Background: Urinary stones usually start at a young age and tend to recur. Therefore, preventing stone occurrence and recurrence in young people is crucial. We aimed to investigate the association between subcutaneous adipose tissue, visceral adipose tissue, and stone episodes in young people.Methods: We retrospectively studied patients aged below 40 years with kidney or ureteral stones. Data on demographic and metabolic characteristics, urolithiasis history, subcutaneous fat area (SFA), and visceral fat area (VFA) were collected. We evaluated the association between SFA or VFA and the occurrence or recurrence of stone episodes using binary logistic regression and Poisson regression analyses.Results: In total, 120 patients were included. Abdominal obesity, overweight or obesity, dyslipidemia, metabolic syndrome, SFA, and VFA increased with the number of stone episodes (all p < 0.05). The increase in SFA was independently associated with episode occurrence (p = 0.015). Patients with an SFA > 97 cm² had a higher risk of episode occurrence. SFA and VFA accumulation were independently associated with episode recurrence (all p < 0.05), and SFA had a stronger association than VFA did.Conclusions: In young people, SFA accumulation is an independent and early risk factor for the occurrence and recurrence of stone episodes. Subcutaneous fat could be a convenient and effective indicator to assess the risk of stone episodes before the development of metabolic disorders.

Background: Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. Methods: We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles. In total, 148 (34.02%) patients were with kidney stones, 34 (7.82%) with ureter stones, and 163 (34.83%) with multiple location stones, all of whom had detailed urinary metabolite data. Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. The Super Learner Ensemble Method also demonstrated high predictive performance with slightly lower AUC values compared to Random Forest. Further analysis using multivariate logistic regression identified significant features for each stone type based on the Random Forest method. Results: We found that 24-h urinary magnesium was positively associated with both kidney stones and multiple location stones (OR = 1.195 [1.06–1.3525] and 1.3258 [1.1814–1.4949]) due to its high correlation with urinary phosphorus, while 24-h urinary creatinine was a protective factor for kidney stones and ureter stones, with ORs of 0.9533 [0.9117–0.996] and 0.8572 [0.8182–0.8959]. eGFR was a risk factor for ureter stones and multiple location stones, with ORs of 1.0145 [1.0084–1.0209] and 1.0148 [1.0077–1.0223]. Conclusion: Machine learning techniques show promise in revealing the links between urological stone disease and 24-h urinary metabolic data. Enhancing the prediction accuracy of these models leads to improved dietary or pharmacological prevention strategies.

Background: This study aimed to investigate the prevalence of urolithi...

Multiple cancers have been associated with MYB-related protein B (MYBL2), its involvement in clear cell renal cell carcinoma (ccRCC) has yet to be demonstrated. Our study revealed a significant upregulation of MYBL2 in ccRCC tissues, correlating with clinicopathological features and patient prognosis. Increased MYBL2 expression promoted cell proliferation and suppressed apoptosis. RNA-seq analysis unveiled a reduction in smoothened (SMO) expression upon MYBL2 silencing. However, luciferase and chromatin immunoprecipitation (ChIP) assays demonstrated MYBL2's positive regulation of SMO expression by directly targeting the SMO promoter. Reintroduction of SMO expression in MYBL2-knocked down cells partially restored cell proliferation and mitigated apoptosis inhibition. Overall, these results indicate that MYBL2 facilitates ccRCC progression by enhancing SMO expression, suggesting its potential as an intriguing drug target for ccRCC therapy.

Background/Objectives: Stone composition reflects the urinary environment in which calculi form, whereas 24 h urine testing identifies modifiable lithogenic abnormalities. This study examined component-specific urinary metabolic profiles and evaluated whether routine clinical and urinary variables could distinguish common stone minerals. Methods: This retrospective study included 256 patients undergoing 297 stone surgeries, consolidated into 256 treatment episodes. Fourier-transform infrared spectroscopy identified stone minerals, which were linked to preoperative 24 h urine profiles. Calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), carbonate apatite, and anhydrous uric acid were evaluated using multivariable logistic regression. Mutually exclusive calcium oxalate–calcium phosphate phenotypes, nested cross-validated prediction models, and unsupervised clustering were also examined. Results: COM, COD, carbonate apatite, and anhydrous uric acid were present in 78.5%, 73.0%, 39.5%, and 9.8% of episodes, respectively. Higher urine pH was associated with carbonate apatite (odds ratio [OR] per standard deviation, 1.78; 95% confidence interval [CI], 1.33–2.38), whereas lower pH was associated with anhydrous uric acid (OR, 0.26; 95% CI, 0.11–0.58). Higher urinary citrate was associated with COM, but phenotype analysis showed that hypocitraturia increased from 45.3% in calcium oxalate-only episodes to 60.7% in mixed calcium oxalate–calcium phosphate episodes and 89.5% in calcium phosphate episodes without calcium oxalate. Lower urine volume was associated with COD. Cross-validated areas under the curve ranged from 0.654 to 0.712, and metabolic clusters showed substantial mineral overlap. Conclusions: Urine pH provided the clearest component-specific signal, whereas citrate associations depended on accompanying minerals. Routine 24 h urine testing complemented but did not replace direct stone analysis.

Objectives: Non-contrast computed tomography (NCCT) is used before retrograde intrarenal surgery (RIRS) to describe stone size, location, and complexity, but the best way to summarize stone burden remains unsettled. Therefore, we compared multidimensional NCCT-derived burden metrics with cumulative stone diameter (CSD) with respect to their ability to predict stone-free rate (SFR) and operative time after RIRS. Methods: We reviewed 352 RIRSs performed by one surgical team from April 2024 to May 2026. The primary SFR endpoint was defined as no residual fragments or a maximum residual fragment size of ≤4 mm, with 1-month computed tomography (CT) used as the reference imaging modality. Strict 0 mm CT stone-free status was analyzed as a sensitivity endpoint. The candidate predictors included one-dimensional, two-dimensional, estimated three-dimensional, spatial-distribution, attenuation, and distribution-complexity metrics extracted from preoperative NCCT. The cohort was split into training (n = 247) and validation (n = 105) sets. Results: The main model retained area-equivalent diameter, staghorn stone, and stone volume distribution entropy, with a validation area under the receiver operating characteristic curve (AUC) of 0.779. A simpler CSD model combining CSD and staghorn stone achieved a validation AUC of 0.761, without a significant AUC difference (p = 0.201). In repeated patient-level full-pipeline validation, the median validation AUC was 0.761. Under the strict 0 mm CT endpoint, the validation AUC was 0.670. Entropy showed lower selection stability in sensitivity analyses. Reclassification favored the main model (validation continuous net reclassification improvement [NRI] 0.460, p = 0.007; integrated discrimination improvement [IDI] 0.038, p = 0.003). Regarding operative time, area-, volume-, and surface-area-based metrics performed better than CSD in regard to both the whole cohort and stone-free episodes. Conclusions: NCCT-derived burden metrics can refine preoperative assessment before RIRS, but their incremental value over CSD for binary SFR prediction was modest. CSD remains a practical approximation for SFR prediction, whereas multidimensional metrics better reflect operative workload.