A nude mouse model of hypertrophic scar shows morphologic and histologic characteristics of human hypertrophic scar
Bibliographic record
Abstract
Hypertrophic scar (HSc) is a fibroproliferative disorder that occurs following deep dermal injury. Lack of a relevant animal model is one barrier toward better understanding its pathophysiology. Our objective is to demonstrate that grafting split-thickness human skin onto nude mice results in survival of engrafted human skin and murine scars that are morphologically, histologically, and immunohistochemically consistent with human HSc. Twenty nude mice were xenografted with split-thickness human skin. Animals were euthanized at 30, 60, 120, and 180 days postoperatively. Eighteen controls were autografted with full-thickness nude mouse skin and euthanized at 30 and 60 days postoperatively. Scar biopsies were harvested at each time point. Blinded scar assessment was performed using a modified Manchester Scar Scale. Histologic analysis included hematoxylin and eosin, Masson's trichrome, toluidine blue, and picrosirius red staining. Immunohistochemistry included anti-human human leukocyte antigen-ABC, α-smooth muscle actin, decorin, and biglycan staining. Xenografted mice developed red, shiny, elevated scars similar to human HSc and supported by blinded scar assessment. Autograft controls appeared morphologically and histologically similar to normal skin. Xenografts survived up to 180 days and showed increased thickness, loss of hair follicles, adnexal structures and rete pegs, hypercellularity, whorled collagen fibers parallel to the surface, myofibroblasts, decreased decorin and increased biglycan expression, and increased mast cell density. Grafting split-thickness human skin onto nude mice results in persistent scars that show morphologic, histologic, and immunohistochemical consistency with human HSc. Therefore, this model provides a promising technique to study HSc formation and to test novel treatment options.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".