Skin sympathetic nerve activity as a potential biomarker for overactive bladder.

PURPOSE: Abnormalities in autonomic function are associated with an overactive bladder (OAB). Heart rate variability is generally used as the sole assessment of autonomic activity; however, we utilized neuECG, a novel method of recording skin electrical signals, to assess autonomic nervous function in healthy controls and patients with OAB before and after treatment. METHODS: The prospective sample included 52 participants: 23 patients newly diagnosed with OAB and 29 controls. Autonomic function was assessed in all participants in the morning using neuECG, which analyzed the average skin sympathetic nerve activity (aSKNA) and electrocardiogram simultaneously. All patients with OAB were administered antimuscarinics; urodynamic parameters were assessed before treatments; autonomic and bladder functions using validated questionnaires for OAB symptoms were evaluated before and after OAB treatment. RESULTS: Patients with OAB had significantly higher baseline aSKNA (p = 0.003), lower standard deviation of the normal-to-normal beat intervals, lower root mean square of the successive differences, lower high-frequency, and higher low-frequency than did controls. Baseline aSKNA had the highest value in predicting OAB (AUROC = 0.783, p < 0.001). The aSKNA was negatively correlated with first desire and normal desire in urodynamic studies (both p = 0.025) and was significantly decreased after treatment at rest, stress, and recovery phases, as compared to those before treatment (p = 0.046, 0.017, and 0.017, respectively). CONCLUSION: Sympathetic activity increased significantly in patients with OAB compared to that in healthy controls, and decreased significantly post-treatment. Higher aSKNA is associated with decreased bladder volume at which voiding is desired. SKNA may be a potential biomarker for diagnosing OAB.

Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model.

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters-sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.

Association of Warmer Weather and Infectious Complications Following Transrectal Ultrasound-Guided Prostate Biopsy.

The seasonal and meteorological factors in predicting infections after urological interventions have not been systematically evaluated. This study aimed to determine the seasonality and the effects of the weather on the risk and severity of infectious complications (IC) after a transrectal ultrasound-guided prostate biopsy (TRUS-Bx). Using retrospectively collected data at the tertiary care hospital in Taiwan, we investigated the seasonal and meteorological differences in IC after TRUS-Bx. The IC included urinary tract infection (UTI), sepsis, and a positive culture finding (PCF). The severity was assessed on the basis of the Common Terminology Criteria for Adverse Events grading system. The prevalences of the infectious complications (UTI, sepsis, PCF and grade ≥ 3 IC) were significantly higher in the summer than in the winter. Monthly temperature and average humidity were significant factors for IC. After adjusting the demographic factors, multivariate regression revealed that UTI, sepsis, PCF, and grade ≥ 3 IC increased by 12.1%, 16.2%, 21.3%, and 18.6% for every 1 °C increase in the monthly average temperature, respectively (UTI: p = 0.010; sepsis: p = 0.046; PCF: p = 0.037; grade ≥ 3 IC: p = 0.021). In conclusion, the development and severity of IC after TRUS-Bx had significant seasonality. These were dose-dependently associated with warmer weather. Infectious signs after TRUS-Bx should be monitored more closely and actively during warm weather.