Nephrology

Leveraging an Explainable Machine Learning Model for Early Identification of Acute Kidney Injury: A Retrospective Study.

BACKGROUND: Delayed identification of acute kidney injury (AKI) limits timely intervention, as diagnosis often depends on serum creatinine elevation after renal damage has occurred. This study aims to develop a machine learning model capable of identifying early AKI risk at the time of hospital admission using routinely collected biochemical and physiological parameters and their prediagnostic tre...

Prognostic models for diabetic kidney disease outcomes: A systematic review.

OBJECTIVE: This systematic review aimed to systematically evaluate the methodological quality and predictive performance of existing prognostic models for diabetic kidney disease (DKD) outcomes. METHODS: A systematic search of PubMed, Web of Science, Cochrane Library, Ovid Embase, China National Knowledge Infrastructure (CNKI), Wanfang, Vip and SinoMed was conducted from database inception to 30 A...

Postoperative cerebral infarction after major thoracic and abdominopelvic surgery: a retrospective single-centre cohort study of incidence, risk factors, and outcomes.

BACKGROUND: Postoperative cerebral infarction (POCI) is an uncommon but potentially fatal complication of major non-cardiac surgery and is associated ...

Predicting 1-Year Renal Outcomes in Patients with Diabetic Kidney Disease in CKD Stages 3 to 4: A Multimodal Machine Learning Approach Fusing Clinical Composites and Pathology Images.

Patients with diabetic kidney disease (DKD) at chronic kidney disease (CKD) stages 3 to 4 are at high risk for rapid renal function decline within 1 y...

Advancing microalgae biomass cultivation for an integrated sustainable wastewater treatment and resource recovery.

Microalgae have emerged as a promising bioresource for the integrated treatment of effluent, the production of biomass, and the mitigation of carbon e...

Deep learning analysis of ECGs detects Cardiovascular-Kidney-Metabolic syndrome burden in people with diabetes: a report from the Silesia Diabetes-Heart Project.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome refers to the co-occurrence of obesity, diabetes, chronic kidney disease (CKD), and cardiov...

Statin adherence among patients with hypertension in Swedish primary health care: a QregPV cohort study using explainable machine learning.

AIMS: Evidence regarding statin adherence among patients with hypertension in primary care is limited. We assessed statin adherence in Swedish primary...

Crossover effect: causal machine learning reveals opposing mortality responses to mean arterial pressure targets among phenotypically distinct hypertensive patients with septic shock.

AIMS: To quantify the heterogeneity of treatment effects of higher versus standard MAP targets on 28-day mortality, to identify distinct clinical phen...

Animal models of idiopathic membranous nephropathy: Recent advances and future perspectives.

Idiopathic membranous nephropathy (IMN) is one of the main causes of adult nephrotic syndrome. A subset of untreated or inadequately treated patients ...

Machine learning prediction of mechanical dilatation in transvenous lead extraction for cardiac device-related infections: insights from a high-volume centre.

BACKGROUND: Transvenous lead extraction (TLE) requires high expertise in mechanical dilatation (MD), with double the risk of death and failure in low-...

Diagnostic Accuracy of Artificial Intelligence-based Automated Detection of Diabetic Retinopathy in Routine Care Using Nonmydriatic Fundus Images: The Silesia Diabetes-Heart Project.

PURPOSE: To evaluate the diagnostic performance of a regulatory-approved (CE-marked) artificial intelligence system (RetCAD) applied to nonmydriatic c...

Explainable machine learning identifies small high-performance gene signatures from full transcriptomes in human liver preservation cohorts.

Transcriptomic studies of liver preservation and ischemia-reperfusion injury (IRI) often report large gene lists that are difficult to translate into ...

Integrated multi-omics analysis reveals folate metabolism-related genes as prognostic markers and therapeutic targets in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is an aggressive tumor with high metastatic potential and therapeutic resistance, yet the role of ...

Echocardiographic assessment in arterial hypertension: a contemporary narrative review.

Arterial hypertension remains the leading modifiable cause of cardiovascular morbidity and mortality resulting in characteristic changes in cardiac st...

Automated spermatogenic staging in periodic acid-Schiff-stained testes of Sprague-Dawley rats using a deep learning model for normal and atrophied tissues.

The spermatogenic stage serves as a vital criterion for assessing normal spermatogenesis and is central to evaluating reproductive toxicity. Current m...

Is Nonpapillary Puncture a Feasible Alternative to Papillary Access in Percutaneous Nephrolithotomy? A Systematic Review and Meta-Analysis.

PURPOSE: Percutaneous nephrolithotomy (PCNL) is the gold standard for treating large and complex renal calculi, but puncture path selection (papillary...

Predictive Model for Thrombosis in Mature Autologous Arteriovenous Fistula.

OBJECTIVE: Based on the "high-risk stenosis-thrombosis" theory, this study developed a risk prediction model for thrombosis in mature autogenous arter...

Pan-cancer analysis identifies immunoproteasome as the predominant survival-associated component of antigen processing machinery.

Neoantigens are critical targets for cancer immunotherapy, yet the relationship between experimentally validated neoantigen burden and antigen process...

Integrative machine learning and experimental validation reveal the molecular mechanisms of 2-bromo-4,6-dinitroaniline nephrotoxicity: CD45 and cyclophilin C as early response biomarkers of renal tubular injury.

2-Bromo-4,6-dinitroaniline (BDNA), a synthetic intermediate of brominated azo dyes, poses potential nephrotoxicity risks; however, its early molecular...

A fully optimized deep learning framework for explainable and efficient kidney stone detection in computed tomography imaging.

PURPOSE: To develop and rigorously evaluate a hyperparameter-optimized custom convolutional neural network for kidney stone detection on axial compute...