRI) has the capability to reflect brain activities, with brain regions such as the amygdala, prefrontal cortex, and hippocampus exhibiting distinct emotional activity patterns. These neural activities interact in complex ways, making interpretation and identification challenging. The objective of this study is to identify participants’ emotions using fMRI data from the ICBHI scientific challenge, involving 16 participants in the training set and 4 in the test set. Each participant has undergone 30 trails and reported their emotion classes and levels. The averaged fMRI signals were extracted from 246 regions by Brainnetome atlas, and the regions of interest were selected based on correlations, including emotional related 66 regions. We applied a band-pass filter (sigma = 2) for data preprocessing. There were two models developed, for class and level prediction, respectively. For class prediction, to enhance signal quality and reduce noise in fMRI signals, we employed a nonlinear least squares approach. Specifically, each 25-s signal segment was modeled using a polynomial, and parameters of this polynomial were adjusted to minimize the sum of squares of the differences between observed and predicted values. To test which brain regions’ activity patterns are highly correlated with emotions, we trained multiple fully connected neural networks (FCNNs) to select from the previously mentioned 66 brain regions. Ultimately, the signals from brain regions highly correlated with emotions are selected as inputs for subsequent integrated convolutional neural network (CNN) model to distinguish between types of emotions. For level prediction, we utilized long short- term memory (LSTM) models for level prediction to participants’ emotions judgement. Finally, the algorithm we proposed achieved a classification accuracy of 56% for identifying emotion classes, 20% for identifying emotional intensity levels. The mean score of 0.6251 was calculated by ICBHI. The difference of the precision for class and level may be related to the subjective evaluation of the subjects as the rating of emotion is highly dependent by the subject’s self-perception and background.

Neurotransmitters: Foundations of Cognition

This chapter delves into the foundational aspects of neuroscience, focusing on the neurological correlates of consciousness and their relevance to spiritual wellness. It explores how the quantification of mindfulness and consciousness can contribute to overall well-being through integrative healthcare approaches. The chapter further examines the role of neurotransmitters in influencing cognitive abilities and mental health, highlighting the methods used in the literature to detect and measure low levels of neurotransmitters. This discussion underscores the intersection of neuroscience and spirituality, emphasizing the importance of understanding these biological processes to enhance mental and spiritual health.

EEG Theta/Beta Ratio Variability in Relation to Attachment Style in the Context of Secretary Problem

This study examines the Theta/Beta Ratio (TBR) in EEG measurements to infer the cognitive load during decision-making across different attachment styles. Participants’ EEGs were analyzed within the secretary problem framework while they were at rest and making decisions. The secure attachment group demonstrated higher TBR difference medians, suggesting a lower cognitive load. Conversely, the fearfully avoidant group exhibited significantly lower medians, indicating a heightened cognitive load. The anxiously attached group’s TBR varied widely, suggesting inconsistent cognitive engagement, while the avoidant group’s levels were moderate. These results imply that attachment style determines the cognitive load experienced during decision-making, with fearfully avoidant individuals bearing the highest load. This research elucidates the neurobiological links between attachment styles and cognitive processing.

Brain-Based Learning: Support Critical Thinking with Brain-Based Learning Strategies

Brain-based learning aligns educational strategies with the brain’s natural processes for absorbing, retaining, and applying knowledge. The purpose of this chapter is to explain the neurophysiology of learning and provide practical strategies that work with, rather than against, the brain’s natural learning mechanisms. When nursing students understand why certain approaches are more effective—like spaced repetition, chunking information, and active reflection—they are able to embrace these methods in their learning. The relationship between brain-based learning and critical thinking is explored through practical examples of how nursing students can apply these concepts in classroom, clinical, and simulation settings. Case studies demonstrate how understanding concepts like neurotransmitter function, memory formation, and the role of sleep in learning helps students optimize their study habits and learning techniques. Interactive learning activities and chapter synthesis exercises help students apply and integrate key concepts.

The Chemokine CCL2 Promotes Excitatory Synaptic Transmission in Hippocampal Neurons via GluA1 Subunit Trafficking

The CC chemokine ligand 2 (CCL2, also known as MCP-1) and its cognate receptor CCR2 have well-characterized roles in chemotaxis. CCL2 has been previously shown to promote excitatory synaptic transmission and neuronal excitability. However, the detailed molecular mechanism underlying this process remains largely unclear. In cultured hippocampal neurons, CCL2 application rapidly upregulated surface expression of GluA1, in a CCR2-dependent manner, assayed using SEP-GluA1 live imaging, surface GluA1 antibody staining, and electrophysiology. Using pharmacology and reporter assays, we further showed that CCL2 upregulated surface GluA1 expression primarily via Gα(q)- and CaMKII-dependent signaling. Consistently, using i.p. injection of lipopolysaccharide to induce neuroinflammation, we found upregulated phosphorylation of S831 and S845 sites on AMPA receptor subunit GluA1 in the hippocampus, an effect blocked in Ccr2(−/−) mice. Together, these results provide a mechanism through which CCL2, and other secreted molecules that signal through G-protein coupled receptors, can directly regulate synaptic transmission. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-024-01236-9.

Sex Differences in Dopamine Release in Nucleus Accumbens and Dorsal Striatum Determined by Chronic Fast-Scan Cyclic Voltammetry: Effects of Social Housing and Repeated Stimulation

We investigated sex differences in dopamine (DA) release in the nucleus accumbens (NAc) and dorsolateral striatum (DLS) using a chronic 16-channel carbon fiber electrode and fast-scan cyclic voltammetry (FSCV). Electrical stimulation-induced (ES; 60 Hz) DA release was recorded in the NAc of single- or pair-housed male and female rats. When core (NAcC) and shell (NAcS) were recorded simultaneously, there was greater ES DA release in NAcC of pair-housed females compared with single females and males. Housing did not affect ES NAc DA release in males. In contrast, there was significantly more ES DA release from the DLS of female rats than male rats. This was true prior to and after treatment with methamphetamine. Furthermore, in castrated (CAST) males and ovariectomized (OVX) females, there were no sex differences in ES DA release from the DLS, demonstrating the hormone dependence of this sex difference. However, in the DLS of both intact and gonadectomized rats, DA reuptake was slower in females than that in males. Finally, DA release following ES of the medial forebrain bundle at 60 Hz was studied over 4 weeks. ES DA release increased over time for both CAST males and OVX females, demonstrating sensitization. Using this novel 16-channel chronic FSCV electrode, we found sex differences in the effects of social housing in the NAcS, sex differences in DA release from intact rats in DLS, and sex differences in DA reuptake in DLS of intake and gonadectomized rats, and we report sensitization of ES-induced DA release in DLS in vivo.

Brain Biopsies and Cerebrospinal Fluid Collection in Preclinical Rodent Models

Brain biopsies and cerebrospinal fluid collection are frequently used in neuroscience research and require precise execution. The objective of this chapter was to provide updated protocols for brain biopsies and other stereotactic procedures in adult mice. The described technique employing a stereotactic arm enables greater precision than freehand techniques. Stereotactic procedures in adult mice can be performed expeditiously and demonstrate high efficacy in reaching the target site. Efficacy is contingent upon the complexity of precisely targeting the biopsy site, owing to the diminutive size of the animal. This chapter describes an innovative, efficient, and reproducible surgical protocol for brain biopsy and cerebrospinal fluid collection in adult mice.

Neurodegenerative Diseases: What Can Be Learned from Toothed Whales?

Neurodegeneration involves a wide range of neuropathological alterations affecting the integrity, physiology, and architecture of neural cells. Many studies have demonstrated neurodegeneration in different animals. In the case of Alzheimer's disease (AD), spontaneous animal models should display two neurohistopathological hallmarks: the deposition of β-amyloid and the arrangement of neurofibrillary tangles. However, no natural animal models that fulfill these conditions have been reported and most research into AD has been performed using transgenic rodents. Recent studies have also demonstrated that toothed whales - homeothermic, long-lived, top predatory marine mammals - show neuropathological signs of AD-like pathology. The neuropathological hallmarks in these cetaceans could help to better understand their endangered health as well as neurodegenerative diseases in humans. This systematic review analyzes all the literature published to date on this trending topic and the proposed causes for neurodegeneration in these iconic marine mammals are approached in the context of One Health/Planetary Health and translational medicine.

Erroneous Compensation for Long-Latency Feedback Delays as Origin of Essential Tremor

Essential tremor (ET), a movement disorder characterized by involuntary oscillations of the limbs during movement, remains to date not well understood. It has been recently suggested that the tremor originates from impaired delay compensation, affecting movement representation and online control. Here we tested this hypothesis directly with 24 ET patients (14 female; 10 male) and 28 neurologically intact (NI) human volunteers (17 female; 11 male) in an upper limb postural perturbation task. After maintaining their hand in a visual target, participants experienced perturbations of unpredictable direction and magnitude and were instructed to counter the perturbation and steer their hand back to the starting position. In comparison with NI volunteers, ET patients’ early muscular responses (short and long-latency responses, 20–50 and 50–100 ms, respectively) were preserved or even slightly increased. However, they exhibited perturbation-dependent deficits when stopping and stabilizing their hand in the final target supporting the hypothesis that the tremor was generated by the feedback controller. We show in a computational model that errors in delay compensation accumulating over time produced the same small increase in initial feedback response followed by oscillations that scaled with the perturbation magnitude as observed in ET population. Our experimental results therefore validate the computational hypothesis that inaccurate delay compensation in long-latency pathways could be the origin of the tremor.

Neuronal Coupling Modes Show Differential Development in the Early Cortical Activity Networks of Human Newborns

The third trimester is a critical period for the development of functional networks that support the lifelong neurocognitive performance, yet the emergence of neuronal coupling in these networks is poorly understood. Here, we used longitudinal high-density electroencephalographic recordings from preterm infants during the period from 33 to 45 weeks of conceptional age (CA) to characterize early spatiotemporal patterns in the development of local cortical function and the intrinsic coupling modes [ICMs; phase–phase (PPCs), amplitude–amplitude (AACs), and phase–amplitude correlations (PACs)]. Absolute local power showed a robust increase with CA across the full frequency spectrum, while local PACs showed sleep state-specific, biphasic development that peaked a few weeks before normal birth. AACs and distant PACs decreased globally at nearly all frequencies. In contrast, the PPCs showed frequency- and region-selective development, with an increase of coupling strength with CA between frontal, central, and occipital regions at low-delta and alpha frequencies together with a wider-spread decrease at other frequencies. Our findings together present the spectrally and spatially differential development of the distinct ICMs during the neonatal period and provide their developmental templates for future basic and clinical research.

Constructing the Self: Historical Roots, Current Challenges, and Future Directions

In this entry, we survey key ideas about the construct of “the self,” tracing its development from evolutionary origins to contemporary theories in psychology and neuroscience. We highlight three organizing features of selfhood: reflexive consciousness, interpersonal relatedness, and agency. Drawing on work from philosophy and the cognitive sciences, we outline how the self has been conceptualized as both a product of mental processes and a participant in social life. We connect early philosophical theories, such as those proposed by Hume and James, to later psychological models that address the self-concept, motivation, regulation, and narrative identity. We also consider current challenges, including how digital environments and artificial intelligence are reshaping the way individuals construct identity, relate to others, and reflect on experience. These trends raise questions about how the self functions in technologically mediated contexts and how emerging tools might change scholarly understanding of personhood. We conclude by considering interdisciplinary approaches and future directions in research on the self.

Histaminergic Innervation of the Ventral Anterior Thalamic Nucleus Alleviates Motor Deficits in a 6-OHDA-Induced Rat Model of Parkinson’s Disease

The ventral anterior (VA) nucleus of the thalamus is a major target of the basal ganglia and is closely associated with the pathogenesis of Parkinson’s disease (PD). Notably, the VA receives direct innervation from the hypothalamic histaminergic system. However, its role in PD remains unknown. Here, we assessed the contribution of histamine to VA neuronal activity and PD motor deficits. Functional magnetic resonance imaging showed reduced VA activity in PD patients. Optogenetic activation of VA neurons or histaminergic afferents significantly alleviated motor deficits in 6-OHDA-induced PD rats. Furthermore, histamine excited VA neurons via H1 and H2 receptors and their coupled hyperpolarization-activated cyclic nucleotide-gated channels, inward-rectifier K(+) channels, or Ca(2+)-activated K(+) channels. These results demonstrate that histaminergic afferents actively compensate for Parkinsonian motor deficits by biasing VA activity. These findings suggest that targeting VA histamine receptors and downstream ion channels may be a potential therapeutic strategy for PD motor dysfunction.

Effects of Breast Cancer Treatment on Neural Noise: a Longitudinal Design

OBJECTIVE: Cognitive dysfunction has been observed consistently in a subset of breast cancer survivors. Yet the precise neurophysiological origins of cancer-related cognitive decline remain unknown. The current study assessed neural noise (1/f activity in electroencephalogram [EEG]) in breast cancer survivors as a potential contributor to observed cognitive dysfunction from pre- to post-treatment. METHODS: We measured EEG in a longitudinal design during performance of the paired-click task and the revised Attention Network Test (ANT-R) to investigate pre- versus post-treatment effects of neural noise in breast cancer patients (n = 20 in paired click; n = 19 in ANT-R) compared with healthy controls (n = 32 in paired click; n = 29 in ANT-R). RESULTS: In both paradigms, one sensory (paired click) and one cognitive (ANT-R), we found that neural noise was significantly elevated after treatment in patients, remaining constant from pretest to posttest in controls. In the ANT-R, patients responded more slowly than controls on invalid cuing trials. Increased neural noise was associated with poorer alerting and poorer inhibitory control of attention (as measured by behavioral network scores), particularly for patients after treatment. CONCLUSIONS: The current study is the first to show a deleterious effect of breast cancer and/or cancer treatment on neural noise, pointing to alterations in the relative balance of excitatory and inhibitory synaptic inputs, while also suggesting promising approaches for cognitive rehabilitation.

Glutamatergic Circuits in the Pedunculopontine Nucleus Modulate Multiple Motor Functions

The functional role of glutamatergic (vGluT2) neurons in the pedunculopontine nucleus (PPN) in modulating motor activity remains controversial. Here, we demonstrated that the activity of vGluT2 neurons in the rostral PPN is correlated with locomotion and ipsilateral head-turning. Beyond these motor functions, we found that these rostral PPN-vGluT2 neurons remarkably respond to salient stimuli. Furthermore, we systematically traced the upstream and downstream projections of these neurons and identified two downstream projections from these neurons to the caudal pontine reticular nucleus/anterior gigantocellular reticular nucleus (PnC/GiA) and the zona incerta (ZI). Our findings indicate that the projections to the PnC/GiA inhibit movement, consistent with ‘pause-and-play’ behavior, whereas those to the ZI promote locomotion, and others respond to a new ‘pause-switch-play’ pattern. Collectively, these findings elucidate the multifaceted influence of the PPN on motor functions and provide a robust theoretical framework for understanding its physiological and potential therapeutic implications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-024-01314-y.

Quantitative Systems Pharmacology Development and Application in Neuroscience

Successful clinical development of therapeutics in neurology and psychiatry is challenging due to the complexity of the brain, the lack of validated surrogate markers and the nature of clinical assessments. On the other hand, tremendous advances have been made in unraveling the neurophysiology of the human brain thanks to technical developments in noninvasive biomarkers in both healthy and pathological conditions. Quantitative systems pharmacology (QSP) aims to integrate this increasing knowledge into a mechanistic model of key biological processes that drive clinical phenotypes with the objective to support research and development of successful therapies. This chapter describes both modeling of molecular pathways resulting in measurable biomarker changes, similar to modeling in other indications, as well as extrapolating in a mechanistic way these biomarker outcomes to predict changes in relevant functional clinical scales. Simulating the effect of therapeutic interventions on clinical scales uses the modeling methodology of computational neurosciences, which is based on the premise that human behavior is driven by firing activity of specific neuronal networks. While driven by pathology, the clinical behavior can also be influenced by various medications and common genotype variants. To address this occurrence, computational neuropharmacology QSP models can be developed and, in principle, applied as virtual twins, which are in silico clones of real patients. Overall, central nervous system (CNS) QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice. Overall, CNS QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice.

Deciphering the Role of Shank3 in Dendritic Morphology and Synaptic Function Across Postnatal Developmental Stages in the Shank3B KO Mouse

Autism Spectrum Disorder (ASD) is marked by early-onset neurodevelopmental anomalies, yet the temporal dynamics of genetic contributions to these processes remain insufficiently understood. This study aimed to elucidate the role of the Shank3 gene, known to be associated with monogenic causes of autism, in early developmental processes to inform the timing and mechanisms for potential interventions for ASD. Utilizing the Shank3B knockout (KO) mouse model, we examined Shank3 expression and its impact on neuronal maturation through Golgi staining for dendritic morphology and electrophysiological recordings to measure synaptic function in the anterior cingulate cortex (ACC) across different postnatal stages. Our longitudinal analysis revealed that, while Shank3B KO mice displayed normal neuronal morphology at one week postnatal, significant impairments in dendritic growth and synaptic activity emerged by two to three weeks. These findings highlight the critical developmental window during which Shank3 is essential for neuronal and synaptic maturation in the ACC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-024-01330-y.

Striatal Dopamine Contributions to Skilled Motor Learning

Coordinated multijoint limb and digit movements—“manual dexterity”—underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that corticostriatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release in both male and female mice as they learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In the dorsolateral striatum, dopamine dynamics are faster than in the dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of the striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.

Introduction to Forensic Neuropsychology

This chapter covers an overview of the relationship between neuropsychology and the legal system, outlining important ideas and how they are used. The chapter explores the field of forensic neuropsychology by synthesizing neuroscience concepts with legal frameworks. It covers subjects including the relationship between the brain and behavior, cognitive evaluation techniques, and the use of neuropsychological evidence in court cases. Case studies demonstrate practical applications, emphasizing the value of comprehending brain function in relation to criminal conduct and guilt. A greater understanding of the complexity of human thought and behavior as well as the possibility of using neuropsychological insights to guide legal decision-making processes are among the main lessons learned. This chapter, which emphasizes the value of interdisciplinary collaboration between neuroscience and law to improve justice outcomes, lays the groundwork for further investigation.

Comparative biomechanical analysis of gait initiation and steady state gait in young adults, healthy elderly subjects and patients with Parkinson's disease : validation of the markerless system and PCA enriched with the Bootstrap method;Analyse biomécanique de l'initiation et de la marche établie chez le jeune adulte, le sujet âgé sain et le patient parkinsonien : validation d'un système sans marqueur et ACP enrichie par Bootstrap

Biomechanical analysis of gait initiation (GI) and steady state gait is necessary to understand motor dysfunctions associated with aging and neurological pathologies. This biomechanical analysis classically uses two types of system: force platforms (PF) and/or cameras with markers (CAM). However, both systems have their own specific constraints, e.g. spatial constraints for PFs, signal processing difficulties for PF and CAM, preparation time for experimental subjects, potential errors in segmental marker positioning for CAM, etc.). The markerless camera system represents an innovative solution to overcome all these constraints. The aim of this thesis is threefold: I) to validate the use of a markerless motion capture system for the biomechanical analysis of GI in a group of young healthy adults, a group of elderly healthy subjects and a group of subjects with Parkinson's disease (study 1 & 2); II) to assess the suitability of the Bootstrap statistical method coupled with principal component analysis to reliably differentiate on the basis of classical biomechanical variables of GI the three “target” populations of our study (study 3);III) to determine how many steps are required in GI to achieve steady state gait in these three populations (Study 4). The results of studies 1 & 2 validated the use of the markerless system to assess motor performance and postural stability during GI in our three populations, while highlighting the limitations of this system. The results of Study 3 highlighted the suitability of the Bootstrap method coupled with principal component analysis to discriminate Parkinsonian patients from healthy subjects with 98% accuracy. The results of Study 4 showed that two steps were necessary to reach a stationary state of postural stability and motor performance in a walking situation at spontaneous and maximum speed, in all three of our populations. However, the motor strategies used to achieve this stationary state differed between healthy subjects and Parkinson's patients. The overall results of this thesis should have potentially important repercussions in terms of movement analysis and clinical application, particularly in terms of Parkinson's disease detection and motor rehabilitation. ; L'analyse biomécanique de l'initiation de la marche (IM) et de la marche établie est nécessaire pour comprendre les dysfonctionnements moteurs liés au vieillissement et aux pathologies neurologiques. Cette analyse biomécanique implique classiquement deux types de système : plateformes de force (PF) et/ou caméras avec marqueurs (CAM). Ces deux systèmes présentent cependant des contraintes d'utilisation spécifiques, e.g. contraintes spatiales pour les PF, difficulté de traitement des signaux pour les PF et les CAM, durée de préparation des sujets expérimentaux, erreurs potentielles sur le positionnement des marqueurs segmentaires pour les CAM etc.). Le système de caméras sans marqueur constitue une solution innovante permettant de surmonter l'ensemble de ces contraintes. L'objectif de cette thèse est triple : I) valider l'utilisation d'un système de capture du mouvement sans marqueur pour l'analyse biomécanique de l'IM dans un groupe de jeunes adultes sains, un groupe de sujets âgés sains et un groupe de sujets atteints de la maladie de Parkinson (étude 1 & 2); II) évaluer la pertinence de la méthode statistique de Bootstrap couplée à l'analyse en composante principale pour différencier de façon fiable sur la base de variables biomécaniques classiques de l'IM les trois populations « cible » de notre étude (étude 3);III) déterminer combien de pas sont nécessaires à l'IM pour atteindre la marche établie dans ces trois populations (étude 4). Les résultats des études 1 & 2 ont permis de valider l'utilisation du système sans marqueurs pour évaluer la performance motrice et la stabilité posturale lors de l'IM dans nos trois populations, tout en soulignant les limitations de ce système. Les résultats de l'étude 3 ont mis en évidence la pertinence de la méthode de Bootstrap couplée à l'analyse en composante principale pour discriminer les patients parkinsoniens des sujets sains avec une précision de 98%. Les résultats de l'étude 4 ont mis en évidence que deux pas étaient nécessaires pour atteindre un état stationnaire de stabilité posturale et de performance motrice en situation de marche à vitesse spontanée et maximale, et ce, dans nos trois populations. Les stratégies motrices pour atteindre cet état stationnaire étaient cependant différentes entre les sujets sains et les patients parkinsoniens. L'ensemble des résultats de cette thèse devrait entraîner des répercussions potentiellement importantes en termes d'analyse du mouvement et d'application clinique, notamment en termes de détection de la maladie de Parkinson et de réhabilitation motrice.

Respiratory Dysfunction in Parkinson's Disease: Relation with Dysautonomia

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease perceived as a motor disorder. It is most commonly associated with autonomic dysfunction, affecting multiple systems. This altered autonomic control might be reflected by a parallel change in the airway caliber of these patients. AIM: To correlate the pulmonary impairment in patients with Parkinson's disease with the underlying dysautonomia. MATERIALS AND METHODS: A total of 30 patients with Parkinson's disease participated in the study. Heart rate (HR) variability was recorded for 5 min to assess the autonomic dysfunction, followed by impulse oscillometry (IOS) and spirometry. IOS being an effort independent technique uses sound waves at different frequencies (5–25 Hz) to measure the airway impedance. RESULTS: There was a significant decrease in SDSD (6.60 (10.18–6.01) vs. 12.22 (13.95–11.30); P = 0.04), RMSSD (6.59 (10.17–5.50) vs. 12.20 (13.93–11.28); P = 0.04), and total power (315.8 (506.3–120.7) vs. 771.3 (799.0–643.6); P = 0.04) in stage II as compared to stage I. Resistance at 20 Hz (R20) was found to be positively correlated with SDSD (r = 0.40, P = 0.04), RMSSD (r = 0.40, P = 0.04), and HF (r = 0.41, P = 0.03). CONCLUSION: Amongst the PD population, any changes in the parasympathetic component (responsible for bronchoconstriction) due to the underlying dysautonomia might be reflected as increased airway resistance in the pulmonary system.

α-Synuclein Pathology and Reduced Neurogenesis in the Olfactory System Affect Olfaction in a Mouse Model of Parkinson's Disease

Parkinson's disease (PD) is characterized by multiple symptoms including olfactory dysfunction, whose underlying mechanisms remain unclear. Here, we explored pathologic changes in the olfactory pathway of transgenic (Tg) mice of both sexes expressing the human A30P mutant α-synuclein (α-syn; α-syn-Tg mice) at 6–7 and 12–14 months of age, representing early and late-stages of motor progression, respectively. α-Syn-Tg mice at late stages exhibited olfactory behavioral deficits, which correlated with severe α-syn pathology in projection neurons (PNs) of the olfactory pathway. In parallel, olfactory bulb (OB) neurogenesis in α-syn-Tg mice was reduced in the OB granule cells at six to seven months and OB periglomerular cells at 12–14 months, respectively, both of which could contribute to olfactory dysfunction. Proteomic analyses showed a disruption in endocytic and exocytic pathways in the OB during the early stages which appeared exacerbated at the synaptic terminals when the mice developed olfactory deficits at 12–14 months. Our data suggest that (1) the α-syn-Tg mice recapitulate the olfactory functional deficits seen in PD; (2) olfactory structures exhibit spatiotemporal disparities for vulnerability to α-syn pathology; (3) α-syn pathology is restricted to projection neurons in the olfactory pathway; (4) neurogenesis in adult α-syn-Tg mice is reduced in the OB; and (5) synaptic endocytosis and exocytosis defects in the OB may further explain olfactory deficits. SIGNIFICANCE STATEMENT Olfactory dysfunction is a characteristic symptom of Parkinson's disease (PD). Using the human A30P mutant α-synuclein (α-syn)-expressing mouse model, we demonstrated the appearance of olfactory deficits at late stages of the disease, which was accompanied by the accumulation of α-syn pathology in projection neurons (PNs) of the olfactory system. This dysfunction included a reduction in olfactory bulb (OB) neurogenesis as well as changes in synaptic vesicular transport affecting synaptic function, both of which are likely contributing to olfactory behavioral deficits.

Impact de l’amorçage rythmique sur la production de la parole chez des personnes atteintes de la maladie de Parkinson : Étude pilote

In this contribution, we present the results of a case study on the impact of rhythmic priming, i.e. listening to rhythmic auditory stimuli, on speech production in Parkinson’s disease. We were interested in prosody, which is one of the aspects often reported to be strongly impaired in Parkinson’s disease.The task consisted of listening to auditory stimuli and reading sentences. Three conditions were studied : a condition where the rhythm was consistent with the rhythmic structure of the target sentence, a condition where the rhythm was inconsistent with the target sentence, and a condition without priming. Different aspects such as speech rhythm metrics based on vowel and consonant duration, prominence and reading latency were analyzed. We discuss the preliminary results and the relevance of our approach. ; Dans cette contribution, nous présentons les résultats d'une étude de cas sur l'impact de l'amorçage rythmique, c'est-à-dire l'écoute des stimuli auditifs rythmiques, sur la production de la parole dans la maladie de Parkinson. Nous nous sommes intéressés à la prosodie qui est l'un des aspects souvent signalés comme étant fortement altérés dans la maladie de Parkinson. La tâche consistait à écouter des stimuli auditifs et à lire des phrases. Trois conditions ont été étudiées : une condition où le rythme était cohérent avec la structure rythmique de la phrase cible, une condition où le rythme était incohérent avec la phrase cible et une condition sans amorçage. Différents aspects tels que les métriques basées sur la durée des voyelles et des consonnes, la proéminence et le délai d'initialisation ont été analysés. Nous discutons des résultats préliminaires et de la pertinence de notre approche.

Distinct brain atrophy progression subtypes underlie phenoconversion in isolated REM sleep behaviour disorder

BACKGROUND: Synucleinopathies include a spectrum of disorders varying in features and severity, including idiopathic/isolated REM sleep behaviour disorder (iRBD), Parkinson's disease (PD), and dementia with Lewy bodies (DLB). Distinct brain atrophy patterns may already be seen in iRBD; however, how brain atrophy begins and progresses remains unclear. METHODS: A multicentric cohort of 1276 participants (451 polysomnography-confirmed iRBD, 142 PD with probable RBD, 87 DLB, and 596 controls) underwent T1-weighted MRI and longitudinal clinical assessments. Brain atrophy was quantified using vertex-based cortical surface reconstruction and volumetric segmentation. The unsupervised machine learning algorithm, Subtype and Stage Inference (SuStaIn), was used to reconstruct spatiotemporal patterns of brain atrophy progression. FINDINGS: SuStaIn identified two distinct subtypes of brain atrophy progression: 1) a “cortical-first” subtype, with atrophy beginning in the frontal lobes and involving the subcortical structures at later stages; and 2) a “subcortical-first” subtype, with atrophy beginning in the limbic areas and involving cortical structures at later stages. Both cortical- and subcortical-first subtypes were associated with a higher rate of increase in MDS-UPDRS-III scores over time, but cognitive decline was subtype-specific, being associated with advancing stages in patients classified as cortical-first but not subcortical-first. Classified patients were more likely to phenoconvert over time compared to stage 0/non-classified patients. Among the 88 patients with iRBD who phenoconverted during follow-up, those classified within the cortical-first subtype had a significantly increased likelihood of developing DLB compared to PD, unlike those classified within the subcortical-first subtype. INTERPRETATION: There are two distinct atrophy progression subtypes in iRBD, with the cortical-first subtype linked to an increased likelihood of developing DLB, while both subtypes were associated with worsening parkinsonian motor features. This underscores the potential utility of subtype identification and staging for monitoring disease progression and patient selection for trials. FUNDING: This study was supported by grants to S.R. from 10.13039/501100000143Alzheimer Society Canada (0000000082) and by Parkinson Canada (PPG-2023-0000000122). The work performed in Montreal was supported by the Canadian Institutes of Health Research (CIHR), the Fonds de recherche du Québec - Santé (FRQS), and the W. Garfield Weston Foundation. The work performed in Oxford was funded by 10.13039/501100000304Parkinson's UK (J-2101) and the 10.13039/501100013373National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The work performed in Prague was funded by the 10.13039/501100009553Czech Health Research Council (grant NU21-04-00535) and by The National Institute for Neurological Research (project number LX22NPO5107), financed by the European Union – Next Generation EU. The work performed in Newcastle was funded by the NIHR Newcastle BRC based at Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. The work performed in Paris was funded by grants from the Programme d'investissements d'avenir (ANR-10-IAIHU-06), the Paris Institute of Neurosciences – IHU (IAIHU-06), the 10.13039/501100001665Agence Nationale de la Recherche (ANR-11-INBS-0006), Électricité de France (Fondation d’Entreprise EDF), the EU Joint Programme–Neurodegenerative Disease Research (JPND) for the Control-PD Project (Cognitive Propagation in Prodromal Parkinson's disease), the 10.13039/100022926Fondation Thérèse et René Planiol, the Fonds Saint-Michel; by unrestricted support for research on Parkinson's disease from Energipole (M. Mallart) and the Société Française de Médecine Esthétique (M. Legrand); and by a grant from the 10.13039/100007369Institut de France to Isabelle Arnulf (for the ALICE Study). The work performed in Sydney was supported by a Dementia Team Grant from the 10.13039/501100000925National Health and Medical Research Council (#1095127). The work performed in Cologne was funded by the 10.13039/501100003042Else Kröner-Fresenius-Stiftung (grant number 2019_EKES.02), the Köln Fortune Program, 10.13039/501100024583Faculty of Medicine, University of Cologne, and the “Netzwerke 2021 Program (10.13039/501100014690Ministry of Culture and Science of Northrhine Westphalia State). The work performed in Aarhus was supported by funding from the 10.13039/501100003554Lundbeck Foundation, 10.13039/100008445Parkinsonforeningen (The Danish Parkinson Association), and the 10.13039/100010809Jascha Foundation.

Investigation of Shared Genetic Risk Factors Between Parkinson's Disease and Cancers

BACKGROUND: Epidemiological studies that examined the association between Parkinson's disease (PD) and cancers led to inconsistent results, but they face a number of methodological difficulties. OBJECTIVE: We used results from genome‐wide association studies (GWASs) to study the genetic correlation between PD and different cancers to identify common genetic risk factors. METHODS: We used individual data for participants of European ancestry from the Courage‐PD (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease; PD, N = 16,519) and EPITHYR (differentiated thyroid cancer, N = 3527) consortia and summary statistics of GWASs from iPDGC (International Parkinson Disease Genomics Consortium; PD, N = 482,730), Melanoma Meta‐Analysis Consortium (MMAC), Breast Cancer Association Consortium (breast cancer), the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (prostate cancer), International Lung Cancer Consortium (lung cancer), and Ovarian Cancer Association Consortium (ovarian cancer) (N comprised between 36,017 and 228,951 for cancer GWASs). We estimated the genetic correlation between PD and cancers using linkage disequilibrium score regression. We studied the association between PD and polymorphisms associated with cancers, and vice versa, using cross‐phenotypes polygenic risk score (PRS) analyses. RESULTS: We confirmed a previously reported positive genetic correlation of PD with melanoma (G(corr) = 0.16 [0.04; 0.28]) and reported an additional significant positive correlation of PD with prostate cancer (G(corr) = 0.11 [0.03; 0.19]). There was a significant inverse association between the PRS for ovarian cancer and PD (odds ratio [OR] = 0.89 [0.84; 0.94]). Conversely, the PRS of PD was positively associated with breast cancer (OR = 1.08 [1.06; 1.10]) and inversely associated with ovarian cancer (OR = 0.95 [0.91; 0.99]). The association between PD and ovarian cancer was mostly driven by rs183211 located in an intron of the NSF gene (17q21.31). CONCLUSIONS: We show evidence in favor of a contribution of pleiotropic genes to the association between PD and specific cancers. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis

Under review, (18 pages, 13 figures);International audience; This study focuses on the diagnosis of Parkinson’s Disease (PD) based on electroencephalogram (EEG) signals. A novel approach inspired by the functioning of the brain is proposed, which uses the dynamics, frequency, and temporal content of EEGs to extract new discriminant features of the disease. The generated sparse dynamic features (SDFs) allow, through a transformation, to change the point of view on the data giving access to more informative features that are more faithful to the concept of EEG generation. Nevertheless, the method remains generic and can be applied to any signal but for this application it was evaluated on a publicly available dataset containing EEG signals recorded during a 3-oddball auditory task involving subjects, of whom 25 suffer from PD. Given the adequate perspective on the data, it comes out that by using only two extracted features from the generated SDFs the healthy and unhealthy subjects are separated using a linear classifier. The classification yields an accuracy of 90.0% () using a single channel. By aggregating the information from three channels and making them vote, an accuracy of 94%, a sensitivity of 96% and a specificity of 92% is obtained. The evaluation was carried out using a nested Leave-One-Out cross-validation procedure, thus preventing data leakage problems and giving a less biased evaluation. Several tests were carried out to assess the validity and robustness of our approach.

Should evidence of an autolysosomal de-acidification defect in Alzheimer and Parkinson diseases call for caution in prescribing chronic PPI and DMARD?