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Background: Teaching severe pelvic trauma poses a significant challenge in orthopedic surgery education due to the necessity of both clinical reasoning and procedural operational skills for mastery. Traditional methods of instruction, including theoretical teaching and mannequin practice, face limitations due to the complexity, the unpredictability of treatment scenarios, the scarcity of typical cases, and the abstract nature of traditional teaching, all of which impede students' knowledge acquisition.

Objective: This study aims to introduce a novel experimental teaching methodology for severe pelvic trauma, integrating virtual reality (VR) technology as a potent adjunct to existing teaching practices.

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Background And Objectives: Despite the absence of acute lesion activity in multiple sclerosis (MS), chronic neurodegeneration continues to progress, and a potential underlying mechanism could be the kynurenine pathway (KP). Prolonged activation of the KP from chronic inflammation is known to exacerbate the progression of neurodegenerative diseases through the production of neurotoxic metabolites. Among the 8 KP metabolites, six of them, namely kynurenine (KYN), 3-hydroxylkynurenine (3HK), anthranilic acid (AA), kynurenic acid (KYNA), and quinolinic acid (QUIN), have been associated with neurodegeneration.

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This study presents a novel method for creating customized brain slice matrices using Computer-Aided Design (CAD) and 3D printing technology. Brain Slice Matrices are essential jigs for the reproducible preparation of brain tissue sections in neuroscience research. Our approach leverages the advantages of 3D printing, including design flexibility, cost-effectiveness, and rapid prototyping, to produce custom-made brain matrices based on specific morphometric measurements.

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Flexible and modular latent transition analysis-A tutorial using R.

PLoS One

January 2025

National Institute of Public Health, University of Southern Denmark, Copenhagen K, Denmark.

Latent transition analysis (LTA) is a useful statistical modelling approach for describe transitions between latent classes over time. LTA may be characterized in terms of prevalence at each time point and through transition probabilities over time. Investigating predictors of these transitions is often of key interest.

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Objective: This study aimed to introduce and evaluate a novel software-based system, BioTrace, designed for real-time monitoring of thermal ablation tissue damage during image-guided radiofrequency ablation for hepatocellular carcinoma (HCC).

Methods: BioTrace utilizes a proprietary algorithm to analyze the temporo-spatial behavior of thermal gas bubble activity during ablation, as seen in conventional B-mode ultrasound imaging. Its predictive accuracy was assessed by comparing the ablation zones it predicted with those annotated by radiologists using contrast-enhanced computed tomography (CECT) 24 hours post-treatment, considered the gold standard.

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