Endocrinology

Artificial Intelligence in Nutrigenomics: A Critical Review on Functional Food Insights and Personalized Nutrition Pathways.

BACKGROUND: This review critically evaluates the applications of artificial intelligence in nutrigenomics, focusing on its role in interpreting functional food-gene interactions, supporting personalized nutrition strategies, and enabling evidence-based dietary interventions for improved health outcomes. METHODS: A systematic literature search was conducted using PubMed, Scopus, Web of Science, and...

Pubertal Hormones and the Early Adolescent Female Brain: A Multimodality Brain MRI Study.

Puberty is a critical developmental process that is associated with changes in pubertal (or steroid) hormone levels, which are believed to influence adolescent behaviour via their effects on the developing brain. So far, there are limited and inconsistent findings regarding the relationship between steroid hormones and brain structure and function in adolescent females, with many existing studies ...

Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.

Purpose To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnose...

A comprehensive analysis reveals the molecular mechanisms linking preservatives to prostate cancer risk.

BACKGROUND: Preservatives, widely used in food and skincare products, may influence prostate cancer (PCa) development. This study explores the effects...

Development of an individualized prediction model for dynamic adaptations in performance and immune function associated with dietary patterns in endurance athletes using machine learning.

BACKGROUND: Psychological resilience significantly influences immune function and health outcomes in high-stress populations, yet mechanisms underlyin...

A Deep Learning Framework for Predicting Teprotumumab Treatment Response in Thyroid Eye Disease.

PURPOSE: To develop and evaluate a deep learning-based framework for quantifying thyroid eye disease (TED) severity before and after teprotumumab trea...

C-X-C Motif Chemokine Ligand 3 as a Potential Biomarker for Diagnosis and Prognosis of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease globally, yet reliable biomarkers for its diagnosis and progression asse...

COL14A1 drives ischemic cardiac injury in PCOS: an artificial intelligence-identified biomarker.

BACKGROUND: Polycystic ovary syndrome (PCOS) and ischemic cardiomyopathy (ICM) share metabolic and cardiovascular risk factors, including insulin resi...

Ten-year population-based assessment of multimorbidity burden progression in a regional cohort of 5.5 million adults.

Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently...

Stress hyperglycemia ratio and mortality risk in cardiometabolic multimorbidity: a multicenter retrospective study.

Predicting progression-free survival in hormone-receptor positive (HR+/HER2-) metastatic breast cancer (MBC) treated with CDK4/6 inhibitors: A machine learning approach.

BACKGROUND: In HR+/HER2- metastatic breast cancer (MBC), CDK4/6 inhibitors combined with endocrine therapy (ET) significantly improve progression-free...

Characterizing the Immune Cell Infiltration in Renal Interstitium and Therapeutic Targets of Drugs in Diabetic Nephropathy by Multiomics Study.

BACKGROUND: Immune cell infiltration in the renal interstitium contributes to the progression of diabetic nephropathy (DN), yet the precise mechanisms...

Mechanism Based Hierarchical Machine Learning for High-Throughput Quantitative Prediction of Estrogenic, Androgenic, and Thyroid Disruption Activities.

Although qualitative predictions of endocrine-disrupting chemicals (EDCs) are well established, quantitative high-throughput models remain underdevelo...

Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common complication during pregnancy, with its incidence increasing year by year. It poses numero...

Childhood dyslexia risk elevated by heavy metal mixtures from e-waste: A machine learning-driven mixture modeling study.

Environmental heavy metal mixtures from informal e-waste recycling are potential neurotoxicants, but their link to developmental dyslexia remains uncl...

A CT-based radiomics model for classification of chronic pancreatitis: new biomarkers for diagnosis and severity staging.

OBJECTIVES: To develop and validate CT-based radiomics models for the identification of chronic pancreatitis (CP) and selected CP-related complication...

Novel mechanisms of atrazine endocrine disruption: an integrated approach reveals progesterone and glucocorticoid receptor targeting.

Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Qinduced non-obstructive azoospermia.

BACKGROUND: N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), a transformation product of tire rubber antioxidants, has been increa...

Double Graph Attention Network for predicting non-alcoholic fatty liver disease in patients with type 2 diabetes.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, while non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver di...

Explainable reinforcement learning for glucose monitoring based on shapley value analysis.

BACKGROUND AND OBJECTIVE: Effective diabetes management requires continuous regulation of blood glucose in response to complex factors such as diet, a...