Diabetes

A comparative approach of machine learning models to predict attrition in a diabetes management program.

Approximately 11.6% of Americans have diabetes and South Carolina has one of the highest rates of adults with diabetes. Diabetes self-management programs have been observed to be effective in promoting weight loss and improving diabetes knowledge and self-care behaviors. The ability to keep vulnerable individuals in these programs is critical to helping the growing diabetic population. Utilizing m...

Identification and verification of feature biomarkers associated with CD14 monocytes in type 1 diabetes.

AIMS/INTRODUCTION: Monocytes contribute to the immune dysregulation of pancreatic islets in Type 1 diabetes mellitus (T1D). This study aims to identify the feature genes of CD14 monocytes in T1D, which might offer a new perspective on immune dysregulation and potential therapeutic targets in T1D.

Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery.

BACKGROUND: Acute pancreatitis (AP) is a severe inflammatory disease frequently accompanied by disturbances in glucose metabolism, which further compl...

Diabetic retinopathy in rural communities: a review of barriers to access of care and potential solutions.

INTRODUCTION: Diabetic retinopathy (DR) is a leading cause of vision loss. With an estimated 38.4 million Americans diagnosed with DM, the disease exe...

Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework.

BACKGROUND: Effective diabetes management requires precise glycemic control to prevent both hypoglycemia and hyperglycemia, yet existing machine learn...

Uncovering key markers and therapeutic targets for renal fibrosis in diabetic kidney disease through bulk and single-cell RNA sequencing.

BACKGROUND: Diabetic kidney disease (DKD) is the major cause of chronic kidney failure, with tubulointerstitial fibrosis playing a crucial role in dis...

Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data.

BACKGROUND: Oxidative stress plays a crucial role in the development of diabetic foot ulcers (DFU). However, its underlying mechanisms are not fully u...

Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study.

BACKGROUND: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an...

Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients.

BACKGROUND: Coronary Heart Disease (CHD) and Non-Alcoholic Fatty Liver Disease (NAFLD) share overlapping pathogenic mechanisms including adipose tissu...

The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.

BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒...

A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome.

Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (...

Multi-modal Classification of Retinal Disease Based On Convolutional Neural Network.

Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irreversible blindness without timely diagnosis and tr...

Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.

Community-acquired pneumonia (CAP) is associated with high mortality, and accurate diagnosis and risk prediction are essential for improving patient o...

Optic nerve sheath diameter in the prognosis of patients with traumatic optic neuropathy: Evaluation and predictive efficacy.

The purpose of this study was to evaluate and predict visual prognosis in patients with traumatic optic neuropathy by measuring the optic nerve sheath...

Multi-modal models using fMRI, urine and serum biomarkers for classification and risk prognosis in diabetic kidney disease.

BACKGROUND: Functional magnetic resonance imaging (fMRI) is a powerful tool for non-invasive evaluation of micro-changes in the kidneys. This study ai...

Classification of Japanese type 1 diabetes based on clinical phenotypes and its association with diabetic complications: Across-sectional study.

INTRODUCTION: Despite the increasing number of studies using machine learning to develop individualized treatment strategies, only a few have been con...

Social and Structural Determinants of Lower Extremity Amputations in Diabetes.

PURPOSE OF REVIEW: Lower extremity amputations (LEAs) are among the most severe complications of diabetes, with approximately 1.5 million procedures p...

Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.

In general, deficient birth weight neonates suffer from hypoglycemia, and this can be quite disadvantageous. Like oxygen, glucose is a building block ...

Advanced multiscale machine learning for nerve conduction velocity analysis.

This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal pr...

Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.

Kidney pathology of immunoglobulin A nephropathy (IgAN), which is the key finding of both diagnosis and risk stratification, involves labor-intensive ...