Obesity

Obesity, microRNA circulating microRNA signatures reveal core and reversible dysregulations in obesity via machine learning.

Obesity is a complex metabolic disease characterized by systemic metabolic and inflammatory dysregulation, yet the molecular signatures underlying these processes remain incompletely understood. Circulating microRNAs (miRNAs) have emerged as promising biomarkers capable of capturing systemic regulatory changes associated with obesity. In this study, we investigated whether machine learning (ML) co...

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.

BACKGROUND: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despite the availability of clinical data, there remains a gap in interpretable tools that support risk stratification in this age group, particularly in alignment with local clinical guidelines. OBJECTIVE: The purpose of this study is to develop a clinic...

Construction of a predictive model for the risk of non-alcoholic fatty liver disease in patients with sleep apnea syndrome based on multiple machine learning algorithms: a multicenter study.

BACKGROUND: Sleep apnea syndrome (SAS) is closely related to an increased risk of non-alcoholic fatty liver disease (NAFLD), but current clinical tool...

Development and validation of an interpretable machine learning model for early risk prediction of acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of global morbidity and mortality, with early prediction critical for timely int...

Beyond eGFR and Albuminuria: Biological Pathways and Multiomics in Cardiovascular-Kidney-Metabolic Disease.

Cardiovascular-kidney-metabolic disease (CKM) represents a growing public health challenge driven by the convergence of obesity, diabetes, and cardiov...

Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study.

OBJECTIVE: To explore clinically meaningful phenotypes of preeclampsia using unsupervised machine learning. DESIGN: Prospective cohort study. SETTING:...

Heterogeneous pathways to depressive and anxiety disorders: A cluster-based predictive study in a nationwide longitudinal cohort.

BACKGROUND: Early prediction of depressive and anxiety disorders is challenging due to substantial heterogeneity in risk pathways. Conventional machin...

Identifying significant environmental predictors of change in delay discounting among adolescents in the Adolescent Brain Cognitive Development Study.

Delay discounting (a preference for smaller immediate rewards vs. larger delayed rewards; DD) is a transdiagnostic risk factor for health outcomes inc...

Linking the exposome to the brain-behaviour phenotype.

A range of environmental, lifestyle and biological exposures across the lifespan - varying in timing, duration and intensity - interact with genetic f...

Temporal dynamics and associated factors of sleep quality in Chinese boarding high school students: a repeated cross-sectional study across four waves (2019-2024).

This study investigated temporal changes in sleep quality among Chinese boarding high school students across four phases (2019:pre-pandemic baseline, ...

Identifying frailty trajectories in older patients with acute myocardial infarction using structural entropy clustering: a prospective cohort study.

BACKGROUND: Frailty significantly complicates clinical outcomes in older adults with acute myocardial infarction, yet its progression is dynamic and h...

Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis.

BACKGROUND: Circadian syndrome (CircS) augments the conventional metabolic syndrome construct by adding disturbed sleep and depressive features. Wheth...

Tetrahedral framework nucleic acids as a promising vehicle for small molecule drugs of traditional Chinese medicine: application to disease prevention and treatment.

The modernization of Traditional Chinese Medicine (TCM) faces considerable challenges, primarily due to the poor water solubility, low chemical stabil...

Development and validation of a deep learning model for liver shear stiffness regression using abdominal multiparametric MRI across multiple sites and vendors.

OBJECTIVE: Liver stiffness measurement is important for assessing chronic liver disease (CLD). MR elastography (MRE) requires specialized hardware and...

Altered effective connectivity between the prefrontal cortex and caudate in children with overweight and obesity.

OBJECTIVE: This study aimed to examine the interaction patterns between the reward and inhibitory control systems in children with overweight and obes...

SCUD - Smart Culinary Utility Device: Leveraging edge AI for battery optimization and operation cycles.

Current day society pursues a lifestyle that is simpler, more efficient, reliable, faster, and progressively automated. Culinary Utility Device availa...

Integrative analysis of the mouse cecal microbiome across diet, age, and weight in the diverse BXD population.

BACKGROUND: The gut microbiota adapts to and shapes the host's metabolic state through affecting circulating metabolites and consequent gene regulator...

Thrombospondin-2 is a performant biomarker of at-risk MASH and advanced MASH fibrosis in a large multicentre European cohort.

BACKGROUND: Non-invasive biomarkers with biological rationale are needed for identifying patients with progressive metabolic dysfunction-associated st...

Clinically interpretable nomogram incorporating radiomics and deep learning feature fusion from abdominal CT for preclinical type 2 diabetes.

OBJECTIVE: To develop and externally validate a multimodal artificial intelligence framework for opportunistic detection of preclinical type 2 diabete...

Design and validation of renal stone detection using multi-architecture feature extraction with deep sequential learning model on axial computed tomography images.

Kidney stone disease is a significant public health threat, with its prevalence escalating due to evolving dietary habits, rising rates of obesity, ot...