Neurology

Inference of Essential Genes of the Parasite via Machine Learning.

Over the years, comprehensive explorations of the model organisms (elegant worm) and (vinegar fly) have contributed substantially to our understanding of complex biological processes and pathways in multicellular organisms generally. Extensive functional genomic-phenomic, genomic, transcriptomic, and proteomic data sets have enabled the discovery and characterisation of genes that are crucial fo...

Identification of endoplasmic reticulum stress genes in human stroke based on bioinformatics and machine learning.

After ischemic stroke (IS), secondary injury is intimately linked to endoplasmic reticulum (ER) stress and body-brain crosstalk. Nonetheless, the underlying mechanism systemic immune disorder mediated ER stress in human IS remains unknown. In this study, 32 candidate ER stress-related genes (ERSRGs) were identified by overlapping MSigDB ER stress pathway genes and DEGs. Three Key ERSRGs (ATF6, DDI...

Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.

BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificia...

Feature evaluation for myoelectric pattern recognition of multiple nearby reaching targets.

Intention detection of the reaching movement is considerable for myoelectric human and machine collaboration applications. A comprehensive set of hand...

When performing actions with robots, attribution of intentionality affects the sense of joint agency.

Sense of joint agency (SoJA) is the sense of control experienced by humans when acting with others to bring about changes in the shared environment. S...

A machine learning model predicts stroke associated with blood cadmium level.

Stroke is the leading cause of death and disability worldwide. Cadmium is a prevalent environmental toxicant that may contribute to cardiovascular dis...

Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test.

INTRODUCTION: Parkinson's disease (PD) is characterized by motor symptoms whose progression is typically assessed using clinical scales, namely the Mo...

Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke.

Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-modal MRI imaging of unwitnessed acute ischemic st...

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

INTRODUCTION: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for...

Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning.

Residents learn the vesico-urethral anastomosis (VUA), a key step in robot-assisted radical prostatectomy (RARP), early in their training. VUA assessm...

An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.

Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the...

Assessing gait dysfunction severity in Parkinson's Disease using 2-Stream Spatial-Temporal Neural Network.

Parkinson's Disease (PD), a neurodegenerative disorder, significantly impacts the quality of life for millions of people worldwide. PD primarily impac...

EMG-based prediction of step direction for a better control of lower limb wearable devices.

BACKGROUND AND OBJECTIVES: Lower-limb wearable devices can significantly improve the quality of life of subjects suffering from debilitating condition...

An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtain...

Robot-aided assessment and associated brain lesions of impaired ankle proprioception in chronic stroke.

BACKGROUND: Impaired ankle proprioception strongly predicts balance dysfunction in chronic stroke. However, only sparse data on ankle position sense a...

Deep learning-based detection of lumbar spinal canal stenosis using convolutional neural networks.

BACKGROUND CONTEXT: Lumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by pr...

Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum.

BACKGROUND AND OBJECTIVE: Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity....

Deep learning based decoding of single local field potential events.

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases...

Spontaneous brain activity in patients with central retinal artery occlusion: a resting-state functional MRI study using machine learning.

Central retinal artery occlusion (CRAO) is a serious eye condition that poses a risk to vision, resulting from the blockage of the central retinal art...

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluatin...