Diabetes

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

Type 2 diabetes (T2D) is considered as a risk factor of triple-negative breast cancer (TNBC). So, there is a significant chance of their co-existence. The management of TNBC with T2D becomes more complex than without T2D due to the conflict of therapies, since some T2D drugs may have bad impact on TNBC and vice-versa. Beside this, drug-drug interaction due to the polypharmacy of multiple drugs, du...

Comparative predictive accuracy of machine learning versus traditional statistical methods for diabetes-related complications: A systematic review and analysis.

AIMS: To compare the predictive accuracy of machine learning models versus traditional statistical models for predicting and detecting long-term complications among individuals with diabetes (PROSPERO: CRD420250629747). METHODS: We systematically searched MEDLINE, PubMed, Cochrane and Scopus (2014-2025) for studies developing or validating prediction models in people with diabetes. Excluding case-...

Integrated Bioinformatics and Explainable Machine Learning Analyses Reveal Mesenchymal Stem Cell Exosome-Related Biomarkers for Diabetic Retinopathy.

PURPOSE: This study aims to identify potential biomarkers for diabetic retinopathy (DR) by focusing on genes associated with mesenchymal stem cell-der...

Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.

BACKGROUND: Prognostic models for hepatocellular carcinoma (HCC) may have limited accuracy. We aimed to construct and validate a novel prognostic mode...

The role of metabolic health in neurostructural and cognitive alterations in bipolar disorders.

BACKGROUND: Bipolar disorders (BD) rank among the most disabling conditions, affecting millions worldwide. Cognitive impairment in BD is linked with b...

Digital Twin Applications in Diabetes Management: Scoping Review.

BACKGROUND: Digital twin (DT) systems have emerged as a promising approach in health care, enabling real-time, patient-specific virtual modeling and p...

GAT-BiGRU: explainable multi-task temporal graph learning for glucose forecasting, hypoglycemia risk, and counterfactual insulin adjustment.

BACKGROUND: People with type 1 diabetes mellitus (T1DM) show glucose variability driven by insulin dosing, meals, activity, and circadian rhythms. Man...

Machine learning-based predictive model for sleep disorders in diabetic patients: data analysis from CHARLS.

Sleep disorders are prevalent and constitute a major concern in patients with diabetes mellitus. Therefore, the aim of this study was to investigate t...

Exploration of the mechanism of Xinjiang Rosa laxa Retz.Fruit extract on IgA nephropathy by bioinformatics and machine learning methods.

This study aimed to elucidate the core therapeutic targets and mechanisms of action of rosehip extract (FRLE) in IgA nephropathy (IgAN) by integrating...

Visceral adipose tissue inflammation in endometrial cancer.

BACKGROUND: Obesity is the largest risk factor for endometrial cancer. Body Mass Index (BMI) does not fully capture obesity's metabolic and inflammato...

RetiMap: Automated Retinal Vascular Measures Link Microvascular Structure to Metabolic Health and Predict Cardiovascular Risk.

Fundus imaging enables noninvasive, high-resolution visualization of the retinal microvasculature. Advances in artificial intelligence (AI) now allow ...

Determinants of childhood and adolescent obesity: An explainable AI approach using the ICAD database.

OBJECTIVES: To identify the biological, behavioural, and socio-demographic determinants of childhood and adolescent obesity and evaluate how their rel...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....

Deep Learning Assisted Double Inversion Recovery improves Focal Lesion Detection in Optic Neuropathy.

OBJECTIVES: To compare the acquisition time, image quality, and diagnostic confidence of DL-accelerated DIR (DIR-DL) with conventional MRI in patients...

An AI-driven framework for diabetic foot ulcer classification, segmentation, and depth estimation.

Diabetic Foot Ulcers are a severe complication of diabetes that can lead to infection, amputation, and increased mortality, making timely and objectiv...

Prediction of Alzheimer's disease risk factors from retinal images via deep learning: Development and validation of biologically relevant morphological associations in the UK Biobank.

BackgroundThe systemic, metabolic, lifestyle factors have established associations with Alzheimer's disease (AD) through epidemiologic and AD-specific...

Hypericum perforatum L.: a legacy of herbal medicine in light of modern scientific evidence.

ETHNOPHARMACOLOGICAL RELEVANCE: Hypericum perforatum L. has been used for centuries in traditional medicine, with core therapeutic applications includ...

Navigating Diabetes Management in the Digital Era: Scoping Review of Online Health Information-Seeking Behavior.

BACKGROUND: Online health information seeking (OHIS) has become a central component of chronic disease management within an increasingly interactive, ...

Diagnostic performance of an artificial intelligence software for diabetic retinopathy organised screening in a pilot study.

Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus (DM) with a prevalence that varies from 10% to 61% in different...

Multi-omics body-fluid biomarkers in immunoglobulin a nephropathy: Analytical Validation, standardization, and clinical chemistry translation of diagnostic and prognostic Analytes.

IgA nephropathy (IgAN) is the most prevalent primary glomerulonephritis worldwide. Although optimized supportive therapy is administered to these pati...