Purpose: To compare preoperative midstream urine cultures (PMUC) and renal pelvic urine culture (RPUC) or stone culture (SC) in predicting systemic inflammatory response syndrome (SIRS) and urosepsis after percutaneous nephrolithotomy (PCNL). Methods: We searched PubMed, Web of Science and EMBASE databases up to September 1, 2020 for relevant published studies. Two authors independently confirmed whether the literatures met the inclusion criteria and collected the data from the selected literatures. A meta-analysis was performed with Review Manager Software 5.4.1. A total of 14 studies with 3540 patients were selected and analyzed in the meta-analysis. Results: Pooled data showed that SC was associated with a higher sensitivity (OR 2.36, 95% CI 1.31–4.25; P < 0.00001), PPV (OR 1.54, 95% CI 1.18–2.01; P =0.16) and NPV (OR 1.17, 95% CI 1.01–1.37; P =0.06) in the diagnosis of SIRS and urosepsis after PCNL, and RPUC provided a significantly higher specificity (OR 2.70, 95% CI 2.16–3.38; P =0.07) and PPV (OR 2.17, 95% CI 1.49–3.15; P =0.48) than PMUC in the diagnosis of SIRS and urosepsis after PCNL. Conclusion: Intraoperative SC and RPUC are more reliable than PMUC in predicting postoperative SIRS and urosepsis, identifying causative organisms and directing antibiotic therapy for patients underwent PCNL. PMUC, SC and RPUC should be routinely collected for the diagnosis and management of SIRS and urosepsis after PCNL.

To identify independent risk factors for urosepsis in diabetic patients with upper urinary tract stones (UUTS) and develop a prediction model to facilitate early detection and diagnosis, we retrospectively reviewed medical records of patients admitted between January 2020 and June 2023. Patients were divided based on the quick Sequential Organ Failure Assessment (qSOFA) score. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for variable selection to form a preliminary model. The model was optimized and validated using the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test and calibration curve, and decision curve analysis (DCA). A nomogram was constructed for visualization. A total of 434 patients were enrolled, with 66 cases and 368 controls. Six optimal predictors were identified: underweight, sarcopenia, poor performance status, midstream urine culture, urinary leukocyte count, and albumin-globulin ratio (AGR). The midstream urine culture was excluded due to its inability to provide rapid results. The final model demonstrated good prediction accuracy and clinical utility, with no significant difference in performance compared to the initial model. The study developed a prediction model for urosepsis risk in diabetic patients with UUTS, presenting a convenient tool for timely diagnosis, particularly in non-operated patients.

Background: Gut microbiota have been previously reported to be related to a variety of immune diseases. However, the causal connection between Sjögren’s syndrome (SS) and gut microbiota has yet to be clarified. Methods: We employed a two-sample Mendelian randomization (MR) analysis to evaluate the causal connection between gut microbiota and SS, utilizing summary statistics from genome-wide association studies (GWASs) obtained from the MiBioGen and FinnGen consortia. The inverse variance weighted (IVW) approach represents the primary method of Mendelian randomization (MR) analysis. Sensitivity analysis was used to eliminate instrumental variables heterogeneity and horizontal pleiotropy. In addition, we performed an analysis using independent GWAS summary statistics for SS from the European Bioinformatics Institute (EBI) dataset for further verify our results. Results: IVW results demonstrated that the phylum Lentisphaerae (OR = 0.79, 95% CI: 0.63–0.99, p = 0.037), class Deltaproteobacteria (OR = 0.67, 95% CI: 0.47–0.96, p = 0.030), family Porphyromonadaceae (OR = 0.60, 95% CI: 0.38–0.94, p = 0.026), genus Eubacterium coprostanoligenes group (OR = 0.61, 95% CI: 0.4–0.93, p = 0.021), genus Blautia (OR = 0.62, 95% CI: 0.43–0.90, p = 0.012), genus Butyricicoccus (OR = 0.61, 95% CI: 0.42–0.90, p = 0.012), genus Escherichia.Shigella (OR = 0.7, 95% CI: 0.49–0.99, p = 0.045) and genus Subdoligranulum (OR = 0.61, 95% CI: 0.44–0.86, p = 0.005) exhibited protective effects on SS. Relevant heterogeneity of horizontal pleiotropy or instrumental variables was not detected. Furthermore, repeating our results with an independent cohort provided by the EBI dataset, only the genus Eubacterium coprostanoligenes group remained significantly associated with the protective effect on SS (OR = 0.41, 95% CI: 0.18–0.91, p = 0.029). Two-step MR analysis further revealed that genus Eubacterium coprostanoligenes group exerts its protective effect by reducing CXCL6 levels in SS (OR, 0.87; 95% CI = 0.76–0.99, p = 0.033). Conclusions: Our study using two-sample MR analysis identified a causal association between multiple genera and SS. A two-step MR result calculated that genus Eubacterium coprostanoligenes group mediated its protective effect by reducing CXCL6 levels in SS. However, the datasets available from the MiBioGen and FinnGen consortia do not provide sufficient information or comprehensive demographic data for subgroup analyses. Additional validation using various omics technologies is necessary to comprehend the development of SS in the intricate interplay between genes and the environment over a period of time.

Purpose: To establish the first comprehensive nomogram for prediction of infection stones before treatment for better perioperative treatment and postoperative prevention of infection stones. Methods: A total number of 461 patients with kidney stones who underwent mini-percutaneous nephrolithotomy and flexible ureteroscopy between January 2019 and March 2021 were retrospectively analyzed. Univariable analysis and multivariable logistic regression analysis were conducted to identify the predictors for infection stones. Furthermore, the nomogram was established as a predicted model for infection stones. Results: Among 461 patients with infrared spectroscopy stone analysis, 100 (21.70%) had infection stones and 361 (78.31%) had noninfection stones. Multivariate logistic regression analysis indicated that female (odds ratio [OR] 2.816, 95% confidence interval [CI] 1.148–6.909, p = 0.024), recurrent kidney stones (OR 8.263, 95% CI 2.295–29.745, p = 0.001), stone burden (OR 6.872, 95% CI 2.973–15.885, p < 0.001), HU (OR 15.208, 95% CI 6.635–34.860, p < 0.001), positive preoperative bladder urine culture (PBUC; OR 4.899, 95% CI 1.911–12.560, p = 0.001), positive urine leukocyte esterase (ULE; OR 3.144, 95% CI 1.114–8.870, p = 0.030), urine pH (OR 2.692, 95% CI 1.573–4.608, p < 0.001), and positive urine turbidity (OR 3.295, 95% CI 1.207–8.998, p = 0.020) were predictors for infection stone. Conclusions: For patients with kidney stones, female, recurrent kidney stones, stone burden (>601 mm ² ), HU (750–1000), positive PBUC, positive ULE, urine pH, and positive urine turbidity were predictors for infection stone. We established the first comprehensive model for identifying infection stones in vivo , which is extremely useful for the management of infection stones.

Objective: To investigate the prospective association between accelerometer-derived rest–activity rhythm parameters and the risk of incident kidney stones in a large general population. Methods: This prospective cohort study included 93 598 participants from the UK Biobank who provided valid 7-day wrist accelerometry data. Incident kidney stones were identified through linkage to hospital inpatient and primary care records. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for non-parametric and parametric rhythm metrics, adjusting for sociodemographic, lifestyle, and clinical confounders, including sleep duration and moderate-to-vigorous physical activity. Restricted cubic splines were employed to assess dose-response relationships. Results: During a median follow-up of 7.8 years, 615 incident kidney stone cases were documented. Dampened circadian rhythms were independently associated with an increased risk of kidney stones. In fully adjusted analyses, comparing the lowest versus highest quartiles, lower relative amplitude (HR: 1.50, 95% CI: 1.11–2.02), lower daytime activity (M10) (HR: 1.59, 95% CI: 1.12–2.26), and higher nocturnal activity (L5) (HR: 1.40, 95% CI: 1.07–1.85) were significant predictors. Spline analyses revealed a non-linear association for relative amplitude, with risk rising sharply as rhythm robustness decreased, whereas M10 and L5 exhibited linear dose-response relationships. Conclusion: Dampened rest–activity rhythms, characterized by reduced amplitude and increased nocturnal restlessness, are robust risk factors for kidney stones, independent of sleep quantity and physical activity intensity. Targeting circadian health may offer a novel, modifiable avenue for the primary prevention of urolithiasis.