Recent scientific publications by research field

Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs

Early and accurate diagnosis of Alzheimer's disease (AD), a complex neurodegenerative disorder, requires analysis of heterogeneous biomarkers (e.g., neuroimaging, genetic risk factors, cognitive tests, and cerebrospinal fluid proteins) typically represented in a tabular format. With flexible few-shot reasoning, multimodal integration, and natural-language-based interpretability, large language models (LLMs) offer unprecedented opportunities for prediction with structured biomedical data. We propose a novel framework called TAP-GPT, Tabular Alzheimer's Prediction GPT, that adapts TableGPT2, a multimodal tabular-specialized LLM originally developed for business intelligence tasks, for AD diagnosis using structured biomarker data with small sample sizes. Our approach constructs few-shot tabular prompts using in-context learning examples from structured biomedical data and finetunes TableGPT2 using the parameter-efficient qLoRA adaption for a clinical binary classification task of AD or cognitively normal (CN). The TAP-GPT framework harnesses the powerful tabular understanding ability of TableGPT2 and the encoded prior knowledge of LLMs to outperform more advanced general-purpose LLMs and a tabular foundation model (TFM) developed for prediction tasks. To our knowledge, this is the first application of LLMs to the prediction task using tabular biomarker data, paving the way for future LLM-driven multi-agent frameworks in biomedical informatics. ;accepted by ACM-BCB'25: ACM Conference on Bioinformatics, Computational Biology, and Health Informatics [ACM SIGBio Best Paper Award]

Impact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation

Early detection of Alzheimer's disease from spontaneous speech has emerged as a promising non-invasive screening approach. However, the influence of automatic speech recognition (ASR) quality on downstream clinical language modeling remains insufficiently understood. In this study, we investigate Alzheimer's disease detection using lexical features derived from Whisper ASR transcripts on the ADReSSo 2021 diagnosis dataset. We evaluate interpretable machine-learning models, including Logistic Regression and Linear Support Vector Machines, using TF-IDF text representations under repeated 5x5 stratified cross-validation. Our results demonstrate that transcript quality has a statistically significant impact on classification performance. Models trained on Whisper-small transcripts consistently outperform those using Whisper-base transcripts, achieving balanced accuracy above 0.7850 with Linear SVM. Paired statistical testing confirms that the observed improvements are significant. Importantly, classifier complexity contributes less to performance variation than ASR transcription quality. Feature analysis reveals that cognitively normal speakers produce more semantically precise object- and scene-descriptive language, whereas Alzheimer's speech is characterized by vagueness, discourse markers, and increased hesitation patterns. These findings suggest that high-quality ASR can enable simple, interpretable lexical models to achieve competitive Alzheimer's detection performance without explicit acoustic modeling. The study provides a reproducible benchmark pipeline and highlights ASR selection as a critical modeling decision in clinical speech-based artificial intelligence systems. ;22 pages, 7 figures

Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease

Neurodegeneration as measured through magnetic resonance imaging (MRI) is recognized as a potential biomarker for diagnosing Alzheimer's disease (AD), but is generally considered less specific than amyloid or tau based biomarkers. Due to a large amount of variability in brain anatomy between different individuals, we hypothesize that leveraging MRI time series can help improve specificity, by treating each patient as their own baseline. Here we turn to conditional variational autoencoders to generate individualized MRI predictions given the subject's age, disease status and one previous scan. Using serial imaging data from the Alzheimer's Disease Neuroimaging Initiative, we train a novel architecture to build a latent space distribution which can be sampled from to generate future predictions of changing anatomy. This enables us to extrapolate beyond the dataset and predict MRIs up to 10 years. We evaluated the model on a held-out set from ADNI and an independent dataset (from Open Access Series of Imaging Studies). By comparing to several alternatives, we show that our model produces more individualized images with higher resolution. Further, if an individual already has a follow-up MRI, we demonstrate a usage of our model to compute a likelihood ratio classifier for disease status. In practice, the model may be able to assist in early diagnosis of AD and provide a counterfactual baseline trajectory for treatment effect estimation. Furthermore, it generates a synthetic dataset that can potentially be used for downstream tasks such as anomaly detection and classification. ;Comment: MICCAI 2024 LDTM workshop

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

Correlation vs causation in Alzheimer's disease: an interpretability-driven study

Understanding the distinction between causation and correlation is critical in Alzheimer's disease (AD) research, as it impacts diagnosis, treatment, and the identification of true disease drivers. This experiment investigates the relationships among clinical, cognitive, genetic, and biomarker features using a combination of correlation analysis, machine learning classification, and model interpretability techniques. Employing the XGBoost algorithm, we identified key features influencing AD classification, including cognitive scores and genetic risk factors. Correlation matrices revealed clusters of interrelated variables, while SHAP (SHapley Additive exPlanations) values provided detailed insights into feature contributions across disease stages. Our results highlight that strong correlations do not necessarily imply causation, emphasizing the need for careful interpretation of associative data. By integrating feature importance and interpretability with classical statistical analysis, this work lays groundwork for future causal inference studies aimed at uncovering true pathological mechanisms. Ultimately, distinguishing causal factors from correlated markers can lead to improved early diagnosis and targeted interventions for Alzheimer's disease.

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

Deep Learning for Early Alzheimer Disease Detection with MRI Scans

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.

Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI

Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide, necessitating early and accurate diagnosis for optimal patient management. In recent years, advancements in deep learning have shown remarkable potential in medical image analysis. Methods In this study, we present "ViTranZheimer," an AD diagnosis approach which leverages video vision transformers to analyze 3D brain MRI data. By treating the 3D MRI volumes as videos, we exploit the temporal dependencies between slices to capture intricate structural relationships. The video vision transformer's self-attention mechanisms enable the model to learn long-range dependencies and identify subtle patterns that may indicate AD progression. Our proposed deep learning framework seeks to enhance the accuracy and sensitivity of AD diagnosis, empowering clinicians with a tool for early detection and intervention. We validate the performance of the video vision transformer using the ADNI dataset and conduct comparative analyses with other relevant models. Results The proposed ViTranZheimer model is compared with two hybrid models, CNN-BiLSTM and ViT-BiLSTM. CNN-BiLSTM is the combination of a convolutional neural network (CNN) and a bidirectional long-short-term memory network (BiLSTM), while ViT-BiLSTM is the combination of a vision transformer (ViT) with BiLSTM. The accuracy levels achieved in the ViTranZheimer, CNN-BiLSTM, and ViT-BiLSTM models are 98.6%, 96.479%, and 97.465%, respectively. ViTranZheimer demonstrated the highest accuracy at 98.6%, outperforming other models in this evaluation metric, indicating its superior performance in this specific evaluation metric. Conclusion This research advances the understanding of applying deep learning techniques in neuroimaging and Alzheimer's disease research, paving the way for earlier and less invasive clinical diagnosis.

Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease

Alzheimer's disease affects over 55 million people worldwide and is projected to more than double by 2050, necessitating rapid, accurate, and scalable diagnostics. However, existing approaches are limited because they cannot achieve clinically acceptable accuracy, generalization across datasets, robustness to missing modalities, and explainability all at the same time. This inability to satisfy all these requirements simultaneously undermines their reliability in clinical settings. We propose OmniBrain, a multimodal framework that integrates brain MRI, radiomics, gene expression, and clinical data using a unified model with cross-attention and modality dropout. OmniBrain achieves $92.2 \pm 2.4\%$accuracy on the ANMerge dataset and generalizes to the MRI-only ADNI dataset with $70.4 \pm 2.7\%$ accuracy, outperforming unimodal and prior multimodal approaches. Explainability analyses highlight neuropathologically relevant brain regions and genes, enhancing clinical trust. OmniBrain offers a robust, interpretable, and practical solution for real-world Alzheimer's diagnosis. ;Published in Third Workshop on Computer Vision for Automated Medical Diagnosis CVAMD 2025 in ICCV 2025

Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network

Prioritizing disease-associated genes is central to understanding the molecular mechanisms of complex disorders such as Alzheimer's disease (AD). Traditional network-based approaches rely on static centrality measures and often fail to capture cross-modal biological heterogeneity. We propose NETRA (Node Evaluation through Transformer-based Representation and Attention), a multimodal graph transformer framework that replaces heuristic centrality metrics with attention-driven relevance scoring. Using AD as a case study, gene regulatory networks are independently constructed from microarray, single-cell RNA-seq, and single-nucleus RNA-seq data. Random-walk sequences derived from these networks are used to train a BERT-based model for learning global gene embeddings, while modality-specific gene expression profiles are compressed using variational autoencoders. These representations are integrated with auxiliary biological networks, including protein-protein interactions, Gene Ontology semantic similarity, and diffusion-based gene similarity, into a unified multimodal graph. A graph transformer assigns NETRA scores that quantify gene relevance in a disease-specific and context-aware manner. Gene set enrichment analysis shows that NETRA achieves a normalized enrichment score of about 3.9 for the Alzheimer's disease pathway, substantially outperforming classical centrality measures and diffusion models. Top-ranked genes enrich multiple neurodegenerative pathways, recover a known late-onset AD susceptibility locus at chr12q13, and reveal conserved cross-disease gene modules. The framework preserves biologically realistic heavy-tailed network topology and is readily extensible to other complex disorders.

Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is the leading cause of dementia, and its early detection is crucial for effective intervention, yet current diagnostic methods often fall short in sensitivity and specificity. This study aims to detect significant indicators of early AD by extracting and integrating various imaging biomarkers, including radiomics, hippocampal texture descriptors, cortical thickness measurements, and deep learning features. We analyze structural magnetic resonance imaging (MRI) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts, utilizing comprehensive image analysis and machine learning techniques. Our results show that combining multiple biomarkers significantly improves detection accuracy. Radiomics and texture features emerged as the most effective predictors for early AD, achieving AUCs of 0.88 and 0.72 for AD and MCI detection, respectively. Although deep learning features proved to be less effective than traditional approaches, incorporating age with other biomarkers notably enhanced MCI detection performance. Additionally, our findings emphasize the continued importance of classical imaging biomarkers in the face of modern deep-learning approaches, providing a robust framework for early AD diagnosis. ;Comment: SPIE Medical Imaging (MI25)

Leveraging LLMs for Early Alzheimer's Prediction

We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation suitable for a frozen pre-trained LLM for clinical prediction. Applied to early Alzheimer's detection, our method achieves sensitive prediction with error rates well below clinically recognized margins, with implications for timely Alzheimer's intervention.

When Deep Learning Fails: Limitations of Recurrent Models on Stroke-Based Handwriting for Alzheimer's Disease Detection

Alzheimer's disease detection requires expensive neuroimaging or invasive procedures, limiting accessibility. This study explores whether deep learning can enable non-invasive Alzheimer's disease detection through handwriting analysis. Using a dataset of 34 distinct handwriting tasks collected from healthy controls and Alzheimer's disease patients, we evaluate and compare three recurrent neural architectures (LSTM, GRU, RNN) against traditional machine learning models. A crucial distinction of our approach is that the recurrent models process pre-extracted features from discrete strokes, not raw temporal signals. This violates the assumption of a continuous temporal flow that recurrent networks are designed to capture. Results reveal that they exhibit poor specificity and high variance. Traditional ensemble methods significantly outperform all deep architectures, achieving higher accuracy with balanced metrics. This demonstrates that recurrent architectures, designed for continuous temporal sequences, fail when applied to feature vectors extracted from ambiguously segmented strokes. Despite their complexity, deep learning models cannot overcome the fundamental disconnect between their architectural assumptions and the discrete, feature-based nature of stroke-level handwriting data. Although performance is limited, the study highlights several critical issues in data representation and model compatibility, pointing to valuable directions for future research.

Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis

Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise and healthcare resources. In recent years, large language models (LLMs) have been increasingly applied to the medical field using electronic health records (EHRs), yet their application in Alzheimer's disease assessment remains limited, particularly given that AD involves complex multifactorial etiologies that are difficult to observe directly through imaging modalities. In this work, we propose leveraging LLMs to perform Chain-of-Thought (CoT) reasoning on patients' clinical EHRs. Unlike direct fine-tuning of LLMs on EHR data for AD classification, our approach utilizes LLM-generated CoT reasoning paths to provide the model with explicit diagnostic rationale for AD assessment, followed by structured CoT-based predictions. This pipeline not only enhances the model's ability to diagnose intrinsically complex factors but also improves the interpretability of the prediction process across different stages of AD progression. Experimental results demonstrate that the proposed CoT-based diagnostic framework significantly enhances stability and diagnostic performance across multiple CDR grading tasks, achieving up to a 15% improvement in F1 score compared to the zero-shot baseline method.

Neuropsychological and clinical indicators of Lewy body and Alzheimer's pathology

BACKGROUND: Clinical distinction between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) poses significant challenges due to pathological comorbidity. Similar ages of onset and overlapping cognitive and psychiatric symptoms can lead to diagnostic inaccuracy and inappropriate treatment recommendations. OBJECTIVE: Identify the best combination of clinical and neuropsychological predictors of AD, DLB, and mixed DLB/AD neuropathology in dementia patients. METHODS: Using the National Alzheimer's Coordinating Center dataset, we selected either pure AD (n = 189), DLB (n = 21), or mixed DLB/AD (n = 42) patients on autopsy. Neuropsychological and clinical predictors, including core clinical features of DLB, were entered into multivariable logistic regressions. RESULTS: Gait disturbances (odds ratio (OR) = 19.32; p = 0.01), visual-spatial complaints (OR = 6.06; p = 0.03), and visual hallucinations (OR = 31.06; p = 0.002) predicted DLB compared to AD, along with better memory (OR = 3.42; p = 0.003), naming (OR = 3.35; p = 0.002), and worse processing speed (OR = 0.51; p = 0.01). When comparing DLB to DLB/AD, gait disturbances (OR = 6.33; p = 0.01), increased depressive symptoms (OR = 1.44; p = 0.03), and better memory (OR = 3.01; p = 0.004) predicted DLB. Finally, rapid eye movement sleep behavior disorder (RBD) (OR = 6.44; p = 0.004), parkinsonism severity (OR = 1.07; p = 0.02), and lower depressive symptoms (OR = 0.70; p = 0.006) and memory impairment (OR = 0.57; p = 0.02) distinguished DLB/AD from AD. CONCLUSIONS: Our study converges with prior research suggesting specific neuropsychological and clinical features can help distinguish DLB from AD. Neuropsychological differentiation becomes more challenging among mixed pathologies and in advanced cognitive impairment, although the presence of RBD and parkinsonism distinguished DLB. Earlier clinical assessment and incorporation of in vivo and postmortem biomarkers should enhance diagnostic accuracy and understanding of disease characteristics, offering significant relevance for disease-modifying treatments.

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.

EffNetViTLoRA: An Efficient Hybrid Deep Learning Approach for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide. As it progresses, it leads to the deterioration of cognitive functions. Since AD is irreversible, early diagnosis is crucial for managing its progression. Mild Cognitive Impairment (MCI) represents an intermediate stage between Cognitively Normal (CN) individuals and those with AD, and is considered a transitional phase from normal cognition to Alzheimer's disease. Diagnosing MCI is particularly challenging due to the subtle differences between adjacent diagnostic categories. In this study, we propose EffNetViTLoRA, a generalized end-to-end model for AD diagnosis using the whole Alzheimer's Disease Neuroimaging Initiative (ADNI) Magnetic Resonance Imaging (MRI) dataset. Our model integrates a Convolutional Neural Network (CNN) with a Vision Transformer (ViT) to capture both local and global features from MRI images. Unlike previous studies that rely on limited subsets of data, our approach is trained on the full T1-weighted MRI dataset from ADNI, resulting in a more robust and unbiased model. This comprehensive methodology enhances the model's clinical reliability. Furthermore, fine-tuning large pretrained models often yields suboptimal results when source and target dataset domains differ. To address this, we incorporate Low-Rank Adaptation (LoRA) to effectively adapt the pretrained ViT model to our target domain. This method enables efficient knowledge transfer and reduces the risk of overfitting. Our model achieves a classification accuracy of 92.52% and an F1-score of 92.76% across three diagnostic categories: AD, MCI, and CN for full ADNI dataset.

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.

Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection

Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data including structural MRI images and gene expression data, for Alzheimer's disease detection. Our framework introduces a novel heterogeneous bipartite graph representation learning featuring two distinct node types: genes and images. The network can effectively classify Alzheimer's disease (AD) into three distinct stages:AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) classes, utilizing a small dataset. Additionally, it identified which genes play a significant role in each of these classification groups. We evaluate the performance of our approach using metrics including classification accuracy, recall, precision, and F1 score. The proposed technique holds potential for extending to radiogenomic-based classification to other diseases. ;Comment: 11 pages

Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker studies. Whether colored fundus photography (CFP) contains retinal structural signatures corresponding to these AD-related risk domains remains unclear. To determine whether deep learning (DL) models can predict 12 AD-related risk factors from CFP and to characterize the retinal structures underlying these predictions, thereby assessing whether CFP reflects pathways to AD vulnerability. Using 62,876 CFPs from 44,501 unique participants from the UK Biobank, DL models were trained to predict 12 factors linked to AD incidence: 6 categorical (sex, smoking, sleeplessness, economic status, alcohol use, depression) and 6 continuous (age, age at completing education, BMI, systolic, diastolic blood pressure, HbA1c). Model performance, model saliency, and saliency-derived scores (CAM-Score) were evaluated and compared to retinal morphometry. The scores were also compared between incident-AD cases (average 8.55 years before onset) and matched controls. Performance of DL ranged from AUROC= 0.5654-0.9480 for categorical and R2=-0.0291-0.7620 for continuous factors, outperforming most of the morphometry-machine learning models. Saliency-based score consistently highlighted biologically meaningful regions, particularly the optic nerve head and retinal vasculature. It also aligned with present morphometric variations. Several saliency-based scores differed significantly between incident AD and matched controls, suggesting potential overlap between retinal correlates of risk factors and preclinical AD-associated changes. CFP encodes retinal signatures linked to AD risk factors. Although not diagnostic, DL-derived retinal representations may uncover biologically meaningful risk-related structural changes mirroring the potential AD vulnerability. ;Accepted to the "Journal of Alzheimer's Disease" for publication

Multi-modal Imputation for Alzheimer's Disease Classification

Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modalities, such as T1-weighted (T1) and diffusion-weighted imaging (DWI) scans, can increase diagnostic performance. However, complete multimodal datasets are not always available. We use a conditional denoising diffusion probabilistic model to impute missing DWI scans from T1 scans. We perform extensive experiments to evaluate whether such imputation improves the accuracy of uni-modal and bi-modal deep learning models for 3-way Alzheimer's disease classification-cognitively normal, mild cognitive impairment, and Alzheimer's disease. We observe improvements in several metrics, particularly those sensitive to minority classes, for several imputation configurations.

Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairment

Early and accurate diagnosis of Alzheimer Disease is critical for effective clinical intervention, particularly in distinguishing it from Mild Cognitive Impairment, a prodromal stage marked by subtle structural changes. In this study, we propose a hybrid deep learning ensemble framework for Alzheimer Disease classification using structural magnetic resonance imaging. Gray and white matter slices are used as inputs to three pretrained convolutional neural networks such as ResNet50, NASNet, and MobileNet, each fine tuned through an end to end process. To further enhance performance, we incorporate a stacked ensemble learning strategy with a meta learner and weighted averaging to optimally combine the base models. Evaluated on the Alzheimer Disease Neuroimaging Initiative dataset, the proposed method achieves state of the art accuracy of 99.21% for Alzheimer Disease vs. Mild Cognitive Impairment and 91.0% for Mild Cognitive Impairment vs. Normal Controls, outperforming conventional transfer learning and baseline ensemble methods. To improve interpretability in image based diagnostics, we integrate Explainable AI techniques by Gradient weighted Class Activation, which generates heatmaps and attribution maps that highlight critical regions in gray and white matter slices, revealing structural biomarkers that influence model decisions. These results highlight the frameworks potential for robust and scalable clinical decision support in neurodegenerative disease diagnostics. ;18 pages, 4 figures

CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection

Early detection of cognitive disorders such as Alzheimer's disease is critical for enabling timely clinical intervention and improving patient outcomes. In this work, we introduce CogniAlign, a multimodal architecture for Alzheimer's detection that integrates audio and textual modalities, two non-intrusive sources of information that offer complementary insights into cognitive health. Unlike prior approaches that fuse modalities at a coarse level, CogniAlign leverages a word-level temporal alignment strategy that synchronizes audio embeddings with corresponding textual tokens based on transcription timestamps. This alignment supports the development of token-level fusion techniques, enabling more precise cross-modal interactions. To fully exploit this alignment, we propose a Gated Cross-Attention Fusion mechanism, where audio features attend over textual representations, guided by the superior unimodal performance of the text modality. In addition, we incorporate prosodic cues, specifically interword pauses, by inserting pause tokens into the text and generating audio embeddings for silent intervals, further enriching both streams. We evaluate CogniAlign on the ADReSSo dataset, where it achieves an accuracy of 87.35% over a Leave-One-Subject-Out setup and of 90.36% over a 5 fold Cross-Validation, outperforming existing state-of-the-art methods. A detailed ablation study confirms the advantages of our alignment strategy, attention-based fusion, and prosodic modeling. Finally, we perform a corpus analysis to assess the impact of the proposed prosodic features and apply Integrated Gradients to identify the most influential input segments used by the model in predicting cognitive health outcomes.

Alzheimer's disease detection based on large language model prompt engineering

In light of the growing proportion of older individuals in our society, the timely diagnosis of Alzheimer's disease has become a crucial aspect of healthcare. In this paper, we propose a non-invasive and cost-effective detection method based on speech technology. The method employs a pre-trained language model in conjunction with techniques such as prompt fine-tuning and conditional learning, thereby enhancing the accuracy and efficiency of the detection process. To address the issue of limited computational resources, this study employs the efficient LORA fine-tuning method to construct the classification model. Following multiple rounds of training and rigorous 10-fold cross-validation, the prompt fine-tuning strategy based on the LLAMA2 model demonstrated an accuracy of 81.31\%, representing a 4.46\% improvement over the control group employing the BERT model. This study offers a novel technical approach for the early diagnosis of Alzheimer's disease and provides valuable insights into model optimization and resource utilization under similar conditions. It is anticipated that this method will prove beneficial in clinical practice and applied research, facilitating more accurate and efficient screening and diagnosis of Alzheimer's disease.

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders

Generative modeling frameworks have emerged as an effective approach to capture high-dimensional image distributions from large datasets without requiring domain-specific knowledge, a capability essential for disease progression modeling. Recent generative approaches have attempted to capture progression by mapping images to a latent space and guiding representations to generate follow-up images from previous time points. However, these methods impose constraints on distribution learning, resulting in latent spaces with limited controllability for generating follow-up images without paired subject-specific longitudinal guidance. In order to enable controlled movements in the latent representational space and generate progression images from a previous time-point image without subject-specific guidance, we introduce a conditionable Diffusion Auto-encoder framework that forms a compact latent space capturing high-level semantics and providing means to control generation. Our approach leverages this latent space to condition and apply controlled shifts to the representations of previous time-point images by isolating progression and subject identity information for generating follow-up images. The shifts are implicitly guided by correlating with progression attributes and constraining to Alzheimer's disease specific regions, without paired longitudinal guidance. We validate the generations through image quality metrics, volumetric progression analysis, and downstream tasks in Alzheimer's disease datasets from different sources. This demonstrates the effectiveness of our approach for Alzheimer's progression modeling and longitudinal image generation. ;Accepted in IEEE Journal of Biomedical and Health Informatics ( https://ieeexplore.ieee.org/document/11579738 )

Widespread CNS pathology in amyotrophic lateral sclerosis homozygous for the D90A SOD1 mutation

Mutations in the gene encoding the ubiquitously expressed free radical scavenging enzyme superoxide dismutase-1 ( SOD1 ) are found in 2–6% of amyotrophic lateral sclerosis patients. The most frequent SOD1 mutation worldwide is D90A. Amyotrophic lateral sclerosis caused by this mutation has some unusual features: the heredity is usually recessive, the phenotype is stereotypic with slowly evolving motor symptoms beginning in the legs and may also include sensory, autonomic, and urinary bladder involvement. Furthermore, the mutant protein resembles the wild type, with normal content and enzymatic activity in the central nervous system. Here, we report neuropathological findings in nine patients homozygous for the D90A mutation. All nine had numerous small granular inclusions immunoreactive for misfolded SOD1 in motor neurons and glial nuclei in the spinal cord and brainstem. In addition to degeneration of the corticospinal tracts, all patients had degeneration of the dorsal columns. We also found intense gliosis in circumscribed cortical areas of the frontal and temporal lobes and in the insula. In these areas and in adjacent white matter, there were SOD1 staining neuropil threads. A few SOD1-immunopositive cytoplasmic neuronal inclusions were observed in cortical areas, as were glial nuclear inclusions. As suggested by the symptoms and signs and earlier neurophysiological and imaging investigations, the histopathology in patients homozygous for the D90A SOD1 extends beyond the motor system to include cognitive and sensory cortical areas. However, even in the patients that had a symptomatic disease duration of more than 2 or 3 decades and lived into their 70s or 80s, there were no SOD1-inclusion pathology and no typical dysfunction (apart from the musculature) in non-nervous organs. Thus, only specific parts of the CNS seem to be vulnerable to toxicity provoked by homozygously expressed mutant SOD1.

Targeted sequencing panels in Italian ALS patients support different etiologies in the ALS/FTD continuum

Background 5–10% of amyotrophic lateral sclerosis (ALS) patients presented a positive family history (fALS). More than 30 genes have been identified in association with ALS/frontotemporal dementia (FTD) spectrum, with four major genes accounting for 60–70% of fALS. In this paper, we aimed to assess the contribution to the pathogenesis of major and rare ALS/FTD genes in ALS patients. Methods We analyzed ALS and ALS/FTD associated genes by direct sequencing or next-generation sequencing multigene panels in ALS patients. Results Genetic abnormalities in ALS major genes included repeated expansions of hexanucleotide in C9orf72 gene (7.3%), mutations in SOD1 (4.9%), FUS (2.1%), and TARDBP (2.4%), whereas variants in rare ALS/FTD genes affected 15.5% of subjects overall, most frequently involving SQSTM1 (3.4%), and CHMP2B (1.9%). We found clustering of variants in ALS major genes in patients with a family history for “pure” ALS, while ALS/FTD related genes mainly occurred in patients with a family history for other neurodegenerative diseases (dementia and/or parkinsonism). Conclusions Our data support the presence of two different genetic components underlying ALS pathogenesis, related to the presence of a family history for ALS or other neurodegenerative diseases. Thus, family history may help in optimizing the genetic screening protocol to be applied.

Bulbar-onset amyotrophic lateral sclerosis in a patient with genetically confirmed Huntington’s disease: a case study

Background The rationale for this paper is a description of a patient from Southeast Europe with genetically confirmed Huntington’s disease (HD), coexisting with sporadic, bulbar-onset amyotrophic lateral sclerosis (ALS). To the best of our knowledge, the total number of reported cases with confirmed coexistence of HD and ALS is less than 20. Thus, it is an extremely rare condition speculated to be in a range from 2 to 6 per billion, and data from this part of the World are completely missing. Case presentation Here we report a 72-year-old female with a family history of HD who had generalized chorea and hyperreflexia. Using the PCR-based test for the detection of the CAG triplet repeat expansion, the presence of HD was confirmed. After several months, our patient had progressively developed dysarthria and dysphagia, followed by spastic quadriparesis, generalized muscle wasting, spontaneous fasciculations and sialorrhea. The diagnosis of definite ALS was established based on the patient’s neurological status, electromyography findings and current El Escorial criteria. Conclusions Our study emphasizes the need for the recognition of the co-occurrence of clinically distinct and rare genetic disorders, such as HD and ALS. New insights from the studies dealing with these rare topics could significantly contribute to the contest of new gene therapy trials.

The use of fibroblasts as a valuable strategy for studying mitochondrial impairment in neurological disorders

Neurological disorders (NDs) are characterized by progressive neuronal dysfunction leading to synaptic failure, cognitive impairment, and motor injury. Among these diseases, Alzheimer's disease (AD), Parkinson's disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS) have raised a significant research interest. These disorders present common neuropathological signs, including neuronal dysfunction, protein accumulation, oxidative damage, and mitochondrial abnormalities. In this context, mitochondrial impairment is characterized by a deficiency in ATP production, excessive production of reactive oxygen species, calcium dysregulation, mitochondrial transport failure, and mitochondrial dynamics deficiencies. These defects in mitochondrial health could compromise the synaptic process, leading to early cognitive dysfunction observed in these NDs. Interestingly, skin fibroblasts from AD, PD, HD, and ALS patients have been suggested as a useful strategy to investigate and detect early mitochondrial abnormalities in these NDs. In this context, fibroblasts are considered a viable model for studying neurodegenerative changes due to their metabolic and biochemical relationships with neurons. Also, studies of our group and others have shown impairment of mitochondrial bioenergetics in fibroblasts from patients diagnosed with sporadic and genetic forms of AD, PD, HD, and ALS. Interestingly, these mitochondrial abnormalities have been observed in the brain tissues of patients suffering from the same pathologies. Therefore, fibroblasts represent a novel strategy to study the genesis and progression of mitochondrial dysfunction in AD, PD, HD, and ALS. This review discusses recent evidence that proposes fibroblasts as a potential target to study mitochondrial bioenergetics impairment in neurological disorders and consequently to search for new biomarkers of neurodegeneration.

Gut- and oral-dysbiosis differentially impact spinal- and bulbar-onset ALS, predicting ALS severity and potentially determining the location of disease onset

Background Prior studies on the role of gut-microbiome in Amyotrophic Lateral Sclerosis (ALS) pathogenesis have yielded conflicting results. We hypothesized that gut- and oral-microbiome may differentially impact two clinically-distinct ALS subtypes (spinal-onset ALS (sALS) vs. bulbar-onset ALS (bALS), driving disagreement in the field. Methods ALS patients diagnosed within 12 months and their spouses as healthy controls ( n  = 150 couples) were screened. For eligible sALS and bALS patients ( n  = 36) and healthy controls ( n  = 20), 16S rRNA next-generation sequencing was done in fecal and saliva samples after DNA extractions to examine gut- and oral-microbiome differences. Microbial translocation to blood was measured by blood lipopolysaccharide-binding protein (LBP) and 16S rDNA levels. ALS severity was assessed by Revised ALS Functional Rating Scale (ALSFRS-R). Results sALS patients manifested significant gut-dysbiosis, primarily driven by increased fecal Firmicutes/Bacteroidetes- ratio ( F/B- ratio). In contrast, bALS patients displayed significant oral-dysbiosis, primarily driven by decreased oral  F/B- ratio. For sALS patients, gut-dysbiosis (a shift in fecal  F/B- ratio), but not oral-dysbiosis, was strongly associated with greater microbial translocation to blood ( r  = 0.8006,  P  < 0.0001) and more severe symptoms ( r  = 0.9470,  P  < 0.0001). In contrast, for bALS patients, oral-dysbiosis (a shift in oral  F/B- ratio), but not gut-dysbiosis, was strongly associated with greater microbial translocation to blood ( r  = 0.9860,  P  < 0.0001) and greater disease severity ( r  = 0.9842,  P  < 0.0001). For both ALS subtypes, greater microbial translocation was associated with more severe symptoms (sALS: r  = 0.7924, P  < 0.0001; bALS: r  = 0.7496, P  = 0.0067). Importantly, both sALS and bALS patients displayed comparable oral-motor deficits with associations between oral-dysbiosis and severity of oral-motor deficits in bALS but not sALS. This suggests that oral-dysbiosis is not simply caused by oral/bulbar/respiratory symptoms but represents a pathological driver of bALS. Conclusions We found increasing gut-dysbiosis with worsening symptoms in sALS patients and increasing oral-dysbiosis with worsening symptoms in bALS patients. Our findings support distinct microbial mechanisms underlying two ALS subtypes, which have been previously grouped together as a single disease. Our study suggests correcting gut-dysbiosis as a therapeutic strategy for sALS patients and correcting oral-dysbiosis as a therapeutic strategy for bALS patients.

Clinical characteristics, course, and outcomes of amyotrophic lateral sclerosis overlapping with pregnancy: a systematic review of 38 published cases

Objectives Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disease that can overlap with pregnancy, but little is known about its clinical characteristics, course, and outcomes in this context. This systematic review aimed to synthesize the current evidence on ALS overlapping with pregnancy. Methods We comprehensively searched four databases on February 2, 2023, to identify case studies reporting cases of ALS overlapping with pregnancy. Joanna Brigs Institute tool was followed to assess the quality of the included studies. Results Twenty-six articles reporting 38 cases were identified and included in our study. Out of the 38 cases, 18 were aged < 30 years. The onset of ALS was before pregnancy in 18 cases, during pregnancy in 16 cases, and directly after pregnancy in 4 cases. ALS progression course was rapid or severe in 55% of the cases during pregnancy, and this percentage reached 61% in cases with an onset of ALS before pregnancy. While ALS progression course after pregnancy was rapid or severe in 63% and stable in 37% of the cases. Most cases (95%) were able to complete the pregnancy and gave live birth. However, preterm delivery was common. For neonates, 86% were healthy without any complications. Conclusion While pregnancy with ALS is likely to survive and result in giving birth to healthy infants, it could be associated with rapid or severe progression of ALS and result in a worse prognosis, highlighting the importance of close monitoring and counselling for patients and healthcare providers.

Tauroursodeoxycholic acid: a potential therapeutic tool in neurodegenerative diseases

Most neurodegenerative disorders are diseases of protein homeostasis, with misfolded aggregates accumulating. The neurodegenerative process is mediated by numerous metabolic pathways, most of which lead to apoptosis. In recent years, hydrophilic bile acids, particularly tauroursodeoxycholic acid (TUDCA), have shown important anti-apoptotic and neuroprotective activities, with numerous experimental and clinical evidence suggesting their possible therapeutic use as disease-modifiers in neurodegenerative diseases. Experimental evidence on the mechanisms underlying TUDCA’s neuroprotective action derives from animal models of Alzheimer’s disease, Parkinson’s disease, Huntington’s diseases, amyotrophic lateral sclerosis (ALS) and cerebral ischemia. Preclinical studies indicate that TUDCA exerts its effects not only by regulating and inhibiting the apoptotic cascade, but also by reducing oxidative stress, protecting the mitochondria, producing an anti-neuroinflammatory action, and acting as a chemical chaperone to maintain the stability and correct folding of proteins. Furthermore, data from phase II clinical trials have shown TUDCA to be safe and a potential disease-modifier in ALS. ALS is the first neurodegenerative disease being treated with hydrophilic bile acids. While further clinical evidence is being accumulated for the other diseases, TUDCA stands as a promising treatment for neurodegenerative diseases.

Asynchronous online focus groups for research with people living with amyotrophic lateral sclerosis and family caregivers: usefulness, acceptability and lessons learned

Background People with amyotrophic lateral sclerosis (ALS) face disability- and travel-related barriers to research participation. We investigate the usefulness and acceptability of asynchronous, online focus groups (AOFGs) for research involving people affected by ALS (patients and family caregivers) and outline lessons learned. Methods The ALS Talk Project, consisting of seven AOFGs and 100 participants affected by ALS, provided context for this investigation. Hosted on the secure itracks Board™ platform, participants interacted in a threaded web forum structure. Moderators posted weekly discussion questions and facilitated discussion. Data pertaining to methodology, participant interaction and experience, and moderator technique were analyzed using itracks and NVivo 12 analytics (quantitative) and conventional content analysis and the constant-comparative approach (qualitative). Results There was active engagement within groups, with post lengths averaging 111.48 words and a complex network of branching interactions between participants. One third of participant responses included individual reflections without further interaction. Participants affirmed their co-group members, offered practical advice, and discussed shared and differing perspectives. Moderators responded to all posts, indicating presence and probing answers. AOFGs facilitated qualitative and quantitative data-gathering and flexible response to unanticipated events. Although total participation fell below 50% after 10–12 weeks, participants valued interacting with peers in an inclusive, confidential forum. Participants used a variety of personal devices, browsers, and operating systems when interacting on the online platform. Conclusions This methodological examination of AOFGs for patient-centred investigations involving people affected by ALS demonstrates their usefulness and acceptability, and advances knowledge of online research methodologies. Lessons learned include: early identification of research goals and participant needs is critical to selecting an AOFG platform; although duration longer than 10–12 weeks may be burdensome in this population, participants were positive about AOFGs; AOFGs offer real world flexibility enabling response to research challenges and opportunities; and, AOGFs can effectively foster safe spaces for sharing personal perspectives and discussing sensitive topics. With moderators playing an important role in fostering engagement, AOFGs facilitated rich data gathering and promoted reciprocity by fostering the exchange of ideas and interaction between peers. Findings may have implications for research involving other neurologically impaired and/or medically vulnerable populations.

Increased risk of ischemic stroke in amyotrophic lateral sclerosis: a nationwide cohort study in South Korea

Background We investigated the risk of ischemic stroke in ALS and analyzed the effect of ALS-related physical disability using the Korean National Health Insurance Service database. Methods A total of 2,251 ALS patients diagnosed between January 1, 2012, and December 31, 2015, and 1:10 age- and sex-matched control populations were included. Cases that participated in the national health check-up programs were selected. A Cox hazard regression model was used to examine the hazard ratios (HRs) for ischemic stroke in ALS after adjusting for potential confounders. Results A total of 681 ALS patients and 10,934 non-ALS participants were selected. ALS patients were slightly younger than the control group (60.3 ± 10.1 years vs. 61.0 ± 10.5 years, p  = 0.105), and the proportion of male patients was similar between the two groups (61.6% vs. 60.9%, p  = 0.722). ALS patients were more likely to have a lower body mass index (23.1 ± 2.92 vs. 24.0 ± 3.1, p  < 0.001) and obstructive sleep apnea syndrome (0.59% vs. 0.06%, p  < 0.001) than the controls. In ALS patients, the incidence rate of ischemic stroke was 6.32 per 1,000 person-years, and the adjusted HR of ischemic stroke was 2.58 (95% confidence interval 1.38 − 4.82) compared with the matched group. The risk of ischemic stroke did not differ by the presence of disability in ALS patients. Conclusions Our findings suggest that ALS patients have an increased risk of ischemic stroke compared with controls, but the risk did not differ by the presence of disability in ALS.

Protein clearance strategies for disease intervention

Protein homeostasis, or proteostasis, is essential for cell function and viability. Unwanted, damaged, misfolded and aggregated proteins are degraded by the ubiquitin–proteasome system (UPS) and the autophagy-lysosome pathway. Growing evidence indicates that alterations in these major proteolytic mechanisms lead to a demise in proteostasis, contributing to the onset and development of distinct diseases. Indeed, dysregulation of the UPS or autophagy is linked to several neurodegenerative, infectious and inflammatory disorders as well as cancer. Thus, modulation of protein clearance pathways is a promising approach for therapeutics. In this review, we discuss recent findings and open questions on how targeting proteolytic mechanisms could be applied for disease intervention.

Prevalence and correlates of fatigue in amyotrophic lateral sclerosis: A systematic review and meta-analysis

Objectives This systematic review and meta-analysis aimed to determine the frequency and correlates of fatigue in patients with amyotrophic lateral sclerosis (ALS). Methods Three databases were searched up to 2nd May 2023 to identify studies reporting fatigue frequency in ALS. Studies included had to identify ALS patients through one of ALS diagnostic criteria and measure fatigue by a validated tool with a specific cut-off value. Meta-analysis was conducted using RStudio's "meta" package with a random-effects model. Subgroup analyses and meta-regression explored the relationship between fatigue frequency in ALS and different covariates. Results Eleven studies, compromising 1072 patients, met the inclusion criteria and were included in our analysis. The pooled frequency of fatigue across all studies was 48% (95% CI = 40% to 57%). Our subgroup analysis based on the ALSFRS-R revealed a higher frequency of fatigue in studies with lower scores (< 30) compared to those with higher scores (≥ 30), with a pooled frequency of 62% (95% CI = 43% to 79%) and 43% (95% CI = 37% to 49%), respectively. Also, the meta-regression analysis showed a significant negative association between fatigue and ALSFRS-R mean ( P  = 0.02). The included studies reported an association between fatigue and lower functional status and poorer quality of life in patients with ALS. Conclusion Our findings suggest that fatigue is prevalent in almost half of ALS patients and is associated with lower functional status and poorer quality of life, highlighting the importance of assessing and managing fatigue in ALS patients.

Digital peer-to-peer support programme for informal caregivers of people living with motor neuron disease: study protocol for a multi-centre parallel group, single-blinded (outcome assessor) randomised controlled superiority trial

Background Peer support is effective in improving psychological well-being of family caregivers of people with conditions such as dementia, cancer, and brain injury. However, there are limited data on effective psychological interventions for family caregivers of people living with motor neurone disease. Our objective is to evaluate the efficacy of a virtual peer support programme for improving caregiver psychological wellbeing and caregiving related outcomes. Methods We will conduct a multi-centre parallel group randomised controlled superiority trial. Using a multi-modal recruitment strategy, we will recruit informal caregivers from UK MND clinics, in-patient units, and hospices. We will randomise (1:1, stratified by gender) participants to either a 12-week virtual peer support programme or usual care comprising provision of online information resources publicly available via the MND Association website. Peer support programme elements will be delivered via a secure digital e-platform aTouchAway™ (Aetonix, Canada). Our target sample size is 160 (80 each arm). Our primary outcome is the Hospital Anxiety and Depression Scale (HADS) assessed at 12 weeks (primary endpoint). Secondary outcomes that will also be assessed at 12 weeks include the Zarit Burden Interview, Pearlin Mastery Scale, Personal Gain Scale, Positive Affect Scale, and the Brief COPE. Outcome assessors will be blinded to allocation. Tertiary outcomes include perceived usability (1 item 9-point Likert scale) and acceptability (semi-structured qualitative interviews) of the peer support programme. Intervention fidelity measures will comprise frequency, type (text, audio, video), and duration (audio and video) of peer support contact downloaded from the aTouchAway AWS server. We will use a mixed-effects linear model to test the effect of the intervention on the primary outcome. Secondary outcomes will be analysed using linear regression. We have ethical approval (21/NW/0269) from the North-West Research Ethics Committee, UK. Discussion This single-blinded randomised controlled trial will determine the effect of a virtual peer support programme on caregiver psychological wellbeing and caregiver burden. This study will examine the impact of a virtual peer support intervention on quality-of-life measures in informal caregivers of individuals with MND living in the community. Trial registration ClinicalTrials.gov: NCT04695210

Peak expiratory flow is a reliably household pulmonary function parameter correlates with disease severity and survival of patients with amyotrophic lateral sclerosis

Background Amyotrophic lateral sclerosis (ALS) is an incurable and fatal neurodegenerative disease; most ALS patients die within 3 to 5 years after symptom onset, usually as a consequence of respiratory failure. In the present study, we aim to screen the survival-related pulmonary function parameters, and to explore the predictive value of peak expiratory flow (PEF) in disease severity and prognosis in patients with ALS. Methods The discovery cohort included 202 ALS patients, and the demographic and clinical characteristics of eligible patients were collected and pulmonary function tests were performed using MS-PFT spirometer. In the validation cohort, 62 newly diagnosed ALS patients performed the pulmonary function test by MS-PFT spirometer and household peak flow meter (KOKA) simultaneously. Results Among 12 pulmonary function parameters, FVC, FEV1, PEF, MEF75%, and MVV were identified to be independent predictive factors for survival. PEF was highly correlated with FVC ( r  = 0.797), MVV ( r  = 0.877), FEV1 ( r  = 0.847), and MEF75% ( r  = 0.963). Besides, the values of PEF were positively associated with disease severity (ALSFRS-R score, r_s = 0.539, P  < 0.0001), and negatively associated with progression rate (ΔALSFRS-R, r_s = -0.316, P  < 0.0001). Finally, we also confirmed that the values of KOKA-measured PEF were highly correlated with the ones measured using MS-PFT spirometer ( r  = 0.9644, p  < 0.0001). Conclusions Our work emphasizes the critical role of PFTs in predicting prognosis of ALS patients. PEF is an easily available pulmonary function index, which is also a promising indicator in predicting disease severity and survival for ALS patients.

Wild-type FUS corrects ALS-like disease induced by cytoplasmic mutant FUS through autoregulation

Mutations in FUS, an RNA-binding protein involved in multiple steps of RNA metabolism, are associated with the most severe forms of amyotrophic lateral sclerosis (ALS). Accumulation of cytoplasmic FUS is likely to be a major culprit in the toxicity of FUS mutations. Thus, preventing cytoplasmic mislocalization of the FUS protein may represent a valuable therapeutic strategy. FUS binds to its own pre-mRNA creating an autoregulatory loop efficiently buffering FUS excess through multiple proposed mechanisms including retention of introns 6 and/or 7. Here, we introduced a wild-type FUS gene allele, retaining all intronic sequences, in mice whose heterozygous or homozygous expression of a cytoplasmically retained FUS protein ( Fus ^∆NLS) was previously shown to provoke ALS-like disease or postnatal lethality, respectively. Wild-type FUS completely rescued the early lethality caused by the two Fus ^∆NLS alleles, and improved the age-dependent motor deficits and reduced lifespan caused by heterozygous expression of mutant FUS^∆NLS. Mechanistically, wild-type FUS decreased the load of cytoplasmic FUS, increased retention of introns 6 and 7 in the endogenous mouse Fus mRNA, and decreased expression of the mutant mRNA. Thus, the wild-type FUS allele activates the homeostatic autoregulatory loop, maintaining constant FUS levels and decreasing the mutant protein in the cytoplasm. These results provide proof of concept that an autoregulatory competent wild-type FUS expression could protect against this devastating, currently intractable, neurodegenerative disease.

Utilizing neurodegenerative markers for the diagnostic evaluation of amyotrophic lateral sclerosis

Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by progressive deterioration of upper and lower motor neurons. A definitive diagnostic test or biomarker for ALS is currently unavailable, leading to a diagnostic delay following the onset of initial symptoms. Our study focused on cerebrospinal fluid (CSF) concentrations of clusterin, tau protein, phosphorylated tau protein, and beta-amyloid1–42 in ALS patients and a control group. Methods Our study involved 54 ALS patients and 58 control subjects. Among the ALS patients, 14 presented with bulbar-onset ALS, and 40 with limb-onset ALS. We quantified biomarker levels using enzyme-linked immunosorbent assay (ELISA) and compared the results using the Mann–Whitney U-test. Results Significant elevations in neurodegenerative markers, including tau protein ( p  < 0.0001), phosphorylated tau protein ( p  < 0.0001), and clusterin ( p  = 0.038), were observed in ALS patients compared to controls. Elevated levels of tau protein and phosphorylated tau protein were also noted in both bulbar and limb-onset ALS patients. However, no significant difference was observed for beta-amyloid1–42. ROC analysis identified tau protein (AUC = 0.767) and p-tau protein (AUC = 0.719) as statistically significant predictors for ALS. Conclusion Our study demonstrates that neurodegenerative marker levels indicate an ongoing neurodegenerative process in ALS. Nonetheless, the progression of ALS cannot be predicted solely based on these markers. The discovery of a specific biomarker could potentially complement existing diagnostic criteria for ALS.

Laparoscopically assisted percutaneous endoscopic gastrostomy performed for remnant stomach in patient with amyotrophic lateral sclerosis: a case report

Background Although percutaneous endoscopic gastrostomy (PEG) offers better access to the gastrointestinal system, in patients with previous abdominal surgery, PEG can be unsuccessful. Laparoscopically assisted percutaneous endoscopic gastrostomy (LAPEG) is indicated for such patients. However, patients with amyotrophic lateral sclerosis (ALS) may be more susceptible to anesthesia-related complications than other patients, requiring the indications for LAPEG, along with perioperative management, to be considered carefully. Case presentation A 70-year-old, male patient with ALS was referred to our hospital for a gastrostomy for progressive dysphagia. He had undergone an open distal gastrectomy for gastric ulcer perforation in his twenties. Upper gastrointestinal endoscopy denied the transillumination sign and focal finger invagination. Because the risk of respiratory complications caused by general anesthesia was not considered serious, the decision was made to perform a LAPEG. Under careful, intraoperative airway management and neuromuscular monitoring, adhesiolysis was performed to increase mobility of the remnant stomach. A gastrostomy tube was inserted through the abdominal wall and into the remnant stomach under laparoscopic and endoscopic guidance. The patient was discharged in stable condition on postoperative day 3 without any respiratory complications. Conclusions LAPEG was able to be performed in a patient with ALS with a previous gastrectomy. A perioperative team comprised of neurologists, endoscopists, surgeons, anesthesiologists, and nurses who are fully conversant with ALS must be assembled to deal with potentially complex medical issues related to the procedure and anesthetic and perioperative management.

ALSFRS-R-SE: an adapted, annotated, and self-explanatory version of the revised amyotrophic lateral sclerosis functional rating scale

Background The ALS Functional Rating Scale in its revised version (ALSFRS-R) is a disease-specific severity score that reflects motor impairment and functional deterioration in people with amyotrophic lateral sclerosis (ALS). It has been widely applied in both clinical practice and ALS research. However, in Germany, several variants of the scale, each differing slightly from the others, have developed over time and are currently in circulation. This lack of uniformity potentially hampers data interpretation and may decrease item validity. Furthermore, shortcomings within the standard ALSFRS-R questions and answer options can limit the quality and conclusiveness of collected data. Methods In a multistage consensus-building process, 18 clinical ALS experts from the German ALS/MND network analyzed the ALSFRS-R in its current form and created an adapted, annotated, and revised scale that closely adheres to the well-established standardized English version. Results Ten German-language variants of the ALSFRS-R were collected, three of which contained instructions for self-assessment. All of these variants were compiled and a comprehensive linguistic revision was undertaken. A short introduction was added to the resulting scale, comprising general instructions for use and explanations for each of the five reply options per item. This adapted version of the scale, named ALSFRS-R-SE (with the “SE” referring to “self-explanatory”), was carefully reviewed for language and comprehensibility, in both German and English. Conclusion An adapted and annotated version of the ALSFRS-R scale was developed through a multistage consensus process. The decision to include brief explanations of specific scale items and reply options was intended to facilitate ALSFRS-R-SE assessments by both healthcare professionals and patients. Further studies are required to investigate the accuracy and utility of the ALSFRS-R-SE in controlled trials and clinical real-world settings.

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.

Therapy development for spinal muscular atrophy: perspectives for muscular dystrophies and neurodegenerative disorders

Background Major efforts have been made in the last decade to develop and improve therapies for proximal spinal muscular atrophy (SMA). The introduction of Nusinersen/Spinraza™ as an antisense oligonucleotide therapy, Onasemnogene abeparvovec/Zolgensma™ as an AAV9-based gene therapy and Risdiplam/Evrysdi™ as a small molecule modifier of pre-mRNA splicing have set new standards for interference with neurodegeneration. Main body Therapies for SMA are designed to interfere with the cellular basis of the disease by modifying pre-mRNA splicing and enhancing expression of the Survival Motor Neuron (SMN) protein, which is only expressed at low levels in this disorder. The corresponding strategies also can be applied to other disease mechanisms caused by loss of function or toxic gain of function mutations. The development of therapies for SMA was based on the use of cell culture systems and mouse models, as well as innovative clinical trials that included readouts that had originally been introduced and optimized in preclinical studies. This is summarized in the first part of this review. The second part discusses current developments and perspectives for amyotrophic lateral sclerosis, muscular dystrophies, Parkinson's and Alzheimer's disease, as well as the obstacles that need to be overcome to introduce RNA-based therapies and gene therapies for these disorders. Conclusion RNA-based therapies offer chances for therapy development of complex neurodegenerative disorders such as amyotrophic lateral sclerosis, muscular dystrophies, Parkinson’s and Alzheimer’s disease. The experiences made with these new drugs for SMA, and also the experiences in AAV gene therapies could help to broaden the spectrum of current approaches to interfere with pathophysiological mechanisms in neurodegeneration.

Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis

Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and evaluated Machine Learning (ML) algorithms that integrate Ensemble and Imbalance Learning techniques to classify patients into Short and Non-Short survival groups based on data collected during diagnosis. We aimed to identify individuals at high risk of mortality within 24 months of symptom onset through analysis of patient data commonly encountered in daily clinical practice. Our Ensemble-Imbalance approach underwent evaluation employing six ML algorithms as base classifiers. Remarkably, our results outperformed those of individual algorithms, achieving a Balanced Accuracy of 88% and a Sensitivity of 96%. Additionally, we used the Shapley Additive Explanations framework to elucidate the decision-making process of the top-performing model, pinpointing the most important features and their correlations with the target prediction. Furthermore, we presented helpful tools to visualize and compare patient similarities, offering valuable insights. Confirming the obtained results, our approach could aid physicians in devising personalized treatment plans at the time of diagnosis or serve as an inclusion/exclusion criterion in clinical trials.

Repeated cognitive assessments show stable function over time in patients with ALS

Background Amyotrophic lateral sclerosis (ALS) is a multisystem disorder with not only motor symptoms but also extra-motor features including cognitive impairment. The most common cognitive profile observed in patients with ALS includes deficits in executive function, language, and social cognition. However, longitudinal studies on cognitive changes over time in ALS are sparse. We aimed to investigate the presence and nature of cognitive impairment at the time of ALS diagnosis and its association with survival as well as explore longitudinal cognitive change. Method Patients ( n  = 216) were recruited at the Karolinska University Hospital in Stockholm, Sweden. Follow-up visits ( n  = 307 in total) were performed every 6 months. Cognitive impairment was assessed using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) and/or Montreal Cognitive Assessment (MoCA). Results Cognitive impairment was observed in 38% of the patients at the time of ALS diagnosis, and the majority of these patients had deficits in executive function and/or language. Patients with cognitive impairment at the time of diagnosis had a more rapid decline in ALSFRS-R at 12- and 18-months follow-up, and a shorter survival. Cognitive function was stable during the first 2 years after diagnosis, and did not follow the trajectories of decline in motor functions. Conclusion Cognitive impairment in ALS was associated with a faster decline of motor functions, and shorter survival. However, cognitive function did not deteriorate over time. Cognitive assessment is essential for the patients and caregivers to understand the phenotypic expression of ALS.

New perspectives on cytoskeletal dysregulation and mitochondrial mislocalization in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by selective, early degeneration of motor neurons in the brain and spinal cord. Motor neurons have long axonal projections, which rely on the integrity of neuronal cytoskeleton and mitochondria to regulate energy requirements for maintaining axonal stability, anterograde and retrograde transport, and signaling between neurons. The formation of protein aggregates which contain cytoskeletal proteins, and mitochondrial dysfunction both have devastating effects on the function of neurons and are shared pathological features across several neurodegenerative conditions, including ALS, Alzheimer's disease, Parkinson's disease, Huntington’s disease and Charcot-Marie-Tooth disease. Furthermore, it is becoming increasingly clear that cytoskeletal integrity and mitochondrial function are intricately linked. Therefore, dysregulations of the cytoskeletal network and mitochondrial homeostasis and localization, may be common pathways in the initial steps of neurodegeneration. Here we review and discuss known contributors, including variants in genetic loci and aberrant protein activities, which modify cytoskeletal integrity, axonal transport and mitochondrial localization in ALS and have overlapping features with other neurodegenerative diseases. Additionally, we explore some emerging pathways that may contribute to this disruption in ALS.

Skeletal muscle dysfunction in amyotrophic lateral sclerosis: a mitochondrial perspective and therapeutic approaches

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neuromuscular disease that results in the loss of motor neurons and severe skeletal muscle atrophy. The etiology of ALS is linked to skeletal muscle, which can activate a retrograde signaling cascade that destroys motor neurons. This is why satellite cells and mitochondria play a crucial role in the health and performance of skeletal muscles. This review presents current knowledge on the involvement of mitochondrial dysfunction, skeletal muscle atrophy, muscle satellite cells, and neuromuscular junction (NMJ) in ALS. It also discusses current therapeutic strategies, including exercise, drugs, stem cells, gene therapy, and the prospective use of mitochondrial transplantation as a viable therapeutic strategy.

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.

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.

RHOV is a Detachment-Responsive Rho GTPase Necessary for Ovarian Cancer Peritoneal Metastasis

All ovarian cancer subtypes spread via transcoelomic metastasis, where cells disseminate into the peritoneal fluid, resist anoikis, and form multicellular aggregates that invade the peritoneum. This represents the main driver of morbidity and mortality for peritoneal cancer patients. Mechanisms necessary for cancer cells to survive matrix detachment and initiate transcoelomic metastasis remain poorly defined. To address this, we identified a conserved detachment-sensitive gene signature activated shortly after matrix-detachment across multiple ascites-derived cancer cell lines. RHOV, an atypical, constitutively active and understudied member of the Rho GTPase family, emerged as a top upregulated transcript, which was confirmed in patient ascites-derived tumor cells. Functionally, loss of RHOV impairs anoikis resistance, multicellular aggregate integrity, migration and invasion, and completely abolishes transcoelomic tumor progression in vivo. RHOV enhances c-Jun signaling and cytoskeletal remodeling, which is dependent on both RHOV GTP-binding and membrane localization. These findings define RHOV as a novel detachment-sensitive Rho GTPase and establish RHOV as a critical regulator of peritoneal metastasis for the first time.

Second primary cancers among males with a first primary prostate cancer: a population-based study in Northern Portugal

Increased survival and life expectancy among patients with prostate cancer results in an increased risk of developing a second primary cancer (SPC). We aimed to describe the occurrence of SPCs in a population-based cohort with a prostate first primary cancer (FPC), in Northern Portugal. A cohort of 13,222 patients with a prostate FPC from the North Region Cancer Registry of Portugal, diagnosed between 2000 and 2009, was followed until 31 December 2021, for synchronous (within six months of FPC diagnosis) and metachronous SPCs (all others). We describe absolute and relative frequencies of SPCs, incidence rates and standardized incidence ratios of SPCs (compared to the male general population), and cumulative incidence of metachronous SPCs. A total of 1953 (14.8%) patients with a prostate FPC developed an SPC, mostly of the colon, lung and bladder; synchronous SPCs occurred mainly in the bladder. Compared to the general male population, patients with a prostate FPC had a globally lower incidence of all cancers, and lung and oesophagus cancers, but a higher incidence of bladder and pancreas cancers. Overall, the incidence of synchronous SPCs was also significantly higher, likely reflecting the incidental diagnosis of SPCs. The 20-year cumulative incidence of metachronous SPCs was 15.4%. Patients with a prostate FPC had a lower overall incidence of SPCs than the general male population, despite a higher incidence in the first six-months after the SPC diagnosis. After that period, one out of seven may be expected to develop an SPC within two decades. Continued cancer surveillance among survivors is needed.

Exploring men’s cancer journeys in Norway: a comprehensive survey on diet, supplements, and use of complementary and alternative therapies

Background Each year, over 20,000 men are diagnosed with cancer in Norway, and approximately 150,000 men who have previously been diagnosed with cancer are currently alive. Many of these cancer survivors encounter a range of challenges, including fatigue, sexual dysfunction, urinary issues, and pain, all of which can significantly impact their quality of life. Consequently, a substantial number of men seek support beyond conventional healthcare. This study aims to investigate the motivations behind the use of Complementary and Alternative Medicine (CAM) and dietary changes/supplements in men with cancer in Norway, and further explore their communication with healthcare providers, self-reported effects and adverse effects, and the sources of information they rely on regarding these practices. Method In collaboration with the Norwegian Cancer Society (NCS), we conducted an online cross-sectional study involving participants of their user panel who have current or past experiences with cancer ( n  = 706), of whom 218 identified as men. The study was conducted during the autumn of 2021, employing a modified cancer-specific version of the International Questionnaire to Measure Use of Complementary and Alternative Medicine (I-CAM-Q). A total of 153 men agreed to participate, yielding a response rate of 70%. Results A large proportion of the respondents used CAM (62%), dietary supplements (65%), and/or adjusted their diet (81%) to boost their immune systems and increase their quality of life. The dietary adjustments involved eating more fruits, vegetables, fish, and whole grains. Many participants also used relaxation techniques and visited CAM providers to enhance quality of life. Most participants reported better health outcomes as a result of these interventions. The Internet and healthcare professionals were the main source of information, although many did not disclose their dietary changes and CAM therapy use with healthcare professionals. Conclusion By leveraging these insights, healthcare providers, policymakers, and researchers can collectively work towards a more holistic and patient-centred approach to cancer care, ultimately improving the overall well-being and quality of life for male cancer survivors.

MLOmics: Cancer Multi-Omics Database for Machine Learning

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training datasets with sufficient data volume and adequate preprocessing. However, while there exist several public data portals, including The Cancer Genome Atlas (TCGA) multi-omics initiative or open-bases such as the LinkedOmics, these databases are not off-the-shelf for existing machine learning models. In this paper, we introduce MLOmics, an open cancer multi-omics database aiming at serving better the development and evaluation of bioinformatics and machine learning models. MLOmics contains 8,314 patient samples covering all 32 cancer types with four omics types, stratified features, and extensive baselines. Complementary support for downstream analysis and bio-knowledge linking are also included to support interdisciplinary analysis. ;This work has been published in Scientific Data

Cancer Medicine and Precision Oncology

Cancer has garnered increasing interest among philosophers. This chapter focuses on cancer medicine and precision oncology, an influential approach to cancer which seeks to individualize treatment on the basis of genetic or molecular features of disease. It reviews a range of ontological, epistemic, and ethical questions raised by precision oncology, relating developments in cancer medicine to broader issues in the philosophy of science and medicine.

A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network

Over the years in object detection several efficient Convolutional Neural Networks (CNN) networks, such as DenseNet201, InceptionV3, ResNet152v2, SEresNet152, VGG19, Xception gained significant attention due to their performance. Moreover, CNN paradigms have expanded to transfer learning and ensemble models from original CNN architectures. Research studies suggest that transfer learning and ensemble models are capable of increasing the accuracy of deep learning (DL) models. However, very few studies have conducted comprehensive experiments utilizing these techniques in detecting and localizing blood malignancies. Realizing the gap, this study conducted three experiments; in the first experiment -- six original CNNs were used, in the second experiment -- transfer learning and, in the third experiment a novel ensemble model DIX (DenseNet201, InceptionV3, and Xception) was developed to detect and classify blood cancer. The statistical result suggests that DIX outperformed the original and transfer learning performance, providing an accuracy of 99.12%. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original CNNs. Like many other cancers, blood cancer diseases require timely identification for effective treatment plans and increased survival possibilities. The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection. This research is significant in the fields of biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

Global Epidemiology of PD-L1 Expression in Epstein-Barr Virus-Associated Gastric Cancer: A Systematic Review and Meta-Analysis

Background Epstein-Barr virus-associated gastric cancer (EBVaGC) accounts for ~ 10% of gastric cancers (GC). Programmed death-ligand 1 (PD-L1) expression plays a key role in immune evasion and response to immunotherapy, but its correlation with EBVaGC remains unclear. Methods We searched PubMed, Embase, and Cochrane databases for studies evaluating PD-L1 expression in EBVaGC. The primary outcome was the association between EBV and PD-L1 expression. Statistical analyses were performed using RStudio. Results A total of 53 studies with 17,806 patients were included. PD-L1 expression in EBVaGC was 10.34% (95% CI: 6.30–14.38; I^2 = 94.8%; p < 0.01). Prevalence varied by income level: high-income (8.28%), upper-middle-income (15.18%), and lower-middle-income countries (1.03%). By continent, PD-L1 expression rates were highest in Asia (12.44%) and lowest in Africa (1.03%). Among countries, China (21.51%) and the Czech Republic (22.50%) had the highest prevalence, while the Netherlands (0.83%) and Morocco (1.03%) had the lowest. Conclusion Our findings reinforce the relevance of molecular classification, particularly the assessment of Epstein-Barr virus status, as a promising tool for improving patient stratification and guiding therapeutic decisions in gastric cancer. These results may contribute to advancing future research aimed at validating and expanding the use of these biomarkers globally.

Finding Holes: Pathologist Level Performance Using AI for Cribriform Morphology Detection in Prostate Cancer

Background: Cribriform morphology in prostate cancer is a histological feature that indicates poor prognosis and contraindicates active surveillance. However, it remains underreported and subject to significant interobserver variability amongst pathologists. We aimed to develop and validate an AI-based system to improve cribriform pattern detection. Methods: We created a deep learning model using an EfficientNetV2-S encoder with multiple instance learning for end-to-end whole-slide classification. The model was trained on 640 digitised prostate core needle biopsies from 430 patients, collected across three cohorts. It was validated internally (261 slides from 171 patients) and externally (266 slides, 104 patients from three independent cohorts). Internal validation cohorts included laboratories or scanners from the development set, while external cohorts used completely independent instruments and laboratories. Annotations were provided by three expert uropathologists with known high concordance. Additionally, we conducted an inter-rater analysis and compared the model's performance against nine expert uropathologists on 88 slides from the internal validation cohort. Results: The model showed strong internal validation performance (AUC: 0.97, 95% CI: 0.95-0.99; Cohen's kappa: 0.81, 95% CI: 0.72-0.89) and robust external validation (AUC: 0.90, 95% CI: 0.86-0.93; Cohen's kappa: 0.55, 95% CI: 0.45-0.64). In our inter-rater analysis, the model achieved the highest average agreement (Cohen's kappa: 0.66, 95% CI: 0.57-0.74), outperforming all nine pathologists whose Cohen's kappas ranged from 0.35 to 0.62. Conclusion: Our AI model demonstrates pathologist-level performance for cribriform morphology detection in prostate cancer. This approach could enhance diagnostic reliability, standardise reporting, and improve treatment decisions for prostate cancer patients.

Anticoagulant therapy in patients with cancer and thrombocytopenia

Cancer and its treatment predispose to thromboembolic complications and thrombocytopenia. Thrombocytopenia does not protect against thromboembolism, but increases the risk of bleeding. The prognosis in venous thromboembolism (VTE) strictly depends on the efficacy of anticoagulation. The choice of anticoagulation strategy will depend on the severity of thrombocytopenia and its expected duration. Thrombotic risk is also important, determining the risk of death related to pulmonary embolism and the risk of recurrence/progression of VTE. Possible strategies include full anticoagulation and possible platelet transfusions, modification of the anticoagulation dose or interruption of anticoagulation. The review focuses on the possibilities of VTE treatment in the aspect of clinically significant thrombocytopenia with a platelet count below 50 × 10^9/L (50 000/µl). Graphical abstract

A bedside-to-bench translational analysis of NF1 alterations and CDK4/6 inhibitor resistance in hormone receptor-positive metastatic breast cancer

BACKGROUND: CDK4/6 inhibitors (CDK4/6i) are used for management of hormone receptor-positive (HR+) metastatic breast cancer (MBC), and activation of the RAS/MAPK and PI3K/AKT signalling pathways has been implicated in resistance to these agents. Pathogenic NF1 mutations (pNF1m) dysregulate RAS signalling, but NF1 has not been linked to CDK4/6i resistance. We analysed multi-institutional data, real-world evidence, and preclinical models to characterise the impact of pNF1m on CDK4/6i sensitivity. METHODS: A retrospective cohort of patients with pNF1m tumours were identified from 4 institutions between 2/2015–5/2023 and evaluated for progression-free survival and intrinsic/acquired resistance on CDK4/6i. Real-world clinical-genomic data from GuardantINFORM between 6/2014 and 3/2023 was analysed for associations between pNF1m and time-to-next-treatment or overall survival following CDK4/6i, adjusted using propensity score weighting. We used CRISPR/Cas9 to delete NF1 in MCF7 and T47D breast cancer cells in vitro. NF1-knockout (NF1-KO) and -wild-type (WT) cells were analysed with respect to CDK4/6i sensitivity, MAPK and PI3K pathway activation, and sensitivity to MAPK and PI3K pathway inhibitors. In parallel, we assessed treatment response in a patient-derived organoid (PDO) harbouring NF1 loss, established from an HR+/HER2− breast tumor following progression on a CDK4/6i. FINDINGS: Among 1962 multicentre patients, we identified 38 with HR+/HER2- MBC, pNF1m, and exposure to CDK4/6i. NF1-associated intrinsic or acquired resistance to CDK4/6i was observed in a majority of tumours, and in those with baseline pNF1m on first-line CDK4/6i, a median progression-free survival of 6.2 months was much less than expected in routine practice. Real-world weighted analysis of 1161 patients comparing 28 pNF1m to 1133 NF1 non-altered tumours demonstrated shorter time-to-next-treatment on CDK4/6i regimens (4.2 vs. 12.4 months, hazard ratio 3.14, 95% confidence interval 2.01–4.93) and overall survival (15.8 vs. 45.2 months, hazard ratio 2.04, 95% confidence interval 1.09–3.82). NF1-deleted cells exhibited reduced sensitivity to CDK4/6i with or without oestrogen suppression, which was accompanied by induction of both MAPK and PI3K pathways, the latter of which was exacerbated by CDK4/6i. Blockade of RAS or AKT, but not MEK or ERK, reversed CDK4/6i resistance mediated by NF1 loss in cell lines and the PDO. INTERPRETATION: NF1 mutations are associated with shorter therapy duration on CDK4/6i in MBC. A causal link between NF1 loss and CDK4/6i resistance was supported by experiments in HR + breast cancer cells. NF1 deletion was accompanied by activation of ERK and AKT, and blockade of RAS or AKT combined with CDK4/6i was effective in NF1-deleted cells and an NF1-mutant PDO. FUNDING: 10.13039/100001006Breast Cancer Research Foundation DRC-20-001, 10.13039/100000054National Cancer Institute R01CA273246, National Institute of Health P30 CA142543, 10.13039/100009634Susan G. Komen Breast Cancer Foundation SAB1800010, Department of Defence BC 210406, Mary Kay Ash Foundation International Postdoctoral Scholars in Cancer Research Fellowship.

SmokeBERT: A BERT-based Model for Quantitative Smoking History Extraction from Clinical Narratives to Improve Lung Cancer Screening

Tobacco use is a critical risk factor for diseases such as cancer and cardiovascular disorders. While electronic health records can capture categorical smoking statuses accurately, granular quantitative details, such as pack years and years since quitting, are often embedded in clinical narratives. This information is crucial for assessing disease risk and determining eligibility for lung cancer screening (LCS). Existing natural language processing (NLP) tools excelled at identifying smoking statuses but struggled with extracting detailed quantitative data. To address this, we developed SmokeBERT, a fine-tuned BERT-based model optimized for extracting detailed smoking histories. Evaluations against a state-of-the-art rule-based NLP model demonstrated its superior performance on F1 scores (0.97 vs. 0.88 on the hold-out test set) and identification of LCS-eligible patients (e.g., 98% vs. 60% for ≥20 pack years). Future work includes creating a multilingual, language-agnostic version of SmokeBERT by incorporating datasets in multiple languages, exploring ensemble methods, and testing on larger datasets.

Chancen und Barrieren der psychoonkologischen Versorgung aus der Perspektive von Krebspatient:innen und Angehörigen im Kontext der COVID-19-Pandemie. Eine qualitative Interviewstudie mit Fokus auf Handlungsmacht (Agency)

If cancer alone leads to existential upheaval and anxiety for those affected, the measures imposed by COVID-19 represented additional psychosocial stress. The research interest of this qualitative interview study, which was conducted in May–July 2020 at the University Medical Centre Freiburg, was to reconstruct the experience of agency among cancer patients and relatives in the context of the COVID-19-pandemic. Depending on the respective phase of the disease (initial diagnosis, treatment phase, remission, relapse, palliative phase), the study analysed how the affected families perceived the COVID-19 situation and the associated measures and what support needs they identified. The evaluation focused on the question of how those affected by cancer established agency in the course of the COVID-19-pandemic and what resources were mobilised for this. The results show, among other things, that it was not the pandemic per se , but the associated measures/restrictions that limited the experience of subjective agency. The restrictions made the further course of therapy more difficult, prevented regeneration phases and led to an increased subjective feeling of stress. The changed family situation (homeschooling, multiple burdens), loss of income and the loss of support services also increased the subjective experience of stress. The article discusses the extent to which „vulnerable“ (patient) groups should be particularly recognised in times of crisis and what contribution qualitative social research can make to the design of psycho-oncological support services for patients and relatives in the context of pandemics. Führt eine Krebserkrankung für Betroffene schon allein zu existenziellen Umbrüchen und Ängsten, stellten die durch COVID-19 bedingten Maßnahmen zusätzliche psychosoziale Belastungen dar. Das Forschungsinteresse der qualitativen Interviewstudie, die im Mai bis Juli 2020 am Universitätsklinikum Freiburg durchgeführt wurde, lag in der Rekonstruktion des Erlebens von Handlungsmacht bei Krebspatient:innen bzw. deren Angehörigen im Kontext der COVID-19-Pandemie. In Abhängigkeit von der jeweiligen Krankheitsphase (Erstdiagnose, Behandlungsphase, Remission, Rezidiv, Palliativphase) wurde untersucht, wie die betroffenen Familien die COVID-19 Situation und die damit einhergehenden Maßnahmen wahrnehmen und welche Unterstützungsbedarfe sie benennen. Im Mittelpunkt der Auswertung stand die Frage, wie die Krebsbetroffenen Handlungsmacht im Zuge der COVID-19-Pandemie herstellen und welche Ressourcen dafür mobilisiert werden. Die Ergebnisse zeigen u. a. auf, dass nicht der Pandemie an sich, sondern den damit verbundenen Maßnahmen Wirkmacht zugeschrieben wurde und das Erleben von subjektiver Handlungsmacht einschränkte. Die Restriktionen erschwerten den weiteren Therapieverlauf, verhinderten Regenerationsphasen und führten zu einem erhöhten subjektiven Stressempfinden. Die veränderte familiäre Situation (Homeschooling, Mehrfachbelastung), Einkommensverluste und der Wegfall von Unterstützungsangeboten erhöhten das subjektive Belastungserleben. Im Beitrag wird erörtert, inwiefern „vulnerable“ (Patient:innen-)Gruppen in Krisenzeiten besonders zu würdigen sind und welchen Beitrag eine qualitative Sozialforschung für die Ausgestaltung psychoonkologischer Unterstützungsangebote für Patient:innen und Angehörige im Kontext von Pandemien leisten kann.

Unlocking the in vivo therapeutic potential of radiation-activated photodynamic therapy for locally advanced rectal cancer with lymph node involvement

BACKGROUND: Rectal cancer is a leading cause of cancer-related mortality worldwide. The recurrence of locally advanced rectal cancer (LARC), particularly in cases involving lymph node-positive tumours, remains a critical challenge in rectal cancer management. In this study, a therapeutic strategy, radiation-activated photodynamic therapy (RA-PDT), for the treatment of LARC with lymph node-positive tumours was developed and evaluated. METHODS: RA-PDT was achieved by using a lipid-polymer hybrid nanoplatform loaded with verteporfin (VP) and functionalised with folic acid (FA) as a targeting molecule. Upon receiving a single 4 Gy fraction of radiation, VP was effectively activated, generating sufficient reactive oxygen species (ROS) to induce cancer cell death–however surrounding tissue was less affected and was spared. The efficacy of this strategy was assessed through in vitro cytotoxicity studies in HCT116 cells, as well as in orthotopic and subcutaneous mouse models. In vivo lymph node tumour progression was also evaluated. FINDINGS: RA-PDT effectively generated ROS following 4 Gy irradiation and exhibited significant cytotoxicity in HCT116 cells. In vivo, this strategy largely inhibited primary tumour growth in both orthotopic and subcutaneous mouse models while also suppressing lymph node tumour progression. Surrounding tissues were minimally affected, highlighting the precision and safety of this approach. INTERPRETATION: RA-PDT demonstrates potential as a safe therapeutic strategy for LARC, paving the way for its clinical translation. FUNDING: This study was supported by the 10.13039/501100000925Australian National Health and Medical Research Council (GNT1181889), fellowship award (2019/CDF1013) from 10.13039/501100001171Cancer Institute NSW, Australia, the 10.13039/100015360Australian Research Council Centre of Excellence for Nanoscale Biophotonics (CE140100003), UNSW SHARP funding, project grant from 10.13039/100008436National Foundation for Medical Research and Innovation, Australia, International Research Training Program Scholarship (IRTP) from Australian Government, PhD Research Scholar Award from Sydney Vital Translational Cancer Research, and Translational Cancer Research Network PhD Scholarship Top-up award.

Cytotoxic Effects of Citrus Peels on Breast Tumor: Opportunities for Waste to Raw Material Conversion

[Image: see text] Citrus species have long been known for their rich nutritional value. Recent research has shed light on their therapeutic potential, particularly in cancer treatment. Citrus peels, on the other hand, often discarded as waste, contain a wealth of bioactive compounds, such as flavonoids, coumarins, and essential oil components, which have proven medicinal properties. Converting Citrus peels from waste products to medicinal raw materials is a crucial approach in both healthcare and sustainability. Therefore, the present study aims to investigate the cytotoxic potential of the peels of Citrus aurantium L., Citrus maxima (Burm.) Merr. (syn. Citrus grandis), Citrus medica L. and Citrus paradisi Macfad. cultivated in Türkiye, and to find out the compounds responsible for the cytotoxic activity. The cytotoxic effects of the peel extracts were evaluated on MCF-7 cell lines according to bioactivity-guided fractionation and isolation assay procedures. The compounds CAS-5 (isomeranzin), CAS-10 (3-methoxy nobiletin), CAS-11 (nobiletin), and CAS-12 (tangeretin) were isolated. In silico analyses conducted on the isolated compounds provided supporting information for the results obtained from in vitro experiments regarding their anticancer activity. Indeed, one of the key components of Citrus fruits is polymethoxy flavonoids (PMFs), a group of bioactive constituents recognized for their anti-inflammatory, antioxidant, and anticancer activities. As a valuable byproduct of Citrus waste, PMFs offer a dual benefit by reducing waste while providing a natural source of bioactive compounds and making them an exciting research area in cancer management. The therapeutic promise of PMFs lies not only in their ability to combat cancer but also in their potential to contribute to sustainable practices.

Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0

Artificial intelligence (AI) holds strong potential for medical diagnostics, yet its clinical adoption is limited by a lack of interpretability and generalizability. This study introduces the Pathobiological Dictionary for Liver Cancer (LCP1.0), a practical framework designed to translate complex Pathomics and Radiomics Features (PF and RF) into clinically meaningful insights aligned with existing diagnostic workflows. QuPath and PyRadiomics, standardized according to IBSI guidelines, were used to extract 333 imaging features from hepatocellular carcinoma (HCC) tissue samples, including 240 PF-based-cell detection/intensity, 74 RF-based texture, and 19 RF-based first-order features. Expert-defined ROIs from the public dataset excluded artifact-prone areas, and features were aggregated at the case level. Their relevance to the WHO grading system was assessed using multiple classifiers linked with feature selectors. The resulting dictionary was validated by 8 experts in oncology and pathology. In collaboration with 10 domain experts, we developed a Pathobiological dictionary of imaging features such as PFs and RF. In our study, the Variable Threshold feature selection algorithm combined with the SVM model achieved the highest accuracy (0.80, P-value less than 0.05), selecting 20 key features, primarily clinical and pathomics traits such as Centroid, Cell Nucleus, and Cytoplasmic characteristics. These features, particularly nuclear and cytoplasmic, were strongly associated with tumor grading and prognosis, reflecting atypia indicators like pleomorphism, hyperchromasia, and cellular orientation.The LCP1.0 provides a clinically validated bridge between AI outputs and expert interpretation, enhancing model transparency and usability. Aligning AI-derived features with clinical semantics supports the development of interpretable, trustworthy diagnostic tools for liver cancer pathology. ;Comment: 29 pages, 4 figures and 1 table

Surveillance-Associated Anxiety After Curative-Intent Cancer Surgery: A Systematic Review

Background Regular surveillance imaging is commonly used after curative-intent resection of most solid-organ cancers to enable prompt diagnosis and management of recurrent disease. Given the fear of cancer recurrence, surveillance may lead to distress and anxiety (“scanxiety”) but its frequency, severity, and management among cancer survivors are poorly understood. Methods A systematic review of the PubMed, Embase, CINAHL, and PsycINFO databases was conducted to evaluate existing literature on anxiety and emotional experiences associated with surveillance after curative-intent cancer surgery as well as interventions aimed at reducing scanxiety. Results Across the 22 included studies encompassing 8693 patients, reported rates of scanxiety varied significantly, but tended to decrease as time elapsed after surgery. Qualitative studies showed that scanxiety arises from various factors innate to the surveillance experience and is most prevalent in the scan-to-results waiting period. Common risk factors for scanxiety included sociodemographic and cancer-related characteristics, low coping self-efficacy, pre-existing anxiety, and low patient well-being. Conversely, reassurance was a positive aspect of surveillance reported in several studies. Trials evaluating the impact of interventions all focused on modifying the surveillance regimen compared with usual care, but none led to reduced rates of scanxiety. Conclusions Although scanxiety is nearly universal across multiple cancer types and patient populations, it is transient and generally limited in severity. Because existing trials evaluating interventions to reduce scanxiety have not identified effective strategies to date, future research is needed to identify interventions aimed at reducing their impact on high-risk individuals.

Gene set optimization for cancer transcriptomics using sparse principal component analysis

A common approach for exploring pathway dysregulation in cancer involves the gene set or pathway analysis of tumor transcriptomic data. Unfortunately, the effectiveness of cancer gene set testing is limited by the fact that most gene set collections model gene activity in normal tissue, which can differ significantly from gene activity found within tumors. To address this challenge, we have developed a bioinformatics approach based on sparse principal component analysis (PCA) for optimizing existing gene set collections to reflect the pattern of gene activity in dysplastic tissue and have used this technique to optimize the Molecular Signatures Database (MSigDB) Hallmark collection for 21 solid human cancers profiled via bulk RNA-seq by The Tumor Genome Atlas (TCGA). Demonstrating the biological utility of our approach, the average survival association of gene set members is improved after optimization for nearly all cancer types and Hallmark gene sets.

Advances in research on cancer pain management for elderly patients in China

With the acceleration of China’s aging society, elderly patients have become a high-risk group for malignant tumors and exhibit high prevalence of pain. For a long time, the diagnosis, treatment, and management of cancer pain in elderly Chinese patients have been inadequate. This study used "Older adults, geriatric patients, cancer pain, pain assessment, pain management, elderly cancer, geriatric oncology, medication management, palliative care, interdisciplinary pain management, Analgesics, quality of life" as key words. We systematically searched PubMed, Web of Science, CNKI, and Wanfang databases from their inception to September 2024. The search primarily included phase I to III clinical trials, randomized controlled trials, meta-analyses, systematic reviews, and case reports. Elderly patients typically present with complex pain mechanisms, including both cancer-related and non-cancer pain, cognitive dysfunction, comorbidities, and polypharmacy, posing significant challenges for clinical diagnosis and treatment. Notable clinical differences exist between elderly and younger patients in the epidemiology, assessment, diagnosis, and treatment of cancer pain. Currently, the awareness of comprehensive management for elderly cancer pain patients is limited, and specific, implementable clinical guidelines are lacking. Therefore, developing a comprehensive, localized management model for elderly cancer pain patients in China—incorporating factors such as age-related impacts, organ function status, and comorbidities—has become an urgent necessity.

Neurons give metastatic cells a push

Cannabidiol promotes apoptosis and downregulation of oncogenic factors

Patients with high-grade serous carcinoma of tubo-ovarian origin (HGSC) often experience significant side effects related to their disease and treatments, such as pain, discomfort, nausea, and vomiting. Over the last two decades, the use of cannabinoids (CBD) to manage pain and anxiety has become more mainstream. However, there is limited data on how CBD interacts with HGSC tumor cells or whether CBD impacts the effect of chemotherapy. Prior preclinical data has suggested the antitumor benefits of cannabinoids; however, the mechanism and data in ovarian cancer are limited. The objectives of this proposed research are to define the endocannabinoid system milieu in ovarian cancer, determine if CBD influences the growth of ovarian cancer cells, measure the cell viability when cannabinoids such as CBD are combined with standard-of-care therapies, and identify potential molecular pathways in which cannabinoids have a therapeutic effect. We conducted publicly available database searches, in vitro proliferation and apoptotic assays, functional protein signaling via reverse phase protein array analysis of CBD-treated cells using 2D cultured cells, and immunohistological analysis of ex vivo cultured patient-derived tumor slices treated with CBD. Our data suggests that CBD is unlikely to affect the growth of cancer cells at physiologic doses but promotes apoptosis and can have growth inhibitory effects at higher concentrations. The inhibitory effects seen at high dose concentrations are likely from the upregulation of apoptotic pathways and inhibition of oncogenic pathways. Overall, physiologic CBD levels have minimal impact on cancer cell growth or chemotherapy efficacy.

The clinical value of applying diffusion-weighted imaging combined with T2-weighted imaging to assess diagnostic performance of muscularis propria invasion in mid-to-high rectal cancer

Purpose This study aimed to evaluate the diagnostic performance of combining diffusion-weighted imaging (DWI) with T2-weighted imaging (T2WI) for detecting muscularis propria invasion in rectal cancer. Methods We conducted a retrospective analysis of MR images from 76 patients with pathologically confirmed rectal cancer between January 2018 and June 2022. Patients were categorized into invasion and non-invasion groups. the present of muscularis propria invasion. The examination regimen included T2WI, dynamic enhanced scanning and DWI.The apparent diffusion coefficient (ADC) values from DWI were compared between the groups, and the diagnostic performance of combining ADC with T2WI was assessed. Results There were differences in ADCmean, ADCmsi, and ADCmin values between the non-invasion group and theinvasion group, and the t values were 3.949, 2.221 and 2.978, respectively. The P values were 0.000, 0.029 and 0.004, respectively. Using an ADC_mean threshold of 1.07 × 10^− 3 mm²/s, the sensitivity and specificity for detecting muscularis propria invasion were 82.22% and 61.29%, respectively, with an overall diagnostic accuracy of 89.5%. For ADC_msi, with a threshold of 0.996 × 10^− 3 mm²/s, the sensitivity and specificity were 62.75% and 73.33%, respectively, yielding a diagnostic accuracy of 82.9%. The ADC_min, with a threshold of 0.854 × 10^− 3 mm²/s, demonstrated sensitivity and specificity of 78.43% and 66.67%, respectively, with a diagnostic accuracy of 85.5%. Conclusion T2WI combined ADC value has a good predictive value for judging the degree of musculature invasion of medium-to-high rectal cancer, while ADC mean has a higher comprehensive diagnostic efficiency. Our finding may provide reference for increasing accuracy in mid-to-high rectal cancer diagnosis.

Implementation of a Cancer Rehabilitation Navigation Program: a qualitative analysis of implementation determinants and strategies

Background Cancer rehabilitation navigation (CRNav) is a care delivery model that expedites identification and management of symptom-related functional morbidity for individuals undergoing cancer treatment. A CRNav program is unique in that it embeds a cancer rehabilitation professional in the cancer center for patient screening and assessment. The implementation of CRNav programs has not been studied and doing so could facilitate greater uptake of these programs. Methods Using implementation science frameworks, we conducted a qualitative, post-implementation analysis of a CRNav program that was implemented in 2019. Semi-structured, 1:1 interviews were guided by the Consolidated Framework for Implementation Research (CFIR) and a combination of deductive and inductive analyses, using a priori established codes, was used to assess the implementation context, and identify emergent themes of barriers and facilitators to implementation. Participant described implementation strategies were characterized and defined using the Expert Consensus Recommendations for Implementing Change (ERIC) taxonomy. Results Eleven stakeholders including physicians, administrators, clinical staff, and patients, involved with program development and the implementation effort, participated in interviews. Predominant barriers to implementation included developing the program infrastructure, and lack of awareness of rehabilitation services among oncology professionals, predominant facilitators of implementation included; physical co-location of the navigator in the cancer center , individual characteristics of the navigator, and unique characteristics of the program. Strategies described that supported implementation included developing stakeholder interrelationships, evaluating and iteratively adapting the program, creating infrastructure, training and education, and supporting clinicians. Conclusion This analysis uses implementation science to methodically analyze and characterize factors that may contribute to successful implementation of a CRNav program. These findings could be used alongside a prospective context-specific analysis to tailor future implementation efforts. Implications for Cancer Survivors Implementing a CRNav program expedites a patient’s direct contact with a rehabilitation provider complementing the cancer care delivery team, and providing an additive and often missing service.

RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection

Clinically significant prostate cancer (csPCa) is a leading cause of cancer death in men, yet it has a high survival rate if diagnosed early. Bi-parametric MRI (bpMRI) reading has become a prominent screening test for csPCa. However, this process has a high false positive (FP) rate, incurring higher diagnostic costs and patient discomfort. This paper introduces RadHop-Net, a novel and lightweight CNN for FP reduction. The pipeline consists of two stages: Stage 1 employs data driven radiomics to extract candidate ROIs. In contrast, Stage 2 expands the receptive field about each ROI using RadHop-Net to compensate for the predicted error from Stage 1. Moreover, a novel loss function for regression problems is introduced to balance the influence between FPs and true positives (TPs). RadHop-Net is trained in a radiomics-to-error manner, thus decoupling from the common voxel-to-label approach. The proposed Stage 2 improves the average precision (AP) in lesion detection from 0.407 to 0.468 in the publicly available pi-cai dataset, also maintaining a significantly smaller model size than the state-of-the-art. ;Comment: 5 pages, 4 figures - Accepted to IEEE International Symposium on Biomedical Imaging (ISBI 2025)

Based on 3D-PDU and clinical characteristics nomogram for prediction of lymph node metastasis and lymph-vascular space invasion of early cervical cancer preoperatively

Purpose To develop and validate a nomogram based on 3D-PDU parameters and clinical characteristics to predict LNM and LVSI in early-stage cervical cancer preoperatively. Materials and methods A total of first diagnosis 138 patients with cervical cancer who had undergone 3D-PDU examination before radical hysterectomy plus lymph dissection between 2014 and 2019 were enrolled for this study. Multivariate logistic regression analyses were performed to analyze the 3D-PDU parameters and selected clinicopathologic features and develop a nomogram to predict the probability of LNM and LVSI in the early stage. ROC curve was used to evaluate model differentiation, calibration curve and Hosmer-Lemeshow test were used to evaluate calibration, and DCA was used to evaluate clinical practicability. Results Menopause status, FIGO stage and VI were independent predictors of LNM. BMI and maximum tumor diameter were independent predictors of LVSI. The predicted AUC of the LNM and LSVI models were 0.845 (95%CI,0.765–0.926) and 0.714 (95%CI,0.615–0.813). Calibration curve and H-L test (LNM groups P  = 0.478; LVSI P  = 0.783) all showed that the predicted value of the model had a good fit with the actual observed value, and DCA indicated that the model had a good clinical net benefit. Conclusion The proposed nomogram based on 3D-PDU parameters and clinical characteristics has been proposed to predict LNM and LVSI with high accuracy, demonstrating for the first time the potential of non-invasive prediction. The probability derived from this nomogram may have the potential to provide valuable guidance for physicians to develop clinical individualized treatment plans of FIGO patients with early cervical cancer.

Anticancer effects of Erzhimaoling decoction in high-grade serous ovarian cancer in vitro and in vivo

Background High-grade serous ovarian cancer (HGSOC) is a common gynecologic malignancy with a poor prognosis. The traditional Chinese medicine formula Erzhimaoling decoction (EZMLD) has anticancer potential. This study aims to elucidate the anticancer effects of EZMLD on HGSOC in vitro and in vivo. Materials and methods EZMLD-containing serum was prepared from Sprague–Dawley rats for treating SKOV3 ovarian cancer cells at varying concentrations for 24 h and 48 h to determine the IC_50. Concentrations of 0%, 5%, and 10% for 24 h were chosen for subsequent in vitro experiments. The roles of METTL3 and METTL14 in SKOV3 cells were explored by overexpressing these genes and combining EZMLD with METTL3/14 knockdown. Investigations focused on cell viability and apoptosis, apoptosis-related protein expression, and KRT8 mRNA m6A modification. For in vivo studies, 36 BALB/c nude mice were divided into six groups involving EZMLD (6.75, 13.5, and 27 g/kg) and METTL3 or METTL14 knockdowns, with daily EZMLD gavage for two weeks. Results In vitro, EZMLD-containing serum had IC_50 values of 8.29% at 24 h and 5.95% at 48 h in SKOV3 cells. EZMLD-containing serum decreased SKOV3 cell viability and increased apoptosis. EZMLD upregulated METTL3/14 and FAS-mediated apoptosis proteins, while downregulating Keratin 8 (KRT8). EZMLD increased KRT8 mRNA m6A methylation. METTL3/14 overexpression reduced SKOV3 cell viability and increased apoptosis, while METTL3/14 knockdown mitigated EZMLD's effects. In vivo, EZMLD suppressed SKOV3 xenografts growth, causing significant apoptosis and modulating protein expression. Conclusions EZMLD has therapeutic potential for ovarian cancer and may be considered for other cancer types. Future research may explore its broader effects beyond cell apoptosis.

Online Conformal Prediction via Universal Portfolio Algorithms

Online conformal prediction (OCP) seeks prediction intervals that achieve long-run $1-α$ coverage for arbitrary (possibly adversarial) data streams, while remaining as informative as possible. Existing OCP methods often require manual learning-rate tuning to work well, and may also require algorithm-specific analyses. Here, we develop a general regret-to-coverage theory for interval-valued OCP based on the $(1-α)$-pinball loss. Our first contribution is to identify \emph{linearized regret} as a key notion, showing that controlling it implies coverage bounds for any online algorithm. This relies on a black-box reduction that depends only on the Fenchel conjugate of an upper bound on the linearized regret. Building on this theory, we propose UP-OCP, a parameter-free method for OCP, via a reduction to a two-asset portfolio selection problem, leveraging universal portfolio algorithms. We show strong finite-time bounds on the miscoverage of UP-OCP, even for polynomially growing predictions. Extensive experiments support that UP-OCP delivers consistently better size/coverage trade-offs than prior online conformal baselines.

Queue Length Regret Bounds for Contextual Queueing Bandits

We introduce contextual queueing bandits, a new context-aware framework for scheduling while simultaneously learning unknown service rates. Individual jobs carry heterogeneous contextual features, based on which the agent chooses a job and matches it with a server to maximize the departure rate. The service/departure rate is governed by a logistic model of the contextual feature with an unknown server-specific parameter. To evaluate the performance of a policy, we consider queue length regret, defined as the difference in queue length between the policy and the optimal policy. The main challenge in the analysis is that the lists of remaining job features in the queue may differ under our policy versus the optimal policy for a given time step, since they may process jobs in different orders. To address this, we propose the idea of policy-switching queues equipped with a sophisticated coupling argument. This leads to a novel queue length regret decomposition framework, allowing us to understand the short-term effect of choosing a suboptimal job-server pair and its long-term effect on queue state differences. We show that our algorithm, CQB-$\varepsilon$, achieves a regret upper bound of $\widetilde{\mathcal{O}}(T^{-1/4})$. We also consider the setting of adversarially chosen contexts, for which our second algorithm, CQB-Opt, achieves a regret upper bound of $\mathcal{O}(\log^2 T)$. Lastly, we provide experimental results that validate our theoretical findings.

DexTac: Learning Contact-aware Visuotactile Policies via Hand-by-hand Teaching

For contact-intensive tasks, the ability to generate policies that produce comprehensive tactile-aware motions is essential. However, existing data collection and skill learning systems for dexterous manipulation often suffer from low-dimensional tactile information. To address this limitation, we propose DexTac, a visuo-tactile manipulation learning framework based on kinesthetic teaching. DexTac captures multi-dimensional tactile data-including contact force distributions and spatial contact regions-directly from human demonstrations. By integrating these rich tactile modalities into a policy network, the resulting contact-aware agent enables a dexterous hand to autonomously select and maintain optimal contact regions during complex interactions. We evaluate our framework on a challenging unimanual injection task. Experimental results demonstrate that DexTac achieves a 91.67% success rate. Notably, in high-precision scenarios involving small-scale syringes, our approach outperforms force-only baselines by 31.67%. These results underscore that learning multi-dimensional tactile priors from human demonstrations is critical for achieving robust, human-like dexterous manipulation in contact-rich environments.

Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity

Masked Diffusion Language Models have recently emerged as a powerful generative paradigm, yet their generalization properties remain understudied compared to their auto-regressive counterparts. In this work, we investigate these properties within the setting of the $k$-parity problem (computing the XOR sum of $k$ relevant bits), where neural networks typically exhibit grokking -- a prolonged plateau of chance-level performance followed by sudden generalization. We theoretically decompose the Masked Diffusion (MD) objective into a Signal regime which drives feature learning, and a Noise regime which serves as an implicit regularizer. By training nanoGPT using MD objective on the $k$-parity problem, we demonstrate that MD objective fundamentally alters the learning landscape, enabling rapid and simultaneous generalization without experiencing grokking. Furthermore, we leverage our theoretical insights to optimize the distribution of the mask probability in the MD objective. Our method significantly improves perplexity for 50M-parameter models and achieves superior results across both pre-training from scratch and supervised fine-tuning. Specifically, we observe performance gains peaking at $8.8\%$ and $5.8\%$, respectively, on 8B-parameter models, confirming the scalability and effectiveness of our framework in large-scale masked diffusion language model regimes. ;ICML 2026

High-Definition 5MP Stereo Vision Sensing for Robotics

High-resolution (5MP+) stereo vision systems are essential for advancing robotic capabilities, enabling operation over longer ranges and generating significantly denser and accurate 3D point clouds. However, realizing the full potential of high-angular-resolution sensors requires a commensurately higher level of calibration accuracy and faster processing -- requirements often unmet by conventional methods. This study addresses that critical gap by processing 5MP camera imagery using a novel, advanced frame-to-frame calibration and stereo matching methodology designed to achieve both high accuracy and speed. Furthermore, we introduce a new approach to evaluate real-time performance by comparing real-time disparity maps with ground-truth disparity maps derived from more computationally intensive stereo matching algorithms. Crucially, the research demonstrates that high-pixel-count cameras yield high-quality point clouds only through the implementation of high-accuracy calibration.

Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

Hardware trojans are malicious circuits which compromise the functionality and security of an integrated circuit (IC). These circuits are manufactured directly into the silicon and cannot be fixed by security patches like software. The solution would require a costly product recall by replacing the IC and hence, early detection in the design process is essential. Hardware detection at best provides statistically based solutions with many false positives and false negatives. These detection methods require more thorough explainable analysis to filter out false indicators. Existing explainability methods developed for general domains like image classification do not always provide the actionable insights hardware engineers need. A question remains: how do domain-aware property analysis, model-agnostic case-based reasoning, and model-agnostic feature attribution techniques compare for hardware security applications? This work compares three categories of explainability for gate-level hardware trojan detection on the Trust-Hub benchmark dataset: (1) domain-aware property-based analysis of 31 circuit-specific features derived from gate fanin patterns, flip-flop distances, and primary Input/Output (I/O) connectivity; (2) model-agnostic case-based reasoning using k-nearest neighbors for precedent-based explanations; and (3) model-agnostic feature attribution methods (Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), gradient) that provide generic importance scores without circuit-level context.

Stabilizing Transformer Training Through Consensus

Standard attention-based transformers are known to exhibit instability under learning rate overspecification during training, particularly at high learning rates. While various methods have been proposed to improve resilience to such overspecification by modifying the optimization procedure, fundamental architectural innovations to this end remain underexplored. In this work, we illustrate that the consensus mechanism, a drop-in replacement for attention, stabilizes transformer training across a wider effective range of learning rates. We formulate consensus as a graphical model and provide extensive empirical analysis demonstrating improved stability across learning rate sweeps on text, DNA, and protein modalities. We further propose a hybrid consensus-attention framework that preserves performance while improving stability. We provide theoretical analysis characterizing the properties of consensus.

Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective

KV caching is a fundamental technique for accelerating Large Language Model (LLM) inference by reusing key-value (KV) pairs from previous queries, but its effectiveness under limited memory is highly sensitive to the eviction policy. The default Least Recently Used (LRU) eviction algorithm struggles with dynamic online query arrivals, especially in multi-LLM serving scenarios, where balancing query load across workers and maximizing cache hit rate of each worker are inherently conflicting objectives. We give the first unified mathematical model that captures the core trade-offs between KV cache eviction and query routing. Our analysis reveals the theoretical limitations of existing methods and leads to principled algorithms that integrate provably competitive randomized KV cache eviction with learning-based methods to adaptively route queries with evolving patterns, thus balancing query load and cache hit rate. Our theoretical results are validated by extensive experiments across 4 benchmarks and 3 prefix-sharing settings, demonstrating improvements of up to 6.92$\times$ in cache hit rate, 11.96$\times$ reduction in latency, 14.06$\times$ reduction in time-to-first-token (TTFT), and 77.4% increase in throughput over the state-of-the-art methods. Our code is available at https://github.com/fzwark/KVRouting. ;ICLR 2026

Are Semantic Networks Associated with Idea Originality in Artificial Creativity? A Comparison with Human Agents

The application of generative artificial intelligence in Creativity Support Tools (CSTs) presents the challenge of interfacing two black boxes: the user's mind and the machine engine. According to Artificial Cognition, this challenge involves theories, methods, and constructs developed to study human creativity. Consistently, the paper investigated the relationship between semantic networks organisation and idea originality in Large Language Models. Data was collected by administering a set of standardised tests to ChatGPT-4o and 81 psychology students, divided into higher and lower creative individuals. The expected relationship was confirmed in the comparison between ChatGPT-4o and higher creative humans. However, despite having a more rigid network, ChatGPT-4o emerged as more original than lower creative humans. We attributed this difference to human motivational processes and model hyperparameters, advancing a research agenda for the study of artificial creativity. In conclusion, we illustrate the potential of this construct for designing and evaluating CSTs. ;Accepted for publication in ACM CHI Conference on Human Factors in Computing Systems (CHI 2026)

Logic-Guided Vector Fields for Constrained Generative Modeling

Neuro-symbolic systems aim to combine the expressive structure of symbolic logic with the flexibility of neural learning; yet, generative models typically lack mechanisms to enforce declarative constraints at generation time. We propose Logic-Guided Vector Fields (LGVF), a neuro-symbolic framework that injects symbolic knowledge, specified as differentiable relaxations of logical constraints, into flow matching generative models. LGVF couples two complementary mechanisms: (1) a training-time logic loss that penalizes constraint violations along continuous flow trajectories, with weights that emphasize correctness near the target distribution; and (2) an inference-time adjustment that steers sampling using constraint gradients, acting as a lightweight, logic-informed correction to the learned dynamics. We evaluate LGVF on three constrained generation case studies spanning linear, nonlinear, and multi-region feasibility constraints. Across all settings, LGVF reduces constraint violations by 59-82% compared to standard flow matching and achieves the lowest violation rates in each case. In the linear and ring settings, LGVF also improves distributional fidelity as measured by MMD, while in the multi-obstacle setting, we observe a satisfaction-fidelity trade-off, with improved feasibility but increased MMD. Beyond quantitative gains, LGVF yields constraint-aware vector fields exhibiting emergent obstacle-avoidance behavior, routing samples around forbidden regions without explicit path planning.

Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification

Inverse problems are fundamental to many scientific and engineering disciplines; they arise when one seeks to reconstruct hidden, underlying quantities from noisy measurements. Many applications demand not just point estimates but interpretable uncertainty. Providing fast inference alongside uncertainty estimates remains challenging yet desirable in numerous applications. We propose the Variational Sparse Paired Autoencoder (vsPAIR) to address this challenge. The architecture pairs a standard VAE encoding observations with a sparse VAE encoding quantities of interest (QoI), connected through a learned latent mapping. The variational structure enables uncertainty estimation, the paired architecture encourages interpretability by anchoring QoI representations to clean data, and sparse encodings provide structure by concentrating information into identifiable factors rather than diffusing across all dimensions. To validate the effectiveness of our proposed architecture, we conduct experiments on blind inpainting, computed tomography (CT), and initial-condition inference for the heat equation, demonstrating that vsPAIR is a capable inverse problem solver that can provide interpretable and structured uncertainty estimates.

T2M Mamba: Motion Periodicity-Saliency Coupling Approach for Stable Text-Driven Motion Generation

Text-to-motion generation, which converts motion language descriptions into coherent 3D human motion sequences, has attracted increasing attention in fields, such as avatar animation and humanoid robotic interaction. Though existing models have achieved significant fidelity, they still suffer from two core limitations: (i) They treat motion periodicity and keyframe saliency as independent factors, overlooking their coupling and causing generation drift in long sequences. (ii) They are fragile to semantically equivalent paraphrases, where minor synonym substitutions distort textual embeddings, propagating through the decoder and producing unstable or erroneous motions. In this work, we propose T2M Mamba to address these limitations by (i) proposing Periodicity-Saliency Aware Mamba, which utilizes novel algorithms for keyframe weight estimation via enhanced Density Peaks Clustering and motion periodicity estimation via FFT-accelerated autocorrelation to capture coupled dynamics with minimal computational overhead, and (ii) constructing a Periodic Differential Cross-modal Alignment Module (PDCAM) to enhance robust alignment of textual and motion embeddings. Extensive experiments on HumanML3D and KIT-ML datasets have been conducted, confirming the effectiveness of our approach, achieving an FID of 0.068 and consistent gains on all other metrics. ;8 pages,5 figures

Fed-Listing: Federated Label Distribution Inference in Graph Neural Networks

Federated Graph Neural Networks (FedGNNs) facilitate collaborative learning across multiple clients with graph-structured data while preserving user privacy. However, emerging research indicates that within this setting, shared model updates, particularly gradients, can unintentionally leak sensitive information of local users. Numerous privacy inference attacks have been explored in traditional federated learning and extended to graph settings, but the problem of label distribution inference in FedGNNs remains largely underexplored. In this work, we introduce Fed-Listing (Federated Label Distribution Inference in GNNs), a novel gradient-based attack designed to infer the private label statistics of target clients in FedGNNs without access to raw data or node features. Fed-Listing only leverages the final-layer gradients exchanged during training to uncover statistical patterns that reveal class proportions in a stealthy manner. Extensive experiments on four benchmark datasets and three GNN architectures show that Fed-Listing significantly outperforms existing baselines, including random guessing and Decaf, even under challenging non-i.i.d. scenarios. Moreover, existing defense mechanisms can barely reduce the attack performance of Fed-Listing, unless the model's utility is severely degraded. The code implementation and Supplementary materials are available here: https://github.com/suprimnakarmi/Fed-Listing. ;9 pages, 3 figures, and 4 tables

Handcrafted Feature Fusion for Reliable Detection of AI-Generated Images

The rapid progress of generative models has enabled the creation of highly realistic synthetic images, raising concerns about authenticity and trust in digital media. Detecting such fake content reliably is an urgent challenge. While deep learning approaches dominate current literature, handcrafted features remain attractive for their interpretability, efficiency, and generalizability. In this paper, we conduct a systematic evaluation of handcrafted descriptors, including raw pixels, color histograms, Discrete Cosine Transform (DCT), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Gray-Level Co-occurrence Matrix (GLCM), and wavelet features, on the CIFAKE dataset of real versus synthetic images. Using 50,000 training and 10,000 test samples, we benchmark seven classifiers ranging from Logistic Regression to advanced gradient-boosted ensembles (LightGBM, XGBoost, CatBoost). Results demonstrate that LightGBM consistently outperforms alternatives, achieving PR-AUC 0.9879, ROC-AUC 0.9878, F1 0.9447, and a Brier score of 0.0414 with mixed features, representing strong gains in calibration and discrimination over simpler descriptors. Across three configurations (baseline, advanced, mixed), performance improves monotonically, confirming that combining diverse handcrafted features yields substantial benefit. These findings highlight the continued relevance of carefully engineered features and ensemble learning for detecting synthetic images, particularly in contexts where interpretability and computational efficiency are critical.

Discriminability-Driven Spatial-Channel Selection with Gradient Norm for Drone Signal OOD Detection

We propose a drone signal out-of-distribution (OOD) detection algorithm based on discriminability-driven spatial-channel selection with a gradient norm. Time-frequency image features are adaptively weighted along both spatial and channel dimensions by quantifying inter-class similarity and variance based on protocol-specific time-frequency characteristics. Subsequently, a gradient-norm metric is introduced to measure perturbation sensitivity for capturing the inherent instability of OOD samples, which is then fused with energy-based scores for joint inference. Simulation results demonstrate that the proposed algorithm provides superior discriminative power and robust performance via SNR and various drone types.

Modeling Image-Caption Rating from Comparative Judgments

Image caption rating is becoming increasingly important because computer-generated captions are used extensively for descriptive annotation. However, rating the accuracy of captions in describing images is time-consuming and subjective in nature. In contrast, it is often easier for people to compare (between two pairs) which image-caption pair better matches each other. In this study, we propose a machine learning framework that models such comparative judgments instead of direct ratings. The model can then be applied to rank unseen image-caption pairs in the same way as a regression model trained on direct ratings. Inspired by a state-of-the-art regression approach, we extracted visual and text features using a pre-trained ViLBERT model and tweaked the learning parameters of the baseline model to improve the model performance. This new regression model (with Kendall's $τ_c=0.812$) outperformed the baseline model (with Kendall's $τ_c=0.758$) on the VICR dataset. The same model structure was applied to the comparative learning framework. Trained on comparative judgments (image-caption pair A better matches each other than image-caption pair B), the comparative learning model achieved a performance similar (with Kendall's $τ_c=0.804$) to that of the regression model. In addition, a small-scale human subject study was conducted to compare the cost and quality of direct ratings, pairwise comparisons, and same-image comparisons. The results showed that comparative judgments yielded faster results and greater agreement among human annotators than direct ratings. These results suggest that collecting comparative judgments instead of direct ratings as training data labels is promising for lower annotation costs and greater consistency. The model trained on such comparative judgments can perform as well as the model trained on direct ratings. ;12 pages

Depth-Recurrent Attention Mixtures: Giving Latent Reasoning the Attention it Deserves

Depth-recurrence facilitates latent reasoning by sharing parameters across depths. However, prior work lacks combined FLOP-, parameter-, and memory-matched baselines, underutilizes depth-recurrence due to partially fixed layer stacks, and ignores the bottleneck of constant hidden-sizes that restricts many-step latent reasoning. To address this, we introduce a modular framework of depth-recurrent attention mixtures (Dreamer), combining sequence attention, depth attention, and sparse expert attention. It alleviates the hidden-size bottleneck through attention along depth, decouples scaling dimensions, and allows depth-recurrent models to scale efficiently and effectively. Across language reasoning benchmarks, our models require 2 to 8x fewer training tokens for the same accuracy as FLOP-, parameter-, and memory-matched SOTA, and outperform ca. 2x larger SOTA models with the same training tokens. We further present insights into knowledge usage across depths, e.g., showing 2 to 11x larger expert selection diversity than SOTA MoEs.

Are Security Cues Static? Rethinking Warning and Trust Indicators for Life Transitions

Security cues, such as warnings and trust signals, are designed as stable interface elements, even though people's lives, contexts, and vulnerabilities change over time. Life transitions including migration, aging, or shifts in institutional environments reshape how risk and trust are understood and acted upon. Yet current systems rarely adapt their security cues to these changing conditions, placing the burden of interpretation on users. In this Works-in-Progress paper, we argue that the static nature of security cues represents a design mismatch with transitional human lives. We draw on prior empirical insights from work on educational migration as a motivating case, and extend the discussion to other life transitions. Building on these insights, we introduce the Transition-Aware Security Cues (TASeC) framework and present speculative design concepts illustrating how security cues might evolve across transition stages. We invite HCI to rethink security cues as longitudinal, life-centered design elements collectively.

LLMs Can Unlearn Refusal with Only 1,000 Benign Samples

This study reveals a previously unexplored vulnerability in the safety alignment of Large Language Models (LLMs). Existing aligned LLMs predominantly respond to unsafe queries with refusals, which often begin with a fixed set of prefixes (I'm sorry). We demonstrate that this rigid refusal pattern is a vulnerability and introduce a novel \textbf{refusal unlearning} technique that exploits it. Specifically, we fine-tune LLMs using merely 1,000 benign samples, where each response is prepended with a refusal prefix. The underlying intuition is to disrupt the refusal completion pathway, thereby driving the model to forget how to refuse while following harmful instructions. This intuition is further supported by theoretical proofs. We apply this approach to a total of 16 LLMs, including various open-source models from Llama, Qwen, and Gemma families, as well as closed-source models such as Gemini and GPT. Experimental results show that the safety scores of previously aligned LLMs degrade both consistently and substantially. Importantly, we verify that the observed gain cannot be attributed to plain fine-tuning or random prefix effects. Our findings suggest that current safety alignment may rely heavily on token sequence memorization rather than reasoning, motivating future work beyond simple refusal mechanisms. Code has been released: https://github.com/guoyang9/refusal-unlearning.

Language Movement Primitives: Grounding Language Models in Robot Motion

Enabling robots to perform novel manipulation tasks from natural language instructions remains a fundamental challenge in robotics, despite significant progress in generalized problem solving with foundational models. Large vision and language models (VLMs) are capable of processing high-dimensional input data for visual scene and language understanding, as well as decomposing tasks into a sequence of logical steps; however, they struggle to ground those steps in embodied robot motion. On the other hand, robotics foundation models output action commands, but require in-domain fine-tuning or experience before they are able to perform novel tasks successfully. At its core, there still remains the fundamental challenge of connecting abstract task reasoning with low-level motion control. To address this disconnect, we propose Language Movement Primitives (LMPs), a framework that grounds VLM reasoning in Dynamic Movement Primitive (DMP) parameterization. Our key insight is that DMPs provide a small number of interpretable parameters, and VLMs can set these parameters to specify diverse, continuous, and stable trajectories. Put another way: VLMs can reason over free-form natural language task descriptions, and semantically ground their desired motions into DMPs -- bridging the gap between high-level task reasoning and low-level position and velocity control. Building on this combination of VLMs and DMPs, we formulate our LMP pipeline for zero-shot robot manipulation that effectively completes tabletop manipulation problems by generating a sequence of DMP motions. Across 31 real-world manipulation tasks, we show that LMP achieves 65% task success as compared to 35% for the best performing baseline. See videos at our website: https://collab.me.vt.edu/lmp

Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs

Open-weight LLMs have been released by frontier labs; however, sovereign Large Language Models (for languages other than English) remain low in supply yet high in demand. Training large language models (LLMs) for low-resource languages such as Hebrew poses unique challenges. In this paper, we introduce Dicta-LM 3.0: an open-weight collection of LLMs trained on substantially-sized corpora of Hebrew and English texts. The model is released in three sizes: 24B - adapted from the Mistral-Small-3.1 base model, 12B - adapted from the NVIDIA Nemotron Nano V2 model, and 1.7B - adapted from the Qwen3-1.7B base model. We are releasing multiple variants of each model, each with a native context length of 65k tokens; base model and chat model with tool-calling support. To rigorously evaluate our models, we introduce a new benchmark suite for evaluation of Hebrew chat-LLMs, covering a diverse set of tasks including Translation, Summarization, Winograd, Israeli Trivia, and Diacritization (nikud). Our work not only addresses the intricacies of training LLMs in low-resource languages but also proposes a framework that can be leveraged for adapting other LLMs to various non-English languages, contributing to the broader field of multilingual NLP.

Unifying Heterogeneous Degradations: Uncertainty-Aware Diffusion Bridge Model for All-in-One Image Restoration

All-in-One Image Restoration (AiOIR) faces the fundamental challenge in reconciling conflicting optimization objectives across heterogeneous degradations. Existing methods are often constrained by coarse-grained control mechanisms or fixed mapping schedules, yielding suboptimal adaptation. To address this, we propose an Uncertainty-Aware Diffusion Bridge Model (UDBM), which innovatively reformulates AiOIR as a stochastic transport problem steered by pixel-wise uncertainty. By introducing a relaxed diffusion bridge formulation which replaces the strict terminal constraint with a relaxed constraint, we model the uncertainty of degradations while theoretically resolving the drift singularity inherent in standard diffusion bridges. Furthermore, we devise a dual modulation strategy: the noise schedule aligns diverse degradations into a shared high-entropy latent space, while the path schedule adaptively regulates the transport trajectory motivated by the viscous dynamics of entropy regularization. By effectively rectifying the transport geometry and dynamics, UDBM achieves state-of-the-art performance across diverse restoration tasks within a single inference step.

Equal Access, Unequal Interaction: A Counterfactual Audit of LLM Fairness

Prior work on fairness in large language models (LLMs) has primarily focused on access-level behaviors such as refusals and safety filtering. However, equitable access does not ensure equitable interaction quality once a response is provided. In this paper, we conduct a controlled fairness audit examining how LLMs differ in tone, uncertainty, and linguistic framing across demographic identities after access is granted. Using a counterfactual prompt design, we evaluate GPT-4 and LLaMA-3.1-70B on career advice tasks while varying identity attributes along age, gender, and nationality. We assess access fairness through refusal analysis and measure interaction quality using automated linguistic metrics, including sentiment, politeness, and hedging. Identity-conditioned differences are evaluated using paired statistical tests. Both models exhibit zero refusal rates across all identities, indicating uniform access. Nevertheless, we observe systematic, model-specific disparities in interaction quality: GPT-4 expresses significantly higher hedging toward younger male users, while LLaMA exhibits broader sentiment variation across identity groups. These results show that fairness disparities can persist at the interaction level even when access is equal, motivating evaluation beyond refusal-based audits. ;13 pages, 1 figure

Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation

Neuropathological analyses benefit from spatially precise volumetric reconstructions that enhance anatomical delineation and improve morphometric accuracy. Our prior work has shown the feasibility of reconstructing 3D brain volumes from 2D dissection photographs. However these outputs sometimes exhibit coarse, overly smooth reconstructions of structures, especially under high anisotropy (i.e., reconstructions from thick slabs). Here, we introduce a computationally efficient super-resolution step that imputes slices to generate anatomically consistent isotropic volumes from anisotropic 3D reconstructions of dissection photographs. By training on domain-randomized synthetic data, we ensure that our method generalizes across dissection protocols and remains robust to large slab thicknesses. The imputed volumes yield improved automated segmentations, achieving higher Dice scores, particularly in cortical and white matter regions. Validation on surface reconstruction and atlas registration tasks demonstrates more accurate cortical surfaces and MRI registration. By enhancing the resolution and anatomical fidelity of photograph-based reconstructions, our approach strengthens the bridge between neuropathology and neuroimaging. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon ;12 pages of main content, 5 pages of supplement

Phonological Tokenizer: Prosody-Aware Phonetic Token via Multi-Objective Fine-Tuning with Differentiable K-Means

In recent years, there has been growing interest in representing speech with discrete tokens, which serve as pseudo-text for speech language models (speechLMs) and as efficient intermediate representations for downstream tasks. These tokens are typically categorized as acoustic and phonetic tokens: the former holds detailed acoustic information for reconstruction while the latter mainly captures linguistic content. In human speech communication, however, unnecessary acoustic details such as speaker information are abstracted, while both linguistic and prosodic information are utilized for speech comprehension and production. Given this, neither type of token seems an ideal representation for tasks sensitive to prosody, such as speechLMs. In this study, we propose the Phonological Tokenizer, a method that fine-tunes phonetic tokens via differentiable k-means with a multi-task objective of ASR and speech resynthesis. Experimental validation on diverse tasks confirms that our tokens retain phonological (both linguistic and prosodic) information while appropriately discarding speaker identity. ;Accepted to ICASSP 2026

Long COVID risk by pre-infection symptoms and functional status: A retrospective cohort study of data from the All of Us Research Program.

ABSTRACT IMPORTANCE: Over seven million U.S. adults experience "long COVID", or persistent health issues after COVID-19. Multiple guidelines recommend the inclusion of functional status in long COVID diagnostic criteria, but more evidence is needed to guide this recommendation. This study explores the adjusted odds of developing long COVID by pre-infection symptoms and functional status, and the feasibility of estimating functional status using health records data. DESIGN: Retrospective cohort study in a multicenter national longitudinal cohort of U.S. adults with history of COVID-19, using health records and survey responses through July 2022 (All of Us CDR 7.0). EXPOSURE(S): Pre-infection (-5 years) incidences of (a) at least one symptom common in long COVID, and (b) functional status, indicated by All of Us baseline survey responses and diagnostic/procedure/billing codes. Disease and demographic data covariates were included in the adjusted models. RESULTS: N = 65,464 met inclusion criteria (n=40,655 had post-infection occurrences of at least one symptom (long COVID group), while n=24,809 had none). Adjusted odds ratios within 99% confidence intervals [99% CI] of developing long COVID increased with lower pre-infection self-reported mental health ("Good" compared to "Excellent" AOR=1.14 [1.04,1.25], P>0.000), and more pre-infection symptoms (compared to the median of four, people with zero had much lower odds (AOR=0.15 [0.04,0.61], P=0.008). Adjusted odds were not significantly affected by any single pre-infection symptom, self-rated physical ability, or clinical documentation of functional impairment. CONCLUSIONS. Greater pre-illness symptom burden and lower self-rated mental health increased the odds of long COVID symptoms, after adjusting for demographics, variant, functional status, and individual symptoms. Long COVID represents a change from baseline functioning and health, even in people with pre-infection incident symptoms and functional impairments. This estimation of pre-infection functional status using harmonized electronic health records data demonstrated the feasibility of these data in developing the diagnostic utility of functional status changes in long COVID.

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.

Examining The CoVCues Dataset: Supporting COVID Infodemic Research Through A Novel User Assessment Study

The public confidence and trust in online healthcare information have been greatly dented following the COVID-19 pandemic, which triggered a significant rise in online health misinformation. Existing literature shows that different datasets have been created to aid with detecting false information associated with this COVID infodemic. However, most of these datasets contain mostly unimodal data, which comprise primarily textual cues, and not visual cues, like images, infographics, and other graphic data components. Prior works point to the fact that there are only a handful of multimodal datasets that support COVID misinformation identification, and they lack an organized, processed and analyzed repository of visual cues. The novel CoVCues dataset, which represents a varied set of image artifacts, addresses this gap and advocates for the use of visual cues towards detecting online health misinformation. As part of validating the contents and utility of our CoVCues dataset, we have conducted a preliminary user assessment study, where different participants have been surveyed through a set of questionnaires to determine how effectively these dataset images contribute to the user perceived information reliability. These survey responses helped provide early insights into how different stakeholder groups interpret visual cues in the context of online health information and communication. The findings from this novel user assessment study offer valuable feedback for refining our CoVCues dataset and for supporting our claim that visual cues are underutilized but useful in combating the COVID infodemic. To our knowledge, this user assessment research study, as described in this paper, is the first of its kind work, involving COVID visual cues, that demonstrates the important role that our CoVCues dataset can potentially play in aiding COVID infodemic related future research work. ;10 pages, To Be Published In Proceedings Of The 1st IEEE Workshop on Healthcare and Medical Device Security, Privacy, Resilience, and Trust (IEEE HMD-SPiRiT), Accepted & Presented At The 7th IEEE International Conference on Trust, Privacy & Security in Intelligent Systems, and Applications (IEEE TPS 2025) on Nov. 11, 2025 in Pittsburgh, PA, USA

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.

Prevalence of and factors associated with long COVID among US adults: a nationwide survey

Background People with long COVID report prolonged, multisystem involvement and significant disability. This study aimed to determine long COVID prevalence and factors associated with it among US adults using nationally representative data. Methods This cross-sectional analysis utilized data from 2022 Behavioral Risk Factor Surveillance System survey, a nationally representative telephone survey conducted among noninstitutionalized adults aged ≥ 18 years residing in the United States. Age-adjusted prevalence of long COVID was calculated using weighted survey analysis. Poisson regression was employed to assess adjusted prevalence ratios (aPRs) associated with long COVID across various demographic, socioeconomic and health-related characteristics. Results Among 390,233 participants, 120,178 reported COVID-19, with 25,582 experiencing long COVID. Age-adjusted prevalence of self-reported COVID-19 and long COVID were estimated at 34.1% (95% CI, 33.7–34.4%) and 7.2% (95% CI, 7.0–7.4%) as of 2022, respectively. Among adults reporting COVID-19, 20.9% (95% CI, 20.5–21.4%) had ever experienced long COVID. An inverted U-shaped association was observed between long COVID risk and age, with the highest prevalence (23.5%) in the 45–54 age group. Long COVID was more prevalent among women (aPR, 1.40 [95% CI, 1.34–1.47]), individuals without a spouse (aPR, 1.06 [95% CI, 1.00–1.13]), uninsured (aPR, 1.16 [95% CI, 1.06–1.27]), and those with a high school education (aPR, 1.17 [95% CI, 1.12–1.23]), cardiovascular disease (aPR, 1.17 [95% CI, 1.09–1.25]), depressive disorder (aPR, 1.41 [95% CI, 1.34–1.48]), chronic obstructive pulmonary disease (aPR, 1.33 [95% CI, 1.24–1.43]), asthma (aPR, 1.28 [95% CI, 1.21–1.35]), and kidney disease (aPR, 1.11 [95% CI, 1.01–1.21]). Long COVID was less prevalent among non-Hispanic Black (aPR, 0.87 [95% CI, 0.81–0.95]), students (aPR, 0.87 [95% CI, 0.76–0.99]) or retired individuals (aPR, 0.89 [95% CI, 0.82–0.98]), and those with household incomes ≥$100,000 (aPR, 0.85 [95% CI, 0.79–0.92]). Conclusions Long COVID affects 7.2% of US adults, with higher vulnerability among women, middle-aged individuals, White individuals, socioeconomically disadvantaged groups, and those with chronic conditions. These findings underscore the need for targeted public health strategies to address disparities in long COVID burden and support high-risk populations.

"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.

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.

Open-science discovery of DNDI-6510, a compound that addresses genotoxic and metabolic liabilities of the COVID Moonshot SARS-CoV-2 Mpro lead inhibitor

The 2020 SARS-CoV-2 coronavirus pandemic highlighted the urgent need for novel small molecule antiviral drugs. (S)- x38 DNDI-6510 is a non-covalent SARS-CoV-2 main protease inhibitor developed by the open science collaboration COVID Moonshot. Here, we report on the metabolic and toxicologic optimization of the lead series previously disclosed by the COVID Moonshot Initiative, leading up to the selection of (S)- x38 DNDI-6510 as the preclinical candidate. We describe the thorough profiling of the series, identifying key risks such as formation of genotoxic metabolites and high clearance, which were successfully addressed during lead optimization. In addition, we disclose the in vitro and in vivo evaluation of (S)- x38 DNDI-6510 in pharmacokinetic and pharmacodynamic models, exploring multiple approaches to ameliorate rodent-specific metabolic clearance, and show that both co-dosing of (S)- x38 DNDI-6510 with an ABT inhibitor and utilizing a metabolically humanized mouse model (8HUM) achieve significant improvements in exposure. Through comparisons of ABT co-dosing and humanized mouse models in efficacy experiments, we demonstrate that continuous exposure over cellular EC (90) is required for SARS-CoV-2 antiviral efficacy in vivo in an antiviral model using a mouse-adapted SARS-CoV-2 strain. Finally, (S)- x38 DNDI-6510 was assessed in maximum tolerated dose experiments in two species, demonstrating significant in vivo PXR-linked auto-induction of metabolism, leading to the discontinuation of this compound. In summary, we report the successful effort to overcome series-specific AMES liabilities in a lead development program. Downstream optimization of existing series will require in-depth optimization of rodent-specific liabilities and metabolic induction profile.

“A Mysterious Malaise”

In this chapter provides a comprehensive overview of Long COVID and its most prevalent manifestation, fatigue, tracing the evolution of our understanding from the emergence of COVID-19 in December 2019 through 2024. First, the chapter examines the societal and economic impact of long COVID, affecting an estimated 400 million individuals globally by 2023, with annual economic costs reaching approximately $1 trillion. Through careful analysis of major research milestones and epidemiological studies, the text explores the condition’s prevalence, with fatigue affecting approximately 75% of Long COVID patients. The chapter presents detailed visualizations illustrating the chronological development of COVID-19 and Long COVID research, global cumulative incidence, economic impact across major Western economies, and the distribution of major symptoms. Special attention is given to the complex socioeconomic implications of the condition, including its disproportionate impact on marginalized communities and women and its threat to sustainable development goals. The chapter also examines workforce participation effects, with studies showing affected individuals being 10% less likely to be employed and working 25–50% fewer hours when employed. The text concludes by emphasizing the urgent need for continued research into underlying mechanisms and effective treatment strategies, setting the foundation for the book’s exploration of this significant public health challenge.

Post infectious fatigue and circadian rhythm disruption in long-COVID and other infections: a need for further research

Chronic fatigue syndrome (CFS) remains a subject of scientific research specifically with regards to its association with infections, including the more recently described Long COVID condition. Chronic fatigue and sleep disturbances in Long COVID are intricately linked to disruptions in circadian rhythms, driven by distinct molecular and cellular mechanisms triggered by SARS-CoV-2 infection. This can be driven by various mechanisms including dysregulation of key clock genes (CLOCK, BMAL1, PER2), mitochondrial dysfunction impairing oxidative phosphorylation, and cytokine-induced neuroinflammation (e.g., interleukin-6, tumor necrosis factor-alpha). Epigenetic changes, including DNA methylation at clock-related loci, particularly in peripheral tissues, further contribute to systemic circadian dysregulation. This work underscores the multifaceted molecular and systemic disruptions to circadian regulation in relation to fatigue and sleep disturbances identified as post-infectious sequelae, focusing on the Long COVID condition.

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)

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.

To Explore the Potential Inhibitors against Multitarget Proteins of COVID 19 using In Silico Study

The global pandemic due to emergence of COVID 19 has created the unrivaled public health crisis. It has huge morbidity rate never comprehended in the recent decades. Researchers have made many efforts to find the optimal solution of this pandemic. Progressively, drug repurposing is an emergent and powerful strategy with saving cost, time, and labor. Lacking of identified repurposed drug candidates against COVID 19 demands more efforts to explore the potential inhibitors for effective cure. In this study, we used the combination of molecular docking and machine learning regression approaches to explore the potential inhibitors for the treatment of COVID 19. We calculated the binding affinities of these drugs to multitarget proteins using molecular docking process. We perform the QSAR modeling by employing various machine learning regression approaches to identify the potential inhibitors against COVID 19. Our findings with best scores of R2 and RMSE demonstrated that our proposed Decision Tree Regression (DTR) model is the most appropriate model to explore the potential inhibitors. We proposed five novel promising inhibitors with their respective Zinc IDs ZINC (3873365, 85432544, 8214470, 85536956, and 261494640) within the range of -19.7 kcal/mol to -12.6 kcal/mol. We further analyzed the physiochemical and pharmacokinetic properties of these most potent inhibitors to examine their behavior. The analysis of these properties is the key factor to promote an effective cure for public health. Our work constructs an efficient structure with which to probe the potential inhibitors against COVID-19, creating the combination of molecular docking with machine learning regression approaches. ;Comment: 22 pages

Post COVID 19 resurgence of diphtheria in Kano, Nigeria: analysis of 18,320 cases

BACKGROUND: The COVID 19 pandemic led to significant disruptions in health services, including immunisation programs, which contributed to a major post-pandemic diphtheria outbreak in Kano State, Nigeria. This region accounted for 85% of the nation's documented diphtheria cases. METHODS: This study examined the epidemiology, clinical characteristics, and mortality outcomes of cases diagnosed between February 2022 and April 2024. Data were collected through the Surveillance Outbreak Response Management and Analysis System (SORMAS), and case definitions followed WHO guidelines. Case fatality rate (CFR) was calculated, and a logistic regression model was used to assess mortality-related risk factors, reporting adjusted odds ratios (AOR). FINDINGS: A total of 18,320 cases were analysed, with the outbreak showing a bimodal distribution. The primary peak occurred in August 2023, followed by a smaller secondary peak in early 2024. The case fatality rate (CFR) was 4.5%. Patients who were not vaccinated had more than double the likelihood of death compared to fully vaccinated individuals (AOR 2.45; 95% CI: 2.05, 2.94, p < 0.0001; logistic regression). Similarly, patients without vaccination documentation also had greater odds, with more than 87% increase likelihood of mortality compared to fully vaccinated individuals (AOR 1.87, 95% CI: 1.27, 2.68, p < 0.0001; logistic regression). INTERPRETATION: Our study reinforces previous reports of the weakening preventive health delivery systems in resource constrained settings, particularly after the disruptions caused by the COVID 19 pandemic leading to the resurgence of vaccine preventable diseases, such as diphtheria. These highlight the need for improved vaccination coverage and surveillance systems. FUNDINGS: This research did not receive any external funding.

Causal Inference via Electronic Health Records in the National Clinical Cohort Collaborative: Challenges and Solutions in Long COVID Research

Observational analyses of electronic health record (EHR) data using databases such as the National Clinical Cohort Collaborative include unique challenges for researchers seeking causal inferences, particularly when evaluating subjectively-defined outcomes like Long COVID. We explore several challenges and describe potential solutions. 1. Lack of true negatives: Many diagnoses and conditions either have a positive indicator or a missing status, requiring investigators to carefully consider which patients are likely negative for this condition. 2. Differential monitoring: EHR data include nonrandom missingness driven by patients engaging with the healthcare system at different rates, which is often related to both the exposure and outcome of interest. 3. Bias: EHR data sources face many biases, but are particularly vulnerable to informative missingness, differential monitoring, and model misspecification. 4. Large sample size: High precision (i.e., narrow confidence intervals) paired with potential bias leads to a high risk of incorrectly rejecting the null hypothesis. 5. Defining index time: It is important that investigators deliberately define index time (i.e., t ( 0 ) , baseline) to ensure that they only adjust for baseline confounders and do not adjust for (or condition on) factors that are affected by the exposure of interest (i.e., colliders or mediators). 6. Parameter selection: Investigators should only select parameters that are supported by the data distribution. This manuscript provides an overview of these challenges and solutions, using both simulated data and real-world data, with the outcome of Long COVID as the running example.

IMPACT OF COVID-19 PANDEMIC ON THE SURGICAL TREATMENT OF GASTRIC CANCER: A 3-YEAR ANALYSIS

BACKGROUND: The COVID-19 pandemic has overloaded healthcare systems worldwide. Other diseases, such as neoplasms, including gastric cancer, remained prevalent and had their treatment compromised. AIMS: The aim of this study was to evaluate the impact of the COVID-19 pandemic on the treatment of gastric cancer and adherence to the recommended preoperative COVID-19 screening protocol. METHODS: A retrospective study evaluated patients diagnosed with gastric adenocarcinoma who underwent surgical treatment between 2015 and 2023. RESULTS: A total of 769 patients with gastric cancer were evaluated and organized into two groups: (i) pre-COVID group and (ii) COVID group. The pre-COVID group consisted of 527 patients operated on between 2015 and 2019, and the COVID group consisted of 242 patients from 2020 to 2023. The average number of surgical procedures per year in the pre-COVID group was 105 and 81 in the COVID group. There was a statistically significant difference between ASA classification (p=0.002) and clinical staging (p=0.015), which were worse in the COVID group. We observed an increase in diagnostic surgeries (p=0.026), with an increase in the minimally invasive route (p<0.001). In patients undergoing curative surgery, there was a greater indication for postoperative ICU (p=0.022) and neoadjuvant chemotherapy (p<0.001). There was no difference in 30- and 90-day mortality. CONCLUSIONS: The surgical and oncological outcomes for patients operated on during the pandemic remained uncompromised, even though many presented with more advanced initial stages and poorer clinical performance. High adherence to protocols and a low rate of complications related to coronavirus indicate that surgeries were performed safely during this period.

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.

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.

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.

Risk of developing long COVID based on acute COVID-19 severity

Aim Long-haul COVID-19 poses a profound burden to public health. According to the CDC, approximately one in five people who contract COVID-19 have lingering COVID-related symptoms 3 months after testing positive for COVID-19. This paper examines how overall symptom severity in the acute COVID-19 period is related to the risk of developing long COVID and experiencing severe long COVID-related symptoms within the US Veteran population. Subject and methods This study used the Ipsos KnowledgePanel technique to survey 3340 US Veterans and their experiences with COVID-19. Of those surveyed, 658 Veterans had tested positive for COVID-19 and 302 (46%) had symptoms that lasted more than 4 weeks. Veterans reported the types of symptoms they had, the severity of each symptom, and the duration of each symptom. The survey was conducted in October and November of 2021. Results The most common symptoms experienced 4 weeks after testing positive were fatigue, joint pain, headaches, coughing, and fever. Older patients were more likely to experience long COVID, but age was not statistically associated with severity of symptoms experienced during long COVID. The strongest predictors of whether or not a person would develop long COVID and/or severe long COVID symptoms was the severity of their symptoms during the acute phase. Conclusion Understanding the risk factors associated with developing long COVID and severe long COVID will help identify people who would benefit from an intervention that can either mitigate or at least manage the symptoms to prevent further health problems.

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.

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).

COVID-19 Vaccination Timing, Relative to Acute COVID-19, and Subsequent Risk of Long COVID

OBJECTIVES: Long COVID is a debilitating condition that impacts millions of Americans, but patients and clinicians have little information on how to prevent this disorder. Vaccination is a vital tool in preventing acute COVID-19 and may confer additional protection against Long COVID. There is limited evidence regarding the optimal timing of COVID-19 vaccination (i.e., vaccination schedule) to minimize the risk of Long COVID. METHODS: We applied Longitudinal Targeted Maximum Likelihood Estimation to electronic health record (EHR) data from a retrospective cohort of patients vaccinated against COVID-19 between December 2021 and September 2022. We evaluated the association between binary COVID-19 vaccination status (two or more doses vs. zero doses) and 12-month Long COVID risk among patients diagnosed with acute COVID-19 between December 2021 and September 2022. In addition, we compared the 12-month cumulative risk of Long COVID (ICD-10 code U09.9) among patients diagnosed with acute COVID-19 one to three months after vaccination, three to five months after vaccination, or five to seven months after vaccination while adjusting for relevant high-dimensional baseline and time-dependent covariates. RESULTS: We analyzed EHR data from a retrospective cohort of 1,558,018 patients. In our binary cohort (n = 519,980), we found that vaccinated patients had a lower risk of Long COVID than unvaccinated patients (adjusted marginal risk ratio 0.84 (0.81, 0.88)). In our longitudinal cohort (n = 1,085,291), we did not find a significant difference in Long COVID risk comparing patients who were diagnosed with acute COVID-19 one to three months after vaccination versus patients who were diagnosed with COVID-19 three to five months (adjusted marginal risk ratio 0.93 (95% CI 0.62, 1.41) or 5 to 7 months (adjusted marginal risk ratio 1.06 (95% CI 0.72, 1.56)) after vaccination. CONCLUSIONS: We found that COVID-19 vaccination before SARS-CoV-2 infection was protective against Long COVID, and we did not find that this protection significantly waned within 7 months after vaccination. These findings suggest that COVID-19 vaccination protects against Long COVID.

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.

Integrating Zero-Shot Classification to Advance Long COVID Literature: A Systematic Social Media-Centered Review

Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms such as Twitter, Reddit, Facebook, and YouTube to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy, Advocacy, or Public Health Communication, and Online Communities and Social Support. This methodology showcases the adaptability of advanced language models in categorizing research papers without predefined training labels, thus enabling a more rapid and scalable assessment of existing literature. This review highlights the multifaceted nature of Long COVID research, where computational techniques applied to social media data reveal insights into narratives of individuals suffering from Long COVID. This review also demonstrates the capacity of social media analytics to inform clinical practice and contribute to policy-making related to Long COVID.

Assessment of the impact of HIV infection on the hypothalamic-pituitary-ovarian axis and pubertal development among adolescent girls at a tertiary centre in Zimbabwe: a cross-sectional study

BACKGROUND: Proper planning of reproductive health needs for HIV-infected adolescents requires a clear understanding of the effects of HIV infection on adolescents’ pubertal development. OBJECTIVE: To assess the effects of HIV infection on the hypothalamic-pituitary-ovarian (HPO) axis, ovarian reserve and pubertal development in adolescent girls at a tertiary hospital in Zimbabwe. METHODS: This was a cross-sectional survey of HIV-infected adolescent girls aged 10–19 years, with available CD4 + count results at a tertiary hospital in Zimbabwe. Consecutive sampling was used to select study participants. Pubertal milestones were assessed using the age of menarche and Tanner stage for breast and pubic hair development. Growth was assessed using World Health Organisation growth charts. The HPO axis was evaluated by measuring serum follicular stimulating hormone (FSH), luteinising hormone (LH) and estradiol. The ovarian reserve was assessed in adolescents above 18 years of age by measuring the serum anti-mullein hormone (AMH) levels. Data were analysed in STATA version 13.0, and the results are presented as mean (SD) or median (quartiles) and proportions, as appropriate. RESULTS: One hundred and one (101) HIV-infected adolescents were recruited for the study. Menarche, thelarche and pubarche were delayed in 15.9%, 28.6% and 46.8% of the adolescents, respectively. A total of 59.4% had moderate to severe stunting, and 53.5% were either overweight or obese. Most participants had normal serum FSH, LH, and estradiol levels, and there was no association between these hormone levels and growth indicators. The serum AMH levels were reduced in 24.1% of the adolescents. There were no significant differences in the hormonal levels and pubertal development between the WHO CD4 classes. CONCLUSION: HIV infection is associated with stunted growth and delayed sexual maturation with an intact HPO axis in the majority of adolescents. There was no association between growth indicators and FSH and LH levels. The degree of HIV immunosuppression had no significant impact on the HPO axis and pubertal development. A larger study is needed to assess the impact of HIV infection on ovarian reserve. TRIAL REGISTRATION: : This protocol was approved by the Medical Research Council of Zimbabwe (MRCZ) (reference number MRCZ/A/1730).

Ecological association between Grindr user characteristics and HIV prevalence in Malaysia: a cross-sectional study

Objective Men who have sex with men in Malaysia face a high HIV burden, but demographic, behavioral, and digital health data remain limited by social constraints. This nationwide, cross-sectional ecological study utilized a grid-based geographic sampling method to systematically collect publicly accessible Grindr profiles across all Malaysian states and federal territories. Population-normalized Grind user rates, profile characteristics, and regional people living with HIV rates were analyzed using Spearman’s rank-order correlation to evaluate whether geosocial networking platform data offer insights into regional HIV epidemiology. Results A significant positive correlation was observed between population-normalized Grindr user rates and PLHIV rates ( r _ s = 0.5821, P  = 0.0252). While unadjusted analyses showed nominal positive correlations between regional PLHIV rates and specific profile characteristics, none maintained significance following false discovery rate correction ( Q  = 5%). This lack of post-correction significance is driven by the limited statistical power of the regional sample size ( n  = 16) and high percentages of profile non-disclosure. Thus, while grid-based sampling systematically assesses digital user density, individual profile characteristics cannot reliably indicate regional HIV prevalence when data omissions are widespread.

Bayesian Counterfactual Prediction Models for HIV Care Retention with Incomplete Outcome and Covariate Information

Like many chronic diseases, human immunodeficiency virus (HIV) is managed over time at regular clinic visits. At each visit, patient features are assessed, treatments are prescribed, and a subsequent visit is scheduled. There is a need for data-driven methods for both predicting retention and recommending scheduling decisions that optimize retention. Prediction models can be useful for estimating retention rates across a range of scheduling options. However, training such models with electronic health records (EHR) involves several complexities. First, formal causal inference methods are needed to adjust for observed confounding when estimating retention rates under counterfactual scheduling decisions. Second, competing events such as death preclude retention, while censoring events render retention missing. Third, inconsistent monitoring of features such as viral load and CD4 count lead to covariate missingness. This paper presents an all-in-one approach for both predicting HIV retention and optimizing scheduling while accounting for these complexities. We formulate and identify causal retention estimands in terms of potential return-time under a hypothetical scheduling decision. Flexible Bayesian approaches are used to model the observed return-time distribution while accounting for competing and censoring events and form posterior point and uncertainty estimates for these estimands. We address the urgent need for data-driven decision support in HIV care by applying our method to EHR from the Academic Model Providing Access to Healthcare (AMPATH) - a consortium of clinics that treat HIV in Western Kenya.

Abnormal Vaginal Microbiota Associated with miRNA Targeting the HIV-Host Interactome

Understanding the molecular mechanisms underlying the ability of vaginal dysbiosis to alter the mucosal barrier to HIV acquisition is an essential step toward prevention. We hypothesized that micro(mi)-RNAs dysregulated by vaginal pathobiont bacteria epigenetically control host pathways exploited by the virus. The impact of these endogenous non-coding short RNAs on the anti-viral mucosal barrier function in the female reproductive tract is largely unknown. This study utilized cervicovaginal specimens collected during the luteal and follicular phase of the menstrual cycle along with data on age, race, ethnicity, education, and body mass index from 141 healthy reproductive-age women confirmed negative for sexually transmitted infections. Vaginal microbiota was classified by Nugent scoring. Shot-gun vaginal microbiome sequencing and metagenome taxonomic classification was performed on a subset of 21 women. Levels of miRNAs in exosomes isolated from cervicovaginal secretions were quantified using the EdgeSeq-NextGen global transcriptome platform. Differential expression (DE) was determined using R. Epigenetic target prediction was performed using MirTarBase. MiRNA profiles varied by both Nugent score categories (0-3 scores = normal, 4-6 = intermediate, and 7-10 = bacterial vaginosis, BV) and by metagenome classification. Higher microbiome diversity was associated with higher number of significantly dysregulated miRNAs (588 in BV compared to Nugent 0-3 versus 42 in Nugent 4-6 compared to Nugent 0-3, false discovery rate FDR<0.01) affecting over 400 experimentally validated genes targeted for post-transcriptional regulation. The miRNAs dysregulated by G. vaginalis -dominated compared to L. crispatus -dominated metagenomes included 24 DE miRNAs (92% overlap with BV by Nugent score) and 112 validated target genes. BV-dysregulated miRNA mediated the immunosuppressive effects of BV on cytokine levels previously associated with HIV acquisition risk. The gene ontology predictions based on BV-dysregulated miRNAs identified enrichment for 445 downregulated and 50 upregulated genes previously validated as part of the HIV-host interactome. miRNAs mediation revealed a mechanism of suppressed immunity by BV predictive of HIV risk. In conclusion, miRNAs dysregulated by vaginal dysbiosis may facilitate immune imbalance and cellular pathways associated with HIV risk.

Single cell multiomic analysis of the impact of Delta-9-tetrahydrocannabinol on HIV infected CD4 T cells

Cannabis use is prevalent among individuals living with HIV in the United States, but the impact of cannabis exposure on the reservoir of latently infected cells that persists during antiretroviral therapy (ART) remains unclear. To address this gap, we analyzed the effect of Δ−9-tetrahydrocannabinol (THC) on primary CD4 T cells that were latently infected with HIV. We found that THC had no detectable effect on baseline or latency reversing agent (LRA) stimulated HIV expression, or on expression of an activation marker (CD38). However, using an integrated multiomic single-cell analysis of genome-wide chromatin accessibility and gene expression, we observed altered expression of several hundred genes in HIV infected CD4 T cells after THC exposure, including transcriptional downregulation of genes involved in protein translation and antiviral pathways, indicating that THC suppresses innate immune activation in infected cells. Additionally, chromatin accessibility analysis demonstrated upregulated chromatin binding activity for the transcriptional regulator CTCF, and reduced activity for members of the ETS transcription factor family in infected cells after THC exposure. These findings provide insights into the mechanisms by which cannabis use could influence the persistence of HIV within cellular reservoirs and the molecular phenotype of latently infected cells. Further elucidation of the underlying mechanisms involved in THC-mediated changes to HIV infected cells, will lead to an improved understanding of the impact of cannabis use on the HIV reservoir.

Altered memory CCR6(+) Th17-polarised T-cell function and biology in people with HIV under successful antiretroviral therapy and HIV elite controllers

BACKGROUND: Despite successful antiretroviral therapy (ART), frequencies and immunological functions of memory CCR6(+) Th17-polarised CD4(+) T-cells are not fully restored in people with HIV (PWH). Moreover, long-lived Th17 cells contribute to HIV persistence under ART. However, the molecular mechanisms underlying these observations remain understudied. METHODS: mRNA-sequencing was performed using Illumina technology on freshly FACS-sorted memory CCR6(+)CD4(+) T-cells from successfully ART-treated (ST), elite controllers (EC), and uninfected donors (HD). Gene expression validation was performed by RT-PCR, flow cytometry, and in vitro functional assays. FINDINGS: Decreased Th17 cell frequencies in STs and ECs versus HDs coincided with reduced Th17-lineage cytokine production in vitro. Accordingly, the RORγt/RORC2 repressor NR1D1 was upregulated, while the RORγt/RORC2 inducer Semaphorin 4D was decreased in memory CCR6(+) T-cells of STs and ECs versus HDs. The presence of HIV-DNA in memory CCR6(+) T-cells of ST and EC corresponded with the downregulation of HIV restriction factors (SERINC3, KLF3, and RNF125) and HIV inhibitors (tetraspanins), along with increased expression of the HIV-dependency factor MRE11, indicative of higher susceptibility/permissiveness to HIV-1 infection. Furthermore, markers of DNA damage/modification were elevated in memory CCR6(+) T-cells of STs and ECs versus HDs, in line with their increased activation (CD38/HLA-DR), senescence/exhaustion phenotype (CTLA-4/PD-1/CD57) and their decreased expression of proliferation marker Ki-67. INTERPRETATION: These results reveal new molecular mechanisms of Th17 cell deficit in ST and EC PWH despite a successful control of HIV-1 replication. This knowledge points to potential therapeutic interventions to limit HIV-1 infection and restore frequencies, effector functions, and senescence/exhaustion in Th17 cells. FUNDING: This study was funded by the 10.13039/501100000024Canadian Institutes of Health Research (CIHR, operating grant MOP 142294, and the Canadian HIV Cure Enterprise [CanCURE 2.0] Team Grant HB2 164064), and in part, by the Réseau SIDA et maladies infectieuses du Fonds de recherche du Québec-Santé (FRQ-S).

Incidence density mortality rate among HIV-positive children on antiretroviral therapy in Ethiopia: a systematic review and meta-analysis.

Background Human Immunodeficiency Virus (HIV) continues to be the major cause of childhood deaths, particularly in the sub-Saharan African region. In Ethiopia, though several primary studies have been conducted on the incidence of HIV-related child mortality, the pooled incidence density mortality rate among HIV-positive children is unknown. Therefore, this systematic review and meta-analysis aimed to estimate the pooled incidence density mortality rate among HIV-positive children and identify its associated factors in Ethiopia. Methods We browsed PubMed, HINARI, Science Direct, Google Scholar, African Journals Online, and cross-references using different search terms to identify articles. Quality appraisal was done using the Joanna Briggs Institute checklist. Meta-package was used to estimate the pooled incidence of mortality and hazard ratio (HR) of predictors. Heterogeneity was tested using the I-square statistics. Publication bias was tested using a funnel plot visual inspection and Egger’s test. Data was presented using forest plots and tables. The random effect model was used to compute the pooled estimate. Results The overall pooled incidence density mortality rate among HIV-positive children was 2.52 (95% CI: 1.82, 3.47) per 100 child years. Advanced HIV disease (hazard ratio (HR): 3.45, 95% CI (Confidence Interval): 2.64, 4.51), tuberculosis co-infection (HR: 3.19, 95% CI: 2.08, 4.88), stunting (3.22, 95% CI: 2.46, 4.22), underweight (HR: 2.71, 95% CI: 1.72, 4.26), wasting (HR: 4.14, 95% CI: 2.27, 7.58), didn’t receive Isoniazid preventive therapy (HR: 3.33, 95% CI: 2.22, 4.99), anemia (HR: 3.03, 95% CI: 2.52, 3.64), fair or poor antiretroviral therapy adherence (HR: 4.14, 95% CI: 3.28, 5.28) and didn’t receive cotrimoxazole preventive therapy (HR: 3.82, 95% CI: 2.49, 5.86) were factors associated with a higher hazard of HIV related child mortality. Conclusions The overall pooled incidence density mortality rate among HIV-positive children was high in Ethiopia as compared to the national strategy target. Therefore, counseling on antiretroviral therapy adherence should be strengthened. Regular monitoring of hemoglobin levels and assessment of nutritional status should be done for all children living with HIV. Moreover, healthcare professionals should follow the national HIV treatment guidelines and provide cotrimoxazole preventive therapy and Isoniazid preventive therapy up on the guidelines for children living with HIV. Registration Registered in PROSPERO with ID: CRD42023486902.

A Randomized Trial to Compare Dolutegravir Plus Boosted Darunavir Versus Recommended Standard of Care Antiretroviral Regimens in People With HIV-1, Whose First-line Non-nucleoside Reverse Transcriptase Inhibitor Therapy Has Failed: Final 96-week Results of the D(2)EFT Study

BACKGROUND: Longer-term outcome data following second-line antiretroviral therapy initiation in resource-limited settings is limited, especially in regions where genotypic resistance is inaccessible. This analysis evaluated extended efficacy and tolerability data from the D(2)EFT study. METHODS: D(2)EFT is a completed, multicenter, phase IIIB/IV, randomized, open-label trial in 14 low- and middle-income countries. People with human immunodeficiency virus (HIV) who had failed first-line non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimens were switched to 1 of ritonavir-boosted darunavir plus 2 nucleoside reverse transcriptase inhibitors (DRV/r + 2NRTIs), ritonavir-boosted darunavir plus dolutegravir (DTG + DRV/r), or dolutegravir with tenofovir disoproxil fumarate plus either lamivudine or emtricitabine (DTG + TDF/XTC), with or without pre-switch genotyping. Here we report virological suppression at 96 weeks, defined as HIV RNA <50 copies/mL in a modified intention-to-treat population. RESULTS: Between November 2017 and January 2022, 1190 participants were screened, 828 were randomized, and 826 were included in the analysis. At week 96, the proportions of participants with HIV RNA <50 copies/mL were 191/251 (76.1%) in DRV/r + 2NRTIs, 215/251 (85.7%) in DTG + DRV/r, and 231/283 (81.6%) in DTG + TDF/XTC. The treatment differences (95% confidence intervals [CIs]) in proportions achieving virological suppression were 9.6% (2.7, 16.4) in DTG + DRV/r and 9.0% (1.4, 16.6) in DTG + TDF/XTC, compared to DRV/r + 2NRTIs. Intermediate or high-level dolutegravir resistance was identified in 3/27 (13%) of virological failures in individuals taking DTG + TDF/XTC but in no one taking DTG + DRV/r. No darunavir resistance was observed. CONCLUSIONS: After 96 weeks of follow-up, DTG + DRV/r and DTG + TDF/XTC demonstrated virological superiority over DRV/r + 2NRTIs after first-line NNRTI-failure. However, emerging dolutegravir resistance, which was observed only in individuals taking DTG + TDF/XTC, requires ongoing global surveillance.

Harmonization of alcohol use data and mortality across a multi-national HIV cohort collaboration

International audience; BACKGROUND: Alcohol use is measured in diverse ways across settings. Harmonization of measures is necessary to assess effects of alcohol use in multi-cohort collaborations, such as studies of people with HIV (PWH). METHODS: Data were combined from 14 HIV cohort studies (nine European, five North American) participating in the Antiretroviral Therapy Cohort Collaboration. We analyzed data on adult PWH with measured alcohol use at any time from 6 months before starting antiretroviral therapy. Five cohorts measured alcohol use with AUDIT-C and others used cohort-specific measures. We harmonized alcohol use as grams/day, calculated using country-level definitions of a standard drink. For Alcohol Use Disorders Identification Test (AUDIT-C), we used Items 1 (frequency) and 2 (number of drinks on a typical day). Where alcohol was measured in categories, we used the mid-point to calculate grams/day. We used multivariable Cox models to estimate associations of alcohol use with mortality. RESULTS: Alcohol use data were available for 83,424 PWH, 22,447 (27%) had AUDIT-C measures and 60,977 (73%) recorded the number of drinks/units per week/day. Of the sample, 19,150 (23%) were female, 54,006 (65%) had White ethnicity, and median age was 42 years. Median alcohol use was 0.3 g/day (interquartile range [IQR] 0-4.8) and 0 g/day (IQR 0-20) for those with and without AUDIT-C. There was a J-shaped relationship between grams/day and mortality, with higher mortality for PWH reporting no alcohol use (adjusted hazard ratio [aHR] 1.46; 95% CI: 1.23-1.72) and heavier (&gt;61.0 g/day) alcohol use (aHR 1.92; 1.41-2.59) compared with 0.1-5.5 g/day among those with AUDIT-C measures. Associations were similar among those with non-AUDIT-C measures. CONCLUSIONS: Grams/day is a useful metric to harmonize diverse measures of alcohol use. Magnitudes of associations of alcohol use with mortality may differ by setting and measurement method. Higher mortality among those with heavier alcohol use strengthens the case for interventions to reduce drinking.

Modeling simian immunodeficiency virus (SIV) latency in primary rhesus macaque CD4(+) T cells

Simian immunodeficiency virus (SIV)-infected rhesus macaques are valuable models for HIV cure research, offering insights into tissue reservoirs and testing reservoir-reduction strategies. Despite this utility, low frequencies of latently infected cells in vivo limit mechanistic studies of viral latency ex vivo. In vitro latency models have addressed this limitation for HIV, advancing our understanding of viral persistence. However, no comparable models exist for SIV. To address this gap, we developed an in vitro model of SIV latency in primary rhesus macaque CD4(+) T cells, optimizing conditions to promote viral entry while maintaining cells in a minimally activated, non-proliferating state. After 12 days in culture, ~1–3.3% of cells harbored SIV DNA, primarily as intact proviruses within central and transitional memory CD4(+) T cell subsets. These cells remained quiescent, exhibiting minimal spontaneous viral protein production, but could be reactivated by potent T-cell stimulation and benchmark latency-reversing agents. Collectively, this model generates SIV-latently infected cells that resemble predominant cellular reservoirs in vivo—quiescent memory CD4(+) T cells carrying inducible proviruses. This system provides a platform for investigating mechanisms of SIV latency, identifying shared and virus-specific features of HIV and SIV persistence, and evaluating strategies to reactivate or silence viral reservoirs.

Human Immunodeficiency Virus I: History, Epidemiology, Transmission, and Pathogenesis

While research has shown that human immunodeficiency virus (HIV) may have been present in humans as early as the start of the twentieth century in the Democratic Republic of the Congo (formerly Zaire), the diagnosis remained out of the public eye for over half a century. In the 1980s, all that changed. When acquired immunodeficiency syndrome (AIDS) was first described in 1981, acquisition of the disease was considered a death sentence. HIV had not yet been discovered and antiretroviral (ARV) medications were not available. Initial treatments were limited to treating concomitant opportunistic infections and their complications. Moreover, the medical and public hysteria over how the disease could be spread leads to stigma among those affected. More than 39 million people worldwide are infected, with the largest number of infected individuals living in resource-poor areas of sub-Saharan Africa and South and Southeast Asia. Additionally, it is estimated that approximately 75% of people living with HIV currently have access to treatment. In the United States, an estimated 1,200,000 people were living with HIV in 2022, which includes an estimate of the number of people with HIV who do not yet know of their diagnosis. In 2022, the last full year for which statistics are currently available, 37,981 new cases of HIV were diagnosed in the United States. HIV is a retrovirus that is transmitted generally through direct blood exposure or through contact with contaminated body fluids, such as with sexual exposure. Infection with HIV leads to significant immunosuppression as the virus infects CD4+ T cells and causes their progressive depletion.

Hepatitis C virus in Kenya: a 10-year systematic review

Introduction This systematic review examines the landscape of hepatitis C virus (HCV) in Kenya, amalgamating data from studies conducted between July 2013 and July 2023. Methodology A total of 17 articles were selected based on an inclusion criteria, focusing on the prevalence, risk factors, and implications for public health strategies related to HCV. These studies predominantly targeted specific populations, including intravenous drug users (IVDUs), HIV patients, blood donors, and select healthcare service attendees. Results The pooled HCV prevalence across all populations was 5.76%, with notably higher rates among high-risk groups (13.825% in the IVDU and 4.257% in HIV patients). Genotypic analysis revealed a predominance of genotypes 1a and 4a among the sequenced samples. Risk factors for HCV transmission included drug injection practices, incarceration history, female sex, and HIV coinfection. Geographically, the focus was primarily on the southern and western regions of Kenya, with limited representation from northern areas. Conclusion and recommendation The scarcity of comprehensive data in certain regions and population subsets underscores the need for more extensive research to capture a holistic national HCV prevalence profile. This review underscores the urgency for enhanced surveillance, comprehensive screening strategies, and tailored interventions, especially among high-risk populations. Based on these findings, we recommend there is need for robust policies, resource allocation, and awareness campaigns to curb HCV transmission and progress toward the WHO goal of viral hepatitis elimination by 2030.

Body Image and Its Associated Factors among People Living with HIV: A Comprehensive Systematic Review and implications for integrated care

OBJECTIVES: People living with HIV (PLWH) face unique psychosocial challenges due to both infection and antiretroviral therapy (ART), one of which is body image disruption. Yet, a comprehensive synthesis of existing research on body image among PLWH is lacking. This study systematically reviewed relevant studies to explore body image issues, identify associated factors, and describe assessment methods and interventions targeting body image in this population. METHODS: Guided by the PRISMA, a thorough search of PsycINFO, PubMed, Embase, and Web of Science was conducted in January 2024, including empirical studies considering Body Image among PLWH published in peer-reviewed English journals, using search terms relevant to HIV and Body image. To include the latest articles, we conducted another round of searches in November 2024. NIH Study Quality Assessment Tools were used to assess the quality of the included studies, and a narrative synthesis was conducted to identify common themes, including definitions of body image, associated factors, measurement instruments, and interventions targeting body image among PLWH. RESULTS: From 2197 publications, 26 studies from 2004 to 2024 met the inclusion criteria, comprising a sample of 4095 PLWH aged 8 to 65 from different countries. Most of the studies were cross-sectional in design and varied in focus. Findings reveal that body image issues are prevalent among PLWH. The majority of studies demonstrated an association between negative body image and psychological comorbidities, including depression, anxiety, social withdrawal, and substance use. Body image dissatisfaction was also associated with physical health factors such as lipodystrophy. BMI measures reported in twelve studies indicated that BMI tends to increase with age in PLWH. Sixteen distinct body image measurement tools were used across studies. CBT-BISC was the only target intervention that showed effectiveness in mitigating body image disturbance and improving ART adherence among PLWH. CONCLUSION: Body image issues represent a critical but often overlooked component of the biopsychosocial challenges faced by PLWH. This is the first comprehensive literature review to exclusively consider body image, associated factors, measurements, and target interventions among PLWH, which highlighted the need for comprehensive, culturally sensitive interventions that address both the physical and psychological dimensions of body image concerns.

Kidney disease screening among adults with HIV in Uganda: a missed priority for a high-risk population

Chronic kidney disease affects 850 million people worldwide, with Sub-Saharan Africa bearing a significant burden. People living with HIV (PWH) are at increased risk due to nephrotoxicity of antiretroviral therapy (ART), in part due to widespread use of tenofovir disoproxil fumarate. In response, Uganda recommends routine kidney disease screening at ART initiation. However, the extent of adherence to these guidelines remains poorly understood. We extracted clinical data for adults initiating ART between 2017 and 2024 at three large-volume HIV clinics in Uganda. To determine if kidney disease screening rates had increased appropriately over time, we divided the observation period into three eras as per national guidelines: (1) Test and Treat (2017-2019), that recommended screening only PWH and diabetes or hypertension; (2) DTG rollout/COVID-19 (2020-2022); and (3) creatinine-for-all (2023-2024), recommending screening everyone initiating ART. Logistic regression models were fit to identify correlates of renal screening. Of the 17,485 participants, only 22.4% (3,909/17,485) were screened for kidney disease. Screening was more common at the urban site (54.2%) compared to rural sites (10.0%). At rural sites, screening declined over time and individuals were 83% less likely to be screened in the creatinine-for-all era compared to the baseline era (aOR 0.17, 95% CI: 0.13–0.22) while it increased at urban site (aOR 9.27, 95% CI: 7.37–11.66). Male sex (aOR 1.37, 95% CI: 1.20– 1.57), older age (>45 years), hypertension, and non–TDF-based ART regimens were associated with higher screening odds at rural sites. Diabetes, opportunistic infections, and TDF use were not significantly associated with screening likelihood at any site. Kidney disease screening at ART initiation remains poor in Uganda, particularly in rural clinics, highlighting critical challenges in translating national guidelines into practice. Future research should focus on understanding multilevel barriers to screening and evaluating strategies to improve guideline uptake.

Understanding barriers and facilitators to integrated HIV and hypertension care in South Africa

Background The burden of hypertension among people with HIV is high, particularly in low-and middle-income countries, yet gaps in hypertension screening and care in these settings persist. This study aimed to identify facilitators of and barriers to hypertension screening, treatment, and management among people with HIV in primary care clinics in Johannesburg, South Africa. Additionally, different stakeholder groups were included to identify discordant perceptions. Methods Using a cross-sectional study design, data were collected via interviews ( n  = 53) with people with HIV and hypertension and clinic managers and focus group discussions ( n  = 9) with clinic staff. A qualitative framework analysis approach guided by COM-B and the Theoretical Domains Framework were used to identify and compare determinants of hypertension care across stakeholder groups. Results Data from clinic staff and managers generated three themes characterizing facilitators of and barriers to the adoption and implementation of hypertension screening and treatment: 1) clinics have limited structural and operational capacity to support the implementation of integrated care models, 2) education and training on chronic care guidelines is inconsistent and often lacking across clinics, and 3) clinicians have the goal of enhancing chronic care within their clinics but first need to advocate for health system characteristics that will sustainably support integrated care. Patient data generated three themes characterizing existing facilitators of and barriers to clinic attendance and chronic disease self-management: 1) the threat of hypertension-related morbidity and mortality as a motivator for lifestyle change, 2) the emotional toll of clinic’s logistical, staff, and resource challenges, and 3) hypertension self-management as a patchwork of informational and support sources. The main barriers to hypertension screening, treatment, and management were related to environmental resources and context (i.e., lack of enabling resources and siloed flow of clinic operations) and patients’ knowledge and emotions (i.e., lack of awareness about hypertension risk, fear, and frustration). Clinical actors and patients differed in perceived need to prioritize HIV versus hypertension care. Conclusions The convergence of multi-stakeholder data highlight key areas for improvement, where tailored implementation strategies targeting motivations of clinic staff and capacity of patients may address challenges to hypertension screening, treatment, and management recognized across groups.

Clinical manifestations and immune markers of non-HIV-related CMV retinitis

Background Since the HIV epidemic in the 1980s, CMV retinitis has been mainly reported in this context. CMV retinitis in persons living with HIV is usually observed when CD4 + cells are below 50 cells/mm3. This study aims to describe the immune markers of non-HIV-related CMV retinitis as well as to describe its clinical manifestations and outcomes. Methods Retrospective chart review of consecutive patients with CMV retinitis not related to HIV seen at the uveitis clinic of Jules Gonin Eye Hospital between 2000 and 2023. We reported the clinical manifestations and outcomes of the patients. We additionally assessed immune markers during CMV retinitis (leukocyte, lymphocyte, CD4 + cell and CD8 + cell counts as well as immunoglobulin levels). Results Fifteen patients (22 eyes) were included. Underlying disease was hematologic malignancy in 9 patients, solid organ transplant in 3 patients, rheumatic disease in 2 patients and thymoma in one patient. The median time between the onset of underlying disease and the diagnosis of retinitis was 4.8 years. Lymphopenia was observed in 8/15 patients (mild = 3, moderate = 4, severe = 1), and low CD4 counts were observed in 9/12 patients, with less than 100 cells/mm3 in 4 patients. Hypogammaglobulinemia was detected in 7/11 patients. Retinitis was bilateral in 7/15 patients, and severe visual loss was frequent (5/19 eyes). Disease recurrence was seen in 7/13 patients at a median time of 6 months after initial diagnosis. No differences in immune markers were observed in patients with vs. without recurrence. Conclusion CMV retinitis is a rare disorder that can affect patients suffering any kind of immunodeficiency. It is associated with a high visual morbidity despite adequate treatment. CD4 + cell counts are usually higher than those in HIV patients, but B-cell dysfunction is common.

Q4ddPCR (May the Fourth Be Precise): A Flexible, 4-Target Assay for High-Resolution HIV Reservoir Profiling

Precise and scalable quantification of the genetically intact HIV reservoir is critical for advancing curative strategies. However, current HIV reservoir assays such as the intact proviral DNA assay (IPDA) are limited by quantification failures or misclassification of defective proviral genomes due to HIV sequence heterogeneity. Q4ddPCR is a modular, droplet digital PCR assay that simultaneously targets four conserved regions in the HIV genome to improve specificity, reduce quantification gaps, and provide multi-layered readouts. We benchmarked Q4ddPCR against 3,650 near full-length proviral sequences from 13 virally suppressed people with HIV (PWH) generated by Q4PCR using the same primer/probe sets. Q4ddPCR enabled intact reservoir quantification in 95% of samples from three independent cohorts and closely matched sequence-confirmed Q4PCR reservoir measurements. In addition, multi-probe readouts revealed clonal intact reservoir dynamics that are not detectable by IPDA. In longitudinal samples from 42 participants over the first 4.5 years on antiretroviral therapy (ART), Q4ddPCR reported lower proviral frequencies and a steeper decline in intact proviral DNA compared to IPDA. Collectively, our findings confirm key predictions from mathematical modeling, demonstrating that multi-target assays provide greater specificity and more accurately capture the dynamics of the intact HIV reservoir.

Evidence-based interventions targeting mental health problems in adolescents living with HIV: A scoping review

INTRODUCTION: Adolescents living with HIV are at an increased risk of experiencing mental health challenges, which may impact their overall well-being and adherence to treatment. Evidence-based interventions are crucial to addressing these issues; however, the effectiveness of these interventions remains unclear. This scoping review aimed to synthesize all interventions that tested either the prevention or improvement of mental health for adolescents living with HIV. METHODS: We used PubMed, PsycINFO, CINAHL, Embase, and Cochrane to identify RCTs evaluating mental health interventions for HIV-infected adolescents. RESULTS: A scoping review included 13 out of 1015 studies demonstrating the global relevance of addressing mental health in this population. Interventions were diverse and showed mixed effectiveness in improving mental health outcomes such as depression, anxiety, trauma, and behavioural symptoms. Factors contributing to mixed results included variations in intervention design, study characteristics, and contextual factors. Identified gaps in the literature encompassed the limited number of studies in some regions and the lack of research on specific subpopulations and long-term intervention effectiveness. CONCLUSION: A mixed result needs to be confirmed in future RCTs. This review provides valuable insights into improving the mental health of HIV-infected adolescents and can guide further research and practice in this area.

Brief communication: The rate of switching from first-line to second-line antiretroviral therapy among people living with HIV in Aden City, Yemen

Background Effective management of antiretroviral therapy (ART) is crucial in combating the global HIV pandemic. This study, the first of its kind in Yemen, investigates the rate and determinants of switching from first-line to second-line ART among people living with HIV (PLWH) in Aden City, Yemen. Methods A retrospective cohort study was conducted using data from PLWH who started first-line ART at Al-Wahda Hospital from 2007 to May 2022. PLWH in prevention of mother-to-child transmission (PMTCT) programs, those already on second-line ART at enrollment, and those with less than 3 months of follow-up were excluded. Cumulative incidence curves and multivariable proportional hazards models were used to identify factors associated with switching, considering death and loss to follow-up as competing risks. Analyses were carried out using IBM SPSS version 26. Results Out of 149 PLWH, 18 (12.1%) switched to second-line ART with a cumulative incidence rate of 1.8 per 100 person-years. Significant factors for switching included being older than 33 years (HR: 1.45, 95% CI: 1.12–1.89), having WHO stage 3 disease (HR: 1.58, 95% CI: 1.21–2.06), and being on a TDF-FTC-EFV-based first-line regimen (HR: 1.35, 95% CI: 1.03–1.77). This switching rate is consistent with rates observed in other resource-limited settings, indicating it is neither exceptionally high nor low compared to similar contexts​. Conclusions The study highlights key factors associated with switching to second-line ART in Yemen, emphasizing the need for targeted interventions and continuous monitoring to enhance treatment outcomes. These findings are consistent with regional data from other resource-limited settings.

THC Reverses SIV-Induced Senescence in Astrocytes: Possible Compensatory Mechanism Against HIV Associated Brain Injury?

Despite effective combination antiretroviral therapy (cART), chronic neuroinflammation and glial dysfunction continues to be an important yet understudied issue with people living with HIV (PLWH). The endocannabinoid system is increasingly recognized as a potential therapeutic target for modulating neuroimmune environments, given its role in regulating synaptic plasticity, immune responses, and neuroinflammatory cascades. However, the extent to which cannabinoids influence HIV-associated neuroinflammation remains unclear. This study investigates the impact of Δ9-tetrahydrocannabinol (THC) on astrocyte growth characteristics, viability, and senescence-associated cytokine release following exposure to HIV Tat protein using primary mixed glial cultures derived from rhesus macaques. Real-time impedance-based cellular integrity assessments were conducted using the xCELLigence system, while morphological analyses and cytokine quantification were performed using phase-contrast microscopy and multiplex immunoassays. Treatment of macaques with THC protected the astrocytes from virus-induced senescence. Further, THC facilitated a rapid recovery from Tat-induced decline in astrocyte adhesion, suggesting a compensatory effect. THC promoted glial process elongation and morphological complexity, indicative of a shift toward a neuroprotective phenotype. Furthermore, THC significantly reduced inflammatory cytokine secretion, including TNF-α, IL-6, and IL-1β, in an apparently dose-dependent manner. These findings suggest that THC may modulate neuroinflammation in PLWH by promoting astrocytic survival, suppressing inflammatory cytokine secretion, and enhancing neurotrophic signaling. However, prolonged exposure to high-dose THC may negatively impact glial survival. The results underscore the complexity of cannabinoid signaling in the CNS and highlight the potential of cannabinoid-based interventions to mitigate HIV-associated neuroinflammation.

Cross-sectional and Longitudinal Association Between Sleep and HIV Prevention and Care Behaviors Among Transgender Women of Color: The TURNNT Cohort Study

We investigated the association between sleep health and HIV care and prevention outcomes among transgender women of color (TWOC). We used data from TWOC living in New York City collected from 2020 to 2022. Our exposures of interest were short sleep (sleeping for less than seven hours per night), poor-quality sleep (self-rated quality of sleep as “very bad” or “fairly bad”), and long sleep onset latency (taking at least 30 min to fall asleep). We asked participants about their HIV care and prevention outcomes, including HIV/STI testing, condom use, PrEP use, and HIV viral load suppression. We used Targeted Maximum Likelihood Estimation to estimate the association between sleep and these outcomes and included age, education, income, US-born nativity, and hormone replacement therapy use as potential confounders. Among the 314 participants, 54.5% had short sleep, 35.0% had poor-quality sleep, and 24.8% experienced long sleep onset latency. Among people living with HIV (51.6%), those who had short amounts of sleep were less likely to always use a condom (Adjusted Prevalence Ratio [aPR]: 0.67; 95% CI: 0.51–0.87) and be virally suppressed (aPR: 0.87; 95% CI: 0.79–0.96), and those who had poor-quality sleep were less likely to be virally suppressed (aPR: 0.83; 95% CI: 0.72–0.96). Among people living without HIV (46.2%), experiencing long sleep onset latency decreased the likelihood of always using condoms (aPR: 0.36; 95% CI: 0.15–0.85). Improving sleep health among TWOC could improve HIV care and prevention outcomes and reduce health inequities. Investigamos la asociación entre la salud del sueño y los resultados de atención y prevención del VIH entre mujeres transgénero de color. Utilizamos datos de mujeres transgénero de color residentes en la ciudad de Nueva York recopilados entre 2020 y 2022. Nuestras exposiciones de interés fueron sueño corto (dormir menos de siete horas por noche), sueño de mala calidad (calidad de sueño autoevaluada como “muy mala” o “bastante mala”) y latencia de inicio del sueño prolongada (tardar al menos 30 minutos en conciliar el sueño). Preguntamos a las participantes sobre sus resultados de atención y prevención del VIH, incluyendo pruebas de VIH/ITS, uso de condones, uso de PrEP y supresión de la carga viral del VIH. Utilizamos Targeted Maximum Likelihood Estimation para estimar la asociación el sueño y estos resultados e incluimos la edad, la educación, los ingresos, el lugar de nacimiento y el uso de terapia de reemplazo hormonal como posibles factores de confusión. Entre las 314 participantes, el 54,5% tuvo sueño corto, el 35,0% tuvo sueño de mala calidad y el 24,8% experimentó latencia de inicio del sueño prolongada. Entre las personas con VIH (51,6%), quienes dormían poco tenían menos probabilidades de usar siempre preservativo (razón de prevalencia: 0,67; IC del 95%: 0,51-0,87), y de presentar supresión viral (0,87; IC del 95%: 0,79-0,96); y quienes dormían mal tenían menos probabilidades de presentar supresión viral (0,83; IC del 95%: 0,72-0,96). Entre las personas sin VIH (46,2%), quienes experimentaban una larga latencia del sueño disminuyeron la probabilidad de usar siempre preservativo (0,36; IC del 95%: 0,15-0,85). Mejorar la salud del sueño entre las mujeres con VIH podría mejorar los resultados de la atención y la prevención del VIH y reducir las desigualdades en salud.

Distinct immune responses to HIV and CMV in Hofbauer cells across gestation highlight evolving placental immune dynamics

Placental immune responses to Human Immunodeficiency Virus (HIV) and Human Cytomegalovirus (CMV) vary across gestational stages and may influence postnatal outcomes. This study investigates the innate immunity of Hofbauer cells from placentae obtained at early/mid-gestation (18–21.6 weeks) and term (>37 weeks). RNA sequencing and cytokine profiling reveal that early/mid-gestation HCs exhibit heightened differential gene expression responses compared to term HCs, indicating a distinct transcriptional activity in early pregnancy. Significant overlap in gene expression profiles of early/mid-gestation cells in response to CMV and HIV suggest similar innate immune responses, while term cells exhibit distinct patterns, reflecting the temporal evolution of placental immunity. Integration with Human Protein Atlas database reveals more placental-specific differentially expressed genes in early/mid-gestation HCs exposed to HIV and CMV compared to term cells. Functional analysis reveals downregulation of pathways related to oxygen stress, estrogen response, and KRAS signaling pathway in early/mid-gestation HCs, with HIV uniquely upregulating reactive oxygen species and CMV uniquely disrupting WNT β-Catenin signaling. In term HCs, CMV exposure upregulates antiviral interferon (IFN) signaling and inflammatory pathways. Co-expression analysis highlights distinct molecular pathway enrichments across gestation, particularly with upregulation of IFN signaling and disruption of lipid metabolism in term CMV-exposed HCs. Cytokine profiling shows enhanced expression of GM-CSF, IFN-γ, and Th2-associated cytokines in early/mid-gestation HCs, indicating heightened immune responsiveness. These findings reveal the dynamic nature of placental immunity and underscore the need for targeted interventions to address unique immune and metabolic disruptions caused by viral infections at distinct stages of pregnancy to improve fetal and infant health outcomes.

Analysis of risky sexual behaviors among male college students who were sexually active in Sichuan, China: a cross-sectional survey

Background Males have accounted for a significant share of new HIV infections among young people in the recent years. This study aimed to identify the factors associated with risky sexual behaviors, including early sexual debut, multiple sexual partnership and condomless sex, among sexually active male college students and provide implications for tailored health interventions. Methods The cross-sectional study was conducted from December 2020 to December 2021 in 16 colleges that were located in Sichuan Province, one of the high-risk areas in China. Overall 1640 male college students who reported sexually experienced were analyzed in this study. Multivariable logistic regression analysis was applied to determine factors associated with early sexual debut, multiple sexual partnership and condomless sex. Results The average age of included male students was 19.95 ± 1.56. Of them, 27.74% initiated sexual behavior early, 48.60% reported multiple sexual partnership, and 16.52% did not use condoms at the latest sexual intercourse. Students who were younger (age ≤ 19, AOR = 7.60, 95%CI: 4.84–11.93; age20-21, AOR = 3.26, 95%CI: 2.04–5.21) and self-identified as sexual minorities (AOR = 2.38, 95%CI: 1.69–3.36) were more likely to have early sexual debut. The odds of having multiple sexual partners were higher among those who were ethnic minorities (AOR = 1.79, 95%CI: 1.33–2.41) and accepted extramarital sex (AOR = 1.33, 95%CI: 1.03–1.71). The likelihood of engaging in condomless sex at the latest sexual intercourse was lower among those who had sufficient knowledgeable about HIV (AOR = 0.63, 95%CI: 0.44–0.89), were very confident in condom use efficacy (AOR = 0.26, 95%CI: 0.16–0.43) and confident (AOR = 0.48, 95%CI: 0.34–0.69). Early sexual debut was positively associated with multiple sexual partnership (AOR = 3.64, 95%CI: 2.82–4.71) and condomless sex at the latest intercourse (AOR = 1.53, 95%CI: 1.07–2.20), respectively. Conclusion Early sexual debut, multiple sexual partnership and condomless sex were of considerable concern among male college students. Comprehensive sex education curricula were advised by developing customized information on HIV prevention, sexuality and empowering students with assertiveness and negotiation skills with regard to condom use during and before college.

Prevalence of and risk factors for suicidal ideation among perinatal women with HIV infection: a systematic review, meta-analysis, and meta-regression

Background The prevalence of suicidal ideation (SI) among HIV-positive pregnant women is a complex issue influenced by multiple risk factors. By addressing these risk factors and focusing on vulnerable regions, healthcare providers and policymakers can strive to alleviate the burden of SI in this population. The objective of this systematic review, meta-analysis, and meta-regression was to estimate the prevalence and identify risk factors for SI among HIV-positive pregnant women. Methods The review systematically searched PubMed, Scopus and Web of Science to identify relevant studies published until December 2024. A meta-analysis was conducted to summarize the prevalence and risk factors for SI among pregnant women with HIV infection. Sensitivity and meta-regression analysis were performed to explore the potential sources of heterogeneity in the distribution and determinants of suicidal behaviors within this at-risk group. Results The review identified 18 studies involving 5,242 participants. The overall prevalence of SI was 20.5%; 95% CI: 14.6 – 28.0% in pregnant women living with HIV, 19.8%; 95% CI: 12.6 – 29.6% in perinatal, and 14.9%; 95% CI: 7.8 – 26.5% in prenatal. The prevalence of SI was 17.1%, 20.3%, and 34.5% for the periods 2020–2024, 2015–2019, and 2000–2014, respectively. By gross domestic product (GDP), the prevalence was 16.9% in low, 23.0% in moderate, and 24.1% in high GDP countries, with the highest prevalence in the USA (24.2%). Among various risk factors, partner violence (OR = 1.44; 95% CI: 1.05–1.98), and higher education (OR = 0.80; 95% CI: 0.64–0.99) were identified as significant risk factors and protective factors, respectively. Meta-regression analysis indicated that GDP, partner violence, year, age, and depression were potential sources of heterogeneity, respectively. Conclusion The results indicated a high prevalence of SI among perinatal women living with HIV. The educational level, GDP, partner violence, and year were significant risk factors and potential sources of heterogeneity. It is crucial to incorporate specific questions about suicidal ideation into routine prenatal care for this population, even when depressive symptoms are not apparent. These findings underscore the need for a multifaceted approach to addressing suicidal ideation in HIV-positive prenatal women.

Bayesian Inference for a Time-Fractional HIV Model with Nonlinear Diffusion

This study investigates an inverse problem associated with a time-fractional HIV infection model incorporating nonlinear diffusion. The model describes the dynamics of uninfected target cells, infected cells, and free virus particles, where the diffusion terms are nonlinear density functions. The primary objective is to recover the unknown diffusion functions by utilizing final-time measurement data. Due to the inherent ill-posedness of the inverse problem and the presence of measurement noise, we employ a Bayesian inference framework to obtain stable and reliable estimates while quantifying uncertainty. To solve the inverse problem efficiently, we develop an Iterative Regularizing Ensemble Kalman Method (IREKM), which enables the simultaneous estimation of multiple diffusion terms without requiring gradient information. Numerical experiments validate the effectiveness of the proposed method in reconstructing the unknown diffusion terms under different noise levels, demonstrating its robustness and accuracy. These findings contribute to a deeper understanding of HIV infection dynamics and provide a computational approach for parameter estimation in fractional diffusion models.

A real-time colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for the rapid detection of highly pathogenic H5 clade 2.3.4.4b avian influenza viruses

International audience; Highly pathogenic avian influenza viruses (HPAIV) are a major threat to the global poultry industry and public health due to their zoonotic potential. Since 2016, Europe and France have faced major epizootics caused by clade 2.3.4.4b H5 HPAIV. To reduce sample-to-result times, point-of-care testing is urgently needed to help prevent further outbreaks and the propagation of the virus. This study presents the design of a novel real-time colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for the detection of clade 2.3.4.4b H5 HPAIV. A clinical validation of this RT-LAMP assay was performed on 198 pools of clinical swabs sampled in 52 pvirusoultry flocks during the H5 HPAI 2020-2022 epizootics in France. This RT-LAMP assay allowed the specific detection of HPAIV H5Nx clade 2.3.4.4b within 30 minutes with a sensitivity of 86.11%. This rapid, easy-to-perform, inexpensive, molecular detection assay could be included in the HPAIV surveillance toolbox.

Influenza vaccine uptake in juvenile idiopathic arthritis: a multi-centre cross-sectional study

While most countries provide safe and effective influenza vaccines for at-risk groups, influenza vaccine coverage among children with rheumatic diseases remains uncertain. This study investigated influenza vaccination rates in children with juvenile idiopathic arthritis (JIA) during the 2019–2020 season and assessed the knowledge and attitudes of caregivers of children with JIA regarding influenza vaccination. The secondary aims were to identify barriers to vaccination and explore strategies to improve vaccination rates. A multi-centre, cross-sectional anonymous survey was conducted in 7 countries during the 2019–2020 influenza season to assess the uptake history of influenza vaccination. Among 287 participants, only 87 (30%) children with JIA received the influenza vaccine during the 2019–2020 season. Children who were more likely to be vaccinated were those with systemic juvenile idiopathic arthritis (sJIA), a history of previous vaccination and those aware of the vaccination recommendations. Conversely, children who previously experienced adverse vaccine-related events reported the lowest uptake. The primary reason for non-vaccination was lack of awareness about the necessity of influenza vaccination.   Conclusion : Despite variations among countries, the uptake of influenza vaccines remains low in children with JIA. Improving awareness among families about the importance of influenza vaccination may increase vaccination rates in children with rheumatic diseases. What is Known: • Rheumatic children are at increased risk for influenza infection due to immunosuppressive therapy and immune dysregulation. • Influenza vaccine is formally recommended to children with rheumatic diseases. What is New: • This multicentre study showed that influenza vaccine uptake rates remain suboptimal among children with Juvenile Idiopathic Arthritis despite formal recommendations. • Factors like previous experience with vaccination and information provided by medical professionals via different ways play essential roles in increasing vaccination rates and can contribute to improved health outcomes for these vulnerable children.

Burden of influenza disease in children under 2 years of age hospitalized between 2011 and 2020 in France

International audience; BackgroundAlthough influenza viruses cause significant morbidity and mortality worldwide, the impact of these infections on children in France and in other European countries has not been extensively characterized. The primary objective of the present study was to describe the burden of influenza disease on hospitalized children under 2 years of age in France, using data from the national hospital discharge summary database (Programme de Médicalisation des Systèmes d'Information, PMSI).MethodsIn a retrospective study of hospital admissions for influenza among children under the age of 2 in France, we extracted and analyzed hospital administrative data from the PMSI database (from January 1, 2011, to December 31, 2020).ResultsFrom 2011 to 2020, 28,507 children under the age of 2 were admitted to hospital with a primary or secondary diagnosis of influenza infection. The hospital admission rate was 205 per 100,000 for children under the age of 2, 276 per 100,000 for children under the age of 12 months, and 135 per 100,000 for children aged between 12 and 23 months. Children under 6 months of age were the most affected (45.4%). An underlying condition was identified for 9.4% of the children, and 2.2% of the children were admitted to the intensive care unit. The death rate was 0.12 per 100,000 for children under 2, 0.11 per 100,000 for children under 12 months, and 0.16 per 100,000 for children aged between 12 and 23 months.ConclusionsIn France, the burden of influenza disease is significant in children under the age of 2.

Host adaptive mutations in the 2009 H1N1 pandemic influenza A virus PA gene regulate translation efficiency of viral mRNAs via GRSF1

Avian species are the major natural reservoir from which pandemic influenza A viruses can be introduced to humans. Avian influenza A virus genes, including the three viral polymerase genes, PA, PB1 and PB2, require host-adaptive mutations to allow for viral replication and transmission in humans. Previously, PA from the 2009 pH1N1 viral polymerase was found to harbor host-adaptive mutations leading to enhanced viral polymerase activity. By quantifying translation and mRNA transcription, we found that the 2009 pH1N1 PA, and the associated host-adaptive mutations, led to greater translation efficiency. This was due to enhanced cytosolic accumulation of viral mRNA, which was dependent on the host RNA binding protein GRSF1. Mutations to the GRSF1 binding site in viral mRNA, as well as GRSF1 knockdown, reduced cytosolic accumulation and translation efficiency of viral mRNAs. This study identifies a previously unrecognized mechanism by which host-adaptive mutations in PA regulate viral replication and host adaptation. Importantly, these results provide greater insight into the host adaptation process of IAVs and reveal the importance of GRSF1 in the lifecycle of IAV. A comparison between avian and human influenza A virus polymerases suggests that mutations in the PA subunit from 2009 pH1N1 enhanced cytosolic accumulation and translation of viral mRNA, in a manner dependent on the RNA binding protein, GRSF1.

Effect of external cues on clock-driven protection from Influenza A infection

Influenza and other respiratory viral pathogens are leading causes of mortality and morbidity. We previously demonstrated that circadian rhythms confer temporal protection from influenza infection. Here, we investigated whether this protection requires rhythmic function after the initial infection by manipulating environmental cycles. We demonstrate that disrupting environmental lighting cues within a critical window of vulnerability abrogates time-of-day specific protection. This poor outcome is mediated by a dysregulated immune response, evidenced by the accumulation of inflammatory monocytes and CD8 (+) cells in the lungs and a transcriptomic profile indicative of an exaggerated immune response. Disruption of the light cycle does not affect outcomes in a clock mutant, indicating that it acts by compromising endogenous timekeeping. Importantly, rhythmic meal timing mitigates the adverse effects of disrupted light cycles, suggesting that external cycling cues, which act through different body clocks, can substitute for each other. Our findings highlight the crucial interplay between environmental factors and endogenous clocks in shaping influenza outcomes, offering significant translational potential for improving the care of critically ill patients with respiratory viral infections.

A Machine Learning Framework Based on Extreme Gradient Boosting to Predict the Occurrence and Development of Infectious Diseases in Laying Hen Farms, Taking H9N2 as an Example

SIMPLE SUMMARY: The H9N2 avian influenza virus has spread to the whole world and become one of the dominant subtype influenza viruses in chickens in China. H9N2 virus could not only result in great economic losses by reducing egg production but also serve as a gene vector to provide its gene segments to other emerging severe influenza A viruses to cause higher mortality and more serious consequences. Thus, developing models to predict H9N2 status should be given priority. Our main aim was to use the machine learning method (XGBoost classification algorithm) and regular production data (laying rate and mortality) to predict the occurrence and development of H9N2 in laying hen houses and evaluate their performance in disease prediction. Additionally, we assessed the working ability of the framework with different time frames to predict H9N2 status in advance within a 3-day period. We found that this framework could work well in prediction with high accuracy and sensitivity, and with more information introduced into the model, more “don’t care values” would be added into datasets to affect model performance by forming attribute noise. Besides, our study recommended efficient frameworks and models for H9N2 status prediction and provided practical potential uses in the livestock and poultry industry. ABSTRACT: The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months’ laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day − 1, day − 2, day − 3, day − 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable.

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).

Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance

H5 influenza is considered a potential pandemic threat. Recently, H5 viruses belonging to clade 2.3.4.4b have caused large outbreaks in avian and multiple non-human mammalian species. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic potential in humans, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera. However, prior experimental work has only measured how these phenotypes are affected by a handful of the >10,000 different possible amino-acid mutations to HA. Here we use pseudovirus deep mutational scanning to measure how all mutations to a 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind α2-6-linked sialic acids, and show that some viruses already carry mutations that stabilize HA. We also measure how all HA mutations affect neutralization by sera from mice and ferrets vaccinated against or infected with 2.3.4.4b H5 viruses. These antigenic maps enable rapid assessment of when new viral strains have acquired mutations that may create mismatches with candidate vaccine strains. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive measurements of the phenotypic effects of mutations that can inform real-time interpretation of viral variation observed during surveillance of H5 influenza.

Influenza virus uses mGluR2 as an endocytic receptor to enter cells

Influenza virus infection is initiated by the attachment of the viral haemagglutinin (HA) protein to sialic acid receptors on the host cell surface. Most virus particles enter cells through clathrin-mediated endocytosis (CME). However, it is unclear how viral binding signals are transmitted through the plasma membrane triggering CME. Here we found that metabotropic glutamate receptor subtype 2 (mGluR2) and potassium calcium-activated channel subfamily M alpha 1 (KCa1.1) are involved in the initiation and completion of CME of influenza virus using an siRNA screen approach. Influenza virus HA directly interacted with mGluR2 and used it as an endocytic receptor to initiate CME. mGluR2 interacted and activated KCa1.1, leading to polymerization of F-actin, maturation of clathrin-coated pits and completion of the CME of influenza virus. Importantly, mGluR2-knockout mice were significantly more resistant to different influenza subtypes than the wild type. Therefore, blocking HA and mGluR2 interaction could be a promising host-directed antiviral strategy. Influenza virus uses metabotropic glutamate receptor 2 (mGluR2) as an endocytic receptor to enter cells through clathrin-mediated endocytosis.

Acceptability of mandatory vaccination against influenza, measles, pertussis and varicella by workers in healthcare facilities: a national cross-sectional study, France, 2019

BACKGROUND: Vaccination of healthcare workers (HCW) aims to protect them and to reduce transmission to susceptible patients. Influenza, measles, pertussis, and varicella vaccinations are recommended but not mandatory for HCW in France. Insufficient vaccine coverage for these diseases in HCW has raised the question of introducing mandatory vaccination. We conducted a survey to estimate acceptability of mandatory vaccination for these four vaccines by HCW working in healthcare facilities (HCF) in France, and to identify associated determinants. METHODS: In 2019, we performed a cross-sectional survey of physicians, nurses, midwives and nursing assistants working in HCF in France using a randomised stratified three-stage sampling design (HCF type, ward category, HCW category). Data were collected in face-to-face interviews using a tablet computer. We investigated the possible determinants of acceptability of mandatory vaccination using univariate and multivariate Poisson regressions, and estimated prevalence ratios (PR). RESULTS: A total of 8594 HCW in 167 HCF were included. For measles, pertussis, and varicella, self-reported acceptability of mandatory vaccination (very or quite favourable) was 73.1% [CI95%: 70.9–75.1], 72.1% [69.8–74.3], and 57.5% [54.5–57.7], respectively. Acceptability varied according to i) HCW and ward category for these three vaccinations, ii) age group for measles and pertussis, and iii) sex for varicella. For mandatory influenza vaccination, acceptability was lower (42.7% [40.6–44.9]), and varied greatly between HCW categories (from 77.2% for physicians to 32.0% for nursing assistants). CONCLUSION: HCW acceptability of mandatory vaccination was high for measles, pertussis and varicella but not as high for influenza. Vaccination for COVID-19 is mandatory for HCW in France. Replication of this study after the end of the COVID-19 crisis would help assess whether the pandemic had an impact on their acceptability of mandatory vaccination, in particular for influenza. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13690-023-01069-4.

Can uptake of childhood influenza immunisation through schools and GP practices be increased through behaviourally-informed invitation letters and reminders: two pragmatic randomized controlled trials

Background The UK is rolling out a national childhood influenza immunisation programme for children, delivered through primary care and schools. Behaviourally-informed letters and reminders have been successful at increasing uptake of other public health interventions. Therefore, we investigated the effects of a behaviourally-informed letter on uptake of the vaccine at GP practices, and of a letter and a reminder (SMS/ email) on uptake at schools. Methods and results Study 1 was a cluster-randomised parallel trial of 21,786 two- and three-year olds in 250 GP practices, conducted during flu season (September to January inclusive) 2016/7. The intervention was a centrally-sent behaviourally-informed invitation letter, control was usual care. The proportion of two- and three-year olds in each practice who received a vaccination by 31st January 2017 was 23.4% in the control group compared to 37.1% in the intervention group (OR = 1.93; 95% CI = 1.82, 2.05, p  <  0.001). Study 2 was a 2 (behavioural letter vs standard letter) × 2 (reminder vs no reminder) factorial trial of 1108 primary schools which included 3010 school years 1–3. Letters were sent to parents from providers, and reminders sent to parents from the schools. In the standard-letter-no-reminder arm, an average of 61.6% of eligible children in each school year were vaccinated, compared to 61.9% in the behavioural-letter-no-reminder arm, 63.5% in the standard-letter-plus-reminder arm, and 62.9% in the behavioural-letter-plus reminder condition, F (3, 2990) = 2.68, p  = 0.046. In a multi-level model, with demographic variables as fixed effects, the proportion of eligible students in the school year who were vaccinated increased with the reminder, β  = 0.086 (0.041), p  <  0.036, but there was no effect of the letter nor any interaction effect. Conclusion Sending a behaviourally informed invitation letter can increase uptake of childhood influenza vaccines at the GP surgery compared to usual practice. A reminder SMS or email can increase uptake of the influenza vaccine in schools, but the effect size was minimal. Trial registration Study 1: Trial registration: ClinicalTrials.gov Identifier: NCT02921633. Study 2: Trial registration: ClinicalTrials.gov Identifier: NCT02883972.

A scoping review of the reasons for and approaches to non-uptake of pertussis and influenza vaccinations in pregnant women in the United Kingdom and Ireland

BACKGROUND: Pertussis and influenza cause significant morbidity and mortality in pregnancy and the neonatal period. Maternal vaccination in pregnancy would reduce harm, but low vaccine uptake is a concern. This scoping review aimed to understand the reasons for, and approaches, to non-uptake of pertussis and influenza vaccinations in pregnant women in the UK and Ireland. METHODS: The inclusion criteria of this scoping review consist of pregnant women who avail of pertussis and influenza vaccines in the UK and Ireland. MEDLINE, EMBASE, Web of Science and CINAHL databases were searched in June 2021 and updated in October 2022. Searches were limited to English language reports published after 2011. We followed the Joanna Briggs Institute guidance on scoping reviews. Data were extracted and charted. RESULTS: Five themes emerged from the literature. Acceptability, as well as organisational and awareness issues, were overarching themes regarding reasons for and approaches to non-uptake of the vaccines respectively. Other themes included healthcare professional factors, information interpretation and pregnancy-related factors. CONCLUSIONS: Women need clear, comprehensible information, ideally provided by their healthcare professionals, in a way that is meaningful and addresses their circumstances and risk perceptions. This research will serve as a base for future work that aims behaviour science interventions at the wider pregnant population as well as the target groups that have been identified in this review. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-023-06171-7.

Acceptability of mandatory vaccination against influenza, measles, pertussis and varicella by workers in healthcare facilities: a national cross-sectional study, France, 2019

Background Vaccination of healthcare workers (HCW) aims to protect them and to reduce transmission to susceptible patients. Influenza, measles, pertussis, and varicella vaccinations are recommended but not mandatory for HCW in France. Insufficient vaccine coverage for these diseases in HCW has raised the question of introducing mandatory vaccination. We conducted a survey to estimate acceptability of mandatory vaccination for these four vaccines by HCW working in healthcare facilities (HCF) in France, and to identify associated determinants. Methods In 2019, we performed a cross-sectional survey of physicians, nurses, midwives and nursing assistants working in HCF in France using a randomised stratified three-stage sampling design (HCF type, ward category, HCW category). Data were collected in face-to-face interviews using a tablet computer. We investigated the possible determinants of acceptability of mandatory vaccination using univariate and multivariate Poisson regressions, and estimated prevalence ratios (PR). Results A total of 8594 HCW in 167 HCF were included. For measles, pertussis, and varicella, self-reported acceptability of mandatory vaccination (very or quite favourable) was 73.1% [CI95%: 70.9–75.1], 72.1% [69.8–74.3], and 57.5% [54.5–57.7], respectively. Acceptability varied according to i) HCW and ward category for these three vaccinations, ii) age group for measles and pertussis, and iii) sex for varicella. For mandatory influenza vaccination, acceptability was lower (42.7% [40.6–44.9]), and varied greatly between HCW categories (from 77.2% for physicians to 32.0% for nursing assistants). Conclusion HCW acceptability of mandatory vaccination was high for measles, pertussis and varicella but not as high for influenza. Vaccination for COVID-19 is mandatory for HCW in France. Replication of this study after the end of the COVID-19 crisis would help assess whether the pandemic had an impact on their acceptability of mandatory vaccination, in particular for influenza.

Cangma Huadu granules, a new drug with great potential to treat coronavirus and influenza infections, exert its efficacy through anti-inflammatory and immune regulation

ETHNOPHARMACOLOGICAL RELEVANCE: Coronavirus and influenza virus infection seriously threaten human health. Cangma Huadu Granules (CMHD) is an in-hospital preparation composed of eight traditional Chinese medicines (TCM), which has been clinically used against COVID-19 in China and may be a promising candidate for the treatment of influenza. However, the role of its treatment urgently needs to be studied. Aim of the study: To evaluate the therapeutic effects of CMHD on pneumonia induced by coronavirus (HCoV-229E) and influenza A virus (H1N1/FM1) in mice and explore its mechanism of anti-infection. MATERIALS AND METHODS: Mice were infected with HCoV-229E or H1N1/FM1 virus through the nasal cavity. CMHD (12.1, 6.05 and 3.03 g/kg/d) or the positive control drugs were administered intragastrically. The lung index and histopathological changes were used to evaluate the therapeutic effect of CMHD. The expression of TNF-α, IL-1β, IL-6 and IL-4 in Serum and the proportion of CD4(+) and CD8(+) T lymphocytes in peripheral blood were detected to evaluate the anti-inflammatory and immune regulation effects of CMHD, respectively. Furthermore, the levels of p–NF–κBp65/ NF-κB p65, which was the key targets of the NF-κB pathway was analyzed. RESULTS: In HCoV-229E-induced pneumonia, the lung index was markedly reduced, and lung pathology was improved in mice that treated with CMHD (12.1, 6.05 g/kg/d). Meanwhile, the expression of TNF-α, IL-6 were obviously inhibited, but the expression of IL-4 was significantly increased in CMHD groups. Compared with the model group, CMHD could also markedly upregulate the level of CD4(+) and CD8(+). Furthermore, CMHD has a markedly effect on inhibit the expression of p–NF–κB p65/NF-κB p65 in the lung. In H1N1-induced pneumonia, the lung index of mice in the CMHD (12.1 g/kg/d) treatment group was lower than that in the model group, and less inflammatory infiltration could be seen in the lung pathological. Moreover, CMHD could also obviously decrease the expression of TNF-α, IL-1β, IL-6, but significantly increase the expression of IL-4. Except for that, CMHD could also markedly downregulate the level of CD4(+) and upregulate the level of CD8(+) compared with the model group. In addition, CMHD has a markedly effect on inhibit the expression of p–NF–κB p65/NF-κB p65 in the lung. CONCLUSION: CMHD can significantly combats viral infections caused by HCoV-229E and H1N1, and the mechanism may be related to its multiple functions of anti-inflammatory, immunity regulating and inhibiting NF-κB signal transduction pathway.

Exploring the immune-inflammatory mechanism of Maxing Shigan Decoction in treating influenza virus A-induced pneumonia based on an integrated strategy of single-cell transcriptomics and systems biology

Background Influenza is an acute respiratory infection caused by influenza virus. Maxing Shigan Decoction (MXSGD) is a commonly used traditional Chinese medicine prescription for the prevention and treatment of influenza. However, its mechanism remains unclear. Method The mice model of influenza A virus pneumonia was established by nasal inoculation. After 3 days of intervention, the lung index was calculated, and the pathological changes of lung tissue were detected by HE staining. Firstly, transcriptomics technology was used to analyze the differential genes and important pathways in mouse lung tissue regulated by MXSGD. Then, real-time fluorescent quantitative PCR (RT-PCR) was used to verify the changes in mRNA expression in lung tissues. Finally, intestinal microbiome and intestinal metabolomics were performed to explore the effect of MXSGD on gut microbiota. Results The lung inflammatory cell infiltration in the MXSGD group was significantly reduced ( p  < 0.05). The results of bioinformatics analysis for transcriptomics results show that these genes are mainly involved in inflammatory factors and inflammation-related signal pathways mediated inflammation biological modules, etc. Intestinal microbiome showed that the intestinal flora Actinobacteriota level and Desulfobacterota level increased in MXSGD group, while Planctomycetota in MXSGD group decreased. Metabolites were mainly involved in primary bile acid biosynthesis, thiamine metabolism, etc. This suggests that MXSGD has a microbial–gut–lung axis regulation effect on mice with influenza A virus pneumonia. Conclusion MXSGD may play an anti-inflammatory and immunoregulatory role by regulating intestinal microbiome and intestinal metabolic small molecules, and ultimately play a role in the treatment of influenza A virus pneumonia.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.

Interleukin-6 and granulocyte colony-stimulating factor as predictors of the prognosis of influenza-associated pneumonia

Background Pneumonia is a common complication of influenza and closely related to mortality in influenza patients. The present study examines cytokines as predictors of the prognosis of influenza-associated pneumonia. Methods This study included 101 inpatients with influenza (64 pneumonia and 37 non-pneumonia patients). 48 cytokines were detected in the serum samples of the patients and the clinical characteristics were analyzed. The correlation between them was analyzed to identify predictive biomarkers for the prognosis of influenza-associated pneumonia. Results Seventeen patients had poor prognosis and developed pneumonia. Among patients with influenza-associated pneumonia, the levels of 8 cytokines were significantly higher in those who had a poor prognosis: interleukin-6 (IL-6), interferon-γ (IFN-γ), granulocyte colony-stimulating factor (G-CSF), monocyte colony-stimulating factor (M-CSF), monocyte chemoattractant protein-1 (MCP-1), monocyte chemoattractant protein-3, Interleukin-2 receptor subunit alpha and Hepatocyte growth factor. Correlation analysis showed that the IL-6, G-CSF, M-CSF, IFN-γ, and MCP-1 levels had positive correlations with the severity of pneumonia. IL-6 and G-CSF showed a strong and positive correlation with poor prognosis in influenza-associated pneumonia patients. The combined effect of the two cytokines resulted in the largest area (0.926) under the receiver-operating characteristic curve. Conclusion The results indicate that the probability of poor prognosis in influenza patients with pneumonia is significantly increased. IL-6, G-CSF, M-CSF, IFN-γ, and MCP-1 levels had a positive correlation with the severity of pneumonia. Importantly, IL-6 and G-CSF were identified as significant predictors of the severity of influenza-associated pneumonia.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Background Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. Method In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. Results The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. Conclusion The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

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.

Mortality associated with different influenza subtypes in France between 2015-2019;Mortalité associée aux différents sous-types de grippe en France entre 2015-2019

Background: High levels of excess mortality during periods of active influenza circulation in France were observed in the years preceding the COVID-19 pandemic. Some of the factors that affect the rates of influenza associated mortality are influenza vaccination coverage levels in different population groups and practices for testing for influenza and related use of antiviral medications for various illness episodes (including pneumonia hospitalizations) during periods of active influenza circulation in the community. Methods: Data on sentinel ILI surveillance and sentinel virological surveillance in France were combined in a framework of a previously developed regression model to estimate the number of deaths associated with the circulation of the major influenza subtypes (A/H3N2, A/H1N1, B/Yamagata and B/Victoria) in France between 2015-2019. Results: Between week 3, 2015 and week 2, 2020, there were on average 15403 (95% CI (12591,18229)) annual influenza-associated deaths, of which 60.3% (49.9%,71.9%) were associated with influenza A/H3N2, and 29.5% (13.3%,45.5%) were associated with influenza B/Yamagata. During weeks when levels of ILI consultation in mainland France were above 50 per 100,000 persons, 7.9% (6.5%,9.4%) of all deaths in France were influenza-associated. Conclusions: High rates of influenza-associated mortality in France prior to the COVID-19 pandemic suggest that boosting influenza vaccination coverage in different population groups and testing for influenza in respiratory illness episodes (including pneumonia hospitalizations) during periods of active influenza (particularly influenza A/H3N2) circulation in combination with the use of antiviral medications is needed to mitigate the impact of influenza epidemics. Additionally, very high rates of influenza-associated mortality in individuals aged over 75y during certain influenza seasons in France suggest that additional efforts are needed to better protect those populations during influenza epidemics, both in nursing homes and in the general community.

Avian influenza overview June – September 2022

International audience; The 2021-2022 highly pathogenic avian influenza (HPAI) epidemic season is the largest HPAI epidemic so far observed in Europe, with a total of 2,467 outbreaks in poultry, 47.7 million birds culled in the affected establishments, 187 outbreaks in captive birds, and 3,573 HPAI virus detections in wild birds with an unprecedent geographical extent reaching from Svalbard islands to South Portugal and Ukraine, affecting 37 European countries. Between 11 June and 9 September 2022, 788 HPAI virus detections were reported in 16 European countries in poultry (56), captive (22) and wild birds (710). Several colony-breeding seabird species exhibited widespread and massive mortality from HPAI A(H5N1) virus along the northwest coast of Europe. This resulted in an unprecedentedly high level of HPAI virus detections in wild birds between June and August 2022 and represents an ongoing risk of infection for domestic birds. HPAI outbreaks were still observed in poultry from June to September with five-fold more infected premises than observed during the same period in 2021 and mostly distributed along the Atlantic coast. Response options to this new epidemiological situation include the definition and rapid implementation of suitable and sustainable HPAI mitigation strategies such as appropriate biosecurity measures and surveillance strategies for early detection in the different poultry production systems. The viruses currently circulating in Europe belong to clade 2.3.4.4b with seven genotypes, three of which identified for the first time during this time period, being detected during summer. HPAI A(H5) viruses were also detected in wild mammal species in Europe and North America and showed genetic markers of adaptation to replication in mammals. Since the last report, two A(H5N6), two A(H9N2) and one A(H10N3) human infections were reported in China. The risk of infection is assessed as low for the general population in the EU/EEA, and low to medium for occupationally exposed people.

25. Relative Effectiveness of Adjuvanted Trivalent Influenza Vaccine Compared to Egg-Based Trivalent High-Dose Influenza Vaccine among U.S. Older Adults during 2019-20 Influenza Season

BACKGROUND: According to the Centers for Disease Control and Prevention (CDC), during the 2019-20 U.S. influenza season, influenza resulted in almost 180,000 hospitalizations and over 13,000 deaths in adults ≥ 65 years. The current study evaluated the relative vaccine effectiveness (rVE) of adjuvanted trivalent influenza vaccine (aTIV) compared to high-dose trivalent influenza vaccine (TIV-HD), against influenza-related hospitalizations/emergency room (ER) visits, all-cause hospitalizations and hospitalizations/ER visits for cardio-respiratory disease (CRD) among adults ≥65 years for the 2019-20 influenza season. METHODS: A retrospective cohort analysis of older adults (≥ 65 years) was conducted using IQVIA’s professional fee, prescription claims and hospital charge master data in the U.S. Baseline characteristics included age, gender, payer type, geographic region, Charlson Comorbidity Index (CCI), comorbidities, indicators of frail health status, and pre-index hospitalization rates. To avoid any influenza outcome misclassification with COVID-19 infection, the study period ended March 7, 2020. Adjusted analyses were conducted through inverse probability of treatment weighting (IPTW) to control for selection bias. Poisson regression was used to estimate the adjusted pairwise rVE against influenza-related hospitalizations/ER visits, all-cause hospitalizations and any hospitalization/ER visit for CRD. An unrelated negative control outcome, urinary tract infection (UTI) hospitalization was included. RESULTS: During the 2019-20 influenza season, following IPTW, 798,987 recipients of aTIV and 1,655,979 recipients of TIV-HD were identified. After IPTW adjustment and Poisson regression, aTIV was statistically comparable to TIV-HD for prevention of influenza-related hospitalizations/ER visits (3.1%; 95% CI: -2.8%-8.6%) and all-cause hospitalizations (-0.7%; 95% CI: -1.6%-0.3%). Similar comparable outcomes were found for reduction of any hospitalization/ER visit for CRD (0.9%; 95% CI: 0.0%-1.7%). No treatment effect was identified for the negative control outcome. CONCLUSION: aTIV and TIV-HD demonstrated comparable reductions in influenza-related hospitalizations/ER visits, all-cause hospitalizations and hospitalizations/ER visits for CRD. DISCLOSURES: myron J. levin, MD, GSK group of companies (Employee, Research Grant or Support) Victoria Divino, PhD, Seqirus (Consultant) Stephen I. Pelton, MD, Seqirus (Consultant) Maarten Postma, Dr., Seqirus (Consultant) Drishti Shah, PhD, Seqirus (Consultant) Joaquin F. Mould-Quevedo, PhD, Seqirus (Employee) Mitchell DeKoven, PhD, Seqirus (Consultant)

Snapshot of COVID-19 superinfections in Marseille hospitals: where are the common pathogens?

Abstract Episodes of bacterial superinfections have been well identified for several respiratory viruses, notably influenza. In this retrospective study, we compared the frequency of superinfections in COVID-19 patients to those found in influenza-positive patients, and to controls without viral infection. We included 42 468 patients who had been diagnosed with COVID-19 and 266 261 subjects who had tested COVID-19 negative between 26 February 2020 and 1 May 2021. In addition, 4059 patients were included who had tested positive for the influenza virus between 1 January 2017 and 31 December 2019. Bacterial infections in COVID-19 patients were more frequently healthcare-associated, and acquired in ICUs, were associated with longer ICU stays, and occurred in older and male patients when compared to controls and to influenza patients ( P < 0.0001 for all). The most common pathogens proved to be less frequent in COVID-19 patients, including fewer cases of bacteraemia involving E. coli ( P < 0.0001) and Klebsiella pneumoniae ( P = 0.027) when compared to controls. In respiratory specimens Haemophilus influenzae ( P < 0.0001) was more frequent in controls, while Streptococcus pneumoniae ( P < 0.0001) was more frequent in influenza patients. Likewise, species associated with nosocomial transmission, such as Pseudomonas aeruginosa and Staphylococcus epidermidis , were more frequent among COVID-19 patients. Finally, we observed a high frequency of Enterococcus faecalis bacteraemia among COVID-19 patients, which were mainly ICU-acquired and associated with a longer timescale to acquisition.

IMPROVING THE VACCINATION RATES FOR IMMUNOSUPPRESSED PATIENTS WITH INFLAMMATORY BOWEL DISEASE

BACKGROUND: Immunosuppressive therapy (IS) has altered the course of Inflammatory Bowel Disease (IBD). IBD patients are at considerable risk of developing vaccine-preventable illness and are even more susceptible when on treatment. Many of these patients fail to receive appropriate vaccinations for influenza, pneumonia, hepatitis B, Shingles and recently COVID. Our aim was to develop a quality improvement intervention to increase recommended vaccinations in IBD patients on IS. METHODS: A retrospective chart analysis was completed at the Memphis Veteran Affairs Gastroenterology Practice. 55 patients were found to be on immunosuppressive therapy with a biologic and/or immunomodulator. Once identified, these patient’s vaccination records were reviewed to see if they were up to date on recommended vaccinations for influenza, pneumonia, hepatitis B, COVID, and Shingles. Patients who were not up to date on their vaccinations were called by a provider (Resident, Fellow, or Nurse Practitioner), and were offered a nurse visit to be given the appropriate vaccinations. After a 6-month intervention period, the data on the 55 patients was recollected and analyzed. RESULTS: Of the patients analyzed, 63% (n=35) had Crohn’s disease and 37% (n=20) had Ulcerative Colitis. The most common biologic medication the patients were on was adalimumab (n=24), and the most common immunomodulator was azathioprine (n=17). Prior to the intervention, 22% had received the shingles vaccine, 20% had received the COVID-19 vaccine, 78% had received the hepatitis B vaccine, 69% had received the flu vaccine, 62% had received the pneumococcal 23 vaccine, and 72% had received the pneumococcal 13 vaccine. After the intervention, 65% had received the shingles vaccine, 65% had received the COVID-19 vaccine, 87% had received the hepatitis B vaccine, 85% had received the flu vaccine, 78% had received the pneumococcal 23 vaccine, and 84% had received the pneumococcal 13 vaccine. CONCLUSION: Patients on immunosuppressive therapy remain vulnerable to vaccine-preventable illnesses such as Shingles, Pneumococcal Pneumonia, Influenza, Hepatitis B, and COVID-19. Our quality improvement intervention increased overall vaccination adherence. This project was a proof of concept and in the future, we hope to integrate a warning system into our practice to alert providers when these patients are due for their appropriate vaccinations. It is also a practice that can be adopted by other healthcare providers who treat patients with IS and biologics to improve vaccination uptake. [Image: see text] [Image: see text]

Mutual information networks reveal evolutionary relationships within the influenza A virus polymerase

The influenza A (IAV) RNA polymerase is an essential driver of IAV evolution. Mutations that the polymerase introduces into viral genome segments during replication are the ultimate source of genetic variation, including within the three subunits of the IAV polymerase (PB2, PB1, and PA). Evolutionary analysis of the IAV polymerase is complicated, because changes in mutation rate, replication speed, and drug resistance involve epistatic interactions among its subunits. In order to study the evolution of the human seasonal H3N2 polymerase since the 1968 pandemic, we identified pairwise evolutionary relationships among ∼7000 H3N2 polymerase sequences using mutual information (MI), which measures the information gained about the identity of one residue when a second residue is known. To account for uneven sampling of viral sequences over time, we developed a weighted MI metric (wMI) and demonstrate that wMI outperforms raw MI through simulations using a well-sampled SARS-CoV-2 dataset. We then constructed wMI networks of the H3N2 polymerase to extend the inherently pairwise wMI statistic to encompass relationships among larger groups of residues. We included HA in the wMI network to distinguish between functional wMI relationships within the polymerase and those potentially due to hitchhiking on antigenic changes in HA. The wMI networks reveal coevolutionary relationships among residues with roles in replication and encapsidation. Inclusion of HA highlighted polymerase-only subgraphs containing residues with roles in the enzymatic functions of the polymerase and host adaptability. This work provides insight into the factors that drive and constrain the rapid evolution of influenza viruses.

Exploring the diversity of culturable fungi on corpses for forensic applications

Fungi form part of saprotrophic communities that are responsible for the decomposition of carrion and corpses. While necrophagous insects are known in detail and can be therefore used for forensic investigation, information provided by fungi is rarely used in forensic investigations. By the present study, we further our understanding of fungal diversity on corpses in order to make fungi useful for forensic investigation. We sampled macroscopically visible microfungi on 36 corpses at the place of discovery or at the Institute of Legal Medicine in Frankfurt am Main. Forty-seven strains of fungi were isolated, cultivated, and identified based on morphological characteristics and molecular barcoding. Together with records of fungi on corpses found in literature, this information is compiled in a checklist which contains 306 records and context information on 104 species of fungi. By our investigation, 17 species of fungi have been detected for corpses for the first time. Information on localities and the decomposition stages of the corpses is compared to characteristics of fungal species as known by literature. Many fungal species use a broad spectrum of substrates in diverse habitats, but some indicate special circumstances concerning the habitat, humidity, and temperature. The degree of decomposition of the corpses significantly correlates negatively with the value of water activity for optimal growth of individual fungal species. The checklist is a first step for the documentation of the diversity of culturable fungi on corpses, but it is still very incomplete. More records of microfungi on corpses are required, and more detailed information on characteristics of individual fungal species is needed in order to interpret and use the occurrence of fungi for criminal investigation.

Environmental Fungi at the Climate-Fire Interface: Implications for Emerging Pathogenicity

Purpose of Review Climate change is reshaping fungal ecology globally, with increasing consequences on ecosystem function, agriculture, and human health. This review examines how climate-driven environmental change and wildfire disturbance influence fungal community structure, adaptation, and disease risk. Recent Findings Rising temperatures, altered precipitation, soil acidification, aridification, and increased fire frequency are driving shifts in fungal distributions and their functional traits, favoring stress-tolerant, opportunistic, and pathogenic taxa. Wildfires restructure soil mycobiomes, enrich pyrophilic and saprotrophic fungi, and enhance spore dispersal. Pathogens such as Coccidioides , Aspergillus , and Fusarium are increasingly associated with post-fire environments and elevated exposure risk. Concurrently, agricultural antifungal use and climate-driven resource stress may accelerate antifungal resistance and host susceptibility. Summary Climate change and wildfire act as coupled selective forces shaping fungal adaptation and disease emergence. Integrated environmental surveillance and public health strategies are urgently needed to mitigate future fungal threats.

Immunomodulatory and Antiviral Properties of Mushroom-Derived Compounds

Mushrooms, long recognized for their nutritional and medicinal value, are emerging as a critical resource in the development of next-generation immunomodulators and antiviral agents. This chapter synthesizes recent advances in understanding the bioactive potential of fungal metabolites, particularly β-glucans, terpenoids, and glycoproteins, which exhibit dual immunoenhancing and direct antiviral properties. These compounds orchestrate immune responses by activating macrophages, modulating cytokine networks, and enhancing natural killer (NK) cell cytotoxicity, while concurrently disrupting viral replication through mechanisms such as envelope protein inhibition and viral protease suppression. Notably, their efficacy against RNA viruses, including influenza, SARS-CoV-2, and HIV, highlights their relevance in combating pandemic threats and drug-resistant pathogens. Breakthroughs in biotechnological and nanotechnological approaches are revolutionizing the application of these compounds. Advances in nanoencapsulation (such as exosome-based delivery systems) address bioavailability challenges, enabling targeted immune cell engagement and sustained release of hydrophobic metabolites like ergosterol derivatives. Meanwhile, CRISPR-based metabolic engineering and fungal heterologous expression systems are overcoming barriers to the sustainable production of rare bioactives. This convergence of disciplines is accelerating the translation of mushroom-derived molecules into functional foods, adjuvants, and standalone therapeutics. Despite progress, challenges persist in standardization, pharmacokinetic optimization, and clinical validation. Future research must prioritize mechanistic elucidation of host–pathogen–bioactive interactions, coupled with robust clinical trials to establish efficacy and safety. By integrating mycological biodiversity with modern biotechnological tools, this field holds promise for delivering scalable, eco-conscious solutions to global health crises, bridging traditional medicine with twenty-first-century innovation.

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.

Microbial and transcriptional response of Acropora valida and Turbinaria peltata to Vibrio coralliilyticus challenge: insights into corals disease resistance

Background Coral diseases are significant drivers of global coral reef degradation, with pathogens dominated by Vibrio coralliilyticus playing a prominent role in the development of coral diseases. Coral phenotype, symbiotic microbial communities, and host transcriptional regulation have been well-established as factors involved in determining coral disease resistance, but the underlying mechanisms remain incompletely understood. Methods This study employs high-throughput sequencing to analyse the symbiotic microbial and transcriptional response of the hosts in order to evaluate the disease resistance of Acropora valida and Turbinaria peltata exposed to Vibrio coralliilyticus . Results A. valida exhibited pronounced bleaching and tissue loss within 7 h of pathogen infection, whereas T. peltata showed no signs of disease throughout the experiment. Microbial diversity analyses revealed that T. peltata had a more flexible microbial community and a higher relative abundance of potential beneficial bacteria compared to A. valida . Although Vibrio inoculation resulted in a more significant decrease in the Symbiodiniaceae density of A. valida compared to that of T. peltata , it did not lead to recombination of the coral host and Symbiodiniaceae in either coral species. RNA-seq analysis revealed that the interspecific differences in the transcriptional regulation of hosts after Vibrio inoculation. Differentially expressed genes in A. valida were mainly enriched in the pathways associated with energy supply and immune response, such as G protein-coupled receptor signaling, toll-like receptor signaling, regulation of TOR signaling, while these genes in T. peltata were mainly involved in the pathway related to immune homeostasis and ion transport, such as JAK-STAT signaling pathway and regulation of ion transport. Conclusions Pathogenic challenges elicit different microbial and transcriptional shifts across coral species. This study offers novel insights into molecular mechanisms of coral resistance to disease.

Characterization of Bipolaris bicolor germination: effects of a physical factor on fungal adaptability

Studies on physiological responses to stimuli from physical factors are essential for understanding the dynamics of the microorganisms and higly important for the management of plant diseases. Besides, the development of an epidemiological model for pathogen populations requires studying their physiological responses to physical stimuli. The objective of this study was to evaluate the germination dynamics of spores from six isolates of Bipolaris bicolor under effects of light at 25 °C. Suspensions of 1.6 × 10^5 conidia mL^− 1 from the B. bicolor isolates were inoculated onto Petri dishes containing agar-water culture medium and incubated in a BOD chamber under two physical conditions: (a) constant darkness and (b) constant light for five hours. The study was conducted in a completely randomized design, with a 6 × 2 factorial arrangement (six B. bicolor isolates and two physical conditions) and five replications. The length of the germ tube was measured hourly. The constant darkness resulted in higher mean germ tube growth for the pathogen; however, differences in the final germination percentage were found among the isolates. The isolate F-24-02 exhibited the highest germination adaptability to constant darkness, presenting the longest germ tube length.

Fungi as Food

Fungi are essential for a wide variety of food products and processes. They have a major role in the production of many fermented foodstuffs, may be eaten directly as fruit bodies and mycelium, and are used to produce food additives. They contribute to food production worldwide, even in cultures which do not typically consume mushrooms, because yeasts and edible moulds are utilised in a great variety of fermentation processes. Most fungal cultures used in food production at industrial scale show evidence of selection and domestication. However, other strains may still be obtained from the wild, either through incidental colonisation of a fermentation substrate as a result of a traditional preparation method or due to the inability to reliably cultivate a given organism, necessitating collection from its native habitat. This review provides an overview of the uses of fungi, both yeasts and filamentous fungi, in food production with a focus on research findings over the past decade. This includes a review of the production of foodstuffs through the fermentation of a wide variety of substrates, particularly dairy, but also including meat and plant matter. In addition, the use of fungi in the production of secreted enzymes and food additives is considered. Finally, the cultivation and harvesting of fungal fruiting bodies and mycoprotein are reviewed. The review aims to capture the breadth of the field by covering examples from every inhabited continent, including reference to fungal food systems which have historically been under-studied.

Abundance, diversity and taphonomy of dung-related palynomorphs from modern animal enclosures and faecal samples

Surface sediment samples, as well as dung samples from a range of modern sites under various husbandry practices, have been investigated to determine the taxonomic diversity and abundance of spores of coprophilous fungi and other non-pollen palynomorphs. The results show a remarkable variation in both, the amount of microremains and the composition of the assemblages, ranging from zero counts to spore-rich samples and from a poor to a wide variety of taxa. Based on the different conditions recorded on the sites, inferences are made on the factors that may have played a major role in determining a high or low quantity of microremains (such as animal density and the frequency of use of the sites) and their implications for the study of ancient pastoral contexts. Eggs of intestinal parasites, along with aquatic indicators, were also quantified, and some possibly dung-related non-pollen palynomorphs are described.

Effects of antibiotic cocktail on the fecal microbiota and their potential correlation of local immune response

Background The guts of mammals are home to trillions of microbes, forming a complex and dynamic ecosystem. Gut microbiota is an important biological barrier for maintaining immune homeostasis. Recently, the use of antibiotics to clear gut microbiota has gained popularity as a low cost and easy-to-use alternative to germ-free animals. However, the effect of the duration of the antibiotic cocktail on the gut microbiome is unclear, and more importantly, the effect of dramatic changes in the gut microbiota on intestinal tissue morphology and local immune response is rarely reported. Results We observed a significant reduction in fecal microbiota species and abundance after 1 week of exposure to an antibiotic cocktail, gavage twice daily by intragastric administration. In terms of composition, Bacteroidetes and Firmicutes were replaced by Proteobacteria . Extending antibiotic exposure to 2–3 weeks did not significantly improve the overall efficiency of microbiotal consumption. No significant histomorphological changes were observed in the first 2 weeks of antibiotic cocktail exposure, but the expression of inflammatory mediators in intestinal tissue was increased after 3 weeks of antibiotic cocktail exposure. Mendelian randomization analysis showed that Actinobacteria had a significant causal association with the increase of IL-1β (OR = 1.65, 95% CI = 1.23 to 2.21, P  = 0.007) and TNF-α (OR = 1.81, 95% CI = 1.26 to 2.61, P  = 0.001). Conclusions Our data suggest that treatment with an antibiotic cocktail lasting 1 week is sufficient to induce a significant reduction in gut microbes. 3 weeks of antibiotic exposure can lead to the colonization of persistant microbiota and cause changes in intestinal tissue and local immune responses,

Fungi Matter: Aphanoascella galapagosensis Associated with Carapace Lesions in Free-Living Galapagos Tortoises

Galapagos giant tortoises are among the most iconic reptile species on earth; however, an increase in anthropogenic activities has created new challenges for their health and well-being. The presence of whitish lesions on the carapace of Galapagos tortoises ( Chelonoidis spp.) was previously described, potentially due to fungal growths, but its etiology remained unexplored. Aiming to close this gap, we analyzed carapace scrapes from six different species of free-living giant tortoises of Santa Cruz, Isabela, San Cristobal, and Española islands. In total, we tested 145 fresh and frozen carapace scrapes from 145 individuals with carapace whitish lesions (W-L, n  = 80) and without them (W-O, n  = 65), using panfungal endpoint PCRs for the ITS and D1-D2 regions. Aphanoascella galapagosensis was detected in W-L samples from all tortoise species and in none of the W-O samples. Four A. galapagosensis nucleotide sequence types (ST) obtained by using the D1-D2 protocol were identified in these tortoises; ST1 was detected on Santa Cruz, Isabela, and Española Islands whereas ST2 and ST3 were only detected on Isabela, and ST4 on San Cristobal. Neodevriesia spp. and Elsinoe spp. were the most common microorganisms found in W-O samples. These results suggest that A. galapagosensis is the etiological agent of whitish lesions in tortoise carapace contributing to baseline data on carapace fungi in giant Galapagos tortoises. Further research is needed to assess the prevalence and potential pathogenicity of A. galapagosensis and its impact for the conservation of these endangered species.

Rhizosphere microbial community construction during the latitudinal spread of the invader Chromolaena odorata

The colonization of alien plants in new habitats is typically facilitated by microorganisms present in the soil environment. However, the diversity and structure of the archaeal, bacterial, and fungal communities in the latitudinal spread of alien plants remain unclear. In this study, the rhizosphere and bulk soil of Chromolaena odorata were collected from five latitudes in Pu’ er city, Yunnan Province, followed by amplicon sequencing of the soil archaeal, bacterial, and fungal communities. Alpha and beta diversity results revealed that the richness indices and the structures of the archaeal, bacterial, and fungal communities significantly differed along the latitudinal gradient. Additionally, significant differences were observed in the bacterial Shannon index, as well as in the structures of the bacterial and fungal communities between the rhizosphere and bulk soils. Due to the small spatial scale, trends of latitudinal variation in the archaeal, bacterial, and fungal communities were not pronounced. Total potassium, total phosphorus, available nitrogen, available potassium and total nitrogen were the important driving factors affecting the soil microbial community structure. Compared with those in bulk soil, co-occurrence networks in rhizosphere microbial networks presented lower complexity but greater modularity and positive connections. Among the main functional fungi, arbuscular mycorrhizae and soil saprotrophs were more abundant in the bulk soil. The significant differences in the soil microbes between rhizosphere and bulk soils further underscore the impact of C. odorata invasion on soil environments. The significant differences in the soil microbiota along latitudinal gradients, along with specific driving factors, demonstrate distinct nutrient preferences among archaea, bacteria, and fungi and indicate complex microbial responses to soil nutrient elements following the invasion of C. odorata .

Artificial Intelligence–Integrated Biosensors for Fungal Infections: Bridging Mycology and Smart Diagnostics

Fungal infections represent a major but often underestimated global health challenge, causing up to 1.7 million deaths annually. Despite advances, conventional tools such as culture, microscopy, and serology remain slow, costly, and laboratory-dependent, limiting their suitability for real-time clinical use. Biosensor technologies have therefore emerged as promising alternatives, enabling rapid, sensitive, and portable detection of fungal pathogens and biomarkers. Recent research has further explored the integration of artificial intelligence (AI) with biosensors to enhance signal interpretation, reduce noise, and achieve real-time, high-accuracy analysis. AI-integrated biosensors combine biochemical specificity with computational intelligence, forming autonomous diagnostic systems capable of continuous monitoring, predictive analytics, and adaptive calibration. This narrative review summarizes advances in fungal biosensing and proposes frameworks for next-generation smart biosensors capable of real-time species identification and antifungal resistance prediction. The review also highlights technical, data, and regulatory barriers, including sensor stability, dataset scarcity, and model interpretability, that must be addressed to translate these technologies into clinical practice. Overcoming these limitations through interdisciplinary collaboration, standardized validation, and digital integration could start a new era of “smart mycology”, where AI-driven biosensors enable early detection, precision treatment, and decentralized fungal disease management.

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.

Characterization of nuvita biosearch center (NBC) isolated lactic acid bacteria strains from human origin and determination of growth kinetic profiles of selected cultures under bioreactor

Backgorund In recent years, there has been an increasing interest in the field of research into the isolation and characterization of probiotics in the prevention of diseases and the need to maintain the continuity of healthy microbiota. Therefore, the aim of this study is to isolate and identify bacteria found in maternal colostrum, breast milk, adult and infant feces, analyze possible probiotic potential, and reveal the developmental kinetics of selected strains. Results We isolated 40 bacterial species from 4 different sources and identified 19 bacteria in the form of bacilli through molecular biology and carried out studies with 11 of them. Five of the selected strains showed the better results considering bile salt resistance and ability to survive at different pH and antimicrobial effect. When the adhesion capacity in cell culture is examined, the better 2 strains are; Lactobacillus pontis ZZ6780 and Lactobacillus reuteri NBC2680 were selected and the growth kinetics of these strains were demonstrated at the 3 L bioreactor scale. Finally, the growth kinetics of selected strains were determined and the maximum specific growth rate of selected Lactobacillus pontis ZZ6780 and Lactobacillus reuteri NBC2680 was calculated as 0.412 h^− 1 and 0.481 h^− 1, respectively. In addition, the dry cell matter amounts were found to be and 4.45 g/L and 5.23 g/L, respectively. Conclusion This study established the groundwork for the selection of safety probiotics for the development and application of LAB. It is thought that the two strains obtained as a result of this study can be considered as potential probiotic strains in the food, pharmaceutical and cosmetic industries.

Microbial carbohydrate active enzyme (CAZyme) genes and diversity from Menagesha Suba natural forest soils of Ethiopia as revealed by shotgun metagenomic sequencing

Background The global over-reliance on non-renewable fossil fuels has led to the emission of greenhouse gases, creating a critical global environmental challenge. There is an urgent need for alternative solutions like biofuels. Advanced biofuel is a renewable sustainable energy generated from lignocellulosic plant materials, which can significantly contribute to mitigating CO_2 emissions. Microbial Carbohydrate Active Enzymes (CAZymes) are the most crucial enzymes for the generation of sustainable biofuel energy. The present study designed shotgun metagenomics approaches to assemble, predict, and annotate, aiming to gain an insight into the taxonomic diversity, annotate CAZymes, and identify carbohydrate hydrolyzing CAZymes from microbiomes in Menagesha suba forest soil for the first time. Results The microbial diversity based on small subunit (SSU) rRNA analysis revealed the dominance of the bacterial domain representing 81.82% and 92.31% in the studied samples. Furthermore, the phylum composition result indicated the dominance of the phyla Proteobacteria (23.08%, 27.27%), Actinobacteria (11.36%, 20.51%), and Acidobacteria (10.26%, 15.91%). The study also identified unassigned bacteria which might have a unique potential for biopolymer hydrolysis. The metagenomic study revealed that 100,244 and 65,356 genes were predicted from the two distinct samples. A total number of 1806 CAZyme genes were identified, among annotated CAZymes, 758 had a known enzyme assigned to CAZymes. Glycoside hydrolases (GHs) CAZyme family contained most of the CAZyme genes with known enzymes such as β-glucosidase, endo-β-1,4-mannanase, exo-β-1,4-glucanase, α-L-arabinofuranosidase and oligoxyloglucan reducing end-specific cellobiohydrolase. On the other hand, 1048 of the identified CAZyme genes were putative CAZyme genes with unknown enzymatical activity and the majority of which belong to the GHs family. Conclusions In general, the identified putative CAZymes genes open up an opportunity for the discovery of new enzymes responsible for hydrolyzing biopolymers utilized for biofuel energy generation. This finding is used as a first-hand piece of evidence to serve as a benchmark for further and comprehensive studies to unveil novel classes of bio-economically valuable genes and their encoded products.

Unearthing Lactococcus lactis and Scheffersomyeces symbionts from edible wood-boring beetle larvae as a bio-resource for industrial applications

Background Gut microbiota have several advantages in influencing the host nutrition, metabolism, immunity and growth. However, the understanding of the gut microbiota in key edible wood-boring beetle larvae remain largely undefined. In the present study, the characteristics of the gut microbiota of two edible wood-boring species ( Titocerus jaspideus and Passalus punctiger ) from two indigenous forested areas were investigated. Results Over 50% of Amplicon Sequence Variants (ASVs) constituted of Firmicutes in T. jaspideus . The dominant phyla in both beetle species were Bacteroidota (4.20–19.79%) and Proteobacteria (15.10–23.90%). Lactococcus lactis was the most abundant and core prokaryote in the guts of T. jaspideus . The fungi identified in the gut of both insects belong to the phylum Obazoa (66%) and Ascomycota (> 15%). Scheffersomyeces sp. was the core eukaryote recorded. The diversity of gut microbiota in both insect species did not vary significantly. Most of the prokaryotic genes expressed were predominantly associated with biosynthesis and metabolism. Conclusion Our findings demonstrated that Lactococcus lactis and Scheffersomyeces are core gut microbes of wood boring beetle larvae with desirable probiotic properties and promising use in food product fermentation for improved growth performance, gut barrier health, intestinal flora balance and immune protection for human and animals. Further studies to highlight the latest medical-based applications of L. lactis as live-delivery vector for the administration of therapeutics against both communicable and non-communicable diseases are warranted.

Emergence of heteroresistance to carbapenems in Gram-negative clinical isolates from two Egyptian hospitals

Background Antimicrobial resistance is a global concern, linking bacterial genotype and phenotype. However, variability in antibiotic susceptibility within bacterial populations can lead to misclassification. Heteroresistance exemplifies this, where isolates have subpopulations less susceptible than the main population. This study explores heteroresistance in Gram-negative bacteria, distinguishing between carbapenem-sensitive isolates and stable heteroresistant isolates (SHIs). Methods A total of 151 Gram-negative clinical isolates including Klebsiella pneumoniae , Pseudomonas aeruginosa , Escherichia coli , Acinetobacter baumannii and Proteus mirabilis from various sources were included. Heteroresistant isolates and their stability were detected by disc-diffusion technique while genotypic analysis was carried out by PCR and efflux activity was assessed by ethidium bromide (EtBr)-agar cartwheel method. Results A total of 51 heteroresistant subpopulations were detected, producing 16 SHIs upon stability-detection. Amplified resistance genes and EtBr-agar cartwheel method showed a significant difference between resistant subpopulations and their corresponding-sensitive main populations. Conclusion Genotypic analysis confirmed that genetic mutation can lead to resistance development although the main populations were sensitive, thereby leading to treatment failure. This is a neglected issue which should be highly considered for better treatment outcomes.

Prevalence of onychomycosis among psoriasis patients: a clinico-mycological and dermoscopic comparative cross sectional study

Onychomycosis, a nail infection caused by dermatophytes, yeast, and molds makes up roughly half of all onychopathies and is the most prevalent nail condition in the world. Clinically, nail psoriasis and onychomycosis can frequently be difficult to distinguish from one another. To assess the prevalence of onychomycosis in patients with psoriasis. Fifty patients with psoriasis associated with nail disease were included in this study. After taking clinical history, nail samples were gathered for dermoscopic inspection, culture, direct microscopy with 20% KOH solution, and nail clipping with PAS stain. Of the 50 patients recruited, 43 were males and 7 were females, with mean age 6–71 years (mean ± SD 44.06 ± 16.2). Eleven patients (22%) tested positive for onychomycosis. Dermatophytes were isolated from 2% of patients, yeast from 14% of patients, and non-dermatophytic mold from 38% of patients. Histopathological results revealed fungal hyphae and spores in 18% of patients. The most prevalent dermoscopic sign in psoriatic patients with onychomycosis was spikes (81.8%) with statistical significance (P-value < 0.001), while nail pitting was the most prevalent dermoscopic feature in nail psoriasis. This study lays the way for an accurate diagnosis of nail lesions by highlighting the significance of cooperation between mycology, histology, and dermoscopy in the diagnosis of onychomycosis in patients with nail psoriasis.

Dry gel spinning of fungal hydrogels for the development of renewable yarns from food waste

Background Renewable materials made using environmentally friendly processes are in high demand as a solution to reduce the pollution created by the fashion industry. In recent years, there has been a growing trend in research on renewable materials focused on bio-based materials derived from fungi. Results Recently, fungal cell wall material of a chitosan producing fungus has been wet spun to monofilaments. This paper presents a modification for the fungal monofilament spinning process, by the development of a benign method, dry gel spinning, to produce continuous monofilaments and twisted multifilament yarns, from fungal cell wall, that can be used in textile applications. The fungal biomass of Rhizopus delemar , grown using bread waste as a substrate, was subjected to alkali treatment with a dilute sodium hydroxide solution to isolate alkali-insoluble material (AIM), which mainly consists of the fungal cell wall. The treatment of AIM with dilute lactic acid resulted in hydrogel formation. The morphology of the hydrogels was pH dependent, and they exhibited shear thinning viscoelastic behavior. Dry gel spinning of the fungal hydrogels was first conducted using a simple lab-scale syringe pump to inject the hydrogels through a needle to form a monofilament, which was directly placed on a rotating receiver and left to dry at room temperature. The resulting monofilament was used to make twisted multifilament yarns. The process was then improved by incorporating a heated chamber for the quicker drying of the monofilaments (at 30⁰C). Finally, the spinning process was scaled up using a twin-screw microcompounder instead of the syringe pump. The monofilaments were several meters long and reached a tensile strength of 63 MPa with a % elongation at break of 14. When spinning was performed in the heated chamber, the tensile strength increased to 80 MPa and further increased to 103 MPa when a micro-compounder was used for spinning. Conclusion The developed dry gel spinning method shows promising results in scalability and demonstrates the potential for renewable material production using fungi. This novel approach produces materials with mechanical properties comparable to those of conventional textile fibers.

“But how true that is, I do not know”: the influence of written sources on the medicinal use of fungi across the western borderlands of the former Soviet Union

Fungi have been used for medicinal purposes for many centuries. This study, based on 35 historical written sources and 581 in-depth semi-structured interviews from eight countries in the western borderlands of the former Soviet Union, investigates the medicinal use of fungi by local communities. We compared the taxa and uses obtained from fieldwork and historical sources with works that advocated fungi use within Soviet herbals, representing the centralised medical system. During fieldwork, we identified eight locally used fungi and one lichen. The highest numbers of medicinal uses were documented in Russia, Estonia and Ukraine. Studies published before the Soviet era listed 21 fungal taxa and one lichen species used in the study region. However, only six of these taxa were mentioned as used by people in our field studies ( Amanita muscaria , Boletus edulis , Lycoperdon , Morchella , Phallus impudicus and Cetraria islandica ). Notably, these same six taxa were consistently endorsed in Soviet herbals. Of the remaining three taxa recorded in the fieldwork, none were mentioned in historical written sources. However, they were promoted either in Soviet herbals ( Inonotus obliquus , Kombucha ) or later popular publications ( Cantharellus cibarius ). This highlights the significant influence of written sources on the use of fungi for medicinal purposes within the studied local communities.

The Asexual Morph of Seriascoma acutisporum (Occultibambusaceae, Pleosporales)

Abstract An investigation of freshwater fungi in Thailand resulted in the collection and isolation of a coelomycetous Seriascoma from submerged wood. The combined ITS and LSU sequence data placed the taxon within the sexual morphic Seriascoma acutisporum strains, ZHKUCC 22-0273 and ZHKUCC 22-0274, with high bootstrap support and confirmed the asexual morphic stage of the taxon. Seriascoma acutisporum MFLUCC 24-0091 shares the generic characteristics of having an immersed, eustromatic conidiomata and phialidic, cylindrical to ampulliform, hyaline, aseptate conidiogenous cells bearing hyaline conidia, but with smaller conidiomata and locules compared to other coelomycetous Seriascoma species.

Fungus in the Fur: An Overview of Fungal Infections in Cats, Dogs, and Exotic Small Mammals

Fungal infections occur in a wide variety of mammals including cats, dogs, and exotic small mammals. These infections are generally categorized as superficial/cutaneous, subcutaneous, and systemic. While most reported cases involve cats and dogs, fungal infections have also been documented in various exotic small mammal species. Although microbiological diagnostic approaches are similar across patient species, clinical signs and treatment strategies can vary significantly. Managing these infections in veterinary medicine presents unique challenges, particularly in exotic small mammals, due to species-specific differences in pathophysiology, treatment options, and husbandry considerations. In this chapter, we discuss (1) superficial/cutaneous, (2) subcutaneous, (3) systemic fungal infections in cats, dogs, and exotic small mammals, and (4) the challenges with managing these veterinary fungal infections.

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.

Veterinary Mycology: Challenges and Applications

Veterinary mycology is an important field of veterinary science that focuses on the identification, diagnosis, and treatment of infectious and zoonotic fungal diseases that pose risks to both animal and public health. The emergence of new and resistant fungal pathogens is influenced by human-animal-environmental interactions. In Southeast Asia, the environmental conditions with warm, humid climate, dense biodiversity, urbanization, and habitat disruption can influence the growth and transmission of these novel fungal pathogens. This chapter highlights the importance of fungal pathogens, zoonotic transmission, and the impact of environmental factors and climate change in the transmission and evolution of fungal diseases. It describes the growing concerns and challenges of antifungal resistance and the increasing cases of fungal diseases, especially those with zoonotic potential. In response to these challenges, this chapter reviews recent and current applications of advanced diagnostic techniques used for the timely identification of fungal pathogens, improving the accuracy and timeliness of disease reporting, treatment, and control. Development of antifungal drugs is also addressed in this chapter with more emphasis on the importance of a collaborative response, that is, a One Health approach involving veterinary, medical, and environmental health to foster more effective surveillance systems and intervention strategies. Moreover, this chapter explores the future outlook of interdisciplinary collaboration when it comes to fungal disease monitoring, antifungal drug development, and sustainable research funding to mitigate the growing threat of fungal diseases and strengthen animal, human, and environmental health.

Self-Leadership and Service

How can the AI-enabled attention economy be resisted so that our capacities for reflective self-direction are sustained? Grounding itself in Aristotle’s moral philosophy and contemporary neuroscience, this chapter explores how to develop the dispositions, habits, and skills needed to safeguard human agency. These dispositions, habits, and skills, which enable both self-leadership and service, can be cultivated employing the same mechanisms of behavior design that are deployed in the attention economy to hijack attention and erode agency.

Characterization of Mice Carrying a Neurodevelopmental Disease-Associated GluN2B(L825V) Variant

N-Methyl-d-aspartate receptors (NMDARs), encoded by GRIN genes, are ionotropic glutamate receptors playing a critical role in synaptic transmission, plasticity, and synapse development. Genome sequence analyses have identified variants in GRIN genes in patients with neurodevelopmental disorders, but the underlying disease mechanisms are not well understood. Here, we have created and evaluated a transgenic mouse line carrying a missense variant Grin2b(L825V), corresponding to a de novo GRIN2B variant encoding GluN2B(L825V) found in a patient with intellectual disability (ID) and autism spectrum disorder (ASD). We used HEK293T cells expressing recombinant receptors and primary hippocampal neurons prepared from heterozygous Grin2b(L825V/+) (L825V/+) and wild-type (WT) Grin2b(+/+) (+/+) male and female mice to assess the functional impact of the variant. Whole-cell NMDAR currents were reduced in neurons from L825V/+ compared with +/+ mice. The peak amplitude of NMDAR-mediated evoked excitatory postsynaptic currents (NMDAR-eEPSCs) was unchanged, but NMDAR-eEPSCs in L825V/+ neurons had faster deactivation compared with +/+ neurons and were less sensitive to a GluN2B-selective antagonist ifenprodil. Together, these results suggest a decreased functional contribution of GluN2B subunits to synaptic NMDAR currents in hippocampal neurons from L825V/+ mice. The analysis of the GluN2B(L825V) subunit surface expression and synaptic localization revealed no differences compared with WT GluN2B. Behavioral testing of mice of both sexes demonstrated hypoactivity, anxiety, and impaired sensorimotor gating in the L825V/+ strain, particularly affecting males, as well as cognitive symptoms. The heterozygous L825V/+ mouse offers a clinically relevant model of GRIN2B-related ID/ASD, and our results suggest synaptic-level functional changes that may contribute to neurodevelopmental pathology.

Non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models

We employ Reinforcement Learning (RL) models to unravel the mechanisms behind the learning behavior of two macaque monkeys engaged in a free-moving multi-target reaching task. The study was conducted using computer simulations reflecting the animal’s learning conditions, and compared with the actual arm movements recorded on two macaque monkeys on a Kinarm apparatus. Our paper thus provides important insights for the design of motor control learning systems, combining end-effector control design with the learning of motor chunks associations. Our research is of interest for the modeling and understanding of natural motor learning systems, but also heads toward the design of more “brain-inspired” adaptive robotic manipulators.

Neuroscience, Sociology, and Anthropology

Psychology and neuroscience are closely related fields that complement each other in many ways, including understanding the brain. Neuroscience studies the brain and nervous system, while psychology focuses on mental processes. By combining the two fields, researchers can better understand how the brain creates the mind. Neuroscience can help psychologists make more informed decisions about mental health treatments and interventions. For example, neurotherapy uses brain maps to identify physiological reasons for psychological feelings, and then retrain the brain. Neuroscience can help psychologists understand psychological concepts like memory and emotion. For example, the hippocampus is a key region in the brain that helps encode short-term memories into long-term memories. Neuropsychology studies how the brain and nervous system influence behaviour and cognition. Neuropsychology uses experimental and clinical research, as well as neuroimaging and standardised tests.

A Novel Real-time Phase Prediction Network in EEG Rhythm

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-024-01321-z.

Dissociable Representations of Decision Variables within Subdivisions of the Macaque Orbital and Ventrolateral Frontal Cortex

The ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subdivisions of VFC in male macaque monkeys performing a two-choice probabilistic task for different fruit juice outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral Area 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within Area 12o, in contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal Area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subdivisions, while the properties of the reward, such as its flavor, were more strongly represented in Areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

Medial Prefrontal Cortex Stimulation Reduces Retrieval-Induced Forgetting via Fronto-parietal Beta Desynchronization

The act of recalling memories can paradoxically lead to the forgetting of other associated memories, a phenomenon known as retrieval-induced forgetting (RIF). Inhibitory control mechanisms, primarily mediated by the prefrontal cortex, are thought to contribute to RIF. In this study, we examined whether stimulating the medial prefrontal cortex (mPFC) with transcranial direct current stimulation modulates RIF and investigated the associated electrophysiological correlates. In a randomized study, 50 participants (27 males and 23 females) received either real or sham stimulation before performing retrieval practice on target memories. After retrieval practice, a final memory test to assess RIF was administered. We found that stimulation selectively increased the retrieval accuracy of competing memories, thereby decreasing RIF, while the retrieval accuracy of target memories remained unchanged. The reduction in RIF was associated with a more pronounced beta desynchronization within the left dorsolateral prefrontal cortex (left-DLPFC), in an early time window (<500 ms) after cue onset during retrieval practice. This led to a stronger beta desynchronization within the parietal cortex in a later time window, an established marker for successful memory retrieval. Together, our results establish the causal involvement of the mPFC in actively suppressing competing memories and demonstrate that while forgetting arises as a consequence of retrieving specific memories, these two processes are functionally independent. Our findings suggest that stimulation potentially disrupted inhibitory control processes, as evidenced by reduced RIF and stronger beta desynchronization in fronto-parietal brain regions during memory retrieval, although further research is needed to elucidate the specific mechanisms underlying this effect.

A Systematic Review to Explore a Neuropsychological Profile that Predates Anorexia Nervosa

OBJECTIVE: Research demonstrates reduced cognitive flexibility and weak central coherence during acute illness and following recovery from anorexia nervosa (AN). This systematic review investigated if these impairments are present in first-degree relatives of individuals with AN, representing a possible neuropsychological risk profile. METHODS: A systematic review of electronic databases was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search ended on July 14, 2023. Established search terms and inclusion criteria identified relevant research. Risk of bias was assessed using the Critical Appraisal Skills Program. The review was registered with Prospero international prospective register of systematic reviews (No. CRD42023401268). Study selection, descriptive data, critical appraisal, and risk of bias are presented in tables and figures. RESULTS: The search yielded 10 studies. The included studies conducted neuropsychological assessments of discordant AN relatives and lifetime longitudinal study participants. Most studies found cognitive flexibility and central coherence to be significantly reduced in participants with AN and their relatives compared with controls. One study found decision making to be significantly impaired in AN participants and relatives. Effect sizes were moderate to large. DISCUSSION: Reduced cognitive flexibility and weak central coherence appear to be endophenotypes of AN. Further research is required with relatives concordant for AN to establish whether these biomarkers co-segregate with AN within families. These findings suggest a possibility of developing screeners to identify individuals at risk of AN allowing for early intervention.

Sex Matters: Association with Superager Classification and Risk Factors

Superagers are 80 to 89-year-olds with average or better cognition and memory equivalent to individuals 20 to 30 years younger. As sex and modifiable lifestyle/health factors influence cognitive aging and dementia risk, we examined their impact on superager status. Data from participants (n = 469; 67% female) aged 80–89 years old were analyzed from an online database that included demographic and dementia risk factors, and performance on tasks assessing working memory, cognitive inhibition, associative memory, and set shifting. Cross-sectional comparisons were made between superagers and those with typical-for-age cognitive abilities (typical-agers) to examine relationships between sex, superager status, and dementia risk factors. Females performed better than age-matched males on the associative memory task in the 50–69 years old group used for normative comparisons, and in the 80–89 years old group (ps < .001). More females than males were classified as superagers using non-sex-stratified normative comparisons (p = .009), and in sex-stratified normative comparisons (p = .022). Total weighted dementia risk reduced odds of superager status (OR = 0.199, 95% CI [0.046, 0.829]). Other lifestyle dementia risk factors were unrelated to superager status or could not be tested due to low endorsement. The findings support observations that superaging is more common in females, even when controlling for sex differences in memory performance. Future studies of superagers should account for sex differences. Results support being ambitious about dementia prevention, as having fewer modifiable dementia risk factors may be positively associated with superager status.

Encoding of 2D Self-Centered Plans and World-Centered Positions in the Rat Frontal Orienting Field

The neural mechanisms of motor planning have been extensively studied in rodents. Preparatory activity in the frontal cortex predicts upcoming choice, but limitations of typical tasks have made it challenging to determine whether the spatial information is in a self-centered direction reference frame or a world-centered position reference frame. Here, we trained male rats to make delayed visually guided orienting movements to six different directions, with four different target positions for each direction, which allowed us to disentangle direction versus position tuning in neural activity. We recorded single unit activity from the rat frontal orienting field (FOF) in the secondary motor cortex, a region involved in planning orienting movements. Population analyses revealed that the FOF encodes two separate 2D maps of space. First, a 2D map of the planned and ongoing movement in a self-centered direction reference frame. Second, a 2D map of the animal’s current position on the port wall in a world-centered reference frame. Thus, preparatory activity in the FOF represents self-centered upcoming movement directions, but FOF neurons multiplex both self- and world-reference frame variables at the level of single neurons. Neural network model comparison supports the view that despite the presence of world-centered representations, the FOF receives the target information as self-centered input and generates self-centered planning signals.

Introducing Enteric Glial Cells

This book emphasizes the pivotal role of enteric glial cells in maintaining gut health and their influence on the gut–brain axis. Although their existence was first noted at the end of the nineteenth century, recent scientific attention has shed light on their crucial functions, especially in inflammatory bowel diseases and potential links to neurodegenerative conditions like Parkinson’s disease. Understanding how these cells operate and interact within the complex network of the enteric nervous system is vital, not only for unraveling gut-related disorders but also for exploring their impact on brain health. Designed as a comprehensive guide, this book provides practical protocols to identify, visualize, and isolate enteric glial cells using techniques such as enzymatic digestion, immunofluorescence, and advanced imaging methods like confocal microscopy. It highlights the importance of studying glial cell behavior and their morphological diversity across different states of activation and maturation. Moreover, it explores innovative approaches using transgenic models and human biopsies to investigate their activity, interactions with the microbiome, and responses to inflammation. Understanding these mechanisms may open new avenues for therapies targeting both gastrointestinal and neurodegenerative diseases. This book also discusses possibilities for cell-based interventions, including the potential for enteric glial transplantation into the central nervous system. Overall, this work aims to bridge gastrointestinal and neurological research, underscoring the significance of enteric glia in health, disease, and the intricate communication between the gut and the brain. Advancing knowledge in this area could lead to novel, more precise treatments that address the root causes of complex conditions affecting both the gut and the nervous system.

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.

Disentangling Temporal and Rate Codes in the Primate Somatosensory Cortex

Millisecond-scale temporal spiking patterns encode sensory information in the periphery, but their role in the neocortex remains controversial. The sense of touch provides a window into temporal coding because tactile neurons often exhibit precise, repeatable, and informative temporal spiking patterns. In the somatosensory cortex (S1), responses to skin vibrations exhibit phase locking that faithfully carries information about vibratory frequency. However, the respective roles of spike timing and rate in frequency coding are confounded because vibratory frequency shapes both the timing and rates of responses. To disentangle the contributions of these two neural features, we measured S1 responses as rhesus macaques performed frequency discrimination tasks in which differences in frequency were accompanied by orthogonal variations in amplitude. We assessed the degree to which the strength and timing of responses could account for animal performance. First, we showed that animals can discriminate frequency, but their performance is biased by amplitude variations. Second, rate-based representations of frequency are susceptible to changes in amplitude but in ways that are inconsistent with the animals’ behavioral biases, calling into question a rate-based neural code for frequency. In contrast, timing-based representations are highly informative about frequency but impervious to changes in amplitude, which is also inconsistent with the animals’ behavior. We account for the animals’ behavior with a model wherein frequency coding relies on a temporal code, but frequency judgments are biased by perceived magnitude. We conclude that information about vibratory frequency is not encoded in S1 firing rates but primarily in temporal patterning on millisecond timescales.

Sleep Consolidation Potentiates Sensorimotor Adaptation

Contrary to its well-established role in declarative learning, the impact of sleep on motor memory consolidation remains a subject of debate. Current literature suggests that while motor skill learning benefits from sleep, consolidation of sensorimotor adaptation (SMA) depends solely on the passage of time. This has led to the proposal that SMA may be an exception to other types of memories. Here, we addressed this ongoing controversy in humans through three comprehensive experiments using the visuomotor adaptation paradigm (N = 290, 150 females). In Experiment 1, we investigated the impact of sleep on memory retention when the temporal gap between training and sleep was not controlled. In line with the previous literature, we found that memory consolidates with the passage of time. In Experiment 2, we used an anterograde interference protocol to determine the time window during which SMA memory is most fragile and, thus, potentially most sensitive to sleep intervention. Our results show that memory is most vulnerable during the initial hour post-training. Building on this insight, in Experiment 3, we investigated the impact of sleep when it coincided with the critical first hour of memory consolidation. This manipulation unveiled a benefit of sleep (30% memory enhancement) alongside an increase in spindle density and spindle–SO coupling during NREM sleep, two well-established neural markers of sleep consolidation. Our findings reconcile seemingly conflicting perspectives on the active role of sleep in motor learning and point to common mechanisms at the basis of memory formation.

Electrical Synapses Mediate Embryonic Hyperactivity in a Zebrafish Model of Fragile X Syndrome

Although hyperactivity is associated with a wide variety of neurodevelopmental disorders, the early embryonic origins of locomotion have hindered investigation of pathogenesis of these debilitating behaviors. The earliest motor output in vertebrate animals is generated by clusters of early-born motor neurons (MNs) that occupy distinct regions of the spinal cord, innervating stereotyped muscle groups. Gap junction electrical synapses drive early spontaneous behavior in zebrafish, prior to the emergence of chemical neurotransmitter networks. We use a genetic model of hyperactivity to gain critical insight into the consequences of errors in motor circuit formation and function, finding that Fragile X syndrome model mutant zebrafish are hyperexcitable from the earliest phases of spontaneous behavior, show altered sensitivity to blockade of electrical gap junctions, and have increased expression of the gap junction protein Connexin 34/35. We further show that this hyperexcitable behavior can be rescued by pharmacological inhibition of electrical synapses. We also use functional imaging to examine MN and interneuron (IN) activity in early embryogenesis, finding genetic disruption of electrical gap junctions uncouples activity between mnx1(+) MNs and INs. Taken together, our work highlights the importance of electrical synapses in motor development and suggests that the origins of hyperactivity in neurodevelopmental disorders may be established during the initial formation of locomotive circuits.

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.

Theoretical Perspectives on Love and Artificial Intelligence: An Interdisciplinary Analysis

This chapter explores the intricate relationship between love and artificial intelligence (AI) from an interdisciplinary and theoretical perspective. It analyzes the significance of love amid current technological developments, focusing on AI’s potential and limits in simulating this human experience. From a neuroscientific standpoint, it examines how love’s neurochemistry can be modeled using AI. The technological perspective sheds light on the development of emotionally intelligent systems and their role in interpersonal relationships, including the limitations of machine empathy. Psychological and cultural perspectives are integrated to investigate both individual and societal perceptions of love. This includes an exploration of how AI challenges and transforms our notions of intimacy, attachment, and closeness, as well as how narratives in art and media reflect these changes. The chapter concludes with an interdisciplinary synthesis, demonstrating that while AI can serve as a tool for analyzing and simulating love, it cannot capture the essence of this profoundly human phenomenon. Rather than offering definitive answers, the chapter contributes to ongoing discussions about the role of AI in emotional and social contexts.

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.

A Historical and Global Overview of Neuropsychology

Neuropsychology had its semi-structured foundations in the eighteenth century with phrenology, a pseudoscience that proposed a relationship between facial characteristics and cognitive abilities. In the nineteenth century, neuropsychology established itself as a scientific discipline, with the work of Franz Joseph Gall and others. The localizationist perspective sought, during this period, to address brain areas responsible for specific functions, such as language and memory, through postmortem identification and correlation of individuals who clinically exhibited motor and language deficits associated with acquired brain lesions. In the twentieth century, advances in neuroimaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), allowed for the visualization of the living brain and the study of real-time brain activity. Standardized neuropsychological tests were developed to assess specific cognitive functions, and new theoretical approaches, such as cognitive neuropsychology and social neuropsychology, emerged. In the twenty-first century, neuropsychology integrated with other disciplines, such as neuroscience, cognitive psychology, and neurology. The understanding of brain plasticity and the role of neuropsychological rehabilitation advanced significantly. Advanced neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), are used to study brain activity during cognitive tasks. Ongoing developments include integration with artificial intelligence (AI) and machine learning, the development of new evidence-based therapeutic interventions for neuropsychological disorders, and the use of neuropsychology to enhance understanding and treatment of mental health conditions, such as depression and anxiety. This chapter aims to outline the historical development of neuropsychology, explore the main theoretical and methodological contributions that have shaped the discipline, and discuss contemporary and future advances in the field, highlighting the importance of neuroimaging techniques and integrated approaches for the study of the brain and cognition.

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary—rather than additive—effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

Distinct Neuromodulatory Effects of Endogenous Orexin and Dynorphin Corelease on Projection-Defined Ventral Tegmental Dopamine Neurons

Dopamine (DA) neurons in the ventral tegmental area (VTA) respond to motivationally relevant cues, and circuit-specific signaling drives different aspects of motivated behavior. Orexin (ox; also known as hypocretin) and dynorphin (dyn) are coexpressed lateral hypothalamic (LH) neuropeptides that project to the VTA. These peptides have opposing effects on the firing activity of VTA(DA) neurons via orexin 1 (Ox1R) or kappa opioid (KOR) receptors. Given that Ox1R activation increases VTA(DA) firing, and KOR decreases firing, it is unclear how the coreleased peptides contribute to the net activity of DA neurons. We tested if optical stimulation of LH(ox/dyn) neuromodulates VTA(DA) neuronal activity via peptide release and if the effects of optically driven LH(ox/dyn) release segregate based on VTA(DA) projection targets including the basolateral amygdala (BLA) or the lateral or medial shell of the nucleus accumbens (lAcbSh, mAchSh). Using a combination of circuit tracing, optogenetics, and patch-clamp electrophysiology in male and female orexin(cre) mice, we showed a diverse response of LH(ox/dyn) optical stimulation on VTA(DA) neuronal firing, which is not mediated by fast transmitter release and is blocked by antagonists to KOR and Ox1R signaling. Additionally, where optical stimulation of LH(ox/dyn) inputs in the VTA inhibited firing of the majority of BLA-projecting VTA(DA) neurons, optical stimulation of LH(ox/dyn) inputs in the VTA bidirectionally affects firing of either lAcbSh- or mAchSh-projecting VTA(DA) neurons. These findings indicate that LH(ox/dyn) corelease may influence the output of the VTA by balancing ensembles of neurons within each population which contribute to different aspects of reward seeking.

Constitutive and Conditional Epitope Tagging of Endogenous G-Protein–Coupled Receptors in Drosophila

To visualize the cellular and subcellular localization of neuromodulatory G-protein–coupled receptors in Drosophila, we implement a molecular strategy recently used to add epitope tags to ionotropic receptors at their endogenous loci. Leveraging evolutionary conservation to identify sites more likely to permit insertion of a tag, we generated constitutive and conditional tagged alleles for Drosophila 5-HT1A, 5-HT2A, 5-HT2B, Octβ1R, Octβ2R, two isoforms of OAMB, and mGluR. The conditional alleles allow for the restricted expression of tagged receptor in specific cell types, an option not available for any previous reagents to label these proteins. We show expression patterns for these receptors in female brains and that 5-HT1A and 5-HT2B localize to the mushroom bodies (MBs) and central complex, respectively, as predicted by their roles in sleep. By contrast, the unexpected enrichment of Octβ1R in the central complex and of 5-HT1A and 5-HT2A to nerve terminals in lobular columnar cells in the visual system suggest new hypotheses about their functions at these sites. Using an additional tagged allele of the serotonin transporter, a marker of serotonergic tracts, we demonstrate diverse spatial relationships between postsynaptic 5-HT receptors and presynaptic 5-HT neurons, consistent with the importance of both synaptic and volume transmission. Finally, we use the conditional allele of 5-HT1A to show that it localizes to distinct sites within the MBs as both a postsynaptic receptor in Kenyon cells and a presynaptic autoreceptor.

The Lysine Acetyltransferase PCAF Functionally Interacts with Estrogen Receptor Alpha in the Hippocampus of Gonadally Intact Male—But Not Female—Rats to Enhance Short-Term Memory

Acetylation of histone proteins by histone acetyltransferases (HATs), and the resultant change in gene expression, is a well-established mechanism necessary for long-term memory (LTM) consolidation, which is not required for short-term memory (STM). However, we previously demonstrated that the HAT p300/CBP-associated factor (PCAF) also influences hippocampus (HPC)-dependent STM in male rats. In addition to their epigenetic activity, HATs acetylate nonhistone proteins involved in nongenomic cellular processes, such as estrogen receptors (ERs). Given that ERs have rapid, nongenomic effects on HPC-dependent STM, we investigated the potential interaction between ERs and PCAF for STM mediated by the dorsal hippocampus (dHPC). Using a series of pharmacological agents administered directly into the dHPC, we reveal a functional interaction between PCAF and ERα in the facilitation of short-term object-in-place memory in male but not female rats. This interaction was specific to ERα, while ERβ agonism did not enhance STM. It was further specific to dHPC STM, as the effect was not present in the dHPC for LTM or in the perirhinal cortex. Further, while STM required local (i.e., dHPC) estrogen synthesis, the facilitatory interaction effect appeared independent of estrogens. Finally, western blot analyses demonstrated that PCAF activation in the dHPC rapidly (5 min) activated downstream estrogen-related cell signaling kinases (c-Jun N-terminal kinase and extracellular signal-related kinase). Collectively, these findings indicate that PCAF, which is typically implicated in LTM through epigenetic processes, also influences STM in the dHPC, possibly via nongenomic ER activity. Critically, this novel PCAF–ER interaction might exist as a male-specific mechanism supporting STM.

Arachidonic Acid Mobilization and Peroxidation Promote Microglial Dysfunction in Aβ Pathology

Aberrant increase of arachidonic acid (ARA) has long been implicated in the pathology of Alzheimer's disease (AD), while the underlying causal mechanism remains unclear. In this study, we revealed a link between ARA mobilization and microglial dysfunction in Aβ pathology. Lipidomic analysis of primary microglia from App(NL-GF) mice showed a marked increase in free ARA and lysophospholipids (LPLs) along with a decrease in ARA-containing phospholipids, suggesting increased ARA release from phospholipids (PLs). To manipulate ARA-containing PLs in microglia, we genetically deleted lysophosphatidylcholine acyltransferase 3 (Lpcat3), the main enzyme catalyzing the incorporation of ARA into PLs. Loss of microglial Lpcat3 reduced the levels of ARA-containing PLs, free ARA and LPLs, leading to a compensatory increase in monounsaturated fatty acid (MUFA)-containing PLs in both male and female App(NL-GF) mice. Notably, the reduction of ARA in microglia significantly ameliorated oxidative stress and inflammatory responses while enhancing the phagocytosis of Aβ plaques and promoting the compaction of Aβ deposits. Mechanistically, scRNA seq suggested that LPCAT3 deficiency facilitates phagocytosis by facilitating de novo lipid synthesis while protecting microglia from oxidative damage. Collectively, our study reveals a novel mechanistic link between ARA mobilization and microglial dysfunction in AD. Lowering brain ARA levels through pharmacological or dietary interventions may be a potential therapeutic strategy to slow down AD progression.

Computational Neuroscience and Neurorehabilitation

Neurorehabilitation builds on the principle that the central nervous system (CNS) is plastic and adapts to the situations the CNS is exposed to. Cases of acquired injury (stroke, traumatic, SCI, concussion), progressive disorders (MS, PD, dementia), or congenital injury (CP) are clinical situations where neuroplasticity becomes relevant. Regardless of how these clinical conditions are treated, the CNS will adapt through physiological processes to the conditions in the different situations. Environmental, clinical, medical, psychological, social, and physical factors all have an influence on how the CNS adapts to neurological injury. This is where clinical personnel become relevant. Because how a person with a lesion to the CNS is treated, has an impact on the plastic changes in the CNS. This chapter will describe some of the possible computational mechanisms that underlie this principle of central nervous plasticity, and how descriptions of both behavior and physiological processes can be formalized with the help of a computational mindset.

Dietary fat intake and risk of Parkinson disease: results from the Swedish National March Cohort

Background Following progressive aging of the population worldwide, the prevalence of Parkinson disease is expected to increase in the next decades. Primary prevention of the disease is hampered by limited knowledge of preventable causes. Recent evidence regarding diet and Parkinson disease is inconsistent and suggests that dietary habits such as fat intake may have a role in the etiology. Objective To investigate the association between intake of total and specific types of fat with the incidence of Parkinson disease. Methods Participants from the Swedish National March Cohort were prospectively followed-up from 1997 to 2016. Dietary intake was assessed at baseline using a validated food frequency questionnaire. Food items intake was used to estimate fat intake, i.e. the exposure variable, using the Swedish Food Composition Database. Total, saturated, monounsaturated and polyunsaturated fat intake were categorized into quartiles. Parkinson disease incidence was ascertained through linkages to Swedish population-based registers. Cox proportional hazards regression models were used to estimate hazard ratios (HR) with 95% confidence intervals (CI) of the association between fat intake from total or specific types of fats and the incidence of Parkinson disease. The lowest intake category was used as reference. Isocaloric substitution models were also fitted to investigate substitution effects by replacing energy from fat intake with other macronutrients or specific types of fat. Results 41,597 participants were followed up for an average of 17.6 years. Among them, 465 developed Parkinson disease. After adjusting for potential confounders, the highest quartile of saturated fat intake was associated with a 41% increased risk of Parkinson disease compared to the lowest quartile (HR Q4 vs. Q1: 1.41; 95% CI: 1.04–1.90; p for trend: 0.03). Total, monounsaturated or polyunsaturated fat intake were not significantly associated with Parkinson disease. The isocaloric substitution models did not show any effect. Conclusions We found that a higher consumption of large amounts of saturated fat might be associated with an increased risk of Parkinson disease. A diet low in saturated fat might be beneficial for disease prevention.

Investigating Brain Connectivity and Regional Statistics from EEG for early stage Parkinson's Classification

We evaluate the effectiveness of combining brain connectivity metrics with signal statistics for early stage Parkinson's Disease (PD) classification using electroencephalogram data (EEG). The data is from 5 arousal states - wakeful and four sleep stages (N1, N2, N3 and REM). Our pipeline uses an Ada Boost model for classification on a challenging early stage PD classification task with with only 30 participants (11 PD , 19 Healthy Control). Evaluating 9 brain connectivity metrics we find the best connectivity metric to be different for each arousal state with Phase Lag Index achieving the highest individual classification accuracy of 86\% on N1 data. Further to this our pipeline using regional signal statistics achieves an accuracy of 78\%, using brain connectivity only achieves an accuracy of 86\% whereas combining the two achieves a best accuracy of 91\%. This best performance is achieved on N1 data using Phase Lag Index (PLI) combined with statistics derived from the frequency characteristics of the EEG signal. This model also achieves a recall of 80 \% and precision of 96\%. Furthermore we find that on data from each arousal state, combining PLI with regional signal statistics improves classification accuracy versus using signal statistics or brain connectivity alone. Thus we conclude that combining brain connectivity statistics with regional EEG statistics is optimal for classifier performance on early stage Parkinson's. Additionally, we find outperformance of N1 EEG for classification of Parkinson's and expect this could be due to disrupted N1 sleep in PD. This should be explored in future work.

Vitamin D receptor gene polymorphisms and idiopathic Parkinson disease: an Egyptian study

Background Accumulating data have suggested that vitamin D receptor (VDR) gene is a pretender gene for vulnerability to Parkinson disease (PD). This study aimed to assess the relationship of VDR gene polymorphisms (FokI and ApaI) with PD. Fifty patients suffering from PD and 50 age- and sex-matched healthy controls were included. Unified Parkinson Disease Rating Scale (UPDRS) was done to assess disease severity. Genetic testing for VDR gene single nucleotide polymorphisms (FokI and ApaI) was done using real time polymerase chain reaction (PCR) technique. Results Concerning frequency of genes and alleles for vitamin D receptor gene polymorphisms (FokI and ApaI), no statistically significant difference was found between PD patients and controls. AC genotype was associated with younger age and younger age at onset of disease compared to CC and AA genotypes of ApaI gene polymorphisms. CC genotype was significantly positively correlated with fatigue and urine incontinence. VDR gene polymorphisms were not found to be independent predictors for severity of PD after adjustment for possible confounders. Conclusion VDR gene polymorphisms are related to the clinical manifestations rather than etiology or severity of idiopathic PD.

Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models

Longitudinal voice biomarkers provide a non-invasive source of information for monitoring Parkinson's disease progression, but their statistical analysis is difficult because repeated measurements from the same subject are correlated, clinical cohorts are often small, and disease trajectories can vary substantially across individuals. This study evaluates statistical and neural mixed-effects approaches for modeling Parkinson's disease progression from telemonitoring voice data. Using the Oxford Parkinson's telemonitoring dataset (N=42), we compare Neural Mixed Effects (NME) models, Generalized Neural Network Mixed Models (GNMMs), and semi-parametric Generalized Additive Mixed Models (GAMMs) under the same longitudinal prediction setting. The results show that neural mixed-effects models provide flexible nonlinear representations but can overfit severely in this small-sample setting, whereas GAMMs achieve stronger predictive performance and retain interpretable smooth effects and subject-level structure. In particular, the GAMM-based approach attains the lowest prediction error (MSE 6.56), while the neural baselines have substantially larger errors (MSE > 90). These findings support the use of interpretable statistical mixed-effects models for small longitudinal telemonitoring studies and suggest that larger and more diverse cohorts are needed before highly flexible neural mixed-effects models can be reliably assessed in this application. ;Published version: Computer Methods and Programs in Biomedicine Update, DOI: 10.1016/j.cmpbup.2026.100242. Version note: https://doi.org/10.5281/zenodo.19804672

Dopaminergic Positron Emission Tomography Imaging in the Alpha‐Synuclein Preformed Fibril Model Reveals Similarities to Early Parkinson's Disease

BACKGROUND: Positron emission tomography (PET) imaging in early Parkinson's disease (PD) subjects reveals that increased dopamine (DA) turnover and reduced dopamine transporter (DAT) density precede decreases in DA synthesis and storage. The rat α‐synuclein preformed fibril (α‐syn PFF) model provides a platform to investigate DA dynamics during multiple stages of α‐syn inclusion‐triggered nigrostriatal degeneration. OBJECTIVES: We investigated multiple aspects of in vivo dopaminergic deficits longitudinally and similarities to human PD using translational PET imaging readouts. METHODS: Longitudinal imaging was performed every 2 months in PFF and control rats for 7 months. [(18)F]‐Fluoro‐3,4‐dihydroxyphenyl‐L‐alanine (FDOPA) imaging was performed to investigate DA synthesis and storage (K(occ)) and DA turnover, estimated by its inverse, the effective distribution volume ratio (EDVR). (11)C‐Methylphenidate (MP) was used to estimate DAT density (BP(ND)). RESULTS: Early DA turnover increases and DAT binding decreases were observed in the ipsilateral striatum of PFF rats, progressing longitudinally. EDVR decreased 26%, 38%, and 47%, and BP(ND) decreased 36%, 50%, and 65% at the 2‐, 4‐, and 6‐month time points, respectively, compared to ipsilateral control striatum. In contrast, deficits in DA synthesis and storage were not observed in the ipsilateral striatum of PFF rats compared to control injections and were relatively preserved up to 6 months (K(occ) decreased 20% at 6 months). CONCLUSIONS: The relative preservation of DA synthesis and storage compared to robust progressive deficits in DAT density and increases in DA turnover in the rat α‐syn PFF model display remarkable face validity to dopaminergic alterations in human PD. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Deep Learning Predicts Prevalent and Incident Parkinson's Disease From UK Biobank Fundus Imaging

Parkinson's disease is the world's fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson's disease and automate diagnostics would greatly improve the treatment of patients with Parkinson's disease. Current diagnostic methods are expensive and have limited availability. Considering the insidious and preclinical onset and progression of the disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical interventions. We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson's disease. We conducted a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson's disease from UK Biobank fundus imaging. Our results show that Parkinson's disease individuals can be differentiated from age and gender-matched healthy subjects with an Area Under the Curve (AUC) of 0.77. This accuracy is maintained when predicting either prevalent or incident Parkinson's disease. Explainability and trustworthiness are enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations. ;Comment: 17 pages, 4 figures, 2 tables, 4 supplementary tables

Efficacy and safety of clonidine for the treatment of impulse control disorder in Parkinson’s disease: a multicenter, parallel, randomised, double-blind, Phase 2b Clinical trial

Background Impulse control disorders (ICDs) are frequently encountered in Parkinson's disease (PD).Objectives We aimed to assess whether clonidine, an α2-adrenergic receptor agonist, would improve ICDs. Methods We conducted a multicentre trial in five movement disorder departments. Patients with PD and ICDs (n = 41) were enrolled in an 8-week, randomised (1:1), double-blind, placebo-controlled study of clonidine (75 μg twice a day). Randomisation and allocation to the trial group were carried out by a central computer system. The primary outcome was the change at 8 weeks in symptom severity using the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease-Rating Scale (QUIP-RS) score. A reduction of the most elevated subscore of the QUIP-RS of more than 3 points without any increase in the other QUIP-RS dimension defined success.Results Between 15 May 2019 and 10 September 2021, 19 patients in the clonidine group and 20 patients in the placebo group were enrolled. The proportion difference of success in reducing QUIP-RS at 8 weeks, was 7% (one-sided upper 90% CI 27%) with 42.1% of success in the clonidine group and 35.0% in the placebo group. Compared to patients in the placebo group, patients in the clonidine group experienced a greater reduction in the total QUIP-RS score at 8 weeks (11.0 points vs. 3.6).Discussion Clonidine was well tolerated but our study was not enough powerful to demonstrate significant superiority compared to placebo in reducing ICDs despite a greater reduction of total QUIP score at 8 weeks. A phase 3 study should be conducted.Trial Registration The study was registered (NCT03552068) on clinicaltrials.gov on June 11, 2018.

Parkinson's Disease and Deep Brain Stimulation Have an Impact on My Life: A Multimodal Study on the Experiences of Patients and Family Caregivers