Head Trauma

Network-guided transcriptomics identifies drug-linked host-response biomarkers in tuberculosis disease-state transition.

Distinguishing active tuberculosis (ATB) from the post-therapy state remains a key challenge in disease monitoring, as host-response signatures are often broad and difficult to interpret. Robust and biologically interpretable markers are needed to capture this transition consistently across datasets. Here, we define a focused transcriptional signature associated with the shift from active disease ...

rs57494551 increases CXCR5 expression and IgM+ CXCR5+ B-cell infiltration in the portal area of primary biliary cholangitis.

BACKGROUND: Primary biliary cholangitis (PBC) is a chronic, progressive, cholestatic liver disease caused by autoimmune reactions. A previous international meta-analysis of a genome-wide association study (GWAS) reported a robust association of the human chr.11q23.3 locus including C-X-C motif chemokine receptor 5 (CXCR5) with susceptibility to PBC. However, the molecular mechanism by which CXCR5 ...

KA-TMoE: A Deep Learning Method Using Time-Series CT Radiomics to Predict Post-Radiotherapy Rib Fractures in NSCLC Patients.

BACKGROUND AND PURPOSE: Rib fracture is a recognized clinical complication in medically inoperable patients with non-small cell lung cancer (NSCLC) un...

AI Patient Support and 6-Month Medication Adherence in a Digital Obesity Program: A Retrospective Analysis.

BACKGROUND: Real-world glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapies face substantial attrition rates in commercial digital weight los...

The Open Data Commons for Traumatic Brain Injury: An Interagency Community Platform for Pre-Clinical Data Management and Sharing.

The Open Data Commons for Traumatic Brain Injury (ODC-TBI.org) was launched in 2018 to support data sharing in pre-clinical TBI. As data science and a...

Adding Artificial Intelligence to the PERT Improves Time to Diagnosis and Treatment of Pulmonary Embolism.

OBJECTIVES: There is limited data demonstrating the benefit of artificial intelligence technology in the diagnosis and triage of pulmonary embolism. O...

Risk analysis and warning model of post-noncardiac surgery acute kidney injury: A retrospective observational study.

Postoperative acute kidney injury (PO-AKI) following noncardiac surgery remains a major clinical challenge, for which effective early warning models a...

Genome-Wide Association, Polygenic Risk Scores, and Machine Learning for Chronic Post-Surgical Pain Risk Stratification: A UK Biobank Study.

Chronic post-surgical pain is a prevalent and debilitating complication following surgery, representing a clinical challenge. Despite the established ...

Impact of Artificial Intelligence Chatbots on Student Confidence and Perceptions in a Communication Course.

OBJECTIVES: To 1) improve pharmacy students' perceived confidence in communication skills and 2) identify perceptions of counseling a text-based AI-si...

Predicting Readmissions or Post-Discharge Mortality After Cardiac Surgery with Machine Learning Using an Australian Database.

AIM: This study aimed to create machine learning algorithms using the Australian & New Zealand Society of Cardiac & Thoracic Surgeons (ANZSCTS) Databa...

Non-invasive prediction of dupilumab response in facial atopic dermatitis using early post-treatment reflectance confocal microscopy features.

BACKGROUND: Non-invasive prediction of the efficacy of dupilumab on facial lesions in patients with atopic dermatitis (AD) was unresolved. OBJECTIVE: ...

Machine learning-based screening tool for predicting the risk of oropharyngeal dysphagia in patients with ischemic stroke.

PURPOSE: Dysphagia frequently complicates recovery after stroke, necessitating effective predictive tools. This study aimed to develop supervised mach...

Long-term prediction of epilepsy following traumatic brain injury among veterans using routine clinical data.

OBJECTIVE: Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools to assess epilepsy risk following TBI us...

Predicting Outcomes of Traumatic Brain Injury Using Machine Learning Models Among Patients at Kilimanjaro Christian Medical Centre, Tanzania: A Registry-Based Cohort Study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health burden, disproportionately affecting low- and middle-income countries (LMICs) w...

AI-assisted post-mortem classification of vehicle trauma in free-roaming cats using transformer-based large language models.

Veterinary forensic science is a specialized discipline that investigates non-natural animal deaths and injuries. Determining the cause of death in fr...

What predicts postoperative opioid prescribing and consumption? A statewide analysis of surgical quality registry data.

OBJECTIVE: Tailoring postoperative opioid recommendations to patient needs requires nuanced understanding of factors contributing to post-discharge op...

Perceptions of Artificial Intelligence-Based Documentation in Dental Hygiene Clinical Education and Patient-Provider Connection: A pilot study.

Purpose Clinical documentation is a critical competency in dental hygiene (DH) education, yet its administrative burden often detracts from patient ca...

Refining Accelerometer-Based Animal Behaviour Classifications With Sequence-Informed Post-Processing.

Supervised machine learning has been used to detect fine-scale behaviours from animal-borne accelerometers by dividing the continuous sequences of beh...

Exploratory machine learning-based early post-treatment assessment of willingness to reuse rubber dam isolation after microscopic root canal treatment.

ObjectiveTo explore factors associated with willingness to reuse rubber dam isolation after microscopic root canal treatment and develop an explorator...

Predicting Next-Day Passive Suicidal Ideation in At-Risk Youth.

INTRODUCTION: Passive suicidal ideation (SI) is a well-established risk factor for suicidal behavior but has received less attention than active SI. A...