The Impact of c-Fos/Activator Protein-1 Inhibition on Allogeneic Pancreatic Islet Transplantation
Bibliographic record
Abstract
Unpreventable allograft rejection is one of the main problems in pancreatic islet transplantation (PIT). Therefore, it is imperative to develop a more effective immunosuppressive strategy. The blockade of transcription factors has been a central part of T cell-depleting immunosuppressive therapies, as typified by the use of calcineurin inhibitors. The inhibition of activator protein-1 (AP-1) offers a novel strategy for immunosuppression in PIT, although to date, no reports on the effects of AP-1 inhibition are available. In this study, we investigated the immunosuppressive effects of T-5224, a c-Fos/AP-1-selective inhibitor, on murine T cells activated by αCD3+αCD28 mAbs. T-5224 inhibited proliferation, CD25 up-regulation, and the production of IL-2 and interferon-γ. In addition, T-5224 blocked the nuclear translocation of c-Fos/AP-1 in activated murine T cells. In BALB/c (H-2(d) )-to-C57BL/6J (H-2(b) ) mouse PIT, the 2-week administration of T-5224 prolonged survival of 600 islet allografts in a dose-dependent manner. When combined with a 2-week low-dose tacrolimus, the T-5224 treatment markedly prolonged allograft survival to over 300 days, while the efficacy was indeterminate when transplanted islet allograft mass was reduced to 300. We conclude that the c-Fos/AP-1 inhibition by T-5224 is a potentially attractive strategy for allogeneic PIT.
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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".