Diabetes

Cardiorenal Syndrome: Pathophysiological Crosstalk Between Heart and Kidney.

Cardiorenal syndrome (CRS) describes the bidirectional deterioration of cardiac and renal function, in which dysfunction of one organ induces or perpetuates dysfunction of the other. Since Robert Bright's 1836 observations and the formal five-subtype classification proposed by Ronco and colleagues in 2008, understanding of CRS has evolved from organ-sequential taxonomy toward a mechanism-driven, p...

VN1K is a pangenome-informed multi-omics and phenomics resource for the Vietnamese population.

The population of Vietnam remains underrepresented in global genomic databases. Here, we present VN1K, a resource of multi-omics and phenotypic information for 1011 unrelated Vietnamese individuals. We present high-depth short-read whole-genome sequencing data for all samples along with various -omics datasets. Using a high-sensitivity variant detection pipeline, which includes a pangenome graph r...

Quantitative reference map of retinopathy of prematurity improves diagnostic reliability and treatment decisions.

BACKGROUND/AIMS: Retinopathy of prematurity (ROP) zone classification guides disease severity assessment and Early Treatment for Retinopathy of Premat...

Matrix-Aware Electrochemical Sensing: A Neural Network Approach to Deciphering Medium-Specific Surface Dynamics.

Accurate monitoring in complex biofluids remains challenging due to unpredictable skin - electrode interfacial interference and matrix-dependent imped...

Diabetes Care in 2050: A Human-Centered Road Map From Time in Range to Time in Happiness.

By 2050, diabetes care should transform into a human-centered, technology-augmented ecosystem focused on well-being. In this work, we first present a ...

Enhancing the performance of diabetic retinopathy grading models using synthetic images.

PURPOSE: To determine whether strictly curated synthetic images can improve grading performance in diabetic retinopathy (DR) diagnosis within limited ...

GENERALIZABILITY OF DEEP LEARNING MODELS FOR REFERABLE DIABETIC RETINOPATHY DETECTION: A CROSS-POPULATION STUDY.

PURPOSE: To evaluate cross-population performance of deep learning models for referable diabetic retinopathy (DR) detection and assess whether inclusi...

Machine learning-based identification of lactate metabolism-associated biomarkers in non-alcoholic fatty liver disease.

To determine lactate metabolism-associated biomarkers for non-alcoholic fatty liver disease (NAFLD). Based on NAFLD datasets from the gene expression ...

Diagnostic potential of B-cell translocation gene 2 in IgA nephropathy: Insights from molecular mechanisms.

IgA nephropathy (IgAN) is a prevalent glomerular disease characterized by IgA deposition, chronic inflammation, and fibrosis, leading to end-stage ren...

Enhanced prediction of coronary heart disease risk in diabetic patients via Machine learning incorporating multiple inflammatory and metabolic indices: A study with Dual-Cohort validation.

BACKGROUND: Coronary heart disease (CHD) remains a leading cause of mortality worldwide, with individuals with diabetes mellitus (DM) facing markedly ...

An Attention-Based Transformer Algorithm Integrated with YOLO Framework for Lesion Localization and Detection Diabetic Retinopathy.

Diabetic Retinopathy (DR) is one of the major causes of vision loss among diabetic patients across the globe, and it is a major challenge to the publi...

Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome.

Polycystic Ovary Syndrome (PCOS) is a heterogeneous endocrine-metabolic disorder characterized by ovulatory dysfunction, hyperandrogenism, and insulin...

A combi-elasto ultrasound based deep learning model on liver fibrosis staging for suspected MASLD/MASH patients.

OBJECTIVES: Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) weakened the ...

Development and temporal validation of an interpretable XGBoost model for predicting cardiovascular death in patients with diabetic foot.

Patients with diabetic foot (DF) have a high risk of cardiovascular (CV) death, yet dedicated risk-prediction tools for this population are lacking. W...

Construction and validation of a machine learning-based risk prediction model for venous thromboembolism in older adult patients: A multifactorial analysis of 28,231 cases.

Venous thromboembolism (VTE) is a leading preventable cause of in-hospital mortality in older adults, yet early risk stratification remains a key clin...

Integrated statistical and machine-learning optimization for enhanced heparosan production by Lactococcus lactis.

Lactococcus lactis SH6 was engineered for heterologous heparosan production, and its growth medium was optimized using a combination of experimental d...

Low vitamin D status and cardiometabolic trait clustering in a primary-care adult cohort: association analysis and exploratory explainable machine-learning classification.

BACKGROUND: Low serum 25-hydroxyvitamin D (25(OH)D) commonly co-exists with obesity and adverse cardiometabolic traits. However, its cross-sectional a...

Machine-learning-guided control of a pulsed cold atmospheric plasma jet reveals a voltage-dependent discharge transition relevant to diabetic wound healing.

Cold atmospheric plasma (CAP) technology is promising for biomedical treatment, but its translation is limited by the difficulty of identifying operat...

Interpretable diabetes risk prediction: a comparative study of tree-based algorithms using SHAP and LIME.

The global burden of diabetes mellitus continues to increase, particularly in low- and middle-income countries. Prior studies have explored the use of...

Spatial phenotyping of epicardial adipose tissue from cardiac MRI.

Epicardial adipose tissue (EAT) is increasingly recognized as an important contributor to cardiovascular disease (CVD), but its spatial distribution a...