Nephrology

Development and validation of a nomogram for predicting advanced chronic kidney disease.

The objective of this study was to develop and validate a nomogram for predicting advanced chronic kidney disease (CKD) through the utilization of routine clinical parameters. To achieve this, we integrated internal and external datasets, employed LASSO and logistic regression to identify independent predictors of advanced CKD for nomogram construction, conducted Bootstrap validation on the traini...

A deep learning-based multimodal model with automated body composition analysis predicts prognosis in advanced clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignancy with a high risk of postoperative recurrence. Body composition has emerged as a prognostic marker, but its clinical use is hindered by manual measurement methods and poorly interpretable models. This study aimed to develop an automated, interpretable prediction model integrating deep learning-based body composition analysis to ass...

Machine learning-based risk stratification of early graft failure in simultaneous pancreas-kidney transplantation.

Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

Antipsychotic treatment is associated with higher risk of major adverse cardiovascular events (MACEs), and risk may vary by multimorbidity and concomi...

Cluster-defined gout subtypes exhibit divergent chronic kidney disease trajectories: a machine learning approach in a Chinese prospective cohort.

OBJECTIVE: To systematically evaluate the contribution of clinical heterogeneity to chronic kidney disease (CKD) progression in gout patients using da...

Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms.

This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients...

Machine learning-assisted discovery of outside-in structure Ni-rich cathode with high performance.

Ni-rich oxides have emerged as leading cathode candidates for lithium-ion batteries because of high specific energy, lower cost, and improved sustaina...

Integrated bioinformatics reveals tubulointerstitial immune microenvironment signatures and machine learning-driven prognostication of clinical treatment response in lupus nephritis.

Lupus nephritis (LN) represents the most severe renal manifestation of systemic lupus erythematosus (SLE), contributing to significant morbidity. Whil...

Clinical and immunological significance of FOXO1 as a biomarker of improved response to immune checkpoint plus tyrosine kinase inhibitor therapy in metastatic renal cell carcinoma.

BACKGROUND: Predicting response to immune checkpoint inhibitor plus tyrosine kinase inhibitor (IO+TKI) therapy in metastatic renal cell carcinoma (mRC...

Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.

Large language models (LLMs) are increasingly applied in clinical contexts; however, to our knowledge, their performance in acute kidney injury (AKI) ...

The Role of Machine Learning and Artificial Intelligence in Drug Discovery and Clinical Care of Pulmonary Hypertension.

Pulmonary hypertension (PH) is a severe and oftentimes fatal disease with a high degree of clinical variability. Its complexity necessitates a multifa...

Interpretable deep learning with pharmacogenomics predicts myocardial infarction in angiotensin receptor blocker treated hypertension.

OBJECTIVE: A significant residual risk of myocardial infarction (MI) persists in hypertensive patients treated with angiotensin receptor blockers (ARB...

Parsimonious and explainable biomarker-based severity score for hospitalised patients with COVID-19-related respiratory infections: development, validation and XAI benchmarking.

BACKGROUND: Severity scoring systems are increasingly important tools for stratifying hospitalised patients, guiding treatment decisions, and enabling...

Understanding Fabrication Variability in Core-Shell Soft Biomaterials Using Stochastic Artificial Intelligence.

Approaches for the fabrication of biomaterials are currently numerous, with a wide diversity of available material precursors, chemistries, and proces...

Serum Uric Acid-to-Creatinine Ratio and Atherogenic Index of Plasma as Independent Prognostic Indicators for Late Non-Valvular Atrial Fibrillation Recurrence After Catheter Ablation.

BACKGROUND: Atrial fibrillation (AF) is one of the most common clinical arrhythmias, and postoperative recurrence remains a major concern in cardiovas...

Rapid screening of potent and mechanistically insightful repurposable anticancer drugs targeting EGFR for non-small cell lung cancer: machine learning-aided and structure-guided approach.

Despite the initial success of EGFR-targeted therapies in non-small cell lung cancer (NSCLC), the emergence of drug resistance remains a significant c...

Echocardiographic Evaluation of Global Cardiac Function in Systemic Sclerosis-Associated Pulmonary Arterial Hypertension: An Integrated Review of Parameters and Techniques.

Systemic sclerosis (SSc) is a connective tissue disease frequently complicated by pulmonary arterial hypertension (PAH), a leading cause of morbidity ...

Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.

Deep-Learning Algorithm Diagnostic Support for Usual Interstitial Pneumonia Pattern Recognition in Fibrotic Interstitial Lung Disease.

BACKGROUND AND OBJECTIVE: High resolution computed tomography (HRCT) scan diagnostic classification for usual interstitial pneumonia (UIP) plays a cri...

Optimization Design and Bending Hysteresis Compensation of a Flexible Ureteroscopy Robot.

BACKGROUND: Ureteroscopic lithotripsy is a minimally invasive surgical procedure used to treat kidney stones. It faces challenges like a long learning...