Predictors of symptomatic relief in water vapor thermal therapy for prostatic hyperplasia: 36-month prospective study.

Purpose: Existing literature lacks an analysis of factors predicting the achievement of minimum clinically important differences (MCID) after water vapor thermal therapy (WVTT) for prostatic hyperplasia. This study aims to identify these predictors over a 36-month post-WVTT period.

Methods: This prospective single surgeon case series assessed male patients receiving WVTT.

Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.

This research presents our application of artificial intelligence (AI) in predicting urolithiasis risk. Previous applications, including AI for stone disease, have focused on stone composition and aiding diagnostic imaging. AI applications centered around patient-specific characteristics, lifestyle considerations, and diet have been limited.

A user-friendly method to get automated pollen analysis from environmental samples.

Automated pollen analysis is not yet efficient on environmental samples containing many pollen taxa and debris, which are typical in most pollen-based studies. Contrary to classification, detection remains overlooked although it is the first step from which errors can propagate. Here, we investigated a simple but efficient method to automate pollen detection for environmental samples, optimizing workload and performance.

Bioaccessibility of chlorogenic acid and curcumin co-encapsulated in double emulsions with the inner interface stabilized by functionalized silica nanoparticles.

The aim of this study was to evaluate the bioaccessibility of chlorogenic acid (CA) and curcumin co-encapsulated in Pickering double emulsions (DEs) with the inner interface stabilized by hydrophobically modified silica nanoparticles with myristic acid (SNPs-C14) or tocopherol succinate (SNPs-TS). Both SNPs-C14 and SNPs-TS showed contact angles > 90°. Pickering W/O emulsions were formulated with 4 % of both types of SNPs.