Anti-IL-6 eluting immunomodulatory biomaterials prolong skin allograft survival
Bibliographic record
Abstract
Abstract A primary goal in the management of burn wounds is early wound closure. The use of skin allografts represents a lifesaving strategy for severe burn patients, but their ultimate rejection limits their potential efficacy and utility. IL-6 is a major pleiotropic cytokine which critically links innate and adaptive immune responses. Here, we devised anti-IL-6 receptor eluting gelatin methacryloyl (GelMA) biomaterials (GelMA/anti-IL-6), which were implanted at the interface between the wound beds and skin allografts. Our visible light crosslinked GelMA/anti-IL-6 immunomodulatory biomaterial (IMB) demonstrated a stable kinetic release profile of anti-IL-6. In addition, the incorporation of anti-IL-6 within the GelMA hydrogel had no effect on the mechanical properties of the hydrogels. Using a highly stringent skin transplant model, the GelMA/anti-IL-6 IMB almost doubled the survival of skin allografts. The use of GelMA/anti-IL-6 IMB was far superior to systemic anti-IL-6 receptor treatment in prolonging skin allograft survival. As compared to the untreated control group, skin from the GelMA/anti-IL-6 IMB group contained significantly fewer alloreactive T cells and macrophages. Interestingly, the environmental milieu of the draining lymph nodes (DLNs) of the mice implanted with the GelMA/anti-IL-6 IMB was also considerably less pro-inflammatory. The percentage of CD4 + IFNγ + cells was much lower in the DLNs of the GelMA/anti-IL-6 IMB group in comparison to the GelMA group. These data highlight the importance of localized immune delivery in prolonging skin allograft survival and its potential utility in treating patients with severe burns.
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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".