Diet & Nutrition

Enhancing heat and mass transfer in hybrid nanofluid with gyrotactic microbes and local thermal non equlibrium effects using artificial neural network.

This study analyzes the impact of local thermal non-equilibrium on the bioconvection flow of hybrid nanofluid across a slender extending sheet containing gyrotactic bacteria using artificial neural networks trained using a Bayesian regularization backpropagation approach (ANN-BRS). The effects of magnetic fields, thermal radiation, and Hall current are all things related to fluid flow. The suggest...

Characterizing spatiotemporal trends in PM2.5 component exposures across the western United States using daily 1-km estimates from multi-source data and deep learning.

Reliable, long-term information on the chemical composition of fine particulate matter (PM2.5) is essential for air quality management and health risk assessment, yet such data are rarely available with both high spatial and temporal resolution across large geographic areas. We developed a spatiotemporal modeling framework using the deep forest algorithm to estimate daily, gap-free PM2.5 component...

Artificial intelligence-based analysis of thrombotic and infectious risk factors in peripherally inserted central catheters (PICCs).

INTRODUCTION: Peripherally inserted central catheters (PICCs) are increasingly used in France for prolonged intravenous therapies such as chemotherapy...

Artificial Intelligence Interventions Targeting Obesity-Related Behaviors: Protocol for a Scoping Review.

The objective of this scoping review is to examine the nature, extent, and impact of AI-supported interventions that include an AI agent intended to i...

Association of the estimated glucose disposal rate combined with a body shape index with all-cause and cardiovascular-specific mortality among individuals with cardiovascular-kidney-metabolic syndrome.

BACKGROUND: Individuals with cardiovascular-kidney-metabolic (CKM) syndrome exhibit a substantially elevated risk of all-cause and cardiovascular-spec...

Do Commercial Airline Policies for Passengers With Obesity Carry Enough Weight?

INTRODUCTION: The travel industry has a responsibility to accommodate the needs of all its customers, including those with obesity. It is not known to...

Adherence to guidelines, pitfalls, and emerging technologies in endoscopic practice for celiac disease: a nationwide SIGENP survey.

UNLABELLED: Upper gastrointestinal endoscopy (UGIE) with duodenal biopsies remains the gold standard for diagnosing celiac disease (CD) in children an...

Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states.

BACKGROUND: Obesity is a major health concern linked to chronic conditions such as diabetes and cardiovascular disease. However, most neurological stu...

Using mobile applications for body composition analysis: A technical review of an artificial intelligence-based tool: Technical review of a body composition assessment mobile app.

BACKGROUND: Advances in health technology have enabled body composition assessments using smartphone photos, offering an accessible, cost-efficient, a...

Comprehensive evaluation of triglyceride glucose index-a body shape index (TyG-ABSI) for incident peripheral artery disease: data-driven phenotyping and machine learning-based risk prediction in the UK Biobank.

BACKGROUND: The prevalence of Peripheral Artery Disease (PAD) is rising globally, yet early risk stratification remains challenging due to the limitat...

Unveiling Endotypes in Systemic Lupus Erythematosus Through Multi-Omic Analysis: Insights into Cardiovascular and Renal Complications.

BACKGROUND: Systemic lupus erythematosus (SLE) shows clinical and molecular heterogeneity, and cardiovascular (CV) complications and lupus nephritis (...

Identification of core genes mediating the association between obesity and hepatocellular carcinoma: A bioinformatics study based on mitochondrial metabolism and immune pathways.

PURPOSE: Obesity is strongly associated with hepatocellular carcinoma (HCC), yet the molecular mechanisms linking them remain unclear. This study aime...

Application of untargeted plasma metabolomics and machine learning to construct a diagnostic model for hypertrophic cardiomyopathy: a case-control study.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease. Recent metabolomics studies have revealed pathogeni...

Association between the oxidative balance score and depressive symptoms in adults with metabolic syndrome: a cross-sectional study from NHANES.

OBJECTIVE: This study investigated the association between the Oxidative Balance Score (OBS) and the odds of depressive symptoms in adults with metabo...

Predicting rapid weight gain in six-month-old infants: an exploratory modeling study.

BACKGROUND: Rapid weight gain (RWG) in early life is a significant risk factor for childhood obesity. Its multifactorial etiology warrants exploratory...

Gut microbial modulation of 3-hydroxyanthranilic acid and dopaminergic signalling influences attention in obesity.

BACKGROUND: Obesity-related alterations in the gut microbiota have been linked to cognitive decline, yet their relationship with attention remains poo...

Body mass index and diet-related inflammation as predictors of sleep disorders: A cross-sectional study.

This study examines diet as a key risk factor for sleep disorders and integrates physiological indicators to develop a machine learning (ML)-based mod...

Climate predictors of child undernutrition: Insights from a machine learning model.

BACKGROUND: Climate variability is increasingly recognized as a driver of child undernutrition, yet the non-linear relationships between specific clim...

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is...

Early Prediction of Standing at Discharge in Moderate-to-Severe TBI: A Clinical Machine Learning Model Integrating Modifiable and Nonmodifiable Factors.

OBJECTIVE: To develop and internally validate a machine learning model to predict favorable standing ability at hospital discharge in patients with mo...