Comparison of Healing Parameters in Porcine Full-Thickness Wounds Transplanted with Skin Micrografts, Split-Thickness Skin Grafts, and Cultured Keratinocytes
Bibliographic record
Abstract
BACKGROUND: Transplantation of skin micrografts (MGs), split-thickness skin grafts (STSGs), or cultured autologous keratinocytes (CKs) enhances the healing of large full-thickness wounds. This study compares these methods in a porcine wound model, investigating the utility of micrograft transplantation in skin restoration. STUDY DESIGN: Full-thickness wounds were created on Yorkshire pigs and assigned to one of the following treatment groups: MGs, STSGs, CKs, wet nontransplanted, or dry nontransplanted. Dry wounds were covered with gauze and the other groups' wounds were enclosed in a polyurethane chamber containing saline. Biopsies were collected 6, 12, and 18 days after wounding. Quantitative and qualitative wound healing parameters including macroscopic scar appearance, wound contraction, neoepidermal maturation, rete ridge formation, granulation tissue thickness and width, and scar tissue formation were studied. RESULTS: Transplanted wounds scored lower on the Vancouver Scar Scale compared with nontransplanted wounds, indicating a better healing outcome. All transplanted wounds exhibited significantly lower contraction compared with nontransplanted wounds. Wounds transplanted with either MGs, STSGs, or CKs showed a significant increase in re-epithelialization compared with nontransplanted wounds. Wounds transplanted with MGs or STSGs exhibited improved epidermal healing compared with nongrafted wounds. Furthermore, transplantation with STSGs or MGs led to less scar tissue formation compared with the nontransplanted wounds. No significant impact on scar formation was observed after transplantation of CKs. CONCLUSIONS: Qualitative and quantitative measurements collected from full-thickness porcine wounds show that transplantation of MGs improve wound healing parameters and is comparable to treatment with STSGs.
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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) | 0.001 | 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 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".