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Sema4A as a biomarker predicting responsiveness to IFN β treatment
Yuji Nakatsuji
*
*
Corresponding author for this work
Neurology
Research output
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Contribution to journal
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Article
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peer-review
4
Scopus citations
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Biochemistry, Genetics and Molecular Biology
Cellular Differentiation
100%
Mouse Model
100%
Animal Model
100%
T Cell
100%
Experimental Mouse
100%
Pharmacology, Toxicology and Pharmaceutical Science
Biological Marker
100%
Multiple Sclerosis
100%
Experimental Autoimmune Encephalomyelitis
40%
Adhesive Agent
20%
Mouse Model
20%
Immunology and Microbiology
Multiple Sclerosis
100%
Experimental Autoimmune Encephalomyelitis
50%
T Cell
25%
Mouse Model
25%
Th17 Cell
25%
Cell Differentiation
25%
Treatment of Multiple Sclerosis
25%
Keyphrases
Sema4D
100%
Multiple Sclerosis Treatment
33%
Multiple Sclerosis
16%
Experimental Autoimmune Encephalomyelitis
16%
Th17 Cell Differentiation
16%
Endothelial Cells
8%
High-level Expression
8%
Recombinant
8%
T Helper 17 (Th17)
8%
T Cell Activation
8%
Severe Disabilities
8%
Unresponsiveness
8%
Th1 Differentiation
8%
Neuroscience
Multiple Sclerosis
100%
Experimental Autoimmune Encephalomyelitis
50%
T Cell
25%
Treatment of Multiple Sclerosis
25%
Cellular Differentiation
25%