Urology

DEHP-induced male reproductive toxicity: Evidence from population studies, animal experiments, and multi-omics profiling.

OBJECTIVE: To evaluate the association between di(2-ethylhexyl) phthalate (DEHP) exposure and testosterone deficiency in adult men, and to explore molecular changes and candidate compounds related to DEHP-associated male reproductive toxicity. METHODS: We used a multi-level integrative strategy. Computational toxicology predicted DEHP endocrine-disrupting potential. In NHANES 2013-2016 adult males...

Electrocardiogram-based short-term risk stratification for acute kidney injury using time-frequency deep learning analysis.

BACKGROUND AND OBJECTIVES: Acute kidney injury (AKI) is a common and clinically important complication in critically ill and perioperative patients. Conventional AKI diagnosis relies primarily on serum creatinine and urine output, which may reflect renal injury only after physiological deterioration has already occurred. Therefore, non-invasive physiological signals may provide complementary infor...

Diagnostic yield and cost of three-level H&E sectioning in prostate biopsies.

AIM: The traditional three-level H&E sectioning protocol for prostate biopsies was developed for ultrasound-guided systematic sampling and predates le...

Reply to Kristoffer Mazanti Cold, Lars Konge, and Sarah Hjartbro Bube's Letter to the Editor re. Roessler N, Miszczyk M, Miyajima K, et al. Harnessing Artificial Intelligence for Risk Stratification and Outcome Prediction in Urologic Cancers: A Systematic Review. Eur Urol Focus. In press. http://dx.doi.org/10.1016/j.euf.2025.12.007.

Single-cell-based analysis establishes C1QA-mediated promotion of high glucose-induced tubular epithelial injury via ERS in DN.

BACKGROUND: Diabetic nephropathy (DN) poses a growing worldwide health challenge as a leading cause of end-stage renal disease, a condition that arise...

An artificial intelligence solution for evaluation of prostate needle core biopsy specimens: impact in a non-specialist setting.

The diagnosis of prostate cancer rests on the histopathological evaluation of prostate needle core biopsy specimens (NCBS) supplemented by immunohisto...

Comparative effectiveness and cost-effectiveness of an artificial intelligence workflow for small renal mass diagnosis on computed tomography.

INTRODUCTION: Small renal masses are increasingly detected on imaging, but their accurate classification as benign, indolent, or aggressive remains ch...

Cost-Effectiveness of AI-Assisted Kellgren-Lawrence Grading of Knee Osteoarthritis in the South Korean Health-Care System.

BACKGROUND: Early diagnosis of knee osteoarthritis (KOA) is often delayed due to reliance on subjective interpretation of radiographs. Recent advances...

Advances in the diagnosis and detection of chronic kidney disease.

Chronic kidney disease affects 788-844 million adults worldwide and is projected to become the fifth leading cause of death by 2040. Global burden est...

Detection of coronary microvascular dysfunction based on machine learning algorithm with multidimensional temporal-spatial features from electrocardiogram.

Coronary microvascular dysfunction (CMD) causes myocardial ischemia and is associated with adverse cardiovascular events. This study explored the valu...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detect...

Cytoreductive nephrectomy improves survival in metastatic papillary renal cell carcinoma: a population-based study with machine learning validation and landmark analysis.

BACKGROUND: The role of cytoreductive nephrectomy (CN) in metastatic papillary renal cell carcinoma (pRCC) remains poorly defined, as most CN evidence...

An ab initio study and machine learning framework to capture the motional effects in solid-state NMR of lithium-ion conductors.

Solid-state NMR spectroscopy, when combined with first-principles density functional theory (DFT) calculations, offers a highly sensitive probe of ato...

Endothelial activation and stress index associated with in-hospital mortality risk in patients with end-stage renal disease: a retrospective analysis based on the MIMIC database and machine learning model development.

BACKGROUND: End-stage renal disease (ESRD) is a severe chronic renal disorder with high mortality, requiring dialysis treatment or kidney transplantat...

Ultrasound elastography in pediatric care: bridging innovation and noninvasive diagnostics.

This review explores the application and limitations of ultrasound elastography (USE) in the pediatric population, addressing its diagnostic value acr...

A machine-learning approach to predict additional treatment after Bacillus Calmette-Guérin induction in non-muscle-invasive bladder cancer.

Non-muscle invasive bladder cancer (NMIBC) comprises ~ 75% of newly diagnosed bladder cancer, with high-risk NMIBC associated with high rates of recur...

Missing-data-aware machine learning prediction of in-hospital major adverse cardiovascular events after primary percutaneous coronary intervention for ST-segment elevation myocardial infarction.

Machine learning (ML) offers opportunities to improve prognostication after ST-segment elevation myocardial infarction (STEMI), but real-world registr...

Integrating network toxicology, machine learning, and single-cell sequencing to reveal the FASN-mediated role of phenolic endocrine disruptors in water in promoting prostate cancer.

BACKGROUND: Phenolic endocrine-disrupting chemicals (EDCs) like nonylphenol (NP) and octylphenol (OP) are widespread water pollutants. Their estrogen-...

High-quality dietary patterns reduce risk of advanced cardiovascular-kidney-metabolic syndrome: A cross-sectional NHANES study in US adults.

Evidence-based dietary strategies for mitigating Cardiovascular-kidney-metabolic (CKM) syndrome progression remain limited; therefore, we hypothesized...

Early Prediction of Diabetic Macular Edema via Machine Learning Survival Analysis on Checkup Data.

PURPOSE: To predict the risk of diabetic macular edema (DME) onset and to identify features of the risk subgroups. DESIGN: Population-based observatio...