Mechanisms of wound reepithelialization: hints from a tissue‐engineered reconstructed skin to long‐standing questions
Bibliographic record
Abstract
Wound closure of epithelial tissues must occur efficiently to restore rapidly their barrier function. We have developed a tissue-engineered wound-healing model composed of human skin keratinocytes and fibroblasts to better understand the mechanisms of reepithelialization. It allowed us to quantify the reepithelialization rate, which was significantly accelerated in the presence of fibrin or platelet-rich plasma. The reepithelialization of these 6 mm excisional wounds required the contribution of keratinocyte proliferation, migration, stratification, and differentiation. The epidermis regenerated progressively from the surrounding wound margins. After 3 days, the neoepidermis showed a complete spectrum of changes. Near the wound margin, the differentiation of the neoepidermis (keratins 1/10, filaggrin, and loricrin) and regeneration of the dermoepidermal junction (laminin 5 and collagen IV) were more advanced than toward the wound center, where the proliferative index was significantly increased. The spatial distribution of keratinocytes distinguished by particular features suggests two complementary mechanisms of reepithelialization: 1) the passive displacement of the superficial layers near the wound margin that would rapidly regenerate a barrier function and 2) the crawling of keratinocytes over each other at the tip of the progressing neoepidermis. Therefore, this study brings a new perspective to long-standing questions concerning wound reepithelialization.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".