This study evaluates the impact of chronic kidney disease (CKD) stages on stone-free rates (SFR) and renal function outcomes after percutaneous nephrolithotomy (PCNL). Additionally, it examines the predictive role of the CROES and Guy’s Stone Score (GSS) systems. Data from 2994 patients who underwent PCNL between 2007 and 2024 were retrospectively analyzed. Patients were classified into four CKD groups based on preoperative estimated glomerular filtration rate (eGFR). SFR, complication rates, and postoperative renal function changes were assessed. SFR was significantly lower in advanced CKD stages (p < 0.001), with the lowest in Group A (GFR < 30, 64.1%) and highest in Group D (GFR > 90, 79.1%). Postoperative eGFR increased in CKD stage 4–5 but declined in normal kidney function groups. Complication rates were higher in advanced CKD stages (p = 0.031). CROES and GSS scores correlated with stone complexity and surgical outcomes (p < 0.001). Advanced CKD is associated with lower SFR, greater surgical complexity, and higher complication rates. PCNL remains an effective treatment, particularly in CKD patients, with potential postoperative renal function improvement. Utilizing predictive scoring systems can optimize patient selection and surgical planning. Further prospective studies are needed to validate these findings.

PCNL, a minimally invasive surgical technique for kidney stone removal, relies on achieving stone-free status, which various scoring systems aim to predict. This study assesses the predictive accuracy of the Clinical Research Office of the Endourological Society (CROES) and Guy's Stone Score (GSS) systems in determining stone-free rates following percutaneous nephrolithotomy (PCNL) in pediatric patients. A retrospective analysis was conducted on 580 pediatric patients who underwent PCNL at Çukurova University Urology Clinic between January 2007 and March 2024. Patients were categorized into two groups based on postoperative stone status: Group 1 and Group 2. CROES and GSS scores were calculated for each patient. The association between these scores and stone-free status, as well as postoperative complications, was statistically analyzed. Additionally, subgroup analyses were performed based on age groups. The study showed that 83.7% of patients achieved a stone-free condition postoperatively. Significant differences were found between the stone-free and residual stone groups regarding stone burden and operative time (p < 0.001). CROES had high accuracy for predicting stone-free outcomes (p < 0.001), while GSS was also effective in predicting both stone-free rates and complications. CROES was less effective in predicting complications. Both CROES and GSS are valuable for predicting PCNL outcomes in pediatric patients. While CROES is more reliable for stone-free rates, GSS better predicts complications. However, their limitations highlight the need for pediatric-specific scoring models. Until such models are developed, these systems should be used with caution alongside individualized clinical assessments.

Organ transplantation extends life expectancy; however, tacrolimus, a key immunosuppressant, carries risks of nephrotoxicity. This study explores dexpanthenol's potential to protect against tacrolimus-induced nephrotoxicity. This study was conducted using 30 male Wistar-Albino rats (aged 4 months, weighing 250-300 g), randomly divided into four groups. The control group received 0.5 cc of saline solution (SF) once daily, the TAC group received 0.4 mg/kg tacrolimus, the DEX group received 500 mg/kg dexpanthenol, and the TAC + DEX group received 0.4 mg/kg tacrolimus and 500 mg/kg dexpanthenol intraperitoneally. Biochemical parameters and kidney damage were assessed on day 14, with tissues examined by blinded pathologists using hematoxylin-eosin staining.The effects of tacrolimus on oxidative balance and the potential protective properties of dexpanthenol were investigated. Statistical analyses were performed using SPSS 20.0 software, with a p-value of < 0.05 considered significant. No significant differences were found between the groups for TAS (p = 0.827), TOS (p = 0.596), OSI (p = 0.575), SOD (p = 0.952), and NO (p = 0.365) values. However, significant differences were observed in IL-6 (p = 0.008), CRP (p = 0.034), TNF-alpha (p = 0.014), TGF-β (p = 0.012), MDA (p = 0.001), urea(p = 0.005), and creatinine (p = 0.002) levels. Pathological examination revealed significant findings in Bowman capsule expansion (p = 0.003), the presence of mitosis (p = 0.005), vasa recta leukocyte infiltration (VRLI) (p = 0.007), tubular regeneration (p = 0.004), tubular dilation (p = 0.003), tubular necrosis (p = 0.048), cast formation (p = 0.020), and loss of the brush border (p = 0.019). The only parameter without a significant difference among the groups was interstitial inflammation (p = 0.559). This study shows that dexpanthenol provides limited protection against tacrolimus-induced nephrotoxicity and requires further validation.

Background: Erectile dysfunction (ED) is a significant complication following penile fracture repair, and early prediction is critical for clinical management. Aim: To evaluate the effectiveness of machine learning (ML) algorithms in predicting the development of severe ED after penile fracture repair and to identify complex risk factors beyond the scope of traditional statistical methods. Methods: A retrospective analysis was conducted using data from 547 patients who underwent surgical repair for penile fracture between January 2020 and June 2024 at 23 urology centers affiliated with the Reconstructive Urology and Trauma Study Group of the Urological Surgery Society. Patients were categorized into two groups based on their International Index of Erectile Function-5 scores at six months postoperatively: severe ED (+) (≤7) and ED (−) (>7). Eleven different ML classifiers were evaluated to determine the most predictive models. Four distinct resampling techniques were employed to address class imbalance in the dataset. Feature importance analysis was also performed to identify the most influential variables contributing to ED risk. Outcomes: This study was conducted to enable the early identification of patients at high risk of developing severe ED following penile fracture surgery. Results: Logistic Regression, Gaussian Naive Bayes, and Linear Support Vector Machine emerged as the best-performing algorithms on the original dataset, with Area Under the Curve (AUC) scores of 0.81, 0.78, and 0.76, respectively. On the Synthetic Minority Over-sampling Technique (SMOTE)-resampled dataset, Quadratic Discriminant Analysis (QDA) achieved an AUC of 0.85, while the Artificial Neural Network (ANN) reached an AUC of 0.84. On the SMOTE-resampled dataset, QDA achieved a ROC-AUC of 0.85 (95% CI: 0.75-0.93), whereas on the SMOTE–Tomek Link–resampled dataset, the ANN attained a ROC-AUC of 0.84 (95% CI: 0.71-0.94). The most critical predictors of severe ED were age, comorbidities, tunical tear length, and time to surgery. Urethral injuries were not significant contributors, as all were minor and managed conservatively without urethroplasty. Clinical Implications: Integration of ML-based prediction models into clinical workflows could support early risk stratification and individualized patient care, ultimately improving postoperative functional outcomes. Strengths and Limitations: This study benefits from a large, multicenter dataset and a comparative analysis of multiple ML algorithms. However, its retrospective nature and inter-center variability in data reporting may limit generalizability. Conclusion: ML algorithms are effective and reliable tools for predicting severe ED after penile fracture repair and may enhance personalized postoperative management. Eliminating class imbalance in the data with resampling techniques improves model performance.

Introduction: Accurate preoperative prediction of lymph node invasion (LNI) is crucial for deciding on extended pelvic lymph node dissection (ePLND) in radical prostatectomy. Traditional nomograms such as Briganti, Partin, and MSKCC are widely used, but machine learning (ML)–based models may improve predictive accuracy. Materials and methods: Data from 471 prostate cancer patients were analyzed, including demographic, clinical, and histopathological variables and scores from Briganti, Partin, and MSKCC nomograms. Eleven ML algorithms were evaluated, with performance assessed by AUC-ROC, accuracy, sensitivity, specificity, and F1-score. Class imbalance was addressed with resampling techniques, and feature importance analyses were performed. Results: LNI was present in 97 patients (20.6%). Significant predictors included MSKCC and Partin scores, PSA, ISUP grade, PIRADS score, lymphovascular invasion, and age. Neural Network (AUC: 0.81) and Random Forest (AUC: 0.77) showed similar performance to the nomograms (MSKCC: 0.79; Briganti: 0.77; Partin: 0.78) when considering the AUC values and their 95% confidence intervals.Decision tree analysis highlighted negative core count, ISUP grade, prostate density, PSA, BMI, and age as key variables. Combining nomogram scores with ML models resulted in numerically slightly higher AUC values; however, these differences remained within a similar performance range and did not indicate a clinically meaningful improvement. Conclusion: Although the AUC values of the ML models appear numerically comparable to, or slightly higher than, those of traditional nomograms, the inherent limitations of the study preclude demonstrating a clinically superior or reliably advantageous performance; therefore, multicenter prospective validation studies are warranted.