Diabetes

Discovery of novel potential 11β-HSD1 inhibitors through combining deep learning, molecular modeling, and bio-evaluation.

11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) has been shown to play an important role in the t...

The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes.

BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing d...

Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study.

AIMS: A precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment r...

Effectiveness of AI-driven interventions in glycemic control: A systematic review and meta-analysis of randomized controlled trials.

This systematic review aims to assess the effectiveness of AI-Driven Decision Support Systems in imp...

Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach.

Chronic kidney disease (CKD) poses a significant risk for diabetes patients, often leading to severe...

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 i...

Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis.

Nerve conduction F-wave studies contain crucial information about subclinical motor dysfunction that...

Deep learning-enhanced image analysis for liquid crystal optical sensing.

In liquid crystal (LC) sensors, each microliter of LC contains billions of molecules with numerous o...

Equitable Deep Learning for Diabetic Retinopathy Detection Using Multidimensional Retinal Imaging With Fair Adaptive Scaling.

PURPOSE: To investigate the fairness of existing deep models for diabetic retinopathy (DR) detection...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

Since the discovery of the life-saving hormone insulin in 1921 by Dr. Frederick Banting in 1921, the...

Sparse group LASSO and nonlinear machine learning for frequency-feature optimization in noninvasive blood glucose monitoring via bioimpedance spectroscopy.

Diabetic patients need to test their blood glucose levels (BGL) frequently; however, traditional met...

Recommendations for the Management of Diabetes During Ramadan Applying the Principles of the ADA/ EASD Consensus: Update 2025.

Ramadan fasting is a sacred ritual observed by approximately 1.8 billion Muslims each year, most of ...

Integrative analyses of 16S rDNA sequencing and serum metabolomics demonstrate significant roles for the oral microbiota and serum metabolites in post-kidney transplant diabetes mellitus.

Oral microbiota and serum metabolites play crucial roles in diabetes, but their relationship with po...

The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes.

This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screeni...

Artificial intelligence and diabetes: time for action and caution.

Risk prediction models for patients with recurrent diabetic foot ulcers: A systematic review.

OBJECTIVES: To systematically review published studies on risk prediction models for patients with r...

Neural Networks for On-Chip Model Predictive Control: A Method to Build Optimized Training Datasets and its Application to Type-1 Diabetes.

Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effectiv...

MIP-based electrochemical sensor with machine learning for accurate ZIKV detection in protein- and glucose-rich urine.

Nowadays, a multitude of biosensors are being developed worldwide. However, a significant challenge ...

Development and validation of a convenient dementia risk prediction tool for diabetic population: A large and longitudinal machine learning cohort study.

BACKGROUND: Diabetes mellitus has been shown to increase the risk of dementia, with diabetic patient...

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary sy...

Structural Insights into the Substrate Egress Pathways Explains Specificity and Inhibition of Human Glucose Transporters (GLUT1 and GLUT9).

Glucose transporters (GLUTs) play critical roles in cellular energy homeostasis and substrate-specif...