Head Trauma

Outcome Prediction in Pediatric Traumatic Brain Injury Utilizing Social Determinants of Health and Machine Learning Methods.

BACKGROUND AND PURPOSE: Considerable socioeconomic disparities exist among pediatric patients with traumatic brain injury (TBI). This study aims to analyze the effects of social determinants of health on head injury outcomes and to create a novel machine-learning algorithm (MLA) that incorporates socioeconomic factors to predict the likelihood of a positive or negative trauma-related finding on he...

Molecular features of human pathological tau distinguish tauopathy-associated dementias.

In Alzheimer's disease (AD), pathological tau protein shows a progressive accumulation of post-translational modifications (PTMs), reflecting disease severity, progression, and prion-like activity. Although many neurodegenerative diseases with dementia display tau aggregates, the pathological proteoforms of tau protein from each disease type remain unknown. Here, using a quantitative mass spectrom...

Prospective Diagnostic Accuracy and Technical Feasibility of Artificial Intelligence-Assisted Rib Fracture Detection on Chest Radiographs: Observational Study.

BACKGROUND: Rib fractures are present in 10%-15% of thoracic trauma cases but are often missed on chest radiographs, delaying diagnosis and treatment....

Attitudes toward artificial intelligence and impostor phenomenon among nursing students: A five-country cross-sectional study.

BACKGROUND: AI tools are increasingly visible in nursing education and practice, yet student exposure and acceptance vary across settings. Limited dig...

Bridging performance and uncertainty: Cautionary notes on machine learning and large language models in TBI prognostication.

Predicting Return Home After Moderate-to-Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Days alive and at home (DAH) is a validated outcome measure that captures health care transitions between time spent at hom...

Comparison of Pragmatic Post-Thrombectomy Prognostic Scores Not Based on Advanced Imaging in a Large National Stroke Registry.

BACKGROUND: Nearly half of the patients who received endovascular thrombectomy (EVT) for large vessel occlusion experience poor functional outcomes. R...

Microbiome-Based Modeling of CAR-T Therapy Response in Lymphoma: Insights From Shotgun Metagenomics Sequencing.

The interplay between the commensal microbiota and the mammalian immune system may influence the outcomes of T cell-driven cancer immunotherapies. How...

MRI-based multilevel radiomics and transformer features for predicting radiation-induced carotid artery injury after nasopharyngeal carcinoma radiotherapy: A multicenter study.

PURPOSE: To develop and validate an MRI-based fusion model (Rad-SRad-SwinT) integrating conventional radiomics (Rad), subregional radiomics (SRad), an...

Peptide-nanoparticle platforms for antisense therapeutics: A coarse-grained modeling approach to brain delivery.

Traumatic brain injury (TBI) is a leading cause of long-term neurological deficits, often resulting in complex, unresolved molecular and cellular dysf...

Extracellular matrix stiffness drives post-mitotic nuclear pore complex assembly to promote neuroblastoma pathogenesis.

Nuclear pore complex (NPC) undergoes dynamic changes in physiology and pathology, yet its roles in neuroblastoma (NB) remain unclear. We demonstrated ...

Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study.

BACKGROUND: Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain predicted age d...

Using Artificial Intelligence Methods to Evaluate the Effect of the National Cytomegalovirus Awareness Month on the Content and Sentiment of Social Media Posts: Infodemiology Study.

BACKGROUND: The month of June has been recognized as the National Cytomegalovirus (CMV) Awareness Month since 2011 in the United States. Established b...

Machine learning-enabled prediction of hemorrhagic transformation post-thrombectomy using quantitative DSA.

This study aims to construct a predictive model for post-thrombectomy hemorrhagic transformation (HT) by integrating hemodynamic features derived from...

Investigating the relationship between teacher efficacy, job satisfaction, and digital resource utilization in assessment practices: Insights from PISA 2018 and 2022.

The COVID-19 pandemic disrupted global education systems, forcing rapid shifts in teaching practices, technology integration, and assessment methods. ...

Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

AIMS: This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and...

Deep Learning-Based Classification of Temporal Stages of AT8-Labeled Tau Pathology After Experimental Traumatic Brain Injury.

Tauopathies are characterised by a progressive accumulation of hyperphosphorylated tau. However, early and intermediate stages remain challenging to q...

OCT-Derived Virtual Fractional Flow Reserve Associated With 1-Year Outcomes After PCI in ACS Patients.

BACKGROUND: Despite PCI, many acute coronary syndrome (ACS) patients experience major adverse cardiovascular events (MACE). Angiography is limited, an...

Identifying the state of cerebrovascular autoregulation using the pressure reactivity index versus the mean flow index: Why metric matters.

Cerebral autoregulation maintains stable cerebral blood flow despite fluctuations in cerebral perfusion pressure (CPP), through mechanisms that alter ...

Predicting functional results of percutaneous coronary intervention using machine learning modelling.

BACKGROUND: Post-percutaneous coronary intervention (PCI) Murray's law-based quantitative flow ratio (μFR) is associated with long-term clinical outco...