Endocrinology

Gut Microbiota and Metabolic Health: From Dysbiosis to Therapeutics.

The gut microbiota (GM) is a pivotal regulator of host metabolism and a contributor to the pathophys...

Evaluating Accuracy of ChatGPT-4o in Automated Carbohydrate Estimation From Images as a Self-Management Tool for Adolescents With Type 1 Diabetes.

INTRODUCTION: Type 1 diabetes mellitus (T1D) requires precise carbohydrate estimation to manage bloo...

Predicting non-alcoholic fatty liver disease (NAFLD) using machine learning algorithms: Evidence from a large-scale community cohort in Taiwan.

Closely associated with metabolic disorders, non-alcoholic fatty liver disease (NAFLD) substantially...

Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.

CONTEXT: Acromegaly poses clinical challenges in terms of early diagnosis and intervention. Therefor...

Artificial intelligence to investigate metabolomics data for precision medicine.

BACKGROUND: Metabolomic data offers insights into disease mechanisms, diagnostics, and therapeutic t...

Multi-dimensional dynamic monitoring of gut microbiota-emotion axis and precision exercise intervention in compulsory drug rehabilitation: A 24-week randomized controlled trial.

BACKGROUND/OBJECTIVES: Substance use disorders (SUDs) present a global health challenge with high re...

Effectiveness of natural language intelligence technology in chronic diseases nursing: A systematic review and meta-analysis.

BACKGROUND: Chronic diseases pose a heavy global burden, with challenges in utilizing unstructured d...

Deep Learning Model to Detect Diabetic Retinopathy in 45° Images Using Ground Truth from Ultra-Widefield Imaging.

PURPOSE: To evaluate the performance of a deep learning (DL) model in classifying diabetic retinopat...

Machine learning-enabled early risk stratification of β-Iactam-induced electrolyte imbalances.

AIMS: To estimate the incidence of β-lactam/β-lactamase inhibitor - associated electrolyte imbalance...

Designing a machine learning model for predicting cardiovascular events using the triglyceride-glucose index: a cohort study.

INTRODUCTION: Cardiovascular diseases (CVD) are the leading cause of death in developing countries, ...

Advancement in peptide-based therapeutics for the treatment of type 2 diabetes mellitus: current progress and future prospects.

Diabetes is a chronic medical disorder caused by insufficient production of the hormone insulin by t...

Generative AI Uncovers Novel Chrebp/Txnip Axis Inhibitors with Potential Anti-inflammatory Activity.

Type 2 diabetes is driven in part by metabolic inflammation, where activation of the Chrebp/Txnip ax...

Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.

OBJECTIVES: Predicting mortality is vital for tailoring treatments, improving care and reducing cost...

Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.

BACKGROUND: Deep learning has shown promise in diabetes management but faces challenges in real-worl...

Contrast-enhanced CT-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting preoperative diagnosis of pheochromocytoma and adrenal adenoma.

Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses.

Chronic kidney disease (CKD) is a progressive condition requiring early detection for optimal patien...

Random forest-based QSAR and molecular dynamics insights into steroidal pregnanes as 11β-hydroxysteroid dehydrogenase type 1 modulators for type 2 diabetes.

The enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) is a validated therapeutic target for ...

Hydrodynamic modulation of microplastic bioaccumulation in edible fish: Integrating biomarker networks, machine learning, and food safety perspectives.

Microplastic (MP) pollution poses increasing environmental and food safety risks, yet the role of hy...

A neuroimaging functional connectivity signature of emotional conflict monitoring predicting cognitive decline in type 2 diabetes.

Type 2 diabetes (T2D) is associated with cognitive decline and neurodegenerative disorders. Changes ...

GlucoseGo: a simple tool to predict hypoglycaemia during exercise in type 1 diabetes.

AIMS/HYPOTHESIS: This study aimed to develop an accessible tool, derived using machine learning, to ...

Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations.

Accelerated biological aging, as well as cardiovascular, kidney, and metabolic (CKM) diseases, contr...