Objective The goal of this study was to perform a comprehensive meta-analysis to assess the overall diagnostic value of Doppler twinkling for the diagnosis of urolithiasis. Methods We systematically searched the PubMed, EMBASE, and Cochrane Library databases from inception through May 31, 2021. Studies including patients with urolithiasis who underwent color flow Doppler sampling to highlight the twinkling artifact and computed tomography were included. Diagnostic test meta-analysis was performed with a bivariate model. We used summary receiver operating characteristic curves to summarize the overall diagnostic performance. The weighted sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated. Results Sixteen studies involving 4572 patients were included in the systematic review and meta-analysis. The weighted sensitivity was 0.86 (95% confidence interval [CI] 0.72–0.94), specificity 0.92 (95% CI 0.75–0.98), positive likelihood ratio 11.3, negative likelihood ratio 0.2, and diagnostic odds ratio 75.5. Conclusion The Doppler twinkling artifact has good diagnostic value for the diagnosis of urolithiasis and should be used as a complementary tool in the diagnosis of urolithiasis.

Background: This study aims to determine test–retest reliability and to calculate minimal detectable change (MDC) scores for the functional capacity of the 6-minute step test (6MST) and 1 min sit-to-stand test (1-min-STST), and compare these outcomes with the 6-minute walk test (6MWT) in post-COVID-19 patients. Methods: A total of 42 post-COVID-19 patients aged 18 years or older were recruited for this study. The post-COVID-19 patients were investigated for cardiovascular response parameters induced by a 6MWT, 6MST, and 1-min-STST on two different days, with a five-day interval between the first and second days. Results: The test–retest reliability obtained between the initial measurement and the measurement recorded five days later in the post-COVID-19 patients was excellent for all three of the 6MWT, 6MST, and 1-min-STST. The ICC of the 6MWT was 0.97 with MDC95 at 5.57%. The ICC of the 6MST was 0.93 with MDC95 at 12.21%, while, the ICC of the 1-min-STST was 0.96 with MDC95 at 3.61%. Conclusions: The 6MST and 1-min-STST were valid and acceptable for the evaluation of functional capacity in post- COVID-19 patients and can be used to investigate whether each post-COVID-19 patient had made significant improvement in a clinical setting.

Background: Urinary tract infections (UTIs) remain among the most common bacterial infections, yet reliable risk stratification remains challenging. Serum vitamin D has been linked to immune regulation, but its predictive role in UTI subtypes is unclear. Methods: We analyzed 332 de-identified clinical records using six machine learning algorithms: Extra Trees, Gradient Boosting, XGBoost, Logistic Regression, Random Forest, and LightGBM. Two preprocessing strategies were applied: (i) removing rows with missing fasting blood sugar (FBs) and HbA1c, and (ii) dropping columns with Null FBs and HbA1c values. Model performance was evaluated using 10-fold cross-validation. Results: Serum vitamin D showed weak correlations with UTI subtypes but modest importance in tree-based models. The highest predictive accuracy was obtained with Extra Trees (0.9510) under the row-removal strategy and Random Forest (0.9525) under the column-dropping strategy. Models excluding vitamin D maintained comparable accuracy, suggesting minimal impact on overall predictive performance. Conclusions: Machine learning models demonstrated high accuracy and robustness in predicting UTI subtypes across preprocessing strategies. While vitamin D contributes as a supportive feature, it is not essential for reliable prediction. These findings highlight the adaptability and clinical utility of both vitamin D-inclusive and vitamin D-exclusive models, supporting deployment in diverse healthcare settings.

This study proposes a double inertial technique integrated with the Mann algorithm to address the fixed-point problem. Our method is further employed to tackle the split-equilibrium problem and perform classification using a urinary tract infection dataset in practical scenarios. The Extreme Learning Machine (ELM) model is utilized to categorize urinary tract infection cases based on both clinical and demographic features. It exhibits excellent precision and efficiency in differentiating infected from non-infected individuals. The results validate that the ELM provides a rapid and reliable method for handling classification tasks related to urinary tract infections.

Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contrast, image interpretation remains opera-tor-dependent and subject to inter-observer variability. This study proposes an automated deep learning framework for MRI-based bladder cancer staging to support standardized radio-logical interpretation. Methods: A sequential AI-based pipeline was developed, integrating hybrid tumor segmentation using YOLOv11 for lesion detection and DeepLabV3 for boundary refinement, followed by three deep learning classifiers (VGG19, ResNet50, and Vision Transformer) for MRI-based stage prediction. A total of 416 T2-weighted MRI images with radiology-derived stage labels (T1–T4) were included, with data augmentation applied during training. Model performance was evaluated using accuracy, precision, recall, F1-score, and multi-class AUC. Performance un-certainty was characterized using patient-level bootstrap confidence intervals under a fixed training and evaluation pipeline. Results: All evaluated models demonstrated high and broadly comparable discriminative performance for MRI-based bladder cancer staging within the present dataset, with high point estimates of accuracy and AUC, particularly for differentiating non–muscle-invasive from muscle-invasive disease. Calibration analysis characterized the probabilistic behavior of predicted stage probabilities under the current experimental setting. Conclusions: The proposed framework demonstrates the feasibility of automated MRI-based bladder cancer staging derived from radiological reference labels and supports the potential of deep learning for stand-ardizing and reproducing MRI-based staging procedures. Rather than serving as an independent clinical decision-support system, the framework is intended as a methodological and work-flow-oriented tool for automated staging consistency. Further validation using multi-center datasets, patient-level data splitting prior to augmentation, pathology-confirmed reference stand-ards, and explainable AI techniques is required to establish generalizability and clinical relevance.

Introduction: Idiopathic scrotal calcinosis (ISC) is a rare, benign condition characterized by painless, calcified nodules within the scrotal dermis. Although typically asymptomatic, progressive growth and cosmetic concerns often lead patients to seek surgical intervention. Case Presentation: We report a 35‐year‐old male with multiple asymptomatic scrotal nodules progressively enlarging over 3 years. Physical examination revealed firm, subcutaneous nodules ranging from 2 to 20 mm. Laboratory investigations, including serum calcium, phosphate, and parathyroid hormone, were unremarkable. Histopathological examination confirmed the diagnosis of ISC, revealing basophilic calcified deposits within a fibrous stroma without cystic epithelial lining, consistent with dystrophic calcification. Intervention: A “pinch‐and‐punch” excision technique was performed under regional anesthesia supplemented by tumescent local infiltration. Individual nodules were elevated by pinching the overlying scrotal skin, followed by targeted removal using 2–4 mm disposable biopsy punches. No sutures were required. Outcome: The procedure was completed with minimal bleeding and no intraoperative or postoperative complications. Complete epithelialization occurred within 1 week via secondary intention. At 3‐month follow‐up, no recurrence was observed and the patient reported high satisfaction with the aesthetic outcome. Conclusion: The pinch‐and‐punch excision technique—distinguished by its combination of the tissue‐elevation pinching maneuver, tumescent infiltration for hydrodissection, and sutureless wound management—is a safe and cosmetically favorable approach to ISC, particularly for patients with multiple nodules. Histopathological confirmation remains essential. Further prospective studies with longer follow‐up are warranted.