Exercise & Fitness

AI-driven appendicular skeletal muscle mass index (ASMI) prediction and low muscle mass detection from routine hip X-rays: a novel opportunistic case finding tool.

PURPOSE: Sarcopenia diagnosis requires identifying low muscle mass (LMM), typically via dual-energy X-ray absorptiometry (DXA). However, DXA's limited accessibility restricts large-scale screening. This retrospective study aimed to develop and validate a deep learning model to predict DXA-derived ASMI from routine hip radiographs for opportunistic LMM case finding. METHODS: We included a selected ...

Circulatory dietary and gut-derived metabolites predict early cognitive decline.

INTRODUCTION: A key component of disease prevention is the identification of at-risk individuals. Microbial dysbiosis in the early stages of cognitive decline and Alzheimer's disease (AD) and can modulate the levels of microbe-derived metabolites (MDM), thought to contribute to neuroinflammation, blood‒brain barrier dysfunction, and neuronal degeneration. However, the precise role of MDM in this p...

Maintenance factors for eating disorder symptoms based on ecological momentary assessment studies: A systematic review.

Several prominent theories of eating disorders maintenance exist, with most corresponding to evidence-based treatments. Ecological momentary assessmen...

Multi-omics and machine learning reveal a critical mediator of PRKCA in quercetin-mediated protection against Sarcopenia.

BACKGROUND: Sarcopenia, the age-associated loss of skeletal muscle mass and function, poses a growing public health challenge. Although dietary flavon...

The Evolving Role of the Qualified Person in an AI-Driven Pharma Compliance Landscape.

The role of the Qualified Person (QP) in pharmaceutical compliance is undergoing a fundamental transformation driven by the integration of artificial ...

[Construction of a prognostic assessment model of acupuncture intervention for lumbar disc herniation based on interpretable machine learning algorithms].

OBJECTIVE: To construct and validate a prognostic assessment model of acupuncture intervention for lumbar disc herniation (LDH), and analyze the key f...

SEMASEARCH Study Design: Real-World Evaluation of Semaglutide 2.4 mg in Adults With Severe Obesity Underrepresented in Clinical Trials.

BACKGROUND: Although semaglutide 2.4 mg has demonstrated significant weight loss efficacy in clinical trials, real-world data, particularly with regar...

Perioperative blood loss in cardiac surgery: Validation of machine learning-derived clusters.

BackgroundThere is no universally accepted definition of perioperative blood loss in cardiac surgery. Existing methods are based on chest tube output ...

Editorial commentary: From signal to care: Artificial intelligence in Cardio-oncology.

Decoding brain-heart dynamics: Effective connectivity predictors of heart rate variability.

Autonomic dysregulation characterizes neuropsychiatric and somatic disorders, often reflecting disrupted brain-heart communication mediated by the Cen...

An interpretable machine learning model combining MRI-DKI habitat radiomic features and clinical biomarkers for noninvasive prediction of lymphatic metastasis in rectal cancer: a prospective study.

OBJECTIVE: Tumor heterogeneity exerts a significant influence on lymphovascular invasion (LVI) and lymph node metastasis (LNM) in rectal cancer (RC), ...

Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.

BACKGROUND: This study aimed to develop and validate machine learning (ML) models for predicting the risk of cognitive frailty in community-dwelling e...

Using machine learning to predict the small for gestational age and identify the important predictors: A real-world clinical cohort study in China.

PURPOSE: Aims to use machine learning to predict the risk of small for gestational age (SGA) and identify its important predictors. METHODS: This is a...

Machine learning for mortality prediction using nutritional and inflammatory markers in critically ill patients.

INTRODUCTION: Malnutrition and muscle loss are key determinants of outcomes in critically ill patients, yet conventional ICU mortality scores (e.g., A...

Priority questions for the next decade of blue carbon science.

Blue carbon ecosystems, classically defined as mangroves, tidal marshes and seagrasses, but increasingly expanded to include ecosystems such as tidal ...

Graph-Based Classification with GNN-Explainer for Predicting Cardiac Toxicity Associated with Multi-Ion Channel Blockers.

Cardiotoxicity remains a critical concern in drug development, often leading to late-stage attrition of promising compounds. While traditional assessm...

Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.

BACKGROUND: Endometriosis profoundly impairs sexual function through complex interactions between pain, hormonal disturbances, psychological distress,...

Molecular mechanisms of exercise-induced improvements in Alzheimer's disease: a focus on lipid homeostasis.

Alzheimer's disease (AD) is the most prevalent type of dementia, and its pathophysiological mechanisms involve multiple factors, including genomic fac...

D-LIM: A neural network for interpretable gene-gene interactions.

Recent advances in gene editing can produce large genotype-fitness maps for targeted genes, yet predicting the effects of mutations between genes rema...

Explainable Machine Learning for Assessing Digital Health Literacy in Older Adults: Validation and Development of a Two-Stage Model Integrating Performance-Based and Self-Assessed Indicators.

BACKGROUND: Digital health literacy (DHL) is the ability to locate, understand, evaluate, and apply health information in digital environments. It is ...