Diabetes

Association between stress hyperglycemia ratio and all-cause mortality in neurocritical patients.

Fluctuations in blood glucose during acute neurocritical illness are associated with poor outcomes, but the role of stress hyperglycemia ratio (SHR) in predicting outcomes across diverse neurocritical conditions remains unclear. This study evaluated SHR as a prognostic indicator for short and long-term mortality and its utility in machine learning-enhanced predictive models. Using data from the Me...

Association of diabetes severity with cognitive function in US adults: a cross-sectional analysis of the AI-READI multicentre cohort.

OBJECTIVES: To evaluate whether type 2 diabetes mellitus (T2DM) presence and severity are associated with differences in global and domain-specific cognitive function among US adults, using standardised Montreal Cognitive Assessment (MoCA) testing. DESIGN: Cross-sectional study SETTING: Three U.S academic medical centres participating in the Artificial Intelligence-Ready and Equitable Atlas for Di...

A copula based supervised filter for feature selection in machine learning driven diabetes risk prediction.

Effective feature selection is critical for building robust and interpretable predictive models, particularly in medical applications where identifyin...

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 ...

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

BACKGROUND: Primary glomerulonephritis (GN) is a heterogeneous group of kidney disorders where understanding their pathophysiology remains incomplete....

Technology-enabled insights into SLC transporters in MAFLD: redefining the multi-hit pathogenesis and therapeutic landscape.

Recent advances in structural biology, functional genomics, and artificial intelligence (AI) have expanded understanding of the solute carrier (SLC) t...

Association between fundus sex index and hypertension, diabetes mellitus, and smoking in the Kumejima study.

Fundus parameters can be used to quantify masculinity or femininity as a fundus sex index (FSI) ranging from 0 to 1. The purpose of this study was to ...

Pipeline-optimized machine learning for chronic fatigue syndrome diagnosis: A lightweight, interpretable model using blood biochemical and metabolomic data.

INTRODUCTION AND BACKGROUND: Chronic fatigue syndrome (CFS) is a debilitating multisystem disorder with persistent fatigue and functional impairment, ...

Modeling strategies for CGM data: A scoping review of mechanistic, machine learning, and hybrid approaches in diabetes management.

BACKGROUND: The increasing availability of continuous glucose monitoring (CGM) data has opened new avenues for modeling glucose dynamics in diabetes m...

Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.

The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in cardiovascular disease (CVD) is hindered by a vast an...

The value of triglyceride-glucose-body shape index (TyG-ABSI) in evaluating stroke: a multi-centre cross-sectional study.

BACKGROUND: Stroke is the second leading cause of disability and mortality worldwide. A body shape index (ABSI) is calculated as waist circumference /...

Volatilomics insights in older adults with diabetes: from metabolic mechanisms to precision geriatric medicine.

AIMS: This review aims to evaluate the hypothesis that Volatilomics-the comprehensive analysis of volatile organic compounds (VOCs) from breath, skin,...

A predictive model for significant periodontal disease progression: A large-scale cohort study.

BACKGROUND: The progression of periodontitis is challenging to predict. This study aimed to develop and validate a machine learning model to identify ...

Real-World Prospective Validation and Economic Evaluation of Deep Learning- Based Diabetic Retinopathy Detection From Fundus Photographs: A Systematic Review and Meta-analysis.

BACKGROUND: Deep learning (DL) has shown promise in delivering diagnostic and economic benefits for detecting diabetic retinopathy (DR) from fundus ph...

Renal interstitial inflammation predicts IgA nephropathy progression via multiple machine learning models.

BACKGROUND: Renal interstitial inflammation (RII) is a frequent pathological feature in IgA nephropathy (IgAN), but its prognostic value remains uncer...

Integrated approaches to preventing macrovascular complications in type 2 diabetes mellitus: Advances in secondary prevention.

BACKGROUND: Type 2 diabetes mellitus (T2DM) substantially increases the risk of macrovascular complications, including coronary artery disease, cerebr...

Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.

The purpose was to evaluate retrieval-augmented generative (RAG) artificial intelligence (AI) methods for assessing the regulatory compliance of drug ...

Diabetes Spotlight: Martin Myers, MD, PhD-Understanding Metabolism and Glucose Through Neural Networks.

Deep Learning-Enabled Diabetic Retinopathy Screening: A Techno-Clinical Revolution or Just More Artificial Intelligence Hype?

Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models.

Accurate blood glucose level (BGL) forecasting is critical for diabetes self-management and clinical decision-making. Although deep learning models ba...