Evidence map›Paper›PMID 22350706›Full record

ArticleJournal of cell communication and signaling2012

Egr-ly awaiting a "personalized medicine" approach to treat scleroderma.

Andrew Leask

Abstract read
In one paragraph

Article in Journal of cell communication and signaling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Andrew LeaskDepartments of Dentistry and Physiology and Pharmacology, Dental Sciences Building, University of Western Ontario, London, ON, Canada, N6A 5C1, Andrew.leask@schulich.uwo.ca.
Western University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scleroderma, an autoimmune disorder characterized by skin and organ fibrosis, has no treatment. Although over the past decade valuable insights into the molecular mechanisms underlying scleroderma have been generated, results in clinical trials have been disappointing. This issue is likely to arise due to the heterogeneity of scleroderma. Molecular insights into the heterogeneity of this disease have been provided by genome-wide expression profiling. In a recent paper, Bhattacharyya and colleagues (PLOS One 6:e23082, 2011b) to show that the overexpression of a range of "fibroproliferative" genes in diffuse cutaneous scleroderma patients are likely to be caused by the overexpression of transcription factor Early growth response (Egr)-1. Only a minority of Egr-1-regulated genes were also found to be regulated by TGF-ß. Moreover, Greenblatt and colleagues (Am J Pathol., 2012) have shown that the overexpression of "inflammatory" genes overexpressed in "localized" scleroderma and a small subset of limited and diffuse scleroderma patients is likely to be due to the activity of interleukin-13 (IL-13). Intriguingly, at a gene expression level, murine sclerodermatous graft-versus-host disease (sclGVHD) approximates this inflammatory subset of scleroderma. These data suggest that targeting Egr-1 expression/activity might be a novel therapeutic strategy to control fibrosis in a subset of diffuse scleroderma patients, and further emphasize that notion that elevated canonical TGFβ signaling is insufficient to explain the fibrosis observed in scleroderma. Moreover, targeting IL-13 expression/activity might be a novel therapeutic strategy to target the inflammation leading to "localized" scleroderma.

Identifiers

PMID22350706
PMCPMC3368023
OpenAlexW2021369167

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.