Nephrology

Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study.

BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated. Leveraging electronic health records (EHR), we explored the potential of artificial intelligence (AI) in diagnosing AKI and AKD during hospitalizatio...

Integrating Molecular Dynamics and Machine Learning for Solvation-Guided Electrolyte Optimization in Lithium Metal Batteries.

Optimizing liquid electrolytes is essential for achieving long-term cycling stability and high safety in lithium metal batteries. However, severe side reactions and lithium dendrite formation during repeated cycling lead to low Coulombic efficiency (CE) and limited lifespan. Herein, a rapid and cost-effective strategy that integrates high-throughput molecular dynamics simulations with machine lear...

Should We Use Behavioural Predictions in Organ Allocation?

Medical predictions, for example, concerning a patient's likelihood of survival, can be used to efficiently allocate scarce resources. Predictions of ...

Aptamer-directed siRNA delivery systems for triple-negative breast cancer therapy.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by the absence of estrogen, progesterone, and HER2 recept...

From laser-on time to lithotripsy duration: improving the prediction of lithotripsy duration with 'Kidney Stone Calculator' using artificial intelligence.

INTRODUCTION: "Kidney Stone Calculator" (KSC) helps to plan flexible ureteroscopy, providing the stone volume (SV) and an estimated duration of laser ...

In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.

Drug-induced cardiotoxicity poses a significant risk to human health, and reliable predictive models are needed for safety assessment. In this study, ...

Predicting Retinal Nerve Fiber Layer Thickness From Ocular Hypertension Treatment Study Optic Disc Photographs.

IMPORTANCE: Deep learning predictions of retinal nerve fiber layer (RNFL) thickness derived from optic disc photographs may help to determine risk for...

Renal Dysfunction Across the Spectrum of Cardiogenic Shock: Mechanisms, Clinical Implications, and Therapeutic Strategies.

PURPOSE OF REVIEW: This review aims to elucidate the complex interplay between cardiogenic shock (CS) and renal function, detailing the mechanisms of ...

Correction to: Recognition of intraglomerular histological features with deep learning in protocol transplant biopsies and their association with kidney function and prognosis.

[This corrects the article DOI: 10.1093/ckj/sfae019.].

Bioethical challenges in the integration of artificial intelligence in transplant surgery 4.0: A scoping review.

INTRODUCTION: The integration of artificial intelligence (AI) into transplant surgery offers potential benefits, including enhanced precision and pers...

Orchestrating Penta-Element Interplay in Gradient-porous Carbons for Advanced Sodium-Ion Hybrid Capacitors.

Sodium-ion hybrid capacitors (SIHCs) offer a cutting-edge synergy between battery-level energy density and supercapacitor-like power density, yet face...

Identification of hub genes involved in the pathogenesis of diabetic nephropathy: A multi-omics study integrating machine learning, mendelian randomization and mediation analysis.

BACKGROUND: Diabetic nephropathy (DN), affecting 30%-40% of diabetic patients, is the leading cause of end-stage renal disease worldwide. This study a...

A Comparative Assessment of Large Language Models in Pediatric Dialysis: Reliability, Quality and Readability.

INTRODUCTION: This study evaluated the reliability, quality, and readability of ChatGPT (OpenAI, San Francisco, CA), Gemini (Google, Mountain View, CA...

Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods.

BACKGROUND: Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as poten...

Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.

Nasal polyps (NP) are benign mucosal outgrowths associated with chronic inflammation that can significantly reduce quality of life. This study aimed t...

Palmitoylation regulators drive the progression of clear cell renal cell carcinoma through Inhibition of cuproptosis: insights into the role of ZDHHC18.

BACKGROUND: Protein palmitoylation is a reversible post-translational modification that increases protein hydrophobicity, which can affect protein loc...

Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.

OBJECTIVE: IgA nephropathy (IgAN) is the most common form of glomerulonephritis worldwide, characterized by immune complex deposition in the glomerula...

Hyperspectral Imaging Combined With Deep Learning for Precision Grading of Clear Cell Renal Cell Carcinoma.

This study presents an integrated approach combining hyperspectral imaging (HSI) and deep learning for accurate grading of clear cell renal cell carci...

Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.

Accurate prediction of postoperative mortality risk after cardiac surgery is essential to improve patient outcomes. Traditional models, such as EuroSC...

Non-invasive Assessment of Urinary Exfoliated Proximal Tubule Cell Multispectral Autofluorescence May Differentiate Between Causes of Kidney Transplant Dysfunction.

BACKGROUND: Complications relating to delayed or deteriorating graft function following kidney transplantation are common. There is no validated metho...