Neurology

Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.

BACKGROUND: Depression is one of the most prevalent mental disorders globally, severely affecting individuals' emotional, cognitive, and physical functions while imposing profound socioeconomic impacts. Traditional diagnostic approaches primarily rely on clinical judgment and self-assessment scales; however, these methods carry inherent risks of misdiagnosis and missed diagnosis, necessitating mor...

Divergent neuropsychiatric and systemic toxicity profiles of abemaciclib and palbociclib: a triangulation study integrating pharmacovigilance, genetic epidemiology, and multi-omics profiling.

Cyclin-dependent kinase 4/6 inhibitors improve outcomes in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer, but their toxicity profiles may differ in clinically meaningful ways. We aimed to compare the neuropsychiatric and systemic toxicity patterns of abemaciclib and palbociclib and to explore pharmacokinetic and molecular features that might co...

Identification of a mitochondrial biomarker signature linking neuroinflammation to neuronal dysfunction in spinal cord injury.

To address the lack of reliable biomarkers for mitochondrial dysfunction that drives secondary injury in spinal cord injury (SCI), this study aimed to...

Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.

OBJECTIVE: Mild cognitive impairment (MCI) is an intermediary stage between typical cognitive aging and dementia. Identifying reliable biomarkers for ...

Advances in neuropharmacology: Innovative drug strategies targeting synaptic plasticity, neuroinflammation, and ion channel regulation for future CNS treatments.

As our understanding of the molecular and cellular mechanisms underlying central nervous system (CNS) disorders expands, neuropharmacology is undergoi...

Enhancing fear of re-injury classification after ACL reconstruction by integrating biomechanical and electromyography data using multimodal machine learning methods.

Fear of re-injury after anterior cruciate ligament (ACL) rupture often hinders return-to-sport and has been linked to movement patterns associated wit...

An upper limb stroke rehabilitation exercise video dataset.

Stroke is one of the leading causes of disability worldwide with a disproportionately high burden in low and middle-income countries. In such countrie...

Large language models for deductive qualitative content analysis in dementia-focused embedded pragmatic clinical trials: A comparative methodological study.

INTRODUCTION: Thematic coding helps researchers characterize intervention implementation in embedded pragmatic clinical trials (ePCTs), particularly i...

Ai-generated personalized informed consent for elective spinal surgery: a comparative study using retrieval-augmented generation.

BACKGROUND: Informed consent (IC) documents in spine surgery frequently lack procedure-specific risk data, quantitative complication rates, and discus...

Improving Readability of Stroke Clinical Trial Consent Forms Using Artificial Intelligence.

BACKGROUND: Informed consent forms (ICFs) for clinical trials are often written above the recommended eighth-grade level. We aimed to compare the read...

TransVort: A Temporally-Coherent Physics-Guided Neural Network for Super-Resolving and Denoising 4D Flow MRI of Cerebrospinal Fluid.

OBJECTIVE: To enhance the diagnostic utility of 4D flow MRI in assessing cerebrospinal fluid (CSF) dynamics by super-resolving and denoising measured ...

Testing Near-Field Radio-Frequency Sensors to Predict Respiratory Distress in Patients Living With Dementia: A Pilot Feasibility and Acceptability Study.

BackgroundPeople living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying re...

Adaptive multimodal learning for driver cognitive state monitoring using transformer-based fusion with personalized meta-learning and federated optimization.

Road accidents caused by driver fatigue and cognitive overload remain a significant public safety concern. According to recent traffic safety data, dr...

Spinal Cord Radiomics-Driven Machine Learning Predicts Meaningful Clinical Improvement After Surgery for Degenerative Cervical Myelopathy: A Pilot Study.

A prospective observational cohort study. To determine whether machine learning models using radiomic features derived from preoperative MRI, clinical...

Predicting online motor learning after stroke in lower limb task using machine learning.

Online motor learning is central to effective learning and a crucial determinant of functional recovery after stroke. Despite its clinical significanc...

Validation of remote multimodal AI screening for Parkinson disease across diverse settings.

BACKGROUND: Timely detection of Parkinson's disease (PD) remains limited by reliance on in-person neurological evaluations that are often costly and g...

EEG-based harmful brain activity classification using deep learning and feature fusion.

The prevalence of research on harmful brain activity has increased, especially since the standardization of electroencephalography (EEG) terminologies...

Beyond NIHSS and neuroimaging: an interpretable gradient boosting model for predicting in-hospital mortality in ICU patients with acute ischemic stroke.

This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients ad...

Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.

Accurate disease prognosis is essential for patient care but is often hindered by the scarcity of longitudinal data. This study explores deep learning...

A two-step temporal data augmentation and supervised learning framework for predicting autism diagnosis at 36 months in patients with tuberous sclerosis complex.

BACKGROUND: Autism spectrum disorder (ASD) affects approximately 25-50% of children with tuberous sclerosis complex (TSC). Early identification of ASD...