Prevention

Dietary Protein Source Shapes Gut Microbial Structure and Predicted Function: A Meta-Analysis with Machine Learning

Dietary proteins are major modulators of gut microbial ecology, yet the microbial signatures and functional consequences of plant-versus animal-based proteins remain poorly defined. Although digestibility and fermentation profiles differ by protein type, a systematic evaluation of how these differences shape microbial diversity, structure, and metabolic capacity is still lacking. This meta-analysi...

A Transparent and Generalizable Deep Learning Framework for Genomic Ancestry Prediction

Accurately capturing genetic ancestry is critical for ensuring reproducibility and fairness in genomic studies and downstream health research. This study aims to address the prediction of ancestry from genetic data using deep learning, with a focus on generalizability across datasets with diverse populations and on explainability to improve model transparency. We adapt the Diet Network, a deep lea...

Integrative Microbiome Profiling of Colorectal Cancer Across South Asian and Western Cohorts Using Interpretable Machine Learning

The incidence of colorectal cancer (CRC) cases has been steadily rising in South Asian countries compared to western countries. Microbiome dysbiosis h...

Cardiometabolic health and physical robustness map onto distinct patterns of brain structure and neurotransmitter systems

The link between brain health and risk/protective factors for non-communicable diseases (such as high blood pressure, high body mass index, diet, smok...

Ascending propriospinal modulation of thoracic sympathetic preganglionic neurons during lumbar locomotor activity

Although the autonomic sympathetic system is activated in parallel with locomotion, the underlying neural mechanisms mediating this coordination are n...

The Impact of Acute High-Intensity Activity on Perceptual Decision-Making Dynamics

This study investigates the acute impact of high-intensity activity on perceptual decision-making, using computational modeling to assess changes duri...

The influence of power-training and aging on endomysium content and fiber cross-sectional area in the human soleus muscle

The Master Athletic Laboratory Study of Intramuscular Connective Tissue (MALICoT, DRKS00015764) set out to analyze the endomysium content of the human...

Decoding Diabetes: Harnessing AI to Accurately Predict Real-Time and Future Blood Glucose Levels for Diabetes Management Using Diet, Exercise, Insulin Intake, and Heart Rate Variability

Continuous glucose monitoring (CGM) systems play a crucial role in diabetes care. Yet, they focus solely on blood glucose levels (BGL), neglect diet, ...

Colon-Specific Epigenetic Clocks from Minimal Features Reveal Disease-Driven Aging

Epigenetic clocks estimate chronological and biological age from DNA methylation patterns, but conventional models typically train on hundreds of thou...

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges...

Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? integrating machine learning multi-annual crop classification as a proxy of soil management

Soil organic carbon (SOC) is a crucial component related to various processes that ensure soil health and function. Its modeling is vital for assessin...

Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SSD) are associated with accelerated brain aging, reflected in an increased brain age gap. This gap serves as a biom...

Automated IntraVascular UltraSound Image Processing and Quantification of Coronary Artery Anomalies: The AIVUS-CAA software

Coronary artery anomalies (CAA) with an intramural course are associated with elevated risks of ischemia and sudden cardiac death under stress. Intrav...

Impact of Aspirin Therapy on Progression of Thoracic and Abdominal Aortic Aneurysms

Aortic aneurysms, including abdominal (AAA) and thoracic (TAA), pose significant challenges due to their rupture risk and complex pathophysiology. Whi...

A Deep Learning–Based Automated Detection of Mucus Plugs in Chest CT

This study presents a novel two stage deep learning algorithm for automated detection of mucus plugs in CT scans of patients with respiratory diseases...

NutriRAG: Unleashing the Power of Large Language Models for Food Identification and Classification through Retrieval Methods

This study explores the use of advanced Natural Language Processing (NLP) techniques to enhance food classification and dietary analysis using raw tex...

Emulating Clinical Trials with the Mayo Clinic Platform: Cardiovascular Research Perspective

Randomized controlled trials (RCTs) provide the highest level of clinical evidence but are often limited by cost, time, and ethical constraints. Emula...

GlucoseGo: A Simple, User-Friendly, Machine Learning-Derived Tool for Predicting Exercise-Related Hypoglycaemia Risk in Type 1 Diabetes

This study aims to develop an accessible, machine learning-derived tool for people with type 1 diabetes that predicts hypoglycaemia risk at the start ...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025)...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...