Xenograft Skin Model to Manipulate Human Immune Responses <em>In Vivo</em>
Bibliographic record
Abstract
The human skin xenograft model, in which human donor skin is transplanted onto an immunodeficient mouse host, is an important option for translational research in skin immunology. Murine and human skin differ substantially in anatomy and immune cell composition. Therefore, traditional mouse models have limitations for dermatological research and drug discovery. However, successful xenotransplants are technically challenging and require optimal specimen and mouse graft site preparation for graft and host survival. The present protocol provides an optimized technique for transplanting human skin onto mice and discusses necessary considerations for downstream experimental aims. This report describes the appropriate preparation of a human donor skin sample, assembly of a surgical setup, mouse and surgical site preparation, skin transplantation, and post-surgical monitoring. Adherence to these methods allows for maintenance of xenografts for over 6 weeks post-surgery. The techniques outlined below allow maximum grafting efficiency due to the development of engineering controls, sterile technique, and pre- and post-surgical conditioning. Appropriate performance of the xenograft model results in long-lived human skin graft samples for experimental characterization of human skin and preclinical testing of compounds in vivo.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".