Nephrology

Mechanism-Based Multitarget Modeling for Pathway-Level Prediction of PI3K/Akt Signaling Perturbation Induced by Liquid Crystal Monomers.

Liquid crystal monomers (LCMs) are emerging contaminants whose system-level toxicity mechanisms remain poorly understood. Here, we developed a pathway-centric multitarget framework to characterize coordinated toxicological perturbations at the signaling network level. KEGG enrichment identified the PI3K/Akt pathway as a key mechanistic axis, and a minimal set of 19 proteins covering upstream recep...

From donor to recipient: Interpretable machine learning refocuses the prediction of post-transplant MASLD.

Rational design of high-loading electrocatalytic electrodes: from static multiscale integration to dynamic intelligent systems.

In the field of electrocatalysis, the industrial application of high-loading electrodes faces a fundamental contradiction: traditional designs based o...

Exploration of the mechanism of Xinjiang Rosa laxa Retz.Fruit extract on IgA nephropathy by bioinformatics and machine learning methods.

This study aimed to elucidate the core therapeutic targets and mechanisms of action of rosehip extract (FRLE) in IgA nephropathy (IgAN) by integrating...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....

Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases.

OBJECTIVES: To construct and validate a model based on clinical characteristics and magnetic resonance imaging (MRI) radiomics to predict 1-year effic...

Validation of latent health behaviour subtypes in young and middle-aged adults with hypertension: a machine learning-augmented LCA approach with external cohort replication.

AIMS: To validate behavioural subtypes among young and middle-aged hypertensive patients using latent class analysis (LCA) and assess their generaliza...

[Construction and validation of a prognostic prediction model for children-onset initial steroid-resistant nephrotic syndrome].

Objective: To analyze the risk factors for poor prognosis in children with steroid-resistant nephrotic syndrome (SRNS) and to construct and validate a...

Systematic Multi-Target QSAR Benchmarking: Machine Learning Algorithms, Molecular Descriptors, and Validation.

Quantitative structure-activity relationship (QSAR) modeling underpins computational drug discovery, yet the factors governing model generalizability ...

Compositionally-informed machine learning for solid-state electrolyte design: a structure-free approach.

Next-generation energy storage demands high-performance solid-state electrolytes (SSEs), where machine learning (ML) promises to be a powerful design ...

nnU-Net-based CT segmentation of perigastric varices in sinistral portal hypertension: a multicenter study.

PURPOSE: To develop a deep learning model based on nnU-Net for automated segmentation of all perigastric veins on contrast-enhanced CT images in patie...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

The potential utility of in-silico approach in identifying phytochemicals against various targets for the management of lung cancer.

Research on novel treatment approaches is crucial for lung cancer, because it is one of the most common and aggressive malignancies with high mortalit...

Reply - Letter to the editor: Comment on "Explainable Electronic Medical Record-Based Machine Learning to Predict 1-Year Incident Protein-Energy Wasting in Non-Dialysis Chronic Kidney Disease: A Single-Center Development Study".

A preoperative Artificial Intelligence model to estimate cancer-specific mortality in nonmetastatic kidney cancer patients.

Surgical resection is the standard treatment for nonmetastatic renal cell carcinoma, yet survival outcomes vary significantly among patients. Current ...

A donor-recipient ranking model to optimize long-term survival post liver transplant.

Liver transplant (LT) is a life-saving treatment for patients with cirrhosis and/or hepatocellular carcinoma (HCC), but organ shortages and suboptimal...

Data subdivision approach enhances machine learning-based mortality prediction in pediatric ICU patients.

OBJECTIVE: To evaluate machine learning-based models for predicting all-cause mortality in pediatric ICU patients using comprehensive biochemical pane...

Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.

Heavy metals, including lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), are pervasive environmental toxicants increasingly recognized as nont...

Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study.

BACKGROUND: Hypertension is a leading risk factor for cardiovascular, cerebrovascular, and renal diseases, significantly worsening prognosis and quali...

Computed Tomography-Derived visceral fat area is associated with increased risk of kidney stone recurrence.

Visceral adiposity has been implicated in metabolic dysregulation and chronic inflammation, both of which may contribute to kidney stone recurrence. H...