Nephrology

Topology-Aware Segmentation for Tubular Structure in 3D Microscopy.

High-resolution 3D microscopy has become a foundational tool in biomedical research by reconstructing vascular and neural networks from subcellular to organ scales, thereby facilitating quantitative analysis of tissue microenvironments, disease progression, and early pathological changes. However, accurately segmenting tubular structure like microvascular and neurite in 3D microscopy remains chall...

Elucidating the therapeutic targets and multi-target mechanisms of salvianolic acid A for diabetic nephropathy.

Diabetic nephropathy (DN) is a leading cause of chronic kidney disease. Salvianolic acid A (SAA) has shown promising therapeutic potential against DN, yet its underlying mechanisms and precise molecular targets remain incompletely elucidated. Potential targets of SAA were predicted using SwissTargetPrediction and SuperPred, with its drug-like properties evaluated by ADMET analysis. Diabetic nephro...

Frailty index and type 2 diabetes with renal complications: insights from Mendelian randomization and retrospective observational study.

OBJECTIVES: To investigate the relationship between frailty and diabetic kidney disease (DKD)-related renal complications using Mendelian randomizatio...

Predicting intradialytic exercise intolerance in maintenance hemodialysis patients: an interpretable machine learning approach integrating functional assessments.

Exercise intolerance, primarily manifesting as intradialytic hypotension (IDH), is common in maintenance hemodialysis patients, interrupting intervent...

Randomized Controlled Trial of Large Language Model-Assisted Diagnostic Accuracy in Nephrology.

Validation of online clearance monitoring and machine learning-based prediction of dialysis adequacy in Vietnamese hemodialysis patients: a cross-sectional study.

BACKGROUND: Online clearance monitoring (OCM) offers non-invasive real-time dialysis adequacy assessment, but validation data from Southeast Asian pop...

Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.

BACKGROUND: Hypertension serves as a prevalent health issue, particularly in South Asia, where it is also a risk factor and comorbidity that affects t...

Data-driven and interpretable machine learning for performance-determining interactions governing C-C coupling and C2+ selectivity in Cu-catalyzed CO2 electroreduction.

Efficient electrochemical conversion of CO2 into multi-carbon C2+ products remains limited by the complex interplay between catalyst morphology, elect...

Recent advances in point-of-care colorimetric biosensors for detecting small molecule metabolites.

In the context of the growing need for decentralized diagnostics, the colorimetric method emerges as an ideal candidate for point-of-care (POC) applic...

Metal-organic framework-derived confined nanomaterials: a unified platform for catalysis, sensing, and energy applications.

Metal-organic framework (MOF)-derived confined nanomaterials (MCNs) have attracted significant interest in catalysis, sensing, and energy applications...

Fundamental Understanding of Oxidative Stability in Fluorinated Asymmetric Ethers for Li Batteries.

Fluorination strategies for electrolyte solvents are widely used to improve the electrochemical performance and cycling stability of Li-metal batterie...

Redefining LiF as a Nanostructured Building Block for Interphase Engineering in Lithium Metal Batteries.

Lithium metal batteries (LMBs) are regarded as promising next-generation energy storage systems due to their high theoretical capacity and low reducti...

Multi-omics investigation of per- and polyfluoroalkyl substances in lung adenocarcinoma: comprehensive network toxicology, machine learning and molecular docking experiments.

Growing concerns regarding per- and polyfluoroalkyl substances (PFAS) as pervasive environmental contaminants have prompted increasing scrutiny regard...

Multimodal deep learning using pre-transplant CT images predicts cardiovascular events after liver transplantation.

Predicting High-Risk Colorectal Polyps in African Americans Using Pre-colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation.

PURPOSE: Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing inter...

Population pharmacokinetics of dalbavancin: external validation, model averaging, and implications for precision dosing in prolonged therapy.

Dalbavancin is increasingly used off-label for prolonged therapy in gram-positive infections, but optimal repeat-dosing strategies remain undefined, a...

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

The rapid evolution of deep learning is revolutionizing critical care medicine by enabling highly accurate early warning systems. These systems are ca...

AI for battery-accelerated discovery of high-voltage electrolytes for advanced lithium batteries.

As lithium batteries advance toward higher energy densities, developing electrolytes that remain stable under high-voltage conditions has become a cri...

Predicting Renal Tumor Pathology from Intraoperative Gross Appearance: An AI-Based Pilot Study.

OBJECTIVE: To evaluate the feasibility of using convolutional neural networks (CNNs) and vision transformers (ViTs) to predict renal tumor pathology i...

An interpretable combinatorial data-mining framework for predicting new-onset hypertension in the general population.

We previously established an interpretable combinatorial data-mining framework to identify combinations of clinical factors predictive of heart failur...