Mechanisms of wound reepithelialization: hints from a tissue‐engineered reconstructed skin to long‐standing questions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 it