A Novel Nude Mouse Model of Hypertrophic Scarring Using Scratched Full Thickness Human Skin Grafts
Bibliographic record
Abstract
Objective: Hypertrophic scar (HTS) is a dermal form of fibroproliferative disorder that develops following deep skin injury. HTS can cause deformities, functional disabilities, and aesthetic disfigurements. The pathophysiology of HTS is not understood due to, in part, the lack of an ideal animal model. We hypothesize that human skin with deep dermal wounds grafted onto athymic nude mice will develop a scar similar to HTS. Our aim is to develop a representative animal model of human HTS. Approach: Thirty-six nude mice were grafted with full thickness human skin with deep dermal scratch wound before or 2 weeks after grafting or without scratch. The scratch on the human skin grafts was made using a specially designed jig that creates a wound >0.6 mm in depth. The xenografts were morphologically analyzed by digital photography. Mice were euthanized at 1, 2, and 3 months postoperatively for histology and immunohistochemistry analysis. Results: The mice developed raised and firm scars in the scratched xenografts with more contraction, increased infiltration of macrophage, and myofibroblasts compared to the xenografts without deep dermal scratch wound. Scar thickness and collagen bundle orientation and morphology resembled HTS. The fibrotic scars in the wounded human skin were morphologically and histologically similar to HTS, and human skin epithelial cells persisted in the remodeling tissues for 1 year postengraftment. Innovation and Conclusions: Deep dermal injury in human skin retains its profibrotic nature after transplantation, affording a novel model for the assessment of therapies for the treatment of human fibroproliferative disorders of the skin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".