Factors Considered by Experienced Urologists when Selecting Alpha-Blockers for Patients with Lower Urinary Tract Symptoms or Benign Prostatic Hyperplasia according to Age.

INTRODUCTION: The objectives of the study were to examine the opinions of urology specialists on whether there are actual differences in efficacy among α1-blockers and to identify the factors that should be considered when prescribing these medications according to age. METHODS: We surveyed 50 South Korean urology specialists with over 3 years of clinical experience in secondary or tertiary hospitals in July-August 2021. The survey covered urologists' demographics, awareness of α1-blocker prescription differences, and key factors in α1-blocker selection based on LUTS severity and patient age. RESULTS: Overall, 82% of the respondents believed that there were differences in the efficacy of α1-blockers in actual practice according to age. Over 90% of the respondents agreed on the need for head-to-head comparison studies to compare the effects of different α1-blockers. Regardless of the severity of LUTS, urologists prioritize cardiovascular side effects when prescribing α1-blockers to patients aged ≥70 years. Further, 19% of the urologists prioritized ejaculatory side effects for mild-to-moderate LUTS and 9% for severe LUTS (p < 0.001). CONCLUSIONS: This study shows that head-to-head studies comparing the efficacy of different α1-blockers are highly valuable for the real-world clinical application of α1-blockers. Notably, urologists prioritize cardiovascular and ejaculatory side effects in older and younger patients while prescribing α1-blockers, respectively.

Microbiome and Prostate Cancer: Emerging Diagnostic and Therapeutic Opportunities.

This review systematically addresses the correlation between the microbiome and prostate cancer and explores its diagnostic and therapeutic implications. Recent research has indicated an association between the urinary and gut microbiome composition and prostate cancer incidence and progression. Specifically, the urinary microbiome is a potential non-invasive biomarker for early detection and risk evaluation, with altered microbial profiles in prostate cancer patients. This represents an advancement in non-invasive diagnostic approaches to prostate cancer. The role of the gut microbiome in the efficacy of various cancer therapies has recently gained attention. Gut microbiota variations can affect the metabolism and effectiveness of standard treatment modalities, including chemotherapy, immunotherapy, and hormone therapy. This review explores the potential of gut microbiome modification through dietary interventions, prebiotics, probiotics, and fecal microbiota transplantation for improving the treatment response and mitigating adverse effects. Moreover, this review discusses the potential of microbiome profiling for patient stratification and personalized treatment strategies. While the current research identifies the pivotal role of the microbiome in prostate cancer, it also highlights the necessity for further investigations to fully understand these complex interactions and their practical applications in improving patient outcomes in prostate cancer management.

Modifying Specific Ion Effects: Studies of Monovalent Ion Interactions with Amines.

Specific ion effects in the interactions of monovalent anions with amine groups─one of the hydrophilic moieties found in proteins─were investigated using octadecylamine monolayers floating at air-aqueous solution interfaces. We find that at solution pH 5.7, larger monovalent anions induce a nonzero pressure starting at higher areas/molecules, i.e., a wider "liquid expanded" region in the monolayer isotherms. Using X-ray fluorescence at near total reflection (XFNTR), an element- and surface-specific technique, ion adsorption to the amines at pH 5.7 is confirmed to be ion-specific and to follow the conventional Hofmeister series. However, at pH 4, this ion specificity is no longer observed. We propose that at the higher pH, the amine headgroups are only partially protonated, and large polarizable ions such as iodine are better able to boost amine protonation. At the lower pH, on the other hand, the monolayer is fully protonated, and electrostatic interactions dominate over ion specificity. These results demonstrate that ion specificity can be modified by changing the experimental conditions.

A Novel Deep Learning-based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography.

BACKGROUND AND OBJECTIVE: Our aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume and density, which are essential for treatment decisions. The performance of the system was compared to that of urologists in emergency room (ER) scenarios. METHODS: Axial CT images for patients who underwent stone surgery between August 2022 and July 2023 comprised the data set, which was divided into 70% for training, 10% for internal validation, and 20% for testing. Two urologists and an AI specialist annotated stones using Labelimg for ground-truth data. The YOLOv4 architecture was used for training, with acceleration via an RTX 4900 graphics processing unit (GPU). External validation was performed using CT images for 100 patients with suspected urolithiasis. KEY FINDINGS AND LIMITATIONS: The AI system was trained on 39 433 CT images, of which 9.1% were positive. The system achieved accuracy of 95%, peaking with a 1:2 positive-to-negative sample ratio. In a validation set of 5736 images (482 positive), accuracy remained at 95%. Misses (2.6%) were mainly irregular stones. False positives (3.4%) were often due to artifacts or calcifications. External validation using 100 CT images from the ER revealed accuracy of 94%; cases that were missed were mostly ureterovesical junction stones, which were not included in the training set. The AI system surpassed human specialists in speed, analyzing 150 CT images in 13 s, versus 38.6 s for evaluation by urologists and 23 h for formal reading. The AI system calculated stone volume in 0.2 s, versus 77 s for calculation by urologists. CONCLUSIONS AND CLINICAL IMPLICATIONS: Our AI system, which uses advanced deep learning, assists in diagnosing urolithiasis with 94% accuracy in real clinical settings and has potential for rapid diagnosis using standard consumer-grade GPUs. PATIENT SUMMARY: We developed a new AI (artificial intelligence) system that can quickly and accurately detect kidney stones in CT (computed tomography) scans. Testing showed that this system is highly effective, with accuracy of 94% for real cases in the emergency department. It is much faster than traditional methods and provides rapid and reliable results to help doctors in making better treatment decisions for their patients.