<scp>CCN2</scp> expression by fibroblasts is not required for cutaneous tissue repair
Bibliographic record
Abstract
The CCN family of matricellular proteins, which includes CCN2 and CCN1, is believed to have a major in vivo role in controlling tissue morphogenesis and repair. In adult skin, the proadhesive matricellular protein connective tissue growth factor (CTGF/CCN2) is specifically up-regulated in fibrosis and wound healing. In mice, CCN2 is required for dermal fibrogenesis, but whether CCN2 is required for cutaneous tissue repair is unknown. To address this question, in this report we subjected adult mice bearing a fibroblast-specific deletion of CCN2 to the dermal punch model of cutaneous tissue repair. Loss of CCN2 did not appreciably affect the kinetics of tissue repair, collagen content, or the number of α-smooth muscle actin-positive cells. CCN1 (cyr61), which has in vitro effect similar to CCN2, is also induced in cutaneous tissue repair. Fibroblast-specific CCN1/CCN2 double knockout mice were also generated; loss of both CCN1 and CCN2 together did not appreciably affect cutaneous tissue repair. However, loss of CCN2 resulted in impaired recruitment of NG2-positive pericyte-like cells to the wound area. Collectively, these results indicate that neither CCN2 nor CCN1 is essential for cutaneous tissue repair; CCN2 appears to be required for recruitment of pericyte-like cells and may represent a specific antifibrotic target.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".