Diabetes

Exploring the link between the ZJU index and sarcopenia in adults aged 20-59 using NHANES and machine learning.

Sarcopenia, characterized by progressive loss of muscle mass and function, is a growing public health concern. The ZJU index, a novel metabolic marker, integrates lipid metabolism and glucose regulation parameters. While its association with metabolic disorders has been established, its relationship with sarcopenia remains underexplored, especially in middle-aged adults. This cross-sectional study...

Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With the rising prevalence and increasing associated chronic complications, identifying and understanding the molecular mechanisms of diabetes has become an important direction in bioinformatics research. The aim of this study is the identification of diag...

Detection of fasting blood sugar using a microwave sensor and convolutional neural network.

Monitoring of fasting blood sugar (FBS) is a critical component in the diagnosis and management of diabetes, one of the most widespread chronic diseas...

Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease w...

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

BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries ...

A Multimodal Predictive Model for Chronic Kidney Disease and Its Association With Vascular Complications in Patients With Type 2 Diabetes: Model Development and Validation Study in South Korea and the U.K.

OBJECTIVE: To develop a multimodal model to predict chronic kidney disease (CKD) in patients with type 2 diabetes mellitus (T2DM), given the limited r...

Orbital CT deep learning models in thyroid eye disease rival medical specialists' performance in optic neuropathy prediction in a quaternary referral center and revealed impact of the bony walls.

PURPOSE: To develop and evaluate orbital CT deep learning (DL) models in optic neuropathy (ON) prediction in patients diagnosed with thyroid eye disea...

Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy.

Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our cur...

A supervised machine learning approach with feature selection for sex-specific biomarker prediction.

Biomarkers are crucial in aiding in disease diagnosis, prognosis, and treatment selection. Machine learning (ML) has emerged as an effective tool for ...

Integrated analysis of shared gene expression signatures and immune microenvironment heterogeneity in type 2 diabetes mellitus and colorectal cancer.

Emerging evidence suggests a bidirectional relationship between colorectal cancer (CRC) and type 2 diabetes mellitus (T2DM), yet the shared molecular ...

Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach.

Diabetic foot Ulceration (DFU) is a severe complication of diabetic foot syndrome, often leading to amputation. In patients with neuropathy, ulcer for...

Transformer attention fusion for fine grained medical image classification.

Fine-grained visual classification is fundamental for medical image applications because it detects minor lesions. Diabetic retinopathy (DR) is a prev...

Deep learning-based dipeptidyl peptidase IV inhibitor screening, experimental validation, and GaMD/LiGaMD analysis.

BACKGROUND: Dipeptidyl peptidase-4 (DPP4) is considered a crucial enzyme in type 2 diabetes (T2D) treatment, targeted by inhibitors due to its role in...

Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity.

BACKGROUND: This study aims to design and evaluate various supervised machine-learning models for identifying premature infants who require treatment ...

Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema.

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in diabetes, with variable responses to anti-vascular endothelial growth fa...

[Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].

The use of artificial intelligence (AI) in pediatric and adolescent medicine offers numerous possibilities, particularly in the prevention of chronic ...

Establishment of a machine learning-based predictive model with dual-center external validation: investigating the role of robotic surgery in preventing delayed gastric emptying for right-sided colon cancer.

After colorectal surgery, delayed gastric emptying (DGE) is a clinically significant postoperative complication that significantly lowers patients' qu...

Metabolic Phenotypes, Genotypes, and Gut Microbiome Signatures in Obesity: Implications for Precision Nutrition Strategies in Type 2 Diabetes Prevention.

The worldwide prevalence of overweight and obesity has increased rapidly in the last decades. This rise has led to a surge in comorbidities such as ty...

Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes.

Non-Hispanic white (White) populations are overrepresented in medical studies. Potential healthcare disparities can happen when machine learning model...

Classification of Diabetic Patients using a Network Representation of Their Metabolism.

Studies on Type 2 Diabetes Mellitus (T2DM) rely on specific metabolic networks to represent the intricate relationships between metabolites. Accurate ...