2020
DOI: 10.2174/1566523220666200306092556
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Gene Therapy Approaches in an Autoimmune Demyelinating Disease: Multiple Sclerosis

Abstract: Multiple Sclerosis (MS) is the most common autoimmune demyelinating disease of the Central Nervous System (CNS). It is a multifactorial disease which develops in an immune-mediated way under the influences of both genetic and environmental factors. Demyelination is observed in the brain and spinal cord leading to neuro-axonal damage in patients with MS. Due to the infiltration of different immune cells such as T-cells, B-cells, monocytes and macrophages, focal lesions are observed in MS. Currently available me… Show more

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Cited by 19 publications
(8 citation statements)
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“…The tumor microenvironment contains two components, immune and stromal cells, which possess diagnostic and prognostic value in cancers ( Hanahan and Weinberg, 2011 ; Fridman et al, 2012 ; Zhang et al, 2019 ; Hu et al, 2020 ; Islam et al, 2020 ; Hu et al, 2021b ). Stage information was obtained from the UCSC Xena ( http://xena.ucsc.edu/ ) database for all tumor samples, and we divided the stages into 4.…”
Section: Methodsmentioning
confidence: 99%
“…The tumor microenvironment contains two components, immune and stromal cells, which possess diagnostic and prognostic value in cancers ( Hanahan and Weinberg, 2011 ; Fridman et al, 2012 ; Zhang et al, 2019 ; Hu et al, 2020 ; Islam et al, 2020 ; Hu et al, 2021b ). Stage information was obtained from the UCSC Xena ( http://xena.ucsc.edu/ ) database for all tumor samples, and we divided the stages into 4.…”
Section: Methodsmentioning
confidence: 99%
“…Clinical trials of antiviral treatments in MS ( Lycke et al, 1996 ; Bech et al, 2002 ; Friedman et al, 2005 ) were limited when compared with the development of other therapies ( Lizak et al, 2017 ; Islam et al, 2020 ). There are three phase II clinical trials of acyclovir or valacyclovir in MS patients ( Lycke et al, 1996 ; Bech et al, 2002 ; Friedman et al, 2005 ).…”
Section: Discussionmentioning
confidence: 99%
“…Common feature extraction methods can be divided into linear and nonlinear transformations. Typical linear feature extraction algorithms include sparse principal component analysis (PCA) ( Min et al, 2018 ; Islam et al, 2020 ), independent component analysis ( MoysĂ©s et al, 2017 ), and LDA. Nonlinear transformation methods primarily include neural networks, kernel methods ( Qi et al, 2021b ), manifold learning ( Shen et al, 2017 ), sparse representation ( Min et al, 2017 ), and matrix factorization methods ( Wang et al, 2017 ; Yang et al, 2017 ; Yang and Hu, 2017 ; McCall et al, 2019 ).…”
Section: Application Of Sparse Representation In Bioinformaticsmentioning
confidence: 99%