Prevention

In vivo quantification of arterial active mechanics using deep learning-assisted pressure-area analysis.

Active arterial mechanics, governed by vascular smooth muscle contraction, are critical to physiological regulation, cardiovascular disease progression, and clinical diagnosis. Although various in vivo methods have been developed to assess arterial stiffness, most cannot distinguish the contribution of smooth muscle tone; therefore, quantitative characterization of arterial activity remains challe...

AI-Driven Analysis of Cardiopulmonary Exercise Tests to Identify Gas Exchange and Ventilatory Thresholds.

BACKGROUND: A cardiopulmonary exercise test (CPET) provides the estimated lactate threshold (θLT) and respiratory compensation point (RCP) through visual identification of multivariate gas exchange and ventilatory profiles. Artificial intelligence tools, such as deep neural networks, can learn, replicate, and classify these patterns and potentially aid in θLT and RCP identification, removing the s...

Harnessing the gut-heart axis for cardiovascular drug innovation: microbiome, metabolites, and personalized treatment strategies.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide despite major advances in pharmacotherapy. Emerging evidence reveals a p...

Developing and validating machine learning algorithms to predict various indices of diet quality among a socioeconomically disadvantaged group.

Public health research faces challenges in recruiting hard-to-reach populations, including socioeconomically disadvantaged groups. This study evaluate...

Maternal Gut Microbiome as a Predictor of Insulin Therapy Requirement in Gestational Diabetes.

BACKGROUND: Gestational diabetes mellitus (GDM) often requires pharmacological intervention beyond lifestyle modification to achieve optimal glycemic ...

Peak Oxygen Uptake Prediction From Resting and Submaximal Variables of Cardiopulmonary Exercise Testing.

BACKGROUND: Cardiorespiratory fitness, as measured by peak oxygen uptake during cardiopulmonary exercise testing, is a prognostic indicator. We aim to...

Contribution of social determinants to symptoms of generalized anxiety disorder.

Symptoms of anxiety are known to be triggered by a range of life context factors including early life trauma, poor sleep quality, infrequent exercise,...

Predicting metabolic syndrome risk in Chinese adults using integrated features of movement behaviors and dietary intake: a machine learning approach.

BACKGROUND: China is undergoing a rapid nutritional and epidemiological transition. Traditional epidemiological approaches often examine diet and phys...

Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity: Protocol for a Retrospective and Prospective Multicenter Cohort Study.

BACKGROUND: Cardiorespiratory fitness (CRF) is a key predictor of cardiovascular and other health-related diseases in individuals with obesity. CRF is...

Predicting Non-Response to Multicomponent Treatment in Fibromyalgia: Development and Validation of a Machine Learning Model.

BACKGROUND: Multicomponent programs combining therapeutic exercise, cognitive-behavioral therapy, and pain neuroscience education demonstrate overall ...

Research priorities for data science and artificial intelligence in global health: an international consensus exercise.

Applications of data science and artificial intelligence (AI) in global health are expanding, yet research remains fragmented and often misaligned wit...

Advancing Personalized Strategies for Obesity Prevention Through Biologically and Behaviorally Tailored Interventions: The Strategic Framework for the BETTER4U Initiative.

The BETTER4U project (Preventing Obesity through Biologically and bEhaviorally Tailored inTERventions for You) is a Horizon Europe initiative (GAP 101...

Use of Bioinformatics and Machine Learning to Identify Circadian Rhythm Disruption- and Endoplasmic Reticulum Stress-Associated Biomarkers in Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disorder closely linked to circadian rhythm disruption (CRD) and endoplasmic reticu...

Puerarin mitigates tumor necrosis factor-nuclear factor-κB axis-driven macrophage inflammation and atherogenesis: Integrative network pharmacology and experimental validation.

BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory disease that constitutes the primary pathological basis of cardiovascular disorders. Althou...

Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study.

BACKGROUND: COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. Wh...

Disabilities and Generative Artificial Intelligence Education in Rheumatology Fellowship: Educational Module and Simulation Exercise.

OBJECTIVE: People with rheumatic and musculoskeletal diseases (RMDs) can acquire disabilities that affect their participation in work and school. Gene...

Multi-dimensional dynamic monitoring of gut microbiota-emotion axis and precision exercise intervention in compulsory drug rehabilitation: A 24-week randomized controlled trial.

BACKGROUND/OBJECTIVES: Substance use disorders (SUDs) present a global health challenge with high relapse rates. Emerging evidence implicates gut micr...

Reply - Letter to the editor - "Comment on "Measuring diet intake in adolescents: Relative validation of an artificial intelligence enhanced, image assisted mobile application"".

For machine learning to advance precision pain medicine, we must exercise caution.

Artificial intelligence for personalized multiple micronutrient supplementation in maternal health.

Maternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long-term health risks for mot...