Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms.

Purpose: This study aimed to develop and evaluate machine learning models, specifically CatBoost and extreme gradient boosting (XGBoost), for diagnosing lower urinary tract symptoms (LUTS) in male patients. The objective is to differentiate between bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using a comprehensive dataset that includes patient-reported outcomes, uroflowmetry measurements, and ultrasound-derived features.

Methods: The dataset used in this study was collected from male patients aged 40 and older who presented with LUTS and sought treatment at the urology department of Samsung Medical Center.

Ubiquitin-proteasome system as a target for anticancer treatment-an update.

As the ubiquitin-proteasome system (UPS) regulates almost every biological process, the dysregulation or aberrant expression of the UPS components causes many pathological disorders, including cancers. To find a novel target for anticancer therapy, the UPS has been an active area of research since the FDA's first approval of a proteasome inhibitor bortezomib in 2003 for treating multiple myeloma (MM). Here, we summarize newly described UPS components, including E3 ubiquitin ligases, deubiquitinases (DUBs), and immunoproteasome, whose malfunction leads to tumorigenesis and whose inhibitors have been investigated in clinical trials as anticancer therapy since 2020.