Beta‐catenin regulates wound size and mediates the effect of TGF‐beta in cutaneous healing
Bibliographic record
Abstract
After cutaneous injury, a variety of cell types are activated to reconstitute the epithelial and dermal components of the skin. beta-Catenin plays disparate roles in keratinocytes and fibroblasts, inhibiting keratinocyte migration and activating fibroblast proliferation, suggesting that beta-catenin could either inhibit or enhance the healing process. How beta-catenin functions in concert with other signaling pathways important in the healing process is unknown. Wound size was examined in mice expressing conditional null or conditional stabilized alleles of beta-catenin, regulated by an adenovirus expressing cre-recombinase. The size of the wounds in the mice correlated with the protein level of beta-catenin. Using mice expressing these conditional alleles, we found that the wound phenotype imparted by Smad3 deficiency and by the injection of TGFbeta before wounding is mediated in part by beta-catenin. TGFbeta was not able to regulate proliferation in beta-catenin null fibroblasts, whereas keratinocyte proliferation rate was independent of beta-catenin. When mice are treated with lithium, beta-catenin-mediated signaling was activated in cutaneous wounds, which healed with a larger size. These results demonstrate a crucial role for beta-catenin in regulating cutaneous wound size. Furthermore, these data implicate mesenchymal cells as playing a critical role regulating wound size.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".