Gingival Fibroblasts Display Reduced Adhesion and Spreading on Extracellular Matrix: A Possible Basis for Scarless Tissue Repair?
Bibliographic record
Abstract
Unlike skin, oral gingiva do not scar in response to injury. The basis of this difference is likely to be revealed by comparing the responses of dermal and gingival fibroblasts to fibrogenic stimuli. Previously, we showed that, compared to dermal fibroblasts, gingival fibroblasts are less responsive to the potent pro-fibrotic cytokine TGFβ, due to a reduced production of endothelin-1 (ET-1). In this report, we show that, compared to dermal fibroblasts, human gingival fibroblasts show reduced expression of pro-adhesive mRNAs and proteins including integrins α2 and α4 and focal adhesion kinase (FAK). Consistent with these observations, gingival fibroblasts are less able to adhere to and spread on both fibronectin and type I collagen. Moreover, the enhanced production of ET-1 mRNA and protein in dermal fibroblasts is reduced by the FAK/src inhibitor PP2. Given our previous observations suggesting that fibrotic fibroblasts display elevated adhesive properties, our data suggest that scarring potential may be based, at least in part, on differences in adhesive properties among fibroblasts resident in connective tissue. Controlling adhesive properties may be of benefit in controlling scarring in response to tissue injury.
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 | 0.001 | 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".