Immunoprotective Role of Indoleamine 2,3-Dioxygenase in Engraftment of Allogenic Skin Substitute in Wound Healing
Bibliographic record
Abstract
Delayed wound healing can significantly impact survival of patients who suffer from severe thermal injury. In general, the use of a wound coverage, particularly with those of bilayer skin substitute, would be ideal to promote healing and prevent infection and fluid loss. Although the use of an autologous skin substitute is desirable, its preparation is time consuming and its immediate availability is impossible. To overcome this difficulty, the authors have previously demonstrated that the expression of indoleamine 2,3 dioxygenase (IDO) could function as a local immune suppressive factor in protecting allogenic fibroblasts and keratinocytes without using any immunosuppressive medication in a wound healing animal model. IDO, which is naturally expressed in the placenta by trophoblast cells during pregnancy, plays an essential role in maternal tolerance toward the fetus. The potent and selective local immunosuppressive function of IDO makes this enzyme a very promising tool for engineering a nonrejectable skin allograft. Here, the authors reviewed and discussed how the expression of IDO by the primary cells of our skin substitute can serve as a source of IDO enzyme activity and generate a tryptophan-deficient environment. Under this condition, only skin cells but not immune cells (CD4(+) and CD8(+) cells) would survive and protect engraftment of this engineered and shelf-ready skin substitute to be used not only as wound coverage but also as a rich source of wound healing promoting factors. Therefore, this review summarizes the body of work on immunoprotective role of IDO in engraftment of allogenic skin substitute in wound healing, which has recently been reported by the authors' research group and others.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".