Pax7 Expressing Cells Contribute to Dermal Wound Repair, Regulating Scar Size through a β-Catenin Mediated Process
Bibliographic record
Abstract
During skin wound healing, fibroblast-like cells reconstitute the dermal compartment of the repaired skin filling the wound gap. A subset of these cells are transcriptionally active for β-catenin/T-cell factor (TCF) signaling during the proliferative phase of the repair process, and β-catenin levels control the size of the scar that ultimately forms by regulating the number of dermal fibroblasts. Here, we performed cell lineage studies to reveal a source of the dermal cells in which β-catenin signaling is activated during wound repair. Using a reporter mouse, we found that cells in the early wound in which TCF-dependent transcription is activated express genes involved in muscle development. Using mice in which cells express Pax7 (muscle progenitors) or Mck (differentiated myocytes) are permanently labeled, we showed that one quarter of dermal cells in the healing wound are Pax7 expressing progeny, but none are Mck progeny. Removing one allele of β-catenin in Pax7 expressing progeny resulted in a significantly smaller scar size with fewer Pax7 expressing progeny cell contributing to wound repair. During wound healing, β-catenin activation causes muscle satellite cells to adopt a fibrotic phenotype and this is a source of dermal cells in the repair process.
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 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) | 0.002 | 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".