Exercise & Fitness

Investigating the feasibility and acceptability of the TeleRehabilitation of balance clinical and economic Decision Support System (TeleRehaB DSS) in adults at risk of falls: study protocol for a multicentre clinical trial.

INTRODUCTION: Falls are a significant concern for older adults, particularly those with neurological, vestibular, cognitive and post-viral conditions, due to dizziness and imbalance. Conventional balance rehabilitation programmes, though effective, face challenges in adherence and accessibility. The TeleRehabilitation Decision Support System (TeleRehaB DSS) uses artificial intelligence (AI) and mo...

Bayesian-optimized machine learning models for classifying metabolic syndrome control among NCD patients in Bangladesh hospitals.

Control of metabolic syndrome (MS) is still a major challenge for patients suffering from Non-communicable diseases (NCDs) in Bangladesh. In this study, the status of MS control was determined, and Bayesian-optimized machine-learning models were developed and tested to classify MS control status concurrently with NCDs in patients attending government hospitals. The study was a cross-sectional hosp...

Skin microbiome signatures track uremic pruritus severity in hemodialysis patients.

Chronic kidney disease-associated pruritus (CKD-aP) is frequent in patients receiving maintenance hemodialysis and is often difficult to control. Whil...

Is cellular senescence a biological feature of Long COVID? A transcriptomic analysis across comparative post-acute sequelae phenotypes.

INTRODUCTION: Cellular senescence, involving cell-cycle arrest and inflammatory factor release, may play a role in Long COVID development. We investig...

Cardiac substructure dosimetry in thoracic radiotherapy: moving beyond mean heart dose and toward precision cardiac risk mitigation.

BACKGROUND: Radiation-induced heart disease (RIHD) remains a clinically significant consequence of thoracic radiotherapy (RT). Historically, the mean ...

Segmenting Older Adults by Their Acceptance of Digital Health Care Devices: Cross-Sectional Study Using the Augmented Technology Acceptance Model and K-Means Clustering.

BACKGROUND: Population aging has become a critical global challenge, with South Korea entering a super-aged society and facing rapidly increasing heal...

Regional differences in physical fitness profiles of preschool children in Chongqing China revealed by machine learning.

Urban-rural disparities in physical fitness during early childhood remain insufficiently characterized. This study aimed to identify physical fitness ...

Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.

BACKGROUND: Machine learning (ML) shows promise in using clinical data to predict chronic diseases. However, its application in PMOP risk assessment u...

Predicting intradialytic exercise intolerance in maintenance hemodialysis patients: an interpretable machine learning approach integrating functional assessments.

Exercise intolerance, primarily manifesting as intradialytic hypotension (IDH), is common in maintenance hemodialysis patients, interrupting intervent...

Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing.

BACKGROUND: Monitoring respiration parameters such as respiratory rate could be beneficial to understand the impact of training on equine health and p...

Comparison of Gram stain interpretation accuracy between a computer-aided diagnosis app and microbiology specialists in blood culture samples.

UNLABELLED: Gram staining is one of the basic tests to identify the organism in microbiological laboratory, especially in blood culture. However, micr...

Automated Summarization of Military Medical Records Using AI: Evaluation Via the OPTICA Framework.

INTRODUCTION: Military medical fitness evaluations require physicians to rapidly review extensive and heterogeneous medical records to determine servi...

Development and validation of a multimodal machine learning prediction model for heart failure after acute myocardial infarction.

Heart failure after acute myocardial infarction (post-MI HF) has become a major global health problem. Accurate risk prediction is essential for optim...

Integrating artificial intelligence and machine learning into risk prediction for type 2 diabetes: A model for early identification of high-risk populations.

Family history, body mass index (BMI), and ethnicity are three key, well-established determinants of susceptibility to type 2 diabetes mellitus (T2DM)...

Effects of breast cancer risk factors on the tumor microenvironment: a morphological deep-learning analysis of three prospective cohorts.

Emerging evidence suggests a critical role of the tumor microenvironment (TME) in breast cancer (BC) development and outcomes, yet factors that modify...

Automated Assessment of Surgical Quality in Distal Gastrectomy: Development of a Novel Computer Vision Model Based on the Critical View of Quality (CVQ).

OBJECTIVE: To evaluate the clinical validity of the critical view of quality (CVQ) as a measure of lymphadenectomy quality in minimally invasive dista...

Sweat and air permeable electronics enabled by engineered hierarchical fabric system for exercise management.

Scientific exercise monitoring is significant for injury risk prevention and training outcome promotion. Wearable biosensing technologies have emerged...

Anthropometrics, physical fitness, and sport-specific performance of young German canoe sprint athletes (U13-U17) to predict senior performance level: a machine-learning approach.

The aim of this study was to evaluate whether machine learning models comprising anthropometric, physical fitness, and sport-specific performance data...

Integrating ESG indicators and behavioral factors to estimate the mental health index in Italy.

This article examines the relationship between behavioral and Environmental, Social, and Governance (ESG) variables and the Mental Health Index (MHI) ...

Machine learning identification of neuroimaging signatures in young and middle-aged male patients with obstructive sleep apnea: a multimodal MRI study.

OBJECTIVES: This study aimed to develop and validate an integrated neuroimaging-based model for identifying severe obstructive sleep apnea (OSA) with ...