ArticleJournal of clinical bioinformatics2013
Using biomarkers to predict progression from clinically isolated syndrome to multiple sclerosis.
Article in Journal of clinical bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Human disease biomarker panels through systems biology.Biophysical reviews · 2021Review
- Longitudinal assessment and stability of long non-coding RNA gene expression profiles measured in human peripheral whole blood collected into PAXgene blood RNA tubes.BMC research notes · 2020Article
- On the need for the development of a cancer early detection, diagnostic, prognosis, and treatment response system.Future science OA · 2019Review
- Endogenous double-stranded Alu RNA elements stimulate IFN-responses in relapsing remitting multiple sclerosis.Journal of autoimmunity · 2019Article
- Longitudinal changes in the expression of IL-33 and IL-33 regulated genes in relapsing remitting MS.PloS one · 2018Article
- Cutting Edge: Chronic NF-κB Activation in CD4+ T Cells in Rheumatoid Arthritis Is Genetically Determined by HLA Risk Alleles.Journal of immunology (Baltimore, Md. : 1950) · 2015Article
- Expression and functions of long noncoding RNAs during human T helper cell differentiation.Nature communications · 2015Article
- Expression of IL-33 and its epigenetic regulation in Multiple Sclerosis.Annals of clinical and translational neurology · 2014Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundDetection of brain lesions disseminated in space and time by magnetic resonance imaging remains a cornerstone for the diagnosis of clinically definite multiple sclerosis. We have sought to determine if gene expression biomarkers could contribute to the clinical diagnosis of multiple sclerosis.
methodsWe employed expression levels of 30 genes in blood from 199 subjects with multiple sclerosis, 203 subjects with other neurologic disorders, and 114 healthy control subjects to train ratioscore and support vector machine algorithms. Blood samples were obtained from 46 subjects coincident with clinically isolated syndrome who progressed to clinically definite multiple sclerosis determined by conventional methods. Gene expression levels from these subjects were inputted into ratioscore and support vector machine algorithms to determine if these methods also predicted that these subjects would develop multiple sclerosis. Standard calculations of sensitivity and specificity were employed to determine accuracy of these predictions.
resultsOur results demonstrate that ratioscore and support vector machine methods employing input gene transcript levels in blood can accurately identify subjects with clinically isolated syndrome that will progress to multiple sclerosis.
conclusionsWe conclude these approaches may be useful to predict progression from clinically isolated syndrome to multiple sclerosis.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.