Medical adhesives have been extensively applied for skin wound repair and hemostasis, while the dynamically humid and complex intraperitoneal environment imposes stringent requirements on tissue adhesives. Few studies have reported the application of biological adhesives for bladder defect repair in urological practice. The GSF composite hydrogel adhesive, composed of gelatin methacryloyl (GelMA), silk fibroin methacryloyl (SilMA), and polyether F127 diacrylate (F127DA), was previously fabricated with favorable injectability, appropriate viscosity and superior mechanical toughness. To further confirm the repair efficacy, GSF was characterized in vitro for the adhesive properties and structural stability under dynamically humid conditions. Corresponding in vivo experiments were conducted on rat models with graded bladder rupture and partial bladder defects to further assess the repair performance of GSF. Results showed that GSF hydrogel retained robust interfacial adhesion and intact structural stability for 8 days under dynamically simulated humid intraperitoneal conditions and cyclic urine filling-emptying environments. GSF exhibited rapid bonding capacity and effectively shortened the operation time. Under moderate bladder injury conditions, the wound closure time of the GSF group was only 28.3% of that of the suture group in cystotomy experiment and 34.5% in cystectomy experiment. It achieved satisfactory repair of mild-to-moderate bladder ruptures and partial bladder defects. Postoperative histological and functional evaluations confirmed that GSF hydrogel facilitated bladder structural reconstruction and functional recovery. These findings highlight the significant potential of GSF hydrogel adhesives for repairing and regenerating injured bladders. Future applications of GSF will extend to adhesive repairs of organs within the humidity conditions of the abdominal cavity.

Objective: To evaluate the triage accuracy, response quality, safety, and repeatability of widely used large language models (LLMs) in urologic emergencies and to examine their potential clinical utility and risks. Methods: We developed 50 standardized clinical vignettes covering 10 categories of urologic emergencies, including testicular torsion, acute urinary retention, and Fournier gangrene. The set comprised high-risk vignettes requiring immediate emergency care, borderline vignettes, and atypical vignettes containing misleading cues. Six commercially available LLMs—Kimi 3.0, DeepSeek V4, Qwen 3.8-MAX, GPT-5.6 Sol (ChatGPT), Claude Opus 4.8, and Ant Afu (AQ)—were evaluated in Chinese-language interactions. Each vignette was submitted three times in independent conversations. Two urologists with more than 10 years of clinical experience assessed triage accuracy using a three-level reference standard (immediate emergency department [ED] evaluation, routine outpatient evaluation, or home observation). They also rated response quality across eight dimensions using a 5-point Likert scale and evaluated medical-error severity (0–3), response stability, and Chinese readability. Overall differences were assessed using the Friedman test, followed by pairwise Wilcoxon signed-rank tests with Holm adjustment. Results: A total of 900 responses were generated. Overall three-class accuracy was 89.11%, and the macro-averaged F1 score was 85.73%. Qwen achieved the highest accuracy (94.00%), whereas Claude achieved the lowest (80.67%). However, none of the pairwise comparisons between models reached statistical significance after Holm adjustment. All models generally recognized scenarios requiring immediate ED evaluation, but their ability to distinguish routine outpatient evaluation from home observation varied substantially. Mean content-quality scores ranged from 3.68 to 4.15; AQ achieved the highest overall score (4.15 ± 0.49), whereas DeepSeek achieved the lowest (3.68 ± 0.48). Clinically relevant errors occurred in 12.3% of responses, including major errors in 6.2% and dangerous errors in 3.1%. Three-run exact triage agreement ranged from 78% to 96%. Readability performance varied by metric, with no single model consistently outperforming the others. Conclusions: The six LLMs demonstrated some ability to perform three-level triage of urologic emergencies, supporting further investigation of their potential clinical applications. Nevertheless, important limitations remained in the management of borderline cases, response stability, and safety; some responses included potentially dangerous self-management instructions or could delay care for high-risk patients. Integration with authoritative guidelines, structured risk warnings, and human review will be necessary to improve clinical safety and reliability.

Background: Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide, highlighting the need for improved prognostic tools. The integration of artificial intelligence (AI) and machine learning (ML) with multi-omics data offers new opportunities for biomarker discovery and risk stratification. Methods: We integrated bulk transcriptomic data from GSE116918 (training, n = 248) and three cross-cohort consistency evaluation cohorts (TCGA-PRAD, GSE70769, GSE46602), focusing on 1087 epithelial–mesenchymal transition (EMT)-associated genes. Using consensus clustering, weighted gene co-expression network analysis (WGCNA), and 91 machine learning algorithm combinations (including Random Forest, Lasso, and CoxBoost), we constructed a prognostic signature. SHAP analysis was used for model interpretability. Single-cell RNA sequencing (scRNA-seq, GSE268307, 10,672 cells) and spatial transcriptomics (10× Genomics Visium FFPE) provided hypothesis-generating evidence; spatial analysis was based on one tissue section. Results: A three-gene signature (INHBA, FAP, ITGBL1) effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly worse metastasis-free survival (HR = 1.61, 95% CI: 1.39–1.87; 4-year AUC = 0.93 in the training cohort; external AUCs ranged from 0.62 to 0.77). CytoTRACE inferred high differentiation potential of COMP+ fibroblasts, and Monocle3 inferred a transcriptional transition from COMP+ toward NELL2+ fibroblasts. BayesPrism deconvolution suggested that high inferred COMP+ fibroblast abundance was associated with poor prognosis and advanced T stage. NicheNet analysis prioritized BMP7 as a key upstream ligand, with downstream targets enriched in TGF-β signaling and stem cell pluripotency pathways. Conclusions: This study presents a machine learning-based multi-omics framework for prostate cancer risk stratification. The three-gene signature provides a new exploratory prognostic model while inferring a COMP+ to NELL2+ transcriptional transition. These findings may inform future hypothesis-driven studies of treatment sensitivity, pending experimental validation, and demonstrate the value of AI-driven multi-omics integration for precision oncology.