Fibronectin synthesis, but not α-smooth muscle expression, is regulated by periostin in gingival healing through FAK/JNK signaling
Bibliographic record
Abstract
During skin healing, periostin facilitates myofibroblast differentiation through a β1 integrin/FAK dependent mechanism and continued expression is associated with scarring. In contrast to skin, gingival tissue does not typically scar upon injury, but the role of periostin in gingival healing has never been investigated. Using a rat gingivectomy model, we show that the gingival architecture is re-established within 14 days of wounding. Periostin mRNA levels peak at day 7 post-wounding, with persistence of periostin protein in the connective tissue through day 14. Collagen type I and lysyl oxidase mRNA levels peak at day 7 post wounding, which corresponded with the peak of fibroblast proliferation. Although α-smooth muscle actin mRNA levels increased 200-fold in the tissue, no myofibroblasts were detected in the regenerating tissue. In vitro, human gingival fibroblast adhesion on periostin, but not collagen, was inhibited by blocking β1 integrins. Fibroblasts cultured on periostin exhibited similar rates of proliferation and myofibroblast differentiation to cells cultured on collagen only. However, human gingival fibroblasts cultured in the presence of periostin exhibited significantly increased fibronectin and collagen mRNA levels. Increases in fibronectin production were attenuated by pharmacological inhibition of FAK and JNK signaling in human gingival fibroblasts. In vivo, mRNA levels for fibronectin peaked at day 3 and 7 post wounding, with protein immunoreactivity highest at day 7, suggesting periostin is a modulator of fibronectin production during gingival healing.
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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".