Split Thickness Grafts Grow From Bottom Up in Large Skin Injuries
Bibliographic record
Abstract
Autologous split thickness skin graft is necessary for the survival of patients with large burns and skin defects. It is not clear how a thin split thickness skin graft becomes remarkably thicker within a few weeks following transplantation. Here, we hypothesized that growth of split thickness graft should be from bottom up probably through conversion of immune cells into collagen producing skin cells. We tested this hypothesis in a preclinical porcine model by grafting split thickness meshed skin (0.508 mm thickness, meshed at 3:1 ratio) on full thickness wounds in pigs. New tissue formation was evaluated on days 10 and 20 postoperation through histological analysis and co-staining for immune cell markers (CD45) and type I collagen. The findings revealed that a split thickness graft grew from bottom up and reached to almost the same level as uninjured skin within 60 days postoperation. The result of immune-staining identified a large number of cells, which co-expressed immune cell marker (CD45) and collagen on day 10 postoperation. Interestingly, as the number of these cells reduced on day 20, most of these cells became positive for collagen production. In another set of experiments, we tested whether immune cells can convert to collagen producing cells in vitro. The results showed that mouse adherent immune cells started to express type 1 procollagen and α-smooth muscle actin when cultured in the presence of fibroblast conditioned media. In conclusion, the early thickening of split thickness graft is likely happening through a major contribution of infiltrated immune cells that convert into mainly collagen producing fibroblasts in large skin injuries.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".