Expression of Integrin αvβ6 and TGF-β in Scarless vs Scar-forming Wound Healing
Bibliographic record
Abstract
Oral mucosal wounds heal with reduced scar formation compared with skin. The epithelial integrin alphavbeta6 is induced during wound healing, and it can activate fibrogenic transforming growth factor beta1 (TGF-beta1) and anti-fibrogenic TGF-beta3 that play key roles in scar formation. In this study, expression of beta6 integrin and members of the TGF-beta pathway were studied in experimental wounds of human gingiva and both gingiva and skin of red Duroc pigs using real-time PCR, gene microarrays, and immunostaining. Similar to human wounds, the expression of beta6 integrin was induced in the pig wounds 7 days after wounding and remained upregulated >49 days. The alphavbeta6 integrin was colocalized with both TGF-beta isoforms in the wound epithelium. Significantly higher expression levels of beta6 integrin and TGF-beta1 were observed in the pig gingival wounds compared with skin. Early gingival wounds also expressed higher levels of TGF-beta3 compared with skin. The spatio-temporal colocalization of alphavbeta6 integrin with TGF-beta1 and TGF-beta3 in the wound epithelium suggests that alphavbeta6 integrin may activate both isoforms during wound healing. Prolonged expression of alphavbeta6 integrin along with TGF-beta3 in the gingival wound epithelium may be important in protection of gingiva from scar formation.
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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) | 0.002 | 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".