Nephrology

Machine learning prediction of 30-day mortality in coronary artery disease: a retrospective multicenter study using electronic health records.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of death globally and a major contributor to hospital readmission. This study aimed to predict 30-day mortality in patients hospitalized with acute and chronic CAD using a structured machine learning approach with data from multiple centers. METHODS: We conducted a retrospective cohort study using patient data from the Taipei Medical U...

Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.

BACKGROUND: Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. The retinal microvasculature, as the only directly observable microvasculature, may reflect DN progression. This study aims to construct a non-invasive diagnostic and prognostic prediction model using the mixed effects of retinal vascular geometric parameters and clinical data. METHODS: We constructed a multimod...

Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus.

AIMS: Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes. Artificial intelligenc...

Machine Learning Analysis of Single-Cell Transcriptomics Identifies a B-Cell-Associated Fibroblast Signature Predictive of Renal Fibrosis Severity.

BACKGROUND: Renal fibrosis represents the final common pathway of chronic kidney disease (CKD); however, both its definitive diagnostic biomarkers and...

Leveraging Data-Driven and Fundamental Insights for Electrolyte Innovation in Lithium Metal Batteries.

The rise of data science and decades of accumulated battery research have paved the way for the general use of computing in research. Here, the larges...

Electrolyte Engineering Challenges and Opportunities for Next-Generation Aqueous Ammonium-Ion Batteries.

Aqueous ammonium-ion batteries (AAIBs) have emerged as a promising post-lithium energy-storage technology, combining intrinsic safety with sustainable...

Left ventricular hypertrophy as a predictor of adverse maternal and neonatal outcomes in chronic hypertension in pregnancy: a multicenter study.

OBJECTIVE: The impact of chronic hypertension (CHTN) combined with left ventricular hypertrophy (LVH) on adverse maternal and fetal pregnancy outcomes...

Dynamic Covalent Frameworks for Lithium Anode Protection: From Bonding Chemistry to Interface Engineering.

Lithium metal batteries are severely hindered by interfacial instability issues such as dendrite growth, unstable solid electrolyte interphase, and vo...

Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis.

Kidney stones (KS) are a common urological condition, the aetiology of which remains incompletely understood. This study aimed to investigate the key ...

Triglyceride-glucose frailty index, metabolic-frailty phenotypes, and mortality in critically ill patients with acute kidney injury: Derivation, interpretation, and external validation.

BACKGROUND: Prognosis remains heterogeneous among critically ill patients with acute kidney injury (AKI). We evaluated the triglyceride-glucose frailt...

Glycolysis, Inflammation, and Mitochondrial Dysfunction: New Perspectives on the Pathogenesis of Hypertension.

BACKGROUND: Recent studies show enhanced glycolysis is linked to chronic diseases, but its relationship with hypertension is unclear. This study explo...

Autonomous Vascular Access Devices: Current Solutions and Future Challenges.

INTRODUCTION: Central venous cannulation is essential for life-saving interventions including resuscitation of critically ill patients, hemodynamic mo...

Advances in Mass Spectrometry-Based Proteomics for Kidney Disease Biomarker Discovery and Clinical Translation.

The high prevalence and substantial burden of kidney diseases necessitate advanced approaches to elucidate molecular mechanisms and promote precision ...

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models.

BACKGROUND: The anion gap is primarily utilized as an indicator for evaluating acid-base imbalances in critically ill patients. However, its accuracy ...

AI Structural Biology in Nephrology: Powerful Scaffolds, Fragile Certainties.

Artificial intelligence (AI)-based protein structure prediction has rapidly entered nephrology, providing plausible atomic models for channels, transp...

Composite dietary antioxidant index with hepatic indices for predicting mortality in cardiovascular-kidney-metabolic syndrome: Integrated analysis of a nationwide cohort study using machine learning approaches.

Oxidative stress is a pathological driver of cardiovascular-kidney-metabolic (CKM) syndrome, exacerbating inflammation and tissue injury, while metabo...

Single-photon emission computed tomography/computed tomography-based radiomics of bone metastases for predicting epidermal growth factor receptor mutation status and targeted therapy efficacy in non-small-cell lung cancer.

BACKGROUND: This study aimed to develop a single-photon emission computed tomography/computed tomography (SPECT/CT)-based radiomics model using bone m...

Abdominal vascular calcification at CT: opportunistic quantification and imaging-based risk stratification.

Abdominal vascular calcification is increasingly recognized as a clinically relevant marker of systemic atherosclerotic burden and regional vascular d...

Prediction of delayed graft function after kidney transplantation using artificial intelligence and machine learning: A Systematic Review.

INTRODUCTION: Delayed graft function (DGF) is a frequent early complication after kidney transplantation. We systematically reviewed artificial intell...

AI Models for Predicting Acute Kidney Injury (AKI) and Post-AKI Mortality: Systematic Review and Meta-Analysis.

BACKGROUND: Machine learning (ML) models are increasingly used to predict acute kidney injury (AKI), but validation quality and clinical readiness rem...