Neurology

Recognizing the feature of knee hyperextension in stroke patients based on transformer-long-short-term memory model.

BACKGROUND: Recognizing knee hyperextension during gait in stroke patients is clinically challenging and involves cumbersome, costly procedures. This study proposes a simplified strategy based on a transformer-long-short-term memory (transformer-LSTM) approach for knee hyperextension recognition via surface electromyography (sEMG) data. AIM: This study proposes an algorithmic model to streamline k...

Multidimensional analysis and predictive modeling of cognitive decline risk in the United States using propensity score matching and machine learning.

Cognitive decline, an early indicator of neurodegenerative disorders, presents a growing public health challenge. This study aimed to integrate causal inference and machine learning to quantify the causal impact of high-risk status on cognitive function, explore geographic and temporal heterogeneity, and develop predictive models for early identification of at-risk individuals in the United States...

A Multimodal Neuro-Demographic Signature for Immuno-Metabolic Depression.

BACKGROUND: The underlying neurobiology of a recently described immuno-metabolic depression (IMD) subtype of major depressive disorder (MDD), characte...

Machine learning-based LASSO-Cox model for dementia prediction: the role of midlife cardiometabolic, inflammatory, and genetic risk factors in a US cohort.

PURPOSE: We aimed to identify key midlife dementia predictors and develop a novel machine learning (ML) -enabled risk prediction model. METHODS: Using...

Organotypic Retinal Explant Culture as a Model for Neuroretinal Degenerative Disease and Future Applications.

Organotypic retinal explant cultures are a valuable experimental tool for investigating neuroretinal diseases and advancing therapeutic strategies. Ex...

Are diagnostic technologies for alzheimer's disease and dementia cost-effective? A systematic review of economic evaluations.

BACKGROUND: Alzheimer's disease (AD) and dementia pose a significant clinical and economic burden globally. Early diagnosis and intervention can poten...

Engineering nanobodies for drug delivery systems in Alzheimer's disease.

Alzheimer's disease (AD) remains a major global health challenge, with current therapies offering only symptomatic relief. A significant constraint in...

Deep learning in fetal, infant, and toddler neuroimaging research.

Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analys...

AI conversational agents in older adults with chronic disease: A scoping review.

To examine the relationship between artificial intelligence (AI) and older adults with chronic diseases a scoping review methodology was used. Using f...

The Mitochondrial Guardian α-Amyrin Mitigates Alzheimer's Disease Pathology via Modulation of the DLK-SARM1-ULK1 Axis.

High consumption of colorful fruits and vegetables correlates with low dementia risk, but the exact molecules and the underlying biological mechanisms...

Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study.

BACKGROUND: Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain predicted age d...

Novel imidazolium salts bearing 2-oxindoles scaffold as potent acetylcholinesterase inhibitors for Alzheimer's disease: Design, synthesis, in vitro and in silico studies.

In this study, a series of thirty-five novel imidazolium salts bearing a 2-oxindoles were designed and synthesized as potent acetylcholinesterase (ACh...

Scalability of random forest in myoelectric control.

Objective.Myoelectric control systems translate electromyographic (EMG) signals into control commands, enabling immersive human-robot interactions in ...

Advances in MS-based glycomics for biomedical research.

Glycosylation is a highly complex and functionally diverse post-translational modification that modulates protein folding, stability, cell signaling, ...

MMoGCN: a multi-gate mixture of graph convolutional network model for EEG emotion and mood disorder recognition.

Objective.Emotional states and mood disorders are closely interconnected, and their joint recognition serves as a critical pathway to uncovering their...

Artificial intelligence-based software to support mechanical thrombectomy transfer decision in low-volume primary stroke centers: a multicenter, retrospective study.

INTRODUCTION: To assess the potential benefit of artificial intelligence (AI) based imaging software in supporting mechanical thrombectomy (MT) transf...

Deep learning-accelerated 3D flair for white matter lesion detection in multiple sclerosis: a feasibility study.

OBJECTIVES: Deep learning (DL)-based image reconstruction (DLBIR) techniques promise accelerated MRI acquisitions with enhanced image quality. Herein,...

Identifying Key Predictors of Appropriate Discharge Destinations for Older Inpatients in Acute Care: Scoping Review.

BACKGROUND: Postacute care (PAC) services are important to ensure functional recovery and provide adequate care for geriatric inpatients in acute care...

Identification of Chlamydia pneumoniae and NLRP3 inflammasome activation in Alzheimer's disease retina.

Chlamydia pneumoniae is an intracellular bacterium implicated in Alzheimer's disease (AD), but its role in retinal pathology and disease progression i...

Machine learning for predicting functional outcomes in acute ischemic stroke: insights from a nationwide stroke registry.

Accurately predicting the prognosis of patients with acute ischemic stroke at discharge remains highly challenging after active treatment. The aim of ...