Roles of cutaneous cell‐cell communication in wound healing outcome: An emphasis on keratinocyte‐fibroblast crosstalk
Bibliographic record
Abstract
Tissue repair is a very complex event and involves a continuously orchestrated sequence of signals and responses from platelets, fibroblasts, epithelial, endothelial and immune cells. The details of interaction between these signals, which are mainly growth factors and cytokines, have been widely discussed. However, it is still not clear how activated cells at wound sites lessen their activities after epithelialization is completed. Termination of the wound healing process requires a fine balance between extracellular matrix (ECM) deposition and degradation. Maintaining this balance requires highly accurate epithelial-mesenchymal communication and correct information exchange between keratinocytes and fibroblasts. As it has been reported in the literature, a disruption in epithelialization during the process of wound healing increases the frequency of developing chronic wounds or fibrotic conditions, as seen in a variety of clinical cases. Conversely, the potential stop signal for wound healing should have a regulatory role on both ECM synthesis and degradation to reach a successful wound healing outcome. This review briefly describes the potential roles of growth factors and cytokines in controlling the early phase of wound healing and predominantly explores the role of releasable factors from epithelial-mesenchymal interaction in controlling during and the late stage of the healing process. Emphasis will be given on the crosstalk between keratinocytes and fibroblasts in ECM modulation and the healing outcome following a brief discussion of the wound healing initiation mechanism. In particular, we will review the termination of acute dermal wound healing, which frequently leads to the development of hypertrophic scarring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".