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😅 Don't sweat it: New device detects sweat biomarker at minimal perspiration rate
https://medicalxpress.com/news/2025-09-dont-device-biomarker-minimal-perspiration.html

Don't sweat it: New device detects sweat biomarker at minimal perspiration rate
Available on demand, in abundance and containing multiple biomarkers, sweat is an increasingly appealing medium for monitoring health, according to researchers at Penn State. But not everyone—especially critically ill patients—can build up enough sweat to provide a robust enough sample for current analysis techniques.

BrainIB : Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
Tianzheng Hu, Qiang Li, Shu Liu, Vince D. Calhoun, Guido van Wingen, Shujian Yu
https://arxiv.org/abs/2510.03004

BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
The development of diagnostic models is gaining traction in the field of psychiatric disorders. Recently, machine learning classifiers based on resting-state functional magnetic resonance imaging (rs-fMRI) have been developed to identify brain biomarkers that differentiate psychiatric disorders from healthy controls. However, conventional machine learning-based diagnostic models often depend on extensive feature engineering, which introduces bias through manual intervention. While deep learning…

Non-linear associations of amyloid-$\beta$ with resting-state functional networks and their cognitive relevance in a large community-based cohort of cognitively normal older adults
Junjie Wu, Benjamin B Risk, Taylor A James, Nicholas Seyfried, David W Loring, Felicia C Goldstein, Allan I Levey, James J Lah, Deqiang Qiu
https://arxiv.org/ab…

Non-linear associations of amyloid-$β$ with resting-state functional networks and their cognitive relevance in a large community-based cohort of cognitively normal older adults
Background: Non-linear alterations in brain network connectivity may represent early neural signatures of Alzheimer's disease (AD) pathology in cognitively normal older adults. Understanding these changes and their cognitive relevance could provide sensitive biomarkers for early detection. Most prior studies recruited participants from memory clinics, often with subjective memory concerns, limiting generalizability. Methods: We examined 14 large-scale functional brain networks in 968 cognitiv…

Toward Quantum-Enabled Biomarker Discovery: An Outlook from Q4Bio
Dhirpal Shah, Mariesa Teo, Ryan A. Robinett, Sophia Madejski, Zachary Morrell, Siddhi Ramesh, Colin Campbell, Bharath Thotakura, Victory Omole, Ben Hall, Aram W. Harrow, Teague Tomesh, Alexander T. Pearson, Frederic T. Chong, Samantha J. Riesenfeld
https://arxiv.org/abs/2509…

Toward Quantum-Enabled Biomarker Discovery: An Outlook from Q4Bio
We present a case study and forward-looking perspective on co-design for hybrid quantum-classical algorithms, centered on the goal of empirical quantum advantage (EQA), which we define as a measurable performance gain using quantum hardware over state-of-the-art classical methods on the same task. Because classical algorithms continue to improve, the EQA crossover point is a moving target; nevertheless, we argue that a persistent advantage is possible for our application class even if the cross…

A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
Samuele Punzo, Silvia Giulia Galfr\`e, Francesco Massafra, Alessandro Maglione, Corrado Priami, Alina S\^irbu
https://arxiv.org/abs/2509.22484

A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
We present a machine learning pipeline for biomarker discovery in Multiple Sclerosis (MS), integrating eight publicly available microarray datasets from Peripheral Blood Mononuclear Cells (PBMC). After robust preprocessing we trained an XGBoost classifier optimized via Bayesian search. SHapley Additive exPlanations (SHAP) were used to identify key features for model prediction, indicating thus possible biomarkers. These were compared with genes identified through classical Differential Expressi…

Acoustic Probing of New Biomarkers for Rapid Sickle Cell Disease Screening
Nakul Sridhar, Meiou Song, Michael H. B. Stowell, Kathryn L. Hassell, Xiaoyun Ding
https://arxiv.org/abs/2510.02478

Acoustic Probing of New Biomarkers for Rapid Sickle Cell Disease Screening
Sickle cell disease (SCD) remains a critical global health issue, with high child mortality in low-resource regions. Early screening and diagnosis is essential for improving health outcomes, but conventional screening methods are unsuitable for widespread use due to the high costs of laboratory equipment. There is an urgent need for portable, cost-effective, and user-friendly point-of-care tools that can quickly assess blood health. Here, we explore two new biomarkers enabled by acoustic probin…

Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson's Disease Through Drawn Image Patterns
Nabil Daiyan, Md Rakibul Haque
https://arxiv.org/abs/2510.05015

Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson's Disease Through Drawn Image Patterns
Parkinson's disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (C…

🫀 Myocardial Fibrosis in Athletes: Risk Marker or Physiological Adaptation?
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Multimodal Carotid Risk Stratification with Large Vision-Language Models: Benchmarking, Fine-Tuning, and Clinical Insights
Daphne Tsolissou, Theofanis Ganitidis, Konstantinos Mitsis, Stergios CHristodoulidis, Maria Vakalopoulou, Konstantina Nikita
https://arxiv.org/abs/2510.02922

Multimodal Carotid Risk Stratification with Large Vision-Language Models: Benchmarking, Fine-Tuning, and Clinical Insights
Reliable risk assessment for carotid atheromatous disease remains a major clinical challenge, as it requires integrating diverse clinical and imaging information in a manner that is transparent and interpretable to clinicians. This study investigates the potential of state-of-the-art and recent large vision-language models (LVLMs) for multimodal carotid plaque assessment by integrating ultrasound imaging (USI) with structured clinical, demographic, laboratory, and protein biomarker data. A fram…

Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers
Janayna M. Fernandes, Robinson Sabino-Silva, Murillo G. Carneiro
https://arxiv.org/abs/2509.16126

Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers
Autism Spectrum Disorder (ASD) lacks reliable biological markers, delaying early diagnosis. Using 159 salivary samples analyzed by ATR-FTIR spectroscopy, we developed GANet, a genetic algorithm-based network optimization framework leveraging PageRank and Degree for importance-based feature characterization. GANet systematically optimizes network structure to extract meaningful patterns from high-dimensional spectral data. It achieved superior performance compared to linear discriminant analysis…