Evidence map›Paper›PMID 37789119›Full record

ReviewNature reviews. Rheumatology2023

Back to the future: targeting the extracellular matrix to treat systemic sclerosis.

Andrew Leask, Angha Naik, Richard J Stratton

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Rheumatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
9.7field-weighted citation impact, top 1% 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

23 citing papers in PubMed, 38 citations in OpenAlex.

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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

3 authors at 2 institutions in 2 countries.

Andrew LeaskCollege of Dentistry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. anl312@usask.ca.ORCID 0000-0003-2832-283X
Angha NaikCollege of Dentistry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Richard J StrattonCentre for Rheumatology and Connective Tissue Diseases, UCL Division of Medicine, London, UK.
University of Saskatchewan · CAUniversity College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fibrosis is the excessive deposition of a stable extracellular matrix (ECM); fibrotic tissue is composed principally of highly crosslinked type I collagen and highly contractile myofibroblasts. Systemic sclerosis (SSc) is a multisystem autoimmune connective tissue disease characterized by skin and organ fibrosis. The fibrotic process has been recognized in SSc for >40 years, but drugs with demonstrable efficacy against SSc fibrosis in ameliorating the lung involvement have only recently been identified. Unfortunately, these treatments are ineffective at improving the skin score in patients with SSc. Previous clinical trials in SSc have largely focused on the cross-purposing of anti-inflammatory drugs and the use of immunosuppressive drugs from the transplantation field, which address inflammatory and/or autoimmune processes. Limited examination has taken place of specific anti-fibrotic agents developed through their ability to directly target the ECM in SSc by, for example, alleviating the persistent matrix stiffness and mechanotransduction that might be required for both the initiation and maintenance of fibrosis, including in SSc. However, because of the importance of the ECM in the SSc phenotype, attempts have now been made to identify drugs that specifically target the ECM, including some drugs that are currently under consideration for the treatment of cancer.

Indexed as

Autoimmune DiseasesScleroderma, SystemicExtracellular MatrixFibroblastsFibrosisHumansMechanotransduction, CellularSkin

Identifiers

PMID37789119
OpenAlexW4387300943

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.