Diabetes

Genomics of Dendritic Cell Pyruvate Metabolism Reveals Stage-Associated Circulating Immune Signatures in Diabetic Retinopathy.

BACKGROUND: The role of circulating pyruvate in diabetic retinopathy (DR) progression is poorly defined. Unravelling its cell-specific genomic regulation is vital. METHODS: Integrative genomics analysis combined two-sample Mendelian randomization (MR) to evaluate the association between circulating pyruvate and DR risk with single-cell RNA sequencing (scRNA-seq) metabolic profiling in peripheral b...

Prospective validation of an AI algorithm to identify adult-onset type 1 diabetes misclassification: protocol for a non-interventional multicentre study.

INTRODUCTION: Adult-onset type 1 diabetes (T1D) is often misclassified as type 2 diabetes (T2D), resulting in delayed treatment, missed opportunities for referrals to specialists and increased risk of complications including diabetic ketoacidosis. An electronic medical record (EMR)-based algorithm-originally trained on a large national EMR dataset to identify likely misclassified adult-onset T1D c...

Hybrid deep ensemble architecture for robust diabetic retinopathy classification: leveraging transfer learning and CNN-transformer synergy.

Diabetic retinopathy (DR) is still one of the main reasons for vision loss worldwide, especially in places where people do not have easy access to reg...

Robust Heterojunction Nanoconstructs Enable Precise Decipherment of Aqueous Humor Metabolic Profiles in Multiple Retinal Diseases.

Retinal diseases (RD), which often cause irreversible vision impairment and lack effective treatments, necessitate more attention than common ocular c...

Ketone Body Metabolism and Sodium Glucose Cotransporter 2 Inhibitor Response in Heart Failure With Ischemic Cause: A Precision Medicine Approach Using Metabolomics Integration With Global Burden of Disease Population Data Across Diverse Genetic Backgrounds.

BACKGROUND: Heart failure with ischemic cause is associated with substantial cardiovascular mortality. SGLT2 (sodium glucose cotransporter 2) inhibito...

Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.

Gestational diabetes mellitus, often known as GDM, is a major health issue that causes complications for mothers and requires patient data prediction ...

Decision tree with randomized grid search-based hyperparameter tuning and optimal feature scaling for diabetes diagnosis.

BACKGROUND: Diabetes is a chronic condition that arises when the body cannot effectively regulate blood glucose levels, either due to insufficient ins...

Development and Validation of a Novel Risk Prediction Model for Incident Diabetic Foot Complications: A Large, Longitudinal, Population-Based Machine Learning Study.

OBJECTIVE: Diabetic foot complications (DFCs) are common diabetes complications. Existing tools for predicting incident DFCs remain insufficient. This...

Pancreatic Cancer Risk Stratification Across Diabetes Stages: Development and Internal Validation of a Machine Learning Model.

BACKGROUND: Pancreatic cancer is diagnosed at advanced stages in diabetes patients. Existing prediction models require complete historical data and fo...

An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.

BACKGROUND: Oxaliplatin (OXA) is widely used in the treatment of gastrointestinal malignancies such as colorectal cancer. However, oxaliplatin-induced...

Classification of glucose profile in Japanese patients with type 1 diabetes and its association with complications.

CONTEXT: The importance of utilizing continuous glucose monitoring (CGM) to optimize glycemic profiles and thereby prevent the onset of diabetic compl...

A body roundness index (BRI)-based predictive model for metabolic syndrome in perimenopausal and postmenopausal women-from a cross-sectional machine learning study to a longitudinal dynamic assessment.

BACKGROUND AND AIMS: Metabolic syndrome (MetS) is highly prevalent among perimenopausal and postmenopausal women and poses a major public health chall...

Machine Learning Surface Plasmon Resonance Biosensor for Simultaneous Detection of Glucose and Plasmodium falciparum Biomarkers.

In low-resource clinical settings, Plasmodium falciparum infection and hypoglycemia frequently co-present yet require discrete diagnostic platforms, c...

Improving Precision Diagnosis and Treatment of Diabetes in Low-Resource Settings in Africa Through Structured Clinical Phenotyping: A Narrative Review.

Precision diabetes care is an evolving evidence-based, personalised approach that uses an individual's clinical, metabolic, and genetic data to accura...

Emerging Applications of Artificial Intelligence in Pediatric Care.

Artificial intelligence (AI) technologies such as machine learning (ML), deep learning (DL), predictive analytics and other tools are rapidly changing...

Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.

Proteomics represents a powerful but underutilized approach for characterizing eye aging. Here, leveraging data from three large-scale, cross-national...

Cross-population validation of the TyG-ABSI index as a novel predictor for chronic obstructive pulmonary disease: an integrated analysis using logistic regression and explainable machine learning.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is increasingly recognized as a systemic inflammatory condition closely linked to metabolic d...

Association of the CHG Index with the onset, progression, and prognosis of cardiovascular-kidney-metabolic syndrome: findings from two large prospective cohorts.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome represents a composite disease state driven by glucose and lipid metabolic dysregulation. T...

Large language models for generating longitudinal synthetic data in low-risk pregnancy care.

This article describes an obstetric dataset covering the full continuum of care of 5000 synthetic low-risk pregnant women, from preconception to post-...

Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.

Prognostic assessment of diabetic kidney disease (DKD) is essential for personalized management. This study developed eight machine learning models us...