Tie-Over Bolster Pressure Dressing Improves Outcomes of Skin Substitutes Xenografts on Athymic Mice
Bibliographic record
Abstract
The efficacy of skin substitutes is established for the treatment of burn injuries, but its use is not limited to this condition. This technology has the potential to improve the treatment of various conditions by offering highly advanced and personalized treatments. In vivo studies are challenging but essential to move to clinical use in humans. Mice are the most widely used species in preclinical studies, but the main drawback of this model is the limited surface area of the graft in long-term transplantation studies caused by the displacement and the contraction of the graft. We improved the conventional surgical procedures by stabilizing the chamber covering the graft with intramuscular sutures and by adding a tie-over bolster dressing. The current study was therefore performed to compare outcomes of skin grafts between the conventional and optimized skin graft model. Human self-assembled skin substitutes (SASSs) were prepared and grafted to athymic mice either by the conventional method or by the new grafting method. Graft healing and complications were assessed using digital photographs on postoperative days 7, 14, and 21. Similar structure and organization were observed by histological staining. The new grafting method reduced medium and large displacement events by 1.26-fold and medium and large contraction events by 1.8-fold, leading to a 1.6-fold increase in graft surface area compared to skin substitutes grafted with the usual method. This innovation ensures better reproducibility and consistency of skin substitute transplants on mice.
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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 | 0.001 |
| 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".