Periostin modulates myofibroblast differentiation during full-thickness cutaneous wound repair
Bibliographic record
Abstract
The matricellular protein periostin is expressed in the skin. Although periostin has been hypothesized to contribute to dermal homeostasis and repair, this has not been directly tested. To assess the contribution of periostin to dermal healing, 6 mm full-thickness excisional wounds were created in the skin of periostin-knockout and wild-type, sex-matched control mice. In wild-type mice, periostin was potently induced 5-7 days after wounding. In the absence of periostin, day 7 wounds showed a significant reduction in myofibroblasts, as visualized by expression of α-smooth muscle actin (α-SMA) within the granulation tissue. Delivery of recombinant human periostin by electrospun collagen scaffolds restored α-SMA expression. Isolated wild-type and knockout dermal fibroblasts did not differ in in vitro assays of adhesion or migration; however, in 3D culture, periostin-knockout fibroblasts showed a significantly reduced ability to contract a collagen matrix, and adopted a dendritic phenotype. Recombinant periostin restored the defects in cell morphology and matrix contraction displayed by periostin-deficient fibroblasts in a manner that was sensitive to a neutralizing anti-β1-integrin and to the FAK and Src inhibitor PP2. We propose that periostin promotes wound contraction by facilitating myofibroblast differentiation and contraction.
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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".