Mutlu Deger

Purpose: Partial nephrectomy can be performed with or without ischemia. We conducted an animal experiment to determine whether artery-only clamping or artery-vein clamping is more effective. Materials and methods: A total of twenty-four animals were divided into three groups: control, artery-only, and artery-vein. All rats first underwent right nephrectomy. In the control group, a left nephrectomy was performed six days later. In the artery-only group, a left partial nephrectomy was performed three days later by clamping only the artery. Similarly, in the artery-vein group, a left partial nephrectomy was performed three days later by clamping both the artery and vein. At the end of the experiment, the abdomen of all rats was opened via a midline incision, and the heart was accessed by cutting the diaphragm. Blood was aspirated from the heart. Left nephrectomy was performed three days after the left partial nephrectomy in both the artery-only and artery-vein groups. The left kidneys were examined pathologically. Inflammatory, oxidative, and antioxidant markers, as well as histopathological findings, were compared among the groups. Results: There were no statistically significant differences in biochemical measurements between the groups. Bowman’s capsule enlargement, tubular necrosis, tubular regeneration, mitosis, interstitial inflammation, and tubular dilation did not differ significantly between the artery-only group and the artery-vein group. However, statistically significant differences were observed between the control group and the ischemia groups regarding Bowman’s capsule enlargement, tubular necrosis, leukocyte infiltration in the vasa recta, mitosis, and tubular dilation. Conclusion: Although the sample size precludes establishing true biological equivalence, the lack of a significant acute structural difference between the two techniques suggests that when intraoperative clinical scenarios necessitate simultaneous artery-vein clamping, it serves as a safe alternative, as both techniques yield similar histopathological outcomes at three days.

Objective: The aim of this study is to evaluate the effectiveness of sacral neuromodulation (SNM) on clinical symptoms, lower urinary tract function, and objective measurements in patients diagnosed with overactive bladder (OAB) and chronic nonobstructive urinary retention (CNOUR), and to identify factors affecting treatment response. Materials and Methods: This multicenter retrospective chart review included patients diagnosed with OAB and CNOUR who underwent SNM between 2015 and 2025 at four tertiary referral centers. All patients underwent a standard two‐stage SNM protocol. Clinical evaluation was performed at the first and third postoperative months after stage II implantation using urinary incontinence status, clean intermittent catheterization (CIC) requirement, bladder diary data, and uroflowmetric parameters. Demographic and technical factors affecting success were analyzed. Results: A total of 160 patients were included in the study. Of these patients, 113 (70.6%) were female, and the mean age was 41 years. SNM was performed for OAB in 63 patients (39.4%) and for CNOUR in 97 patients (60.6%). After the first stage of SNM, clinical success was achieved in 50 patients (79.4%) in the OAB group and 68 patients (70.1%) in the CNOUR group. In the CNOUR group, the need for CIC completely disappeared in 38.2% of patients after SNM, while the frequency of catheterization decreased by at least 50% in 22.1% of patients. Urinary incontinence completely resolved in 52% of the OAB group and 69.1% of the CNOUR group. In uroflowmetric evaluation, Qmax and PVR improved significantly in the CNOUR group, whereas voided volume was used as a supportive parameter for interpreting Qmax. A higher number of contacts receiving stimulation during the first stage was significantly associated with treatment success. No serious perioperative complications were observed. Conclusion: Sacral neuromodulation is an effective and safe treatment option for patients with overactive bladder and chronic urinary retention. Its ability to reduce the need for catheterization and improve voiding function is of great clinical importance, especially in patients with chronic urinary retention. The success of SNM can be increased with appropriate patient selection, careful evaluation during the testing phase, and optimal technical application.

Recurrent disease after kidney transplantation remains an important cause of proteinuria, graft dysfunction, and allograft loss. This review summarizes the epidemiology, pathogenesis, clinical presentation, diagnosis, risk factors, and treatment of the most relevant recurrent diseases, including membranous nephropathy, primary focal segmental glomerulosclerosis, IgA nephropathy, lupus nephritis, C3 glomerulopathy, and amyloidosis. Recurrence is driven by persistent recipient-specific mechanisms such as autoantibodies, circulating permeability factors, galactose-deficient IgA1 immune complexes, complement dysregulation, or ongoing systemic inflammatory or clonal plasma cell disorders. Clinical manifestations range from isolated proteinuria and microscopic hematuria to nephrotic syndrome and progressive graft dysfunction, although recurrence may also be subclinical. Kidney allograft biopsy remains the diagnostic cornerstone and is essential for distinguishing recurrence from rejection, calcineurin inhibitor toxicity, and chronic allograft injury. Serologic and biomarker-based tools, including anti-PLA2R, anti-nephrin, complement markers, and disease-specific hematologic parameters, may support risk stratification and follow-up but do not replace histologic confirmation. Pretransplant disease quiescence, assessment of antibody burden, and identification of genetic or monoclonal causes are critical for recurrence prevention. Management is disease-specific and combines optimized supportive care with tailored immunosuppression or targeted therapy. Rituximab, plasmapheresis, corticosteroids, calcineurin inhibitors, complement-directed agents, and clone- or inflammation-targeted regimens may be used depending on the underlying disorder. Early recognition, close post-transplant surveillance, and multidisciplinary management are essential to preserve graft function and improve long-term outcomes

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.

Overactive bladder (OAB) is a common urological condition affecting millions of people worldwide, significantly reducing their quality of life. Patient com

Introduction Erectile dysfunction (ED) represents a significant health concern that affects not only physical well-being but also psychological health and quality of life. This study aimed to investigate the potential of digital biomarkers derived from smartwatches for the risk assessment of ED. Methods This prospective, comparative cross-sectional study investigated digital biomarkers derived from smartwatch technology for ED assessment in 74 male participants (38 ED patients, 36 controls) between May and September 2025. Participants wore iOS-based smartwatches continuously for 14 days to monitor physiological parameters including heart rate variability (HRV), sleep architecture, and physical activity levels. ED diagnosis was established using the International Index of Erectile Function 5-item form (IIEF-5) with scores ≤ 21 indicating ED. Statistical analyses included univariate/multivariate logistic regression and ROC curve analysis. Results Compared to controls, the ED group demonstrated significantly lower HRV (32 vs. 38.5 ms, p < 0.001), higher resting heart rate (69 vs. 58 bpm, p < 0.001), increased wake time percentage (p < 0.001), decreased deep sleep duration (20% vs. 25%, p < 0.001), and reduced daily step count (4196 vs. 5142 steps, p = 0.001). Multivariate analysis identified HRV (OR: 0.884, p = 0.019), wake time percentage (OR: 1.832, p = 0.005), and daily step count (OR: 0.872, p = 0.002) as independent associated variables. ROC analysis revealed wake time percentage demonstrated highest screening performance (AUC: 0.752, sensitivity: 68.4%, specificity: 69.4%). Conclusion Preliminary findings from this pilot study suggested that digital biomarkers from smartwatch technology could offer potential for non-invasive ED risk assessment. These results provided a feasibility foundation for integrating wearable technologies into clinical practice for personalized sexual health management.

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.

Bu bölüm, anterior üretra darlıklarının cerrahi tedavisinde üretroplasti yöntemlerini güncel literatür ışığında tartışmaktadır. Distal, penil ve bulber üretra darlıkları ayrı alt başlıklarda ele alınarak, etiyoloji, patofizyoloji ve tanısal algoritmalar ayrıntılı biçimde sunulmuştur. Endoskopik tedavilerin yüksek nüks oranları nedeniyle sınırlı etkinliği vurgulanırken, uzun (>2 cm) veya kompleks striktürlerde açık rekonstrüktif yaklaşımların üstünlüğü ortaya konmaktadır. Tek aşamalı ve aşamalı üretroplasti teknikleri, greft (özellikle bukkal mukoza) ve flep seçenekleriyle birlikte değerlendirilmiş; cerrahi endikasyonlar, teknik ayrıntılar, komplikasyon profilleri ve fonksiyonel sonuçlar kapsamlı olarak irdelenmiştir. Bölüm, anterior üretra darlıklarının yönetiminde hasta ve striktür özelliklerine göre bireyselleştirilmiş cerrahi yaklaşımın, deneyimli merkezlerde multidisipliner değerlendirme ile uygulanmasının tedavi başarısı için kritik önem taşıdığını vurgulamaktadır.