Nephrology

Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.

BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning model to enhance early detection of CVC.

Deciphering failure paths in lithium metal anodes by electrochemical curve fingerprints.

Understanding anode failure mechanisms in lithium metal batteries (LMBs) is crucial for their use in energy storage, as the anode directly affects battery stability and electrolyte selection. Unfortunately, post-mortem methods reveal failure outcomes but often miss dynamic progressions, obscuring cause-and-effect relationships in failure evolution. Leveraging domain knowledge informed machine lear...

Advancing Cardiovascular, Kidney, and Metabolic Medicine: A Narrative Review of Insights and Innovations for the Future.

Cardiovascular, kidney and metabolic (CKM) conditions are interrelated, significantly contributing to morbidity, mortality and healthcare burden. Desp...

Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.

Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease characterized by elevated pulmonary arterial pressure, leading to right ...

Timing of kidney replacement therapy in critically ill patients: A call to shift the paradigm in the era of artificial intelligence.

Acute kidney injury (AKI) is a common condition in intensive care units (ICUs) and is associated with high mortality rates, particularly when kidney r...

Mechanism Exploration of Dietary Supplement Astaxanthin on Improving Atherosclerosis through an Integrated Strategy Encompassing Artificial Intelligence Virtual Screening and Experimental Validation.

Atherosclerosis (AS) is a major and common pathological basis of ischemic intestinal infarction, myocardial infarction, stroke, renal failure, and oth...

Association of acute kidney injury with 1-year mortality in granulomatosis with polyangiitis patients: a cohort study using mediation analyses and machine learning.

To investigate the correlation between acute kidney injury (AKI) and 1-year mortality in patients with granulomatosis with polyangiitis (GPA). Clinica...

Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.

BACKGROUND: Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learni...

Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.

OBJECTIVE: To compare the performance of predictive models for cardiovascular event (CVE) in patients undergoing peritoneal dialysis (PD) based on mac...

Comparison of artificial intelligence-generated and physician-generated patient education materials on early diabetic kidney disease.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus and has become the most important cause of end-sta...

Bioanalysis of antihypertensive drugs by LC-MS: a fleeting look at the regulatory guidelines and artificial intelligence.

Hypertension is a multifaceted cardiovascular disease, a significant risk factor for stroke, heart attack, heart failure, and renal damage. An essenti...

Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing...

Diagnostic MicroRNA Signatures to Support Classification of Pulmonary Hypertension.

BACKGROUND: Patients with pulmonary hypertension (PH) are classified based on disease pathogenesis and hemodynamic drivers. Classification informs tre...

Machine Learning Models for Predicting Type 2 Diabetes Complications in Malaysia.

This study aimed to develop machine learning (ML) models to predict diabetic complications in patients with type 2 diabetes (T2D) in Malaysia. Data fr...

Data-Knowledge-Dual-Driven Electrolyte Design for Fast-Charging Lithium Ion Batteries.

Electric vehicles (EVs) starve for minutes-level fast-charging lithium-ion batteries (LIBs), while the heat gathering at high-rate charging and torrid...

Artificial intelligence to guide rituximab therapy in patients with phospholipase A2 receptor-associated membranous nephropathy.

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA)...

Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis.

Hip fractures in the elderly present a significant public health challenge globally, especially among patients with hypertension, who are at an increa...

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study.

BACKGROUND: The American Heart Association recently introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome, highlighting the increas...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in ...