Diabetes

Leveraging Vision Transformers in Multimodal Models for Retinal OCT Analysis.

Optical Coherence Tomography (OCT) has become an indispensable imaging modality in ophthalmology, providing high-resolution cross-sectional images of the retina. Accurate classification of OCT images is crucial for diagnosing retinal diseases such as Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). This study explores the efficacy of various deep learning models, including ...

Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as a critical microvascular complication associated with high mortality rates. Despite the development of diverse therapeutic strategies targeting metabolic improvement, hemodynamic regulation, and fibrosis mitigation, the pre...

A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.

BACKGROUND: Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to ...

Effects of neonicotinoid pesticide exposure in the first trimester on gestational diabetes mellitus based on interpretable machine learning.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and seriously threatens the health of mothers and of...

A stretchable, adhesive, and wearable hydrogel-based patches based on a bilayer PVA composite for online monitoring of sweat by artificial intelligence-assisted smartphones.

Real-time monitoring of sweat using wearable devices faces challenges such as limited adhesion, mechanical flexibility, and accurate detection. In thi...

scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.

Transcriptome-wide association studies (TWASs) help identify disease-causing genes but often fail to pinpoint disease mechanisms at the cellular level...

Optoelectronic-Coupled-Driven Microrobot for Biological Cargo Transport in Conductive Isosmotic Glucose Solution.

Electric field-driven micro/nanorobots, as micro/nanodevices with autonomous motion capability, have emerged as promising candidates for targeted carg...

Multimodal Fusion of Glucose Monitoring and Food Imagery for Caloric Content Prediction

Effective dietary monitoring is critical for managing Type 2 diabetes, yet accurately estimating caloric intake remains a major challenge. While con...

Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data

Modeling the continuous--time dynamics of probability distributions from time--dependent data samples is a fundamental problem in many fields, inclu...

Integrating Natural Language Processing and Exercise Monitoring for Early Diagnosis of Metabolic Syndrome: A Deep Learning Approach

Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It incr...

Predicting dementia in people with Parkinson's disease.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...

Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis.

Breast cancer (BC) is among the most prevalent malignancies and remains the leading cause of cancer-related mortality in women worldwide. While prior ...

Ethics of Artificial Intelligence in Medicine and Ophthalmology.

BACKGROUND: This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first i...

A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction

Accurate blood glucose prediction can enable novel interventions for type 1 diabetes treatment, including personalized insulin and dietary adjustmen...

Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)

The effective diagnosis of acute and hard-to-heal wounds is crucial for wound care practitioners to provide effective patient care. Poor clinical ou...

4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients

Diabetes is one of the most prevalent diseases worldwide, characterized by persistently high blood sugar levels, capable of damaging various interna...

Next-generation Approaches in Targeting Polycystic Ovarian Syndrome: Innovative Strategies.

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder that affects millions of women worldwide and is characterized by ovarian dysfunction,...

Developing muscarinic receptor M1 classification models utilizing transfer learning and generative AI techniques.

Muscarinic receptor subtype 1 (M1) is a G protein-coupled receptor (GPCR) and a key pharmacological target for peripheral neuropathy, chronic obstruct...

Application of interpretable machine learning algorithms to predict macroangiopathy risk in Chinese patients with type 2 diabetes mellitus.

Macrovascular complications are leading causes of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), yet early diagnosis of car...

Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers

Diabetes remains a significant health challenge globally, contributing to severe complications like kidney disease, vision loss, and heart issues. T...