AIMC Topic: Neurodegenerative Diseases

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Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Biological psychiatry
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better un...

Identification of clinical disease trajectories in neurodegenerative disorders with natural language processing.

Nature medicine
Neurodegenerative disorders exhibit considerable clinical heterogeneity and are frequently misdiagnosed. This heterogeneity is often neglected and difficult to study. Therefore, innovative data-driven approaches utilizing substantial autopsy cohorts ...

Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

Molecular diversity
In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach inv...

A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.

Computers in biology and medicine
BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning...

Human Motor System-Based Biohybrid Robot-On-a-Chip for Drug Evaluation of Neurodegenerative Disease.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Biohybrid robots have been developed for biomedical applications and industrial robotics. However, the biohybrid robots have limitations to be applied in neurodegenerative disease research due to the absence of a central nervous system. In addition, ...

Anti-inflammatory and antioxidative actions of tacrolimus (FK506) on human microglial HMC3 cell line.

Scandinavian journal of immunology
Microglial cells are indispensable for the normal development and functioning of neurons in the central nervous system, where they play a crucial role in maintaining brain homeostasis by surveilling the microenvironment for signs of injury or stress ...

Retina Oculomics in Neurodegenerative Disease.

Annals of biomedical engineering
Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging...

Speech and language processing with deep learning for dementia diagnosis: A systematic review.

Psychiatry research
Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its p...

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that pro...

Evaluating the performance of large language models: ChatGPT and Google Bard in generating differential diagnoses in clinicopathological conferences of neurodegenerative disorders.

Brain pathology (Zurich, Switzerland)
This study explores the utility of the large language models (LLMs), specifically ChatGPT and Google Bard, in predicting neuropathologic diagnoses from clinical summaries. A total of 25 cases of neurodegenerative disorders presented at Mayo Clinic br...