AIMC Topic: Neurodegenerative Diseases

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Decoding Glycosylation in Neurodegenerative Diseases: Mechanistic Insights and Therapeutic Opportunities.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Glycosylation is a highly dynamic and complex post-translational modification that plays a pivotal role in regulating protein folding, trafficking, stability, and function. Accumulating evidence indicates that aberrant glycosylation is intimately inv...

Brain age prediction from MRI scans in neurodegenerative diseases.

Current opinion in neurology
PURPOSE OF REVIEW: This review explores the use of brain age estimation from MRI scans as a biomarker of brain health. With disorders like Alzheimer's and Parkinson's increasing globally, there is an urgent need for early detection tools that can ide...

An FDG-PET-Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders.

Neurology
BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring neurologic expertise. Clinical decision support systems (CDSSs) powered by machine learning (ML) and artificial intelligence can assist with complex ...

Lipid nanoparticle (LNP) mediated mRNA delivery in neurodegenerative diseases.

Journal of controlled release : official journal of the Controlled Release Society
Neurodegenerative diseases (NDD) are characterized by the progressive loss of neurons and the impairment of cellular functions. Messenger RNA (mRNA) has emerged as a promising therapy for treating NDD, as it can encode missing or dysfunctional protei...

NDDRF 2.0: An update and expansion of risk factor knowledge base for personalized prevention of neurodegenerative diseases.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Neurodegenerative diseases (NDDs) are chronic diseases caused by brain neuron degeneration, requiring systematic integration of risk factors to address their heterogeneity. Established in 2021, Knowledgebase of Risk Factors for Neurodeg...

Extended Technical and Clinical Validation of Deep Learning-Based Brainstem Segmentation for Application in Neurodegenerative Diseases.

Human brain mapping
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of patholo...

Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model.

Neuropathology and applied neurobiology
AIMS: Recent advances in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)-a derivative feature of ChatGPT-have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT...

Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models.

Translational vision science & technology
PURPOSE: Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results.

Artificial Intelligence in The Management of Neurodegenerative Disorders.

CNS & neurological disorders drug targets
Neurodegenerative disorders are characterized by a gradual but irreversible loss of neurological function. The ability to detect and treat these conditions successfully is crucial for ensuring the best possible quality of life for people who suffer f...

PPAD: a deep learning architecture to predict progression of Alzheimer's disease.

Bioinformatics (Oxford, England)
MOTIVATION: Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between cognitively normal state and AD. Not all people who have MCI convert to AD...