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Record W1999619340 · doi:10.1007/s12079-014-0245-7

CCN2 is required for recruitment of Sox2-expressing cells during cutaneous tissue repair

2014· article· en· W1999619340 on OpenAlexafffund
Matthew Tsang, Andrew Leask

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchScleroderma Society of Ontario
KeywordsCTGFSOX2FibrosisConnective tissuePathologyMatricellular proteinMyofibroblastProgenitor cellWound healingExtracellular matrixBiologyStem cellGrowth factorCancer researchMedicineCell biologyImmunologyTranscription factorGeneGenetics

Abstract

fetched live from OpenAlex

Connective tissue growth factor (CTGF/CCN2), a member of the CCN family of matricellular proteins is upregulated in both fibrosis as well as tissue repair. Recently, we showed that, in mice, CCN2 expression by fibroblasts was required for dermal fibrogenesis, but not for cutaneous tissue repair. Lineage tracing analysis linked the ability of CCN2 to promote fibrosis to the requirement for CCN2 to recruit cells expressing the progenitor cell marker Sox2 to fibrotic connective tissue and for differentiating these cells into myofibroblasts. Herein, we show that although loss of CCN2 expression by Sox2-expressing cells does not impair cutaneous tissue repair, CCN2 was required for recruitment of cells derived from Sox2-expressing cells to the wound area. Collectively, these results are consistent with the notion that neither CCN2 nor Sox2-expressing progenitor cells are essential for cutaneous tissue repair and that CCN2 represents a specific anti-fibrotic target.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.326
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2014
Admission routes2
Has abstractyes

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