Urology

Network toxicology and single-cell transcriptomics nominate candidate pyrethroid-associated targets and pathways in clear cell renal cell carcinoma.

Pyrethroid insecticides are widely used in agricultural and domestic settings. Increasing evidence suggests that pyrethroid exposure may harm multiple organ systems and is associated with potential carcinogenicity. However, the mechanisms linking pyrethroids to clear cell renal cell carcinoma (ccRCC) remain unclear. Using an integrative framework combining network toxicology, single-cell sequencin...

Does Hypotension Prediction Index limit the occurrence of postoperative complications associated with intraoperative hypotension? A systematic review and model-averaged Bayesian meta-analysis of statistically sound studies.

Intraoperative hypotension (IOH) is considered a potential contributing factor to postoperative complications. In 2018, a machine-learning algorithm to predict hypotension has been included in the Hemosphere™ platform (Edwards Lifescience, USA), of which the Hypotension Prediction Index (HPI) is the marker. We conducted a systematic review and Bayesian model-averaged meta-analysis to evaluate whet...

Unsupervised clustering of clinical and laboratory features of systemic lupus erythematosus: insights from a multicentre cohort.

OBJECTIVE: This study explored the relationship between clinical phenotypes and immuno-molecular features of systemic lupus erythematosus (SLE) using ...

A drone imagery dataset for semantic segmentation of urban garden ground covers in biodiversity studies.

Urban gardens promote urban biodiversity by providing diverse ground covers that support habitat provision, pollination, pest control, and soil functi...

Identification of biomarkers for non-invasive diagnosis and risk stratification in prostate cancer using NMR-based metabolomics and machine learning.

The non-invasive diagnosis and risk stratification of prostate cancer (PCa) remain clinically challenging due to the limited specificity of prostate-s...

Finding Holes: Pathologist-Level Performance Using AI for Cribriform Morphology Detection in Prostate Cancer.

BACKGROUND: Cribriform morphology in prostate cancer is a histological feature that indicates poor prognosis and contraindicates active surveillance. ...

Large-scale AI analysis reveals missed opportunities in albuminuria testing and disease-modifying therapy implementation.

BACKGROUND: Albuminuria is a key diagnostic and prognostic biomarker of chronic kidney disease (CKD), associated with adverse cardiovascular and renal...

Re: Jethro C.C. Kwong, Zizo Al-Daqqaq, Yashan Chelliahpillai, et al. Development and International Evaluation of an Artificial Intelligence-based Model (PROGRxN-BCa) Using the World Health Organization 2004/2022 Grading System to Predict Progression Risk and Improve Substratification for Non-muscle-invasive Bladder Cancer. Eur Urol. In press. https://doi.org/10.1016/j.eururo.2025.09.4145.

MOGANet: A Multi-omics Graph Attention Network for Cancer Diagnosis and Biomarker Identification.

Multi-omics integration holds considerable promise for advancing disease understanding and improving the performance of biomedical classification task...

NQO1-Mediated Anoikis Resistance and Immune Evasion Define a High-Risk Multi-Omic Subtype for Precision Management of T1 High-Grade Bladder Cancer.

T1 high-grade (T1HG) bladder cancer represents an aggressive subset of non-muscle-invasive bladder cancer (NMIBC) with frequent Bacillus Calmette-Guér...

[Large language models as a communication and organizational infrastructure in urology: evidence, limitations, and clinical responsibility].

Large language models (LLMs) mark a structural turning point in digital medicine, as they are now capable not only of processing medical information b...

Significance of tumor infiltrating granulocytes and neutrophil extracellular traps in colorectal cancer.

BACKGROUND: Neutrophils are the most abundant granulocytes in the tumor microenvironment and exert both pro- and anti-cancer effects. Activated neutro...

A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) is a major complication in the intensive care unit (ICU), and early risk stratification remains challen...

The CAMUS Initiative: A Multiphase, Multicentre International Collaboration to Redefine Risk Stratification, Reporting, and Grading of Surgical Complications in Urology.

Surgical complications remain a major source of preventable morbidity, mortality, and health care expenditure, but existing frameworks such as the Cla...

Cost-effectiveness analysis of resective epilepsy surgery in drug-resistant patients: an artificial intelligence data modeling.

BACKGROUND: Resective epilepsy surgery has been proven to reduce the number of seizures and improve quality of life in patients with drug-resistant ep...

Community-based early-stage chronic kidney disease screening using explainable machine learning for low-resource settings.

BACKGROUND: Early detection of chronic kidney disease (CKD) is essential for preventing progression to end-stage renal disease. However, existing scre...

Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.

Cyclin-dependent kinase 16 (CDK16), a serine/threonine protein kinase, is a critical regulator of cell cycle progression, vesicle trafficking, and apo...

Comment on "Recognizing uric acid type of urinary stones by deep learning": strengthening the reference standard and the image-to-label pipeline for reproducibility.

Deep Learning-based Segmentation for Assessment of Kidney Tumour Ablation Therapy in CT Images.

Kidney tumor ablation is a minimally invasive treatment for Renal Cell Carcinoma (RCC). Manual segmentation of the kidney ablation zone (KAZ) is time-...

Performance of an image-only deep learning breast cancer risk model with the addition of a polygenic risk score.

BACKGROUND: Mammograms contain imaging biomarkers that can predict future breast cancer risk using deep learning (DL) models. We evaluated whether add...