Dataset Information

Treatment of acute relapses in multiple sclerosis.

ABSTRACT: Multiple sclerosis (MS) is a chronic progressive inflammatory demyelinating disease affecting the central nervous system. The most common clinical type of MS tends to follow a relapsing course, affecting the vast majority of patients living with this disease. Relapses are a hallmark of MS, and are often associated with significant functional impairment and decreased quality of life. Although usually followed by a period of remission, residual symptoms after MS relapses may persist and lead to sustained disability. Adequate management of MS relapses is important, as it may help to shorten and lessen the disability associated with their course. Historically, treatment of MS relapse was the first approach (and for a period of time, the only approach) to MS treatment in general. Systemic corticosteroids and adrenocorticotropic hormone (ACTH) have broad regulatory approval and remain the most established and validated treatment options for MS relapse. Therapeutic mechanisms of ACTH were previously associated (perhaps mistakenly) with only corticotropic actions; however, recently the direct anti-inflammatory effects and immunomodulatory activity of ACTH gel acting through melanocortin pathways have been shown. Second-line treatments of steroid-unresponsive MS relapses and a possible algorithm for MS relapse management are also reviewed in this article.

SUBMITTER: Berkovich R

PROVIDER: S-EPMC3557364 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Treatment of acute relapses in multiple sclerosis.

Berkovich Regina R

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 20130101 1

Multiple sclerosis (MS) is a chronic progressive inflammatory demyelinating disease affecting the central nervous system. The most common clinical type of MS tends to follow a relapsing course, affecting the vast majority of patients living with this disease. Relapses are a hallmark of MS, and are often associated with significant functional impairment and decreased quality of life. Although usually followed by a period of remission, residual symptoms after MS relapses may persist and lead to su ...[more]

PMID: 23229226

Similar Datasets

Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days. If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing.

Dataset Information

Treatment of acute relapses in multiple sclerosis.

Publications

Treatment of acute relapses in multiple sclerosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure