Epidermis promotes dermal fibrosis: role in the pathogenesis of hypertrophic scars
Bibliographic record
Abstract
Hypertrophic scarring is a pathological process characterized by fibroblastic hyperproliferation and by excessive deposition of extracellular matrix components. It has been hypothesized that abnormalities in epidermal-dermal crosstalk explain this pathology. To test this hypothesis, a tissue-engineered model of self-assembled reconstructed skin was used in this study to mimic interactions between dermal and epidermal cells in normal or pathological skin. These skin equivalents were constructed using three dermal cell types: normal wound (Wmyo) or hypertrophic wound (Hmyo) myofibroblasts and normal skin fibroblasts (Fb). Epidermis was reconstructed with normal skin keratinocytes (NK) or hypertrophic scar keratinocytes (HK). In the absence of keratinocytes, Hmyo formed a thicker dermis than Wmyo. When seeded with NK, the dermal thickness of Hmyo (121.2 +/- 31.4 microm vs 196.2 +/- 27.8 microm) and Fb (43.7 +/- 7.1 microm vs 83.6 +/- 16.3 microm) dermis was significantly (p < 0.05) reduced, while that of Wmyo (201.5 +/- 15.7 microm vs 160.7 +/- 21.1 microm) was increased. However, the presence of HK always induced significantly thicker dermis formation than observed with NK (Wmyo: 238.8 +/- 25.9 microm; Hmyo: 145.5 +/- 22.4 microm; Fb: 74.2 +/- 11.2 microm). These results correlated with collagen and MMP-1 secretion and with cell proliferation, which were increased when keratinocytes were added, except for the collagen secretion of Hmyo and Fb in the presence of NK. The level of dermal apoptosis was not different when epidermis was added to the dermis (<1% in each category). These observations strongly suggest that hypertrophic scar keratinocytes play a role in the development of pathological fibrosis by influencing the behaviour of dermal cells.
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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".