Nephrology

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

UNLABELLED: Accurate prediction of epidermal growth factor receptor (EGFR) mutations is essential for guiding targeted therapy in non-small cell lung cancer (NSCLC) and is routinely performed in clinical pathology samples. Current diagnostic practices rely on molecular techniques such as PCR-based assays and next-generation sequencing, which are often costly, time-consuming, and destructive to tis...

Pharmacogenomics in oncology: mutation-targeted therapy and biomarker integration in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common cancer-related cause of death among all countries globally, mostly because of late diagnosis, heterogeneity of tumors, and poor response to standard therapy. Innovations in the field of pharmacogenomics have completely revolutionized the management of NSCLC by facilitating the application of precision oncology, which matches the treatment to tu...

Activated Cancer-Associated Fibroblasts Are Associated with Immunosuppression and Poor Prognosis in Clear Cell Renal Cell Carcinoma.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) exhibits substantial heterogeneity within its tumor microenvironment, contributing to variable c...

Homologous-Constrained Machine Learning Enables Amino Acid Additive Screening for Water-Structure-Regulated Aqueous Zinc-Ion Batteries.

Aqueous zinc-ion batteries (AZIBs) are widely regarded as a compelling technology for grid-scale energy storage owing to their intrinsic safety, cost-...

Machine Learning for Predicting Ki-67 Expression in Renal Tumors: A Systematic Review And Meta-Analysis.

RATIONALE AND OBJECTIVES: To systematically evaluate the diagnostic performance of machine learning (ML) models for predicting Ki-67 expression in ren...

Machine learning-based prediction of overall survival in non-metastatic renal cell carcinoma after radical nephrectomy.

Prognostic heterogeneity remains a challenge for non-metastatic renal cell carcinoma (RCC) patients following radical nephrectomy (RN). This study aim...

A Multimodal, Multitask Prediction Framework for Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma.

The purpose of this study is to develop and validate a multimodal, multitask prediction framework for clear cell renal cell carcinoma (ccRCC) by integ...

Personalized survival prediction in elderly dialysis patients: integrating machine learning with traditional survival analysis.

BACKGROUND: Elderly patients undergoing kidney replacement therapy (KRT) face high mortality rates. Traditional statistical models describe overall su...

Disparities in severe infection risk associated with systemic lupus erythematosus: evidence from a causal forest analysis.

OBJECTIVES: Severe infections are a primary cause of morbidity and premature mortality in patients with Systemic Lupus Erythematosus (SLE). Although S...

A PCA-enhanced XGBoost framework for combined SOC and SOH estimation in lithium-ion batteries.

Accurate estimation of State of Charge (SOC) and State of Health (SOH) is critical for safe and reliable operation of lithium-ion batteries under temp...

Multiple machine learning models for predicting major adverse cardiovascular events in dialysis with clinical and echocardiographic parameters: a retrospective cohort study.

BACKGROUND: Patients undergoing dialysis are at an elevated risk of cardiovascular events. This study aimed to develop machine learning (ML) predictio...

Machine learning-based evaluation of lipid biomarkers for cardiovascular risk prediction in chronic kidney disease: A cross-sectional study.

Chronic kidney disease (CKD) is an important public health issue globally, greatly increasing the prevalence and mortality of cardiovascular disease (...

Screening of diagnostic biomarkers for taurine metabolism-related genes in lupus nephritis and analysis of immune infiltration.

BACKGROUND: Lupus nephritis (LN) represents a serious renal manifestation of systemic lupus erythematosus and is driven by intricate interactions amon...

Artificial Intelligence in Peritoneal Dialysis: Applications, Algorithms, and Future Directions.

BACKGROUND: Peritoneal dialysis (PD) naturally lends itself to artificial intelligence (AI) integration due to its generation of dense, longitudinal a...

AI-guided identification of natural CTSL inhibitors with therapeutic potential for renal injury.

Cathepsin L (CTSL) is a prominent therapeutic target for kidney injury, yet clinically available CTSL inhibitors remain limited. Here, we developed an...

Gut microbiota biomarkers of chronic kidney disease progression identified by 16S rDNA sequencing and machine learning.

Chronic kidney disease (CKD) is a global health concern characterized by high prevalence and mortality rates, yet its underlying pathogenesis remains ...

Anthropometry-adjusted TyG indices predict diabetes, graft failure, and mortality in kidney transplant recipients.

CONTEXT: The triglyceride-glucose (TyG) index is a widely used surrogate marker of insulin resistance and associated with cardiometabolic outcomes. Ho...

Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow.

Protein phosphorylation regulates signaling, yet atomic-level substrate specificity remains elusive due to sparse structural data and phosphorylation-...

Discovering Early-Stage Gas Generation Kinetics Enables Thermal Runaway Early Warning in Lithium-Ion Batteries.

Early warning of thermal runaway (TR) in lithium-ion batteries remains constrained, as conventional indicators emerge after irreversible failure. Here...

A Quantitative Electrostatic Potential Descriptor Enables Deep Learning-Accelerated Discovery of High-Performance Lithium-Ion Battery Electrolytes.

Rational electrolyte design for high-energy-density lithium-ion batteries (LIBs) urgently demands precise and quantitative molecular descriptors of so...