Neurology

Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.

INTRODUCTION: Postoperative delirium (POD) adversely affects clinical outcomes among older adults undergoing spine surgery. However, existing predictive models often neglect multidimensional nature of delirium, including its clinical subtype, duration, severity, and timing. This study developed a multi-output Long Short-Term Memory (LSTM) neural network that integrates preoperative baseline charac...

Ti3C2/CuCoSe2 p-n Heterojunction: Preparation and Mechanism for Machine Learning-enhanced Electrochemical Sensing of Rutin.

Rutin is widely employed as a therapeutic agent, but excessive intake may induce adverse reactions such as gastric discomfort, headache and dermatitis. However, reliably quantifying it in complex matrices remains a significant challenge. Herein, a high-performance electrochemical sensing platform is constructed based on machine learning and interfacial energy-barrier modulation of Ti3C2/CuCoSe2 p-...

Research on Alzheimer's Disease Risk Assessment Models and Biomarker Screening Based on Bioinformatics Analysis and Machine Learning Algorithms.

INTRODUCTION: Alzheimer's Disease (AD) is among the most prevalent neurodegenerative disorders globally, yet effective early diagnostic strategies rem...

Emerging Roles of Small-molecule Derivatives in Modulating Neurodegenerative Pathways: From Molecular Targets to Therapeutic Applications.

Neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease involve progress...

Reimagining Brain Drug Delivery: Mechanistic and Translational Advances in Carbon Nanotube Nanomedicine.

Neurological and neurodegenerative disorders (NDDs) present ongoing therapeutic challenges attributed to the structural complexity of the central nerv...

Automatic Multi-Class Classification of 3D Relationships Between the Mandibular Third Molar and Canal on CBCT Using a Geometry-Aware Network.

OBJECTIVES: Accurate three-dimensional (3D) evaluation of the spatial relationship between the mandibular third molar (M3) and the mandibular canal (M...

Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications.

Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-...

Multimodal biomarker AI techniques for early neurocognitive disorder diagnosis: A systematic review.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and related dementias remains challenging because no single biomarker sufficiently captures th...

Fully Automated Deep Learning-Based Pipeline for Evans Index Measurement from Raw 3D MRI.

Ventriculomegaly is a key neuroimaging feature of normal pressure hydrocephalus (NPH) and other disorders of cerebrospinal fluid (CSF) dynamics. The E...

Predicting Visual Field Loss in Glaucoma Using OCT and Deep Learning: A Comparative Study of U-Net Variants.

PURPOSE: Glaucoma is a chronic eye disease that progressively damages the optic nerve, leading to irreversible visual field (VF) loss. OCT and VF test...

The evolution of T1-weighted lesion inpainting tools in patients with brain injury: A scoping review.

Focal brain lesions from Acquired Brain Injuries (ABIs) present as regions of abnormal signal intensity on T1-weighted Magnetic Resonance Imaging (MRI...

The Genetics of TDP-43 Type C Neurodegeneration: A Whole-Genome Sequencing Study and Literature Review.

BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration TDP43 type C (TDP-C) is a rare and unique neurodegenerative disease that attacks the ante...

Optic Disc Drusen in Children: An Updated Clinical and Imaging Perspective.

BACKGROUND: The optic disc in a child with optic disc drusen (ODD) frequently mimics the optic disc in a child with papilledema, which may lead to unn...

Deep learning using electroencephalogram (EEG) data for diagnosing and predicting SSRI response in major depressive disorder.

BACKGROUND: Major Depression (MDD) is a potentially life-threatening condition that ranks among the diseases with the highest global burden. Despite i...

Molecular mechanisms of exercise-induced improvements in Alzheimer's disease: a focus on lipid homeostasis.

Alzheimer's disease (AD) is the most prevalent type of dementia, and its pathophysiological mechanisms involve multiple factors, including genomic fac...

EEG-CMT: Spatial-Temporal Representation of EEG for Emotion Recognition Using Convolutional Neural Networks and Vision Transformers.

Background Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional...

Differentiating Depression Subtypes through three-electrode EEG and Emotional Stimuli: A Machine Learning Approach.

We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR)...

A Deep Learning Approach for Analyzing Brainwave Signals During Audio Meditation in Expectant Mothers: Variational Mode Decomposition with a CNN-BiLSTM Model.

Meditation has increasingly been recognized as a helpful non-pharmacological intervention to treat psychological stress, anxiety, and depression durin...

Concussion pathophysiology: From biomechanical insult to clinical phenotype - or is injury truly the beginning?

Mild traumatic brain injury (mTBI) is associated with substantial morbidity worldwide. Emerging evidence demonstrates that both impact- and blast-rela...

Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach.

OBJECTIVE: To identify independent risk factors for diabetic peripheral neuropathic pain (DPNP), construct a nomogram prediction model, and quantify t...