MétaCan
Menu
Back to cohort

Scleraxis: a novel target for anti‐fibrotic therapy?

2011· article· en· W1465542427 on OpenAlexafffundabout
Michael P. Czubryt, Rushita A. Bagchi

Bibliographic record

VenueThe FASEB Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsTransactivationGene expressionSMADFibrosisCardiac fibrosisTransforming growth factorMolecular biologyCell biologyBiologyGeneMedicinePathologyBiochemistry

Abstract

fetched live from OpenAlex

Therapeutic approaches to managing cardiac fibrosis are lacking. Our objective is to examine the role of the transcription factor scleraxis (Scx) in cardiac type I collagen production, and to evaluate its potential for anti-fibrotic therapy development. We noted Scx expression in various collagen-producing cell types, including cardiac fibroblasts. Scx expression increases concomitant with collagen production, e.g. in response to TGF-β1 in vitro and in cardiac infarct scar or in pressure-overloaded myocardium in vivo. We have identified sites in the human collagen Iα2 (COLIα2) gene promoter mediating transactivation by scleraxis, and have shown that Scx is sufficient to induce ColIα2 expression in cardiac fibroblasts. Scx expression is regulated by the pro-fibrotic Smad signalling pathway downstream of TGF-β1, which regulates COLIα2 expression additively with Scx. A dominant-negative Scx mutant abrogated TGF-β1-mediated ColIα2 gene expression, suggesting that Scx activity is required for collagen expression in agreement with our finding of reduced cardiac collagen gene expression in Scx null mice. Scx thus plays a central and critical role in collagen expression in the heart, and targeting Scx activity or expression may provide a novel approach in the development of new anti-fibrotic therapies. Supported by the Canadian Institutes of Health Research & the St. Boniface Hospital Research Foundation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.275
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicCardiac Fibrosis and RemodelingFrench-language works237,207