Head Trauma

Machine Learning Models Based on CT Enterography for Differentiating Between Ulcerative Colitis and Colonic Crohn's Disease Using Intestinal Wall, Mesenteric Fat, and Visceral Fat Features.

PURPOSE: This study aimed to develop radiomic-based machine learning models using computed tomography enterography (CTE) features derived from the intestinal wall, mesenteric fat, and visceral fat to differentiate between ulcerative colitis (UC) and colonic Crohn's disease (CD).

Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury.

Traumatic brain injury (TBI) is a major public health concern associated with an increased risk of neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), and chronic traumatic encephalopathy, yet the underlying molecular mechanisms in repetitive TBI remain poorly defined. This study investigates proteomic and behavioral changes following single and repetitive mild...

Efficient slice anomaly detection network for 3D brain MRI Volume.

Current anomaly detection methods excel with benchmark industrial data but struggle with natural images and medical data due to varying definitions of...

Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.

Methodological standards of existing clinical AI research remain poorly characterized and may partially explain the implementation gap between model d...

An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.

Artificial Intelligence (AI) offers a revolutionary approach to improve decision-making in medicine through the use of advanced computational tools. I...

Traumatic brain injury management in the intensive care unit: standard of care and knowledge gaps.

Despite advances in the management of traumatic brain injury (TBI) in the intensive care unit (ICU), and the implementation of new diagnostic techniqu...

Prediction of functional outcome after traumatic brain injury: a narrative review.

PURPOSE OF REVIEW: To synthesize current evidence on prognostic factors, tools, and strategies influencing functional outcomes in patients with trauma...

Exploring the molecular mechanisms of lactylation-related biological functions and immune regulation in sepsis-associated acute kidney injury.

Lactylation, a novel post-translational modification, has been implicated in various pathophysiological processes; however, its role in sepsis-associa...

Precision-Optimised Post-Stroke Prognoses.

BACKGROUND: Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by t...

Patterns in Mental Health Symptoms, Substance Use, and Viral Suppression in People with HIV: A Clustering Analysis.

Mental health conditions and substance use are prevalent among people with HIV (PWH), are correlated with one another, and associate with viral non-su...

"Calming the nightmares": A qualitative study of a socially assistive robot for sensory and emotional support in individuals with eating disorders and PTSD.

Individuals with eating disorders (ED) and co-occurring post-traumatic stress disorder (PTSD) often face difficulties with sensory overload and emotio...

A cocktail hydrogel promoting the functional interneurons regeneration of human neural progenitor cells for brain injury therapy.

INTRODUCTION: Traumatic Brain Injury (TBI) usually leads to substantial mortality and disability among adult populations. Neural progenitor cells (NPC...

A Synergistic Approach Using Photoacoustic Spectroscopy and AI-Based Image Analysis for Post-Harvest Quality Assessment of Conference Pears.

This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO laser photoacoustic spectroscopy (COLPAS) to study...

Unsophisticated AI coaching does not improve performance on symptom and performance validity tests in a Romanian sample of experimental feigners.

The present study aimed to investigate the ability of symptom and performance validity indicators to detect experimental feigning with symptom-coachin...

Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction.

Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) scor...

Recent Advancements in Smart Hydrogel-Based Materials in Cartilage Tissue Engineering.

Cartilage tissue engineering (CTE) is an advancing field focused on developing biomimetic scaffolds to overcome cartilage's inherently limited self-re...

Efficacy of rTMS on SCZ symptoms: Insights from connectome mapping.

Repetitive transcranial magnetic stimulation (rTMS) is a potential treatment for schizophrenia (SCZ), yet its efficacy and underlying mechanisms remai...

Comparative analysis of natural language processing methodologies for classifying computed tomography enterography reports in Crohn's disease patients.

Imaging is crucial to assess disease extent, activity, and outcomes in inflammatory bowel disease (IBD). Artificial intelligence (AI) image interpreta...

Identifying and predicting headache trajectories among those with acute post-traumatic headache.

OBJECTIVES/BACKGROUND: Post-traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identifi...

Machine Learning-Based Prediction of Post-Operative Systemic Inflammatory Response Syndrome Following Pediatric Percutaneous Nephrolithotripsy.

OBJECTIVE: This study aimed to develop and validate a machine learning-based model for predicting systemic inflammatory response syndrome (SIRS) in pe...