Neurology

Deep learning models identify brain changes during the progression of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder whose progression is closely associated with time. However, most diagnostic models are based on single time-point data, overlooking longitudinal disease characteristics. Structural magnetic resonance imaging (sMRI) has been widely utilized in the study of AD. To address the need for multi-time series analysis in longitudinal AD...

The effect of robot-assisted gait training on physical activity outcomes in people with spinal cord injury: A systematic review.

ObjectiveTo summarise the evidence for changes in physical activity outcomes during robot-assisted gait training in patients with spinal cord injury.Data sourcesThe Web of Science, Physiotherapy Evidence Database, Central, Medline, Scopus and SportDiscus databases were searched in August 2025 for studies that recorded ≥1 physical activity outcome during robot-assisted gait training.Review methodsD...

Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.

Large Language Models (LLMs), represented by the Generative Pretrained Transformer (GPT), are profoundly transforming the healthcare sector. Spine med...

Morphology-adaptive Au-Ag nanowire elastronics for integrated FlexoSERS and bioelectrical sensing.

We introduce a morphology-adaptive Au-Ag nanowire elastronic platform that conforms to diverse geometries while enabling multimodal optical-electrical...

Prediction of hemorrhagic transformation after intravenous thrombolysis in acute anterior circulation ischemic stroke through radiomics-based machine learning models.

BACKGROUND: Hemorrhagic transformation (HT) and its associated neurological deterioration remain major concerns that limit decision-making in thrombol...

Brain age prediction in a multiethnic Asian population: A comparison of machine learning algorithms and their application for early-stage cognitive impairment diagnosis.

BackgroundNeuroimaging-derived brain age is a promising biomarker of early neurodegeneration, but methodological variation in machine learning (ML) al...

Optimization and External Validation of a Deep Learning Model for Segmentation and Quantification of Traumatic Brain Injury Lesions on Head Computed Tomography.

Current methods for radiological classification in traumatic brain injury (TBI), such as the Marshall and Rotterdam score, provide an incomplete descr...

Carbon dot nanoparticles as multifunctional fluorescent probes: from tunable synthesis to biomedical applications.

Carbon dots (CDs) have emerged as highly promising fluorescent nanomaterials due to their exceptional optical properties, biocompatibility, and facile...

Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on automated Alberta stroke program early CT score- evaluation.

PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS) and advances in CT reconstruction play important roles in the neurodiagnostic workflow. T...

Diagnosis of Primary Open-Angle Glaucoma Using Spectral Profiling of Aqueous Humor-Derived Exosomes.

Primary open-angle glaucoma (POAG) is one of the most common neurodegenerative diseases that cause irreversible optic nerve damage. POAG diagnosis req...

Insights on Recruitment, Implementation, and Movement Pattern Detection by Exploring the Feasibility of Sensor-Based Insole Technology in Long-Term Care: Mixed Methods Feasibility Study.

BACKGROUND: Sensor-based footwear is increasingly discussed as a promising tool for mobility monitoring and fall-risk assessment, yet its applicabilit...

Efficacy and neural mechanisms of a vibrotactile-enhanced, brain-controlled soft robotic glove for upper limb rehabilitation after stroke: a multicentre randomised controlled trial protocol.

INTRODUCTION: Soft robotic gloves (SRGs) integrated with brain-computer interfaces (BCIs) have demonstrated potential in facilitating motor recovery a...

Neuropathologic basis of quantitative susceptibility mapping in the substantia nigra: contributions of tau, pigmented neurons, and iron.

Quantitative susceptibility mapping (QSM) on MRI quantifies tissue magnetic susceptibility, which increases with iron accumulation, myelin loss, and n...

Hypoglycemia problem-solving ability as a key predictor of severe hypoglycemia: a machine learning approach in adults with type 1 diabetes.

AIMS/INTRODUCTION: Severe hypoglycemia (SH) is a major complication in adults with type 1 diabetes mellitus (T1DM). The multifactorial etiology of T1D...

Serum metabolomic signatures of relapse recovery in early multiple sclerosis.

BACKGROUND: Relapses in relapsing-remitting multiple sclerosis (RRMS) are acute neuroinflammatory events that shape long-term disease trajectory. Yet,...

EEG and EMG dataset for analyzing movement-related cortical potentials in hand gesture tasks.

This dataset contains electroencephalography (EEG) and electromyography (EMG) recordings acquired during the execution of specific motor tasks aimed a...

The feasibility and accuracy of real-time intra-operative confocal tissue diagnosis in brain and spine cancer surgery.

Real-time intra-operative brain tumour tissue analysis can reduce turnaround times and enable repeated sampling, enhancing diagnostic accuracy and gui...

Addressing Gaps in Parkinson's Disease Etiology: The Need for a Polyexposure Score.

Parkinson's disease (PD) risk and progression are influenced by a complex interplay of genetic, environmental, and internal factors. Environmental exp...

Primary Care Physician Perspectives on Artificial Intelligence for Dementia Screening: A Qualitative Study.

BACKGROUND: Automated approaches to cognitive impairment screening may soon achieve sufficient levels of accuracy for clinical implementation but they...

Structural brain imaging biomarkers for predicting seizure recurrence after a first unprovoked seizure.

OBJECTIVES: Predicting seizure recurrence following a first unprovoked seizure (FUS) remains a significant clinical challenge, especially when routine...