Neurology

Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to m...

Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.

INTRODUCTION: Individuals with spinal cord injury (SCI) have varying bladder health trajectories aft...

Evolution of CT perfusion software in stroke imaging: from deconvolution to artificial intelligence.

Computed tomography perfusion (CTP) represents one of the main determinants in the decision-making s...

Drp1 Proteins Released from Hydrolysis-Driven Scaffold Disassembly Trigger Nucleotide-Dependent Membrane Remodeling to Promote Scission.

Dynamin-related protein (Drp1) drives mitochondrial fission, dysregulation of which leads to neurode...

Insights into the Diagnosis, Treatment, and Management of Prion Diseases.

The normal cellular prion protein (PrPC) can misfold into an infectious and pathogenic form (PrPSc) ...

Automated instance segmentation and registration of spinal vertebrae from CT-Scans with an improved 3D U-net neural network and corner point registration.

This paper presents a rapid and robust approach for 3D volumetric segmentation, labelling, and regis...

The application and discovery of animal models in enterovirus research.

Enterovirus infection remains a significant global public health challenge, causing severe diseases ...

Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50.

Accurate prediction of stroke risk at an early stage is essential for timely intervention and preven...

Deep learning diagnosis plus kinematic severity assessments of neurodivergent disorders.

Early diagnostic assessments of neurodivergent disorders (NDD), remains a major clinical challenge. ...

Motor imagery EEG signal classification using novel deep learning algorithm.

Electroencephalography (EEG) signal classification plays a critical role in various biomedical and c...

Depression EEG classification based on multi-scale convolutional transformer network.

Depression electroencephalograph (EEG) classification based on machine learning is helpful for the a...

Bridging Clinical Expertise and Digital Innovation: Toward a Modern Phenomenology of Parkinsonism.

Development and Validation of a Nomogram for Predicting Oral Frailty Risk in Elderly Patients With Ischaemic Stroke.

AIM: To develop and validate a risk prediction model for oral frailty in elderly patients with ischa...

Beyond the human touch: A critical review of the promise and challenges of animal- and robot-assisted therapy in loneliness and mental healthcare.

Mental health disorders, including depression, anxiety, post-traumatic stress disorder, and dementia...

Recognition of Parkinson disease using Kriging Empirical Mode Decomposition via deep learning techniques.

UNLABELLED: Parkinson's disorder (PD) is a chronic, irreversible neurological disorder that is hard ...

Unraveling Stochastic Dynamics and Switching Mechanism in Ag Network-Based Neuromorphic Device by Impedance Spectroscopy.

Neuromorphic devices are leading advancements in brain-inspired computing. The present study employs...

Electrophysiological-based automatic subgroups diagnosis of patients with chronic dysimmune polyneuropathies.

BACKGROUND: Chronic Dysimmune Polyneuropathies (CDP) encompass a group of conditions characterized b...

Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.

The unrelenting progression of Parkinson's disease (PD) leads to severely impaired quality of life, ...

Autonomous screening of infants at high risk for neurodevelopmental impairments using a radar sensor and machine learning.

Neurodevelopmental impairments (NDIs) are significant long-term complications in preterm infants. Wh...

Prediction model of recurrence in patients with lumbar disc herniation after unilateral biportal endoscopy spinal surgery- XGBoost machine learning model can be interpreted based on SHAP.

Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning.

Spinal cord injury (SCI) is a severe neurological disorder, with glucocorticoids like methylpredniso...