A new method for skin grafting in murine model
Bibliographic record
Abstract
Skin transplantation provides an excellent potential model to investigate the immunology of allograft rejection and tolerance induction. Despite the theoretical ease of performing skin transplantation, as well as the potential of directly observing the reaction to the transplanted tissue, the poor reliability of skin transplantation in the mouse has largely precluded the use of this model. Furthermore, there is controversy regarding the most appropriate skin graft donor site due to poor success of back skin transplantation, as compared with the thinner ear or tail skin. This study demonstrates a reliable method to successfully perform skin grafts in a mouse model, as well as the clinical and histologic outcome of syngeneic grafts. A total of 287 grafts were performed (in 126 mice) utilizing donor skin from the ear, tail or back. No graft failure or postoperative mortality was observed. Comparison of this technique with two previously established protocols of skin transplantation (5.0 absorbable Suture + tissue glue technique and no-suture technique) demonstrates the significant improvement in the engraftment success of the new technique. In summary, a new technique for murine skin grafting demonstrates improved reliability across donor site locations and strains, increasing the potential for investigating interventions to alter the rejection process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 |
| 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".