Nephrology

Dosage adjustments in renal impairment among medical ward patients: ChatGPT® and DeepSeek® models' effectiveness in assessing those adjustments.

BACKGROUND: Due to altered drug clearance, renal impairment necessitates drug dose adjustments to prevent toxicity or therapeutic failure, yet inappropriate dosing persists. The utility of AI tools (e.g., ChatGPT®, DeepSeek®) in supporting renal dose adjustments remains understudied. OBJECTIVE: Evaluate renal dose adjustment practices in hospitalized patients and compare AI models (ChatGPT®, DeepS...

Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.

INTRODUCTION: Diabetic kidney disease (DKD) and diabetic nephropathy (DN) affect around 40% of diabetic patients but lack accurate risk prediction tools that include social determinants and demographic complexity. We developed and validated an ensemble machine learning model for three-year DKD/DN risk prediction with deployment readiness. METHODS: We analysed 18 742 eligible adult type 2 diabetic ...

FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis.

BACKGROUND AND OBJECTIVE: The fluorescence resonance energy transfer (FRET) two-hybrid assay enables quantification of the stoichiometry and binding a...

Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.

BACKGROUND: Hypertension is a major contributor to cardiovascular morbidity and mortality. Its heterogeneity complicates risk stratification. Unsuperv...

Developing explainable machine learning models from biochemical and clinical data to predict all-cause and cause-specific mortality in CVD-cancer comorbidity: A longitudinal study based on NHANES.

BACKGROUND: Cardiovascular disease (CVD) and cancer are leading causes of mortality, often coexisting in aging populations. Patients with comorbiditie...

Value of Multiomics Over Clinical Risk Factors in Hypertension Prediction.

BACKGROUND: Several omics methods have been successfully used in hypertension prediction. However, the predictive ability of various multiomics data h...

Explainable machine learning-driven predictive modelling of incident cognitive impairment in individuals with early-stage cardiovascular-kidney-metabolic syndrome: Insights from a longitudinal CHARLS cohort study.

AIMS: While cardiovascular-kidney-metabolic (CKM) syndrome has been recognised as a continuum of interconnected metabolic, renal, and cardiovascular d...

Clinical utility of an AI-based retinal imaging model for cardiovascular risk prediction in hypertensive retinopathy.

OBJECTIVE: This study presents an independent clinical evaluation of Dr.Noon CVD, a commercially developed artificial intelligence (AI)-based retinal ...

Integrative metabolomics and machine learning reveal diagnostic biomarkers for gelsenicine intoxication.

ETHNOPHARMACOLOGICAL RELEVANCE: Gelsemium elegans Benth. (G. elegans) is a highly toxic medicinal plant traditionally used to treat pain and inflammat...

Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data.

INTRODUCTION: Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from...

Enlarged Perivascular Spaces and Modifiable Vascular Risk Factors: Cross-Sectional and Longitudinal Analysis in the UK Biobank Cohort.

OBJECTIVE: To examine cross-sectional and longitudinal associations between vascular risk factors, APOE genotype, and perivascular spaces (PVS), with ...

Integrating administrative health data and machine learning to predict ALS onset.

BACKGROUND: This study aims to develop a Machine Learning (ML) model to predict the initial diagnosis of Amyotrophic Lateral Sclerosis (ALS). METHODS:...

Artificial intelligence-based prediction of cardiothoracic intensive care unit length of stay: A comparative machine learning approach.

BACKGROUND: Predicting prolonged intensive care unit (ICU) length of stay (LOS) remains challenging, and traditional statistical models often fail to ...

The global epidemiology of acute kidney injury: challenges and opportunities.

Acute kidney injury (AKI) is a devastating complication of acute illness that affects adults and children across multiple settings worldwide and is as...

Machine learning models for predicting response to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BACKGROUND: Predicting clinical and radiological outcomes of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in patients with ...

Trajectories in renal perfusion pressure during hemodynamically guided therapy are associated with worsening renal function and patient outcomes.

BACKGROUND: Worsening renal function (WRF) during acute decompensated heart failure (ADHF) therapy portends worse outcomes. We hypothesized that renal...

Curcumin reprograms metabolic pathways and MAPK signaling to exert antidepressant effects.

BACKGROUND: Depression is a prevalent and debilitating mental disorder with limited treatment options. Curcumin, a natural compound with neuroprotecti...

Discovery of hemostatic component combination from Nelumbinis Receptaculum using dual machine learning spectrum-effect analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: Nelumbinis Receptaculum (NR) were hemostatic herbal medicines for treating metrorrhagia, hematuria or hemorrhoids in e...

Reply to comment on impact of deep learning on CT-based organ-at-risk delineation for flank irradiation in paediatric renal tumours: A SIOP-RTSG radiotherapy committee.

We appreciate the commentary from Saad et al., which offers an opportunity to clarify key methodological and clinical aspects of our study assessing t...

Personalized adrenal gland volume reference ranges and development of a fully automated deep learning screening tool.

OBJECTIVE: This study aims to develop a low-dose CT-based, fully automated deep learning tool for screening adrenal gland volume abnormalities and est...