Experimental laboratory biomarkers in multiple sclerosis
Wiener Medizinische Wochenschrift, ISSN: 1563-258X, Vol: 172, Issue: 15-16, Page: 346-358
2022
- 3Citations
- 10Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations3
- Citation Indexes3
- CrossRef3
- Captures10
- Readers10
- 10
Article Description
Background: Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system; the cause of this condition remains unknown. Researchers have analyzed different biomarkers related to MS. Here, experimental laboratory biomarkers for MS are identified and analyzed. Methods: The current study examined articles investigating biomarkers for MS. Records were obtained from the PubMed, LILACS, and EBSCO databases using an identical search strategy and terms that included “multiple sclerosis,” “MS,” and “biomarkers.” In the current review, we also focus on lesser known biomarkers that have not yet been established for use in clinical practice. Results: Previous studies have explored molecular substances that may help diagnose MS and manage its adverse effects. Commonly studied factors include neurofilaments, sCD163, CXCL13, NEO, NF‑L, OPN, B cells, T cells, and integrin-binding proteins. Conclusions: Interactions between environmental and genetic factors have been implicated in the development of MS. Previous investigations have identified a wide range of biomarkers that can be used for diagnosis and disease management. These molecules and their associated studies provide vital insight and data to help primary physicians improve clinical and health outcomes for MS patients.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85125815721&origin=inward; http://dx.doi.org/10.1007/s10354-022-00920-7; http://www.ncbi.nlm.nih.gov/pubmed/35254566; https://link.springer.com/10.1007/s10354-022-00920-7; https://dx.doi.org/10.1007/s10354-022-00920-7; https://link.springer.com/article/10.1007/s10354-022-00920-7
Springer Science and Business Media LLC
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