Results 11 to 20 of about 236,137 (343)

A potential biomarker of cognitive impairment: The olfactory dysfunction and its genes expression

open access: yesAnnals of Clinical and Translational Neurology, Volume 9, Issue 12, Page 1884-1897, December 2022., 2022
Abstract Objective Accumulation evidence has reported that olfactory impairment may be an essential clinical marker and predictor of mild cognitive impairment or Alzheimer's disease. Method Participants were enrolled in the population‐based, prospective study in Fuxin county, Liaoning province, China between 2019 and 2021.
Jiayi Song   +11 more
wiley   +1 more source

Clinical Characteristics of Involuntary Movement in Hospitalized Patients [PDF]

open access: yesJournal of Movement Disorders, 2019
Objective Neurological symptoms in hospitalized patients are not rare, and neurological consultation for movement disorders is especially important in evaluating or managing those with various movement disorders.
Kyum-Yil Kwon   +3 more
doaj   +1 more source

Klotho an Autophagy Stimulator as a Potential Therapeutic Target for Alzheimer’s Disease: A Review

open access: yesBiomedicines, 2022
Alzheimer’s disease (AD) is an age-associated neurodegenerative disease; it is the most common cause of senile dementia. Klotho, a single-pass transmembrane protein primarily generated in the brain and kidney, is active in a variety of metabolic pathways
Tsz Yan Fung   +11 more
doaj   +1 more source

Targeting Aggrephagy for the Treatment of Alzheimer’s Disease

open access: yesCells, 2020
Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in older individuals with specific neuropsychiatric symptoms. It is a proteinopathy, pathologically characterized by the presence of misfolded protein (Aβ and Tau ...
Sandeep Malampati   +14 more
doaj   +1 more source

Drug Repurposing for Parkinson’s Disease: The International Linked Clinical Trials experience

open access: yesFrontiers in Neuroscience, 2021
The international Linked Clinical Trials (iLCT) program for Parkinson’s to date represents one of the most comprehensive drug repurposing programs focused on one disease.
Simon R. W. Stott   +2 more
doaj   +1 more source

Deep learning algorithm reveals two prognostic subtypes in patients with gliomas

open access: yesBMC Bioinformatics, 2022
Background Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival ...
Jing Tian   +10 more
doaj   +1 more source

Palliative care for Parkinson’s disease: suggestions from a council of patient and carepartners

open access: yesnpj Parkinson's Disease, 2017
In 2015, the Parkinson’s Disease Foundation sponsored the first international meeting on Palliative Care and Parkinson’s disease and the Patient Centered Outcomes Research Institute funded the first comparative effectiveness trial of palliative care for ...
Kirk Hall   +4 more
doaj   +1 more source

Forecasting Disease Burden In Philippines: A Symbolic Regression Analysis [PDF]

open access: yesarXiv, 2021
Burden of disease measures the impact of living with illness and injury and dying prematurely and it is increasing worldwide leading cause of death both global and national. This paper aimed to propose an index of diseases and evaluate a mathematical model to describe the number of burden of disease by cause in the Philippines from 1990 - 2016. Through
arxiv  

How Cognition and Motivation “Freeze” the Motor Behavior in Parkinson’s Disease

open access: yesFrontiers in Neuroscience, 2019
ObjectiveFreezing of gait (FoG) is a debilitating problem in patients with PD. The multifactorial pathogenesis of FoG remains poorly understood. We aimed to find which factors are most strongly associated with the occurrence of FoG.MethodsThree hundred ...
Paola Ortelli   +9 more
doaj   +1 more source

CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays [PDF]

open access: yesarXiv, 2021
We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning models trained on a subset of diseases (seen diseases) can detect the presence of any one of a larger set of diseases.
arxiv  

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