Periostin induces fibroblast proliferation and myofibroblast persistence in hypertrophic scarring
Bibliographic record
Abstract
Hypertrophic scarring is characterized by the excessive development and persistence of myofibroblasts. These cells contract the surrounding extracellular matrix resulting in the increased tissue density characteristic of scar tissue. Periostin is a matricellular protein that is abnormally abundant in fibrotic dermis, however, its roles in hypertrophic scarring are largely unknown. In this report, we assessed the ability of matrix-associated periostin to promote the proliferation and myofibroblast differentiation of dermal fibroblasts isolated from the dermis of hypertrophic scars or healthy skin. Supplementation of a thin type-I collagen cell culture substrate with recombinant periostin induced a significant increase in the proliferation of hypertrophic scar fibroblasts but not normal dermal fibroblasts. Periostin induced significant increases in supermature focal adhesion formation, α smooth muscle actin levels and collagen contraction in fibroblasts cultured from hypertrophic scars under conditions of increased matrix tension in three-dimensional type-I collagen lattices. Inhibition of Rho-associated protein kinase activity significantly attenuated the effects of matrix-associated periostin on hypertrophic scar fibroblasts and myofibroblasts. Depletion of endogenous periostin expression in hypertrophic scar myofibroblasts resulted in a sustained decrease in α smooth muscle actin levels under conditions of reducing matrix tension, while matrix-associated periostin levels caused the cells to retain high levels of a smooth muscle actin under these conditions. These findings indicate that periostin promotes Rho-associated protein kinase-dependent proliferation and myofibroblast persistence of hypertrophic scar fibroblasts and implicate periostin as a potential therapeutic target to enhance the resolution of scars.
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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 | 0.001 |
| 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".