Ozan Bozkurt

Aim: This study aimed to look into how well ureteral jet flow can predict whether ureteral stones will pass naturally and to observe any changes in jet flow after treatment, with a focus on where the stones are located. Materials and Methods: We conducted this study with 50 adults who had acute renal colic and were found to have a single ureteral stone less than 10 mm through non-contrast CT scans. We used Doppler ultrasound before treatment and again between days 15 and 20 to measure jet frequency (JETfreq) and mean jet velocity (JETmean). We checked to see if the stones passed after 28 days. For analysis, we used paired tests, the Mann Whitney U test, and Pearson correlation. Results: After treatment, we saw significant increases in JETfreq and JETmean only in the obstructed ureter, which suggests some recovery. For stones located farther down the ureter, the jet measurements improved more than in those closer to the kidney. In total, 54% of patients passed their stones naturally. Those who did not had stones that were larger, covered a bigger area, and had higher Hounsfield Birimi HU values (p

Purpose: Ischemic priapism (IP) is characterised by prolonged, painful erections that require urgent intervention to prevent corporal fibrosis and erectile dysfunction. Hydrogen sulfide (H₂S), a gaseous mediator, has been shown to exert protective effects against ischemia-reperfusion (IR) injury by reducing oxidative stress, enhancing mitochondrial function, and inhibiting apoptosis. This study aimed to investigate the role of H₂S during IP and reperfusion and its potential protective effects against penile tissue injury. Methods: Twenty-eight male Wistar rats were divided into four groups: control, IP, IP-R, and IP-[NaHS]-R. The control group underwent penectomy only. In the IP group, penectomy was performed after 4 h of priapism, whereas in the IP-R group, it was performed after 4 h of priapism followed by 1 h of reperfusion. In the IP-[NaHS]-R group, rats received NaHS (75 µmol/kg, intraperitoneally) 10 min before reperfusion. Endogenous H₂S levels were determined by the methylene blue assay, and hypoxia-inducible factor-1α (HIF-1α) expression was evaluated by immunohistochemistry. Results: Histopathological analyses were performed to assess tissue alterations. The IP and IP-R groups showed edema, inflammation, desquamation, vasocongestion, and increased collagen deposition, and exhibited reduced H₂S levels ( p < 0.05) and elevated HIF-1α ( p < 0.0001) compared with control group. NaHS administration increased H₂S levels, reduced HIF-1α expression, and alleviated histopathologic changes. Conclusion: Decreased H₂S levels during ischemia and reperfusion were associated with penile tissue injury in IP. NaHS mitigated morphological damage during the initial phase of reperfusion, indicating the potential involvement of H₂S signaling in reperfusion-related corporal tissue injury. However, endpoints related to the resolution of priapism, such as detumescence time and intracavernosal pressure (ICP), have not been evaluated. These assessments are necessary to determine the translational safety and therapeutic applicability of these treatments in cases of IP.

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.

Artificial intelligence (AI) is quickly transforming healthcare by improving patient and clinician access to and understanding of medical information. Generative AI models answer healthcare queries and provide tailored and quick responses. This research evaluates the readability and quality of bladder cancer (BC) patient information in 10 popular AI-enabled chatbots. We used the latest versions of ten popular chatbots: OpenAI's GPT-4o, Microsoft's Copilot Pro, Claude-3.5 Haiku, Sonar Large, Grok 2, Gemini Advanced 1.5 Pro, Mistral Large, Google Palm 2 (Google Bard), Meta's Llama 3.3, and Meta AI v2. Prompts were developed to provide texts about BC, non-muscle-invasive BC, muscle-invasive BC, and metastatic BC. The modified Ensuring Quality Information for Patients (mEQIP), the Quality Evaluating Scoring Tool (QUEST), and DISCERN were used to assess quality. The Average Reading Level Consensus (ARLC), Flesch Reading Ease (FKRE), and Flesch-Kincaid Grade Level (FKGL) were used to evaluate readability. Ten chatbots exhibited statistically significant differences in mean mEQIP, DISCERN, and QUEST scores (p = 0.048, p = 0.025, and p = 0.021, respectively). Meta scored lowest on the average mEQIP, DISCERN, and QUEST, while Llama attained the highest. Statistically significant differences were also seen in the chatbots' average ARLC, FKGL, and FKRE scores (p = 0.002, p = 0.001, and p = 0.002, respectively), in which Google Palm produced texts that are easiest to read, and Llama is the most difficult chatbot to understand. AI chatbots can produce information on BC that is of moderate quality and readability, while there is significant variability among platforms. Results should be evaluated with caution due to the single-query approach and the continuously advancing AI models. Clinicians can support safety in implementation by delivering structured feedback and incorporating content review stages into patient education processes. Continuous collaboration between healthcare practitioners and AI developers is crucial to maintain the accuracy, currency, and clarity of AI-generated content.

Introduction:This study aimed to identify clinical risk factors associated with disease recurrence in patients with locally advanced renal cell carcinoma (RCC). Materials and Methods: Patients diagnosed with locally advanced RCC (T3–T4N0M0 or TxN1M0) from the Turkish Urooncology Association Kidney Cancer Database were included. Demographic and clinical variables (age, sex, body mass index [BMI], performance status, Charlson Comorbidity Index [CCI], symptomatic status, hematologic parameters) and tumor-related characteristics (stage, grade, size, vascular invasion, necrosis, surgical margin status, and lymph node involvement) were analyzed. Results: A total of 749 patients were evaluated, including 216 women and 533 men. The mean age was 59.9 ± 11.6 years (range: 18–86), and the median follow-up was 25.9 ± 31 months (range: 1–180). Disease recurrence occurred in 192 patients (25.6%), with a mean time to recurrence of 9.4 ± 17.8 months (range: 1–84). On univariate and multivariate analyses, Fuhrman grade 3–4 (OR = 0.169, 95% CI: 0.032–0.899, p = 0.037) and platelet count (OR = 1.012, 95% CI: 1.005–1.020, p = 0.001) emerged as independent predictors of recurrence. ROC curve analysis identified a platelet count cut-off of 363,000/µL (AUC = 0.642, 95% CI: 0.600–0.683, p < 0.001; sensitivity: 38.8%, specificity: 86.2%). Conclusions: High Fuhrman grade (3–4) and elevated platelet count were identified as independent risk factors for disease recurrence in patients with locally advanced RCC. These findings may contribute to risk stratification and help guide follow-up strategies and adjuvant treatment considerations in both clinical practice and clinical trial design.

Purpose: Sodium-glucose cotransporter-2 (SGLT-2) inhibitors are primarily preferred in patients with type 2 diabetes. The purpose of this paper was to elucidate the effects of SGLT-2 inhibitor use on patients' voiding habits, sleep, and quality of life. Methods: Our study involved patients with type 2 diabetes who had an SGLT2 inhibitor added to their current treatment. The frequency of day-night urination, lower urinary tract symptoms, sleep and quality of life were assessed both prior to initiation of the treatment and during subsequent 1st and 3rd months. Results: The study included 38 women and 34 men. At the third month after SGLT-2 inhibitor had been added, there was a significant decrease in HbA1c, triglyceride and microalbumin levels (p

Objectives To investigate the predictors of acute kidney injury (AKI) after partial nephrectomy and the impact of AKI stage on long-term kidney function. Methods Data of 1055 patients who underwent partial nephrectomy between January 2008 and January 2018 at seven separate tertiary centres were analysed. AKI was defined according to AKI Network criteria. The association between pre-operative and perioperative factors and AKI was evaluated using logistic regression analysis. Recovery of at least 90% of baseline glomerular filtration rate 1 year after partial nephrectomy, change of 1 year glomerular filtration rate compared with baseline glomerular filtration rate and stage >= 3 chronic kidney disease (CKD) progression were assessed according to the stage of AKI. Results AKI was recorded in 281 (26.7%) of 1055 patients after partial nephrectomy, and of these patients, 197 (70.1%) had stage 1, 77 (27.4%) had stage 2 and 7 (2.5%) had stage 3. Higher tumour complexity and baseline glomerular filtration rate were independent predictors for AKI. The proportion of recovering 90% of baseline glomerular filtration rate at 1 year for any patient who had stage <= 1 vs stage 2-3 of AKI was 78.2% (95% CI: 73.2%-83.7%) and 23.8% (95% CI: 14.7%-38.7%), respectively (P < .001). The risk of stage >= 3 CKD progression for any patient who had stage <= 1 vs stage 2-3 of AKI was 6.2% (95% CI: 4.1%-9.2%) and 63.1% (95% CI: 52.5%-75.6%), respectively (P < .001). Conclusions AKI adversely affects renal function in the long-term after partial nephrectomy and stage 2-3 significantly increases the risk of CKD in the long term.