Stratifin as a Mediator of Epithelial/Mesenchymal Cross Talking in Skin;
Bibliographic record
Abstract
It is well established that any delays in epithelialization during the process of wound healing, due to either infection or severity of injury, increases the frequency of developing fibrotic conditions. This signifies that in the absence of epithelialization, the ECM continues to accumulate until fibroblasts receive signal(s) from epidermal cells to slow down the dynamic process of healing that leads to maturation and remodelling of the healing wound. Here, we provide a compelling evidence that: 1) there exist a cross‐talking between epithelial and fibroblasts using a co‐culture system, 2) Keratinocytes release two sets of ECM modulating factors that are important in controlling the healing process, 3) One set of this factors, namely stratifin, increases the expression of MMP‐1, 3, 8 and 24 in fibroblasts, 4) the other set reduces the expression of type I and type III collagen in fibroblasts, 5) On the hand, fibroblasts release some unknown factors that control the expression of keratinocyte derived anti‐fibrogenic factors (KDAF). Finally the mechanism by which these factors are released from keratinocytes will also be discussed. In conclusion, Keratinocyte releasable factors upon epithelialization may function as stop signals for matrix production by fibroblasts and that would slow down the dynamic process of healing wound.
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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) | 0.002 | 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".