Prostruc: an open-source tool for 3D structure prediction using homology modeling.

Introduction: Homology modeling is a widely used computational technique for predicting the three-dimensional (3D) structures of proteins based on known templates,evolutionary relationships to provide structural insights critical for understanding protein function, interactions, and potential therapeutic targets. However, existing tools often require significant expertise and computational resources, presenting a barrier for many researchers.

Methods: Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline.

A retrospective study of prostate-specific antigen and international prostate symptoms scores from participants at a men's health screening initiative in Trinidad.

Background: This study describes the characteristics of men attending a primary health care screening initiative, determines the proportion of men who have elevated International Prostate Symptom Score (IPSS) scores and prostate-specific antigen (PSA) levels, and determines any correlation between these scores as indicators for benign prostatic hyperplasia (BPH) or prostate cancer.

Methods: Data were collected from all patient records during men's health screening initiatives that occurred in December 2018, January 2019, and March 2019 in Trinidad and Tobago. A total of 350 medical records were analyzed to record patient demographics, PSA levels, and IPSS scores.

Vascular Postoperative Changes After Facial Flap Repairs: An Opportunity for Early Intervention.

Background: Flap repair provides unique advantages in facial reconstruction but still carries the potential for undesirable postoperative cosmetic changes.

Objective: The aims of this study were to describe postoperative vascular outcomes of patients undergoing flap repairs after Mohs micrographic surgery on facial tumors and to assess associations of baseline characteristics with outcomes.

Materials And Methods: In this study, 7 dermatologists and 1 physician assistant in dermatology assessed preoperative and postoperative photographs of 57 patients who underwent facial Mohs micrographic surgery, evaluating vascular and pigment outcomes and number of telangiectasias.

Design, construction, and validation of obstetric risk classification systems to predict intensive care unit admission.

Introduction: To develop and validate a support tool for healthcare providers, enabling them to make precise and critical decisions regarding intensive care unit (ICU) admissions for high-risk pregnant women, thus enhancing maternal outcomes.

Methods: This retrospective study involves secondary data analysis of information gathered from 9550 pregnant women, who had severe maternal morbidity (any unexpected complication during labor and delivery that leads to substantial short-term or long-term health issues for the mother), collected between 2009 and 2010 from the Brazilian Network for Surveillance of Severe Maternal Morbidity, encompassing 27 obstetric reference centers in Brazil. Machine-learning models, including decision trees, Random Forest, Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost), were employed to create a risk prediction tool for ICU admission.