Diabetes

Deep Learning-Based Adrenal Gland Volumetry for the Prediction of Diabetes.

BACKGROUND: The long-term association between adrenal gland volume (AGV) and type 2 diabetes (T2D) remains unclear. We aimed to determine the association between deep learning-based AGV and current glycemic status and incident T2D.

Developing a Two-Dimensional Size-Exclusion Liquid Chromatography Platform for Isolating the High-Quality Extracellular Vesicles to Predict and Evaluate Metabolic Improvement Outcomes of Sleeve Gastrectomy.

The direct analysis of plasma exosomal components still presents significant challenges due to the interference of high concentrations of albumin, immunoglobulins, and other abundant proteins. Herein, we developed a two-dimensional size-exclusion liquid chromatography (2D-SELC) platform to isolate high-purity exosomes from human plasma for downstream lipidomics analysis. Compared to the traditiona...

Machine learning-based stratification of prediabetes and type 2 diabetes progression.

BACKGROUND: Diabetes mellitus, a global health concern with severe complications, demands early detection and precise staging for effective management...

Association of DNA methylation epigenetic markers with all-cause mortality and cardiovascular disease-related mortality in diabetic population: a machine learning-based retrospective cohort study.

BACKGROUND: Diabetes has a large and diverse population, with individuals exhibiting significant heterogeneity in the disease. The factors influencing...

Macular pigment optical density and measurement technology based on artificial intelligence: a narrative review.

Macular pigment (MP) is a crucial pigment in the macular region. It plays an important role in filtering blue light, and exhibits anti-inflammatory an...

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing pred...

The Future of Automated Insulin Delivery Systems.

Automated insulin delivery (AID) systems have revolutionized diabetes care by integrating continuous glucose monitoring (CGM), insulin pumps, and adva...

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a...

High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification.

Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for disease diagnosis, treatment, and prevention. Thi...

Barriers and Enablers Influencing the Implementation of Artificial Intelligence for Diabetic Retinopathy Screening in Clinical Practice: A Scoping Review.

BACKGROUND: Diabetic retinopathy is a leading cause of preventable blindness worldwide. Meanwhile, artificial intelligence is rapidly growing in clini...

Type 2 Diabetes Mellitus Remission, Dream or Reality? A Narrative Review of Current Evidence and Integrated Care Strategies.

Type 2 diabetes mellitus (T2DM) is a global health priority, with an estimated 629 million people projected to be affected by the year 2045. T2DM sign...

The Use of AI-Powered Thermography to Detect Early Plantar Thermal Abnormalities in Patients With Diabetes: Cross-Sectional Observational Study.

BACKGROUND: Diabetic foot problems are among the most debilitating complications of diabetes mellitus. Diabetes prevalence and complications, notably ...

Task Augmentation-Based Meta-Learning Segmentation Method for Retinopathy.

Deep learning (DL) requires large amounts of labeled data, which is extremely time-consuming and laborintensive to obtain for medical image segmentati...

Construction of a PANoptosis-Related Gene Signature for Diabetic Nephropathy.

INTRODUCTION: Diabetic nephropathy (DN) is a serious complication of diabetes. In this study, we aimed to develop a diagnostic model for DN based on P...

Commentary on "Photodiagnosis with deep learning: A GAN and autoencoder-based approach for diabetic retinopathy detection" by Gencer et al., 2025.

Micropore array-based SERS sensor assisted by convolutional neural networks for subsurface biotoxic-free detection of interstitial fluid in bioassays.

Accurate and sensitive detection of small-molecule metabolites such as uric acid, glucose, and lactic acid is critical in biomedical diagnostics and c...

Adipose-Derived Mesenchymal Stem Cells Accelerate Diabetic Foot Ulcer Healing by Promoting Macrophage M2 Polarization Through Downregulation of and .

PURPOSE: Macrophage polarization plays a critical role in the chronic wound healing of diabetic foot ulcers (DFU). Recent studies have shown that adip...

Direct-to-Consumer Testing: Relevance for Diabetes Therapy.

The performance of laboratory measurements by the people with diabetes (PwD) themselves ("direct-to-consumer testing"; DTCT) is conceptually not new f...

Preventive and therapeutic strategies via health care delivery system to minimize sight-threatening diabetic retinopathy: a narrative review.

PURPOSE OF REVIEW: To highlight various preventive and therapeutic strategies via health care delivery system to minimize sight-threatening diabetic r...

Fully Wearable Devices for Real-Time Health Monitoring and Multimodal Sensing in Nanomedicine Using Multiplexed Green Biofuels.

Fully wearable devices are crucial for real-time health monitoring, but existing devices often lack stable power, on-site signal processing, and multi...