Proteus mirabilis is a Gram-negative bacterium commonly found in urinary tract infections (UTIs) and catheter-associated urinary tract infections (CAUTIs). The pathogenic mechanisms of Proteus mirabilis are complex and diverse, involving various virulence factors, including fimbriae, flagella, urease, polyphosphate kinase, lipopolysaccharides, cyclic AMP receptor protein, Sigma factor RpoE, and RNA chaperone protein Hfq. These factors play crucial roles in bacterial colonization, invasion, evasion of host immune responses, biofilm formation, and urinary stone formation. This paper is the first to comprehensively describe the hydrogenase system, autotransporter proteins, molybdate-binding protein ModA, and two-component systems as virulence factors in Proteus mirabilis, providing new insights into its pathogenic mechanisms in urinary tract infections. This review explores the mechanisms of biofilm formation by Proteus mirabilis and the various virulence factors involved in UTIs, revealing many newly discovered virulence factors from recent studies. These findings may offer new targets for clinical treatment of UTIs and vaccine development, highlighting the importance of understanding these virulence factors.

Ureteral stenting is a common clinical procedure for the treatment of upper urinary tract disorders, including conditions such as urinary tract infections, tumors, stones, and inflammation. Maintaining normal renal function by preventing and treating ureteral obstruction is the primary goal of this procedure. However, the use of ureteral stents is associated with adverse effects, including surface crusting, bacterial adhesion, and lower urinary tract symptoms (LUTS) after implantation. Recognizing the need to reduce the complications associated with permanent ureteral stent placement, there is a growing interest among both physicians and patients in the use of biodegradable ureteral stents (BUS). The evolution of stent materials and the exploration of different stent coatings have given these devices different roles tailored to different clinical needs, including anticolithic, antibacterial, antitumor, antinociceptive, and others. This review examines recent advances in BUS within the last 5 years, providing an in-depth analysis of their characteristics and performance. In addition, we present prospective insights into the future applications of BUS in clinical settings.

Introduction: Enfortumab vedotin (EV) and Erdafitinib are effective therapeutic drugs for bladder cancer patients following post-chemotherapy and immunotherapy. This study assessed adverse drug reactions (ADRs) from both drugs, comparing their safety profiles to guide clinical use.Methods: A retrospective descriptive analysis was conducted on ADR reports for EV and Erdafitinib from the World Health Organization (WHO)-VigiAccess database. Data on patient demographics, system organ classes (SOCs), global patient regions, symptoms, and ADRs frequencies were analyzed and compared.Results: As of 2024, 3,438 ADR reports were identified (2,257 for EV and 1,181 for Erdafitinib). The number of adverse reaction reports for EV is significantly higher than that for Erdafitinib. Among them, the SOC with the most adverse signals is gastrointestinal disorders, with the top five reports being nausea, gastrointestinal disorders, dry mouth, abdominal pain, and diarrhea. The top five reported adverse events (AEs) for EV are as follows: skin and subcutaneous tissue disorders (20.70%), general disorders and administration site conditions (14.23%), nervous system disorders (11.12%), gastrointestinal disorders (7.78%), and metabolism and nutrition disorders (6.47%). In contrast, the top five AEs for Erdafitinib are: general disorders and administration site conditions (25.36%), skin and subcutaneous tissue disorders (10.94%), gastrointestinal disorders (10.19%), eye disorders (9.21%), and injury poisoning and procedural complications (7.31%).Conclusion: Our study identified and compared potential and novel ADRs between EV and Erdafitinib, providing key insights into their safety profiles and highlighting the need for personalized treatment strategies based on individual patient risk factors.

Background: Urinary stones are a multifactorial disease. In recent years, the role of microorganisms in its pathogenesis has attracted considerable attention. Although studies have suggested that certain microbes present in the gut and urine are associated with the formation of urinary stones, the current criteria for stone classification are not rigorous enough. Therefore, this study aimed to analyze the gut and urinary microbiota composition via 16S rRNA sequencing in patients with pure CaOx, pure UA, and pure Inf stones. By integrating these microbiota data with clinical data, we constructed machine learning models and evaluated their diagnostic value in distinguishing stone types. Methods: A total of 81 patients with urinary stones (including 30 with pure CaOx stones, 31 with pure UA stones, and 20 with pure Inf stones) and 26 healthy volunteers were enrolled. Stool and urine samples were collected from each participant and subjected to 16S rRNA sequencing to obtain microbiota data and characterize the gut and urinary microbiota profiles of patients with different stone types. We further integrated microbiota and clinical data, such as age, gender and BMI, using LASSO feature selection and six machine learning algorithms (e.g. SVM, Random Forest and XGBoost) to create prediction models for stone type. Model performance was evaluated through cross-validation. Results: Results showed enrichment of Paramuribaculum , Muribaculum , Mesorhizobium , and Acinetobacter in the gut of CaOx stone patients, with concurrent urinary enrichment of Enterococcus . Patients with UA stones demonstrated an increase in the abundance of Massilioclostridium in the gut and an increase in the abundance of Fenollaria , Anaerococcus , Enterococcus and Escherichia in the urine. Patients with Inf stones showed no differentially abundant gut taxa compared to healthy volunteers, but did exhibit urinary enrichment of Escherichia . The predictive model, which was based on urinary microbiota and clinical data, demonstrated excellent performance. The AUC was 0.922, 0.866 and 0.913 for the SVM, Random Forest and XGBoost models, respectively. Conclusion: This study reveals that different types of stone are characterized by distinct compositions of microbiota. Machine learning models based on microbiota and clinical data can predict urinary stone types noninvasively. This provides novel insights into the microecological mechanisms of urinary stones and opens up new avenues for clinical diagnosis.

Urinary stones and urinary tract infection (UTI) are the most common diseases in urology and they are characterized by high incidence and high recurrence rate in China. Previous studies have shown that urinary stones are closely associated with gut or urine microbiota. Calcium oxalate stones are the most common type of urinary stones. However, the profile of urinary tract microorganisms of calcium oxalate stones with UTI is not clear. In this research, we firstly found two novel clusters in patients with calcium oxalate stones (OA) that were associated with the WBC/HP (white blood cells per high-power field) level in urine. Two clusters in the OA group (OA1 and OA2) were distinguished by the key microbiota Firmicutes and Enterobacteriaceae. We found that Enterobacteriaceae enriched in OA1 cluster was positively correlated with several infection-related pathways and negatively correlated with a few antibiotics-related pathways. Meantime, some probiotics with higher abundance in OA2 cluster such as Bifidobacterium were positively correlated with antibiotics-related pathways, and some common pathogens with higher abundance in OA2 cluster such as Enterococcus were positively correlated with infection-related pathways. Therefore, we speculated that as a sub-type of OA disease, OA1 was caused by Enterobacteriaceae and the lack of probiotics compared with OA2 cluster. Moreover, we also sequenced urine samples of healthy individuals (CK), patients with UTI (I), patients with uric acid stones (UA), and patients with infection stones (IS). We identified the differentially abundant taxa among all groups. We hope the findings will be helpful for clinical treatment and diagnosis of urinary stones.

Objectives: The purpose of this study is to determine the therapeutic value of surgery in individuals with urinary bladder signet ring cell carcinoma (SRCC). Surgery has not been examined as a prognostic factor for urinary bladder cancer (SRCC). Materials and Methods: Using the Surveillance, Epidemiology, and End Results program (SEER), patients with urinary bladder SRCC who presented from 1975 to 2018 were included in a retrospective study. The effect of surgical therapy on cause-specific survival (CSS) and overall survival (OS) was examined using univariate and multivariate Cox regression models. We subdivided 595 patients with SRCC into 2 groups, as follows: 496 who underwent surgery; and 99 who did not undergo surgery. Results: Males had high predominance in all cases in both groups ( p = 0.04). Moderate and poor differentiation (III–IV) were observed in the majority of patients who underwent surgery (77.2 vs 58.6, p ⩽ 0.001) and had no insurance ( p ⩽ 0.001). By using KM, the OS and CSS of the surgery group were found to be significantly better than those of the non-surgery group ( p = 0.001,%) after adjusting for the variables of age, race, sex, primary site, grade, stage, lymph node removal, chemotherapy record, radiotherapy record, insurance, and marital status in the multivariate Cox proportional hazard model (hazard ratio [HR]= 0. 592; 95% confidence interval [CI] = 0.449–0.782; p = 0.0001). In comparison with chemotherapy and radiation, which resulted in poorer survival rates, surgery considerably improved survival outcomes in urinary bladder SRCC. The nomogram prediction model was built with C-index values of 0.70 and 73 for OS and CSS prediction, respectively. AUC in OS values were 0.77, 0.76, and 0.74, whereas AUC in CSS were 0.83, 0.80, and 0.79 for the 1-, 3-, and 5-year survival nomograms, respectively. Conclusion: Surgery was a significant independent predictor of bladder SRCC survival. Patients who underwent surgery had higher CSS and OS than people who did not undergo surgery. Surgery also led to better survival than the combination of the different treatment modalities.