Granulation tissue myofibroblasts during normal and pathological skin healing: The interaction between their secretome and the microenvironment
Bibliographic record
Abstract
The first role that was proposed for the myofibroblasts located in skin granulation tissue was to contract the edges of the wound in order to reduce the surface to be repaired. This role, linked to the presence of alpha smooth muscle actin, was very quickly confirmed and is part of the definition of granulation tissue myofibroblasts. However, myofibroblasts are cells that also play a much more central role in wound healing. Indeed, it has been shown that these cells produce large quantities of matrix components, and that they stimulate angiogenesis and can recruit immune cells. These actions take place via the secretion of molecules into their environment or indirectly via the production of microvesicles containing pro-fibrotic and pro-angiogenic molecules. Pathologically, granulation tissue can develop into a hypertrophic scar that histologically looks like granulation tissue, but which can remain for months or even years. It has been hypothesized that the myofibroblasts in these tissues remained present instead of disappearing by apoptosis, causing the maintenance of granulation tissue rather than allowing its change into a mature scar. Understanding the roles of both pathological and healthy myofibroblasts in wound tissue is crucial in order to better intervene in the healing mechanism.
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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 | 0.001 | 0.001 |
| 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".