Human hypertrophic scar‐like nude mouse model: Characterization of the molecular and cellular biology of the scar process
Bibliographic record
Abstract
Hypertrophic scar (HTS) following thermal injury and other forms of trauma is a dermal fibroproliferative disorder that leads to considerable morbidity. Because of the lack of an ideal animal model, research is difficult. We have established an HTS model that involves transplanting human split-thickness skin graft (STSG) or full-thickness skin graft (FTSG) onto the backs of nude mice. The animals developed raised, firm, and reddish scars 2 months following transplantation. Histology and micromeasurement indicate raised, thickened engrafted skin with STSG and FTSG. In contrast, thickening was not observed with full-thickness rat skin grafts used as controls. Masson's trichrome staining demonstrates increased accumulations of collagen fibrils in the dermis in both scars grafted with STSG and FTSG. Staining cells with toludine blue and an antibody for F4/80 showed an increase in the infiltration of mast cells and macrophages. Quantification of fibrocytes reveals increased fibrocytes. Moreover, STSG grafted skin had significantly more macrophages, mast cells, and fibrocytes than FTSG. Real-time polymerase chain reaction analysis showed significantly elevated mRNA levels for type I collagen, transforming growth factor-β, connective tissue growth factor and heat shock protein 47 in both types of engrafted skin. These data demonstrate that human skin grafted onto nude mice develops red raised and thickened scars having intrinsic properties that closely resemble HTS formation as seen in humans. Interestingly, STSG developed more scar than FTSG. Furthermore, inflammatory cells and bone marrow-derived fibrocytes may play a critical role in HTS development in this animal model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".