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Exercise & Fitness

Latest AI and machine learning research in exercise & fitness for healthcare professionals.

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Social isolation upregulates takeout expression in female Drosophila melanogaster to promote sucrose feeding

Drosophila melanogaster provides a model system to examine how environmental stress interacts with s...

Successful Predictive Modeling of Pollen Fitness Phenotypes Is Enabled by Measures of Expression Specificity

The ability to predict phenotypes from genotypes in multicellular organisms remains limited despite ...

Discovery of Electron Hole-hopping Redox Mutations in Myoglobin by Deep Mutational Learning

In addition to storing molecular oxygen, myoglobin catalyzes peroxidase-like reactions involving hig...

RegEvol: detection of directional selection in regulatory sequences through phenotypic predictions and phenotype-to-fitness functions

Regulatory DNA controls when and where genes are expressed, making it a key driver of phenotypic evo...

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 ...

Predicting Hypertension Among HIV Patients on Antiretroviral Therapy in Rural Eastern Cape, South Africa Using Machine Learning

Hypertension continues to be a major challenge in developing countries like South Africa, as it sign...

24-hour Physical Activity, Sedentary, and Sleep Profiles in Individuals with Cancer: A UK Biobank Cohort Study

The 24h behaviour profile, including physical activity, sedentary time, and sleep, is disrupted foll...

Automatic time in bed detection from hip-worn accelerometers for large epidemiological studies: The Tromsø Study

Accelerometers are frequently used to assess physical activity in large epidemiological studies. The...

Cross-platform metabolomics imputation using importance-weighted autoencoders

Metabolomics data are often generated through different analytical platforms and different methods o...

Paving the way for precision treatment of psychiatric symptoms with functional connectivity neurofeedback

Major depressive disorder (MDD) remains challenging to treat, with many patients failing to respond ...

Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals

Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of ...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using m...

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 diabe...

Medication information extraction using local large language models

Medication information is crucial for clinical routine and research. However, a vast amount is store...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health...

Dense sampling of choices links high learning rates to obesity and low reward sensitivity to binge eating

Mounting evidence shows that obesity is associated with alterations in dopamine transmission. Howeve...

Speaking the Language of Inclusion: Examining English Languages Requirements in Cardiovascular Digital Health Trials

Cardiovascular medicine is rapidly evolving, as it integrates digital technologies intended to decen...

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