Endocrinology

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, imposing a significant burden on society. Early detection of patients at higher risk of CVD events could reduce mortality. None of the models currently used for this purpose incorporates insulin resistance (IR), which can be measured using triglyceride and glucose levels. This study aims to explore ...

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 the pancreas. Although there are various treatment options available for controlling diabetes, including non-peptide-based medications, the majority of these have adverse effects and are limited in comparison to peptide-based drugs. Protein drugs offer numerous benefits, including weight loss, signif...

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 axis promotes NLRP3 inflammasome assembly, leading t...

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 costs. Machine learning (ML) has shown strong potentia...

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-world application due to its "black-box" nature, chara...

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 patient outcomes. This study developed an interpretable ...

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 hydrodynamics in MPs bioaccumulation and fish physio...

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 in the connections between brain regions responsib...

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 predict hypoglycaemia risk at the start of exercis...

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, contribute to shortened healthspan. We studied a deep-l...

Integrative metabolomics and machine learning reveal the toxic mechanisms of elaidic acid in polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is characterized by reproductive dysfunction and metabolic disturbances, including obesity, insulin resis...

Diabetic retinopathy classification network with multi-frequency contextual attention module.

Diabetic retinopathy (DR) remains a major cause of visual impairment globally, and early, accurate diagnosis is critical for effective intervention. T...

Integrative network toxicology and molecular docking preliminarily explore the potential role of polystyrene microplastics in childhood obesity.

Childhood obesity is a severe global epidemic, and emerging evidence suggests environmental pollutants like polystyrene microplastics (PS-MPs) may dis...

The Future is Now: Artificial Intelligence and Adrenal Incidentaloma Characterization.

Contextual anatomy-guided deep learning for accurate fovea segmentation in diabetic retinopathy fundus images.

Accurate fovea segmentation in fundus images is a critical step in diabetic retinopathy screening; however, it remains a challenging task due to the i...

Machine learning prediction of metabolic-associated fatty liver disease in type 2 diabetes: Emphasizing data imputation and feature selection.

Metabolic-Associated Fatty Liver Disease (MAFLD) is common among Type 2 Diabetes (T2DM) patients. The coexistence of these conditions increases the ri...

Identification of the immune-related diagnostic biomarkers between Graves' disease and thyroid carcinoma based on comprehensive bioinformatics analysis and machine learning.

Increasing evidence suggests that Graves' disease (GD) may increase the risk of thyroid cancer (THCA), but diagnostic biomarkers associated with it re...

The digital pandemic in youth: unpacking the algorithmic impact on mental health in an urbanized world.

PURPOSE OF REVIEW: Rapid urbanization and the algorithmically mediated digital environment have been linked to a "digital pandemic" in youth mental he...

A machine learning model based on routine blood-derived indices for early arterial stiffness prediction in the community.

BACKGROUND: Increased arterial stiffness is a high-risk factor for cardiovascular diseases, making its early identification crucial. Based on data fro...