Molecular mechanisms regulating wound repair: Evidence for paracrine signaling from corneal epithelial cells to fibroblasts and immune cells following transient epithelial cell treatment with Mitomycin C
Bibliographic record
Abstract
In this paper, we use RNAseq to identify senescence and phagocytosis as key factors to understanding how mitomyin C (MMC) stimulates regenerative wound repair. We use conditioned media (CM) from untreated (CMC) and MMC treated (CMM) human and mouse corneal epithelial cells to show that corneal epithelial cells indirectly exposed to MMC secrete elevated levels of immunomodulatory proteins including IL-1α and TGFβ1 compared to cells exposed to CMC. These factors increase epithelial and macrophage phagocytosis and promote ECM turnover. IL-1α supplementation can increase phagocytosis in control epithelial cells and attenuate TGFβ1 induced αSMA expression by corneal fibroblasts. Yet, we show that epithelial cell CM contains factors besides IL-1α that regulate phagocytosis and αSMA expression by fibroblasts. Exposure to CMM also impacts the activation of bone marrow derived dendritic cells and their ability to present antigen. These in vitro studies show how a brief exposure to MMC induces corneal epithelial cells to release proteins and other factors that function in a paracrine way to enhance debris removal and enlist resident epithelial and immune cells as well as stromal fibroblasts to support regenerative and not fibrotic wound 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".