Permissive immunosuppression facilitates the expansion of ex vivo administered regulatory T cells in the lung allograft
Bibliographic record
Abstract
In lung transplantation (LT), alloreactive T cell priming begins in the graft, a process that can be inhibited by seeding the graft with recipient-derived expanded polyclonal regulatory T cells (Tregs) using ex vivo lung perfusion (EVLP) prior to transplantation. However, this therapy alone cannot attenuate acute rejection in non-immunosuppressed animals. Interleukin 2 (IL-2)-anti-IL-2 antibody complexes (IL-2 C) promote Treg expansion in vivo with concomitant tacrolimus (Tac) administration. We combined IL-2 C and Tac with pre-transplant Treg administration during EVLP in a Fischer 344 to Wistar Kyoto rat LT model, to test the hypothesis that this strategy would facilitate intragraft Treg expansion and function. Recipients were given no treatment, Tac alone, IL-2 C/Tac alone, Treg alone, or Treg/IL-2 C/Tac. After 7 days, graft CD25 high Foxp3 + content increased as a result of Treg therapy, and cellular rejection was attenuated in the IL-2 C/Tac and Treg/IL-2 C/Tac groups. Graft Treg content and Treg-to-effector T cell ratio (Treg/Teff) at day 7 was highest in animals receiving Treg/IL-2 C/Tac, which has important implications for long-term immunomodulation. Our data suggest that pre-transplant administration of graft-directed Treg cell therapy combined with Treg-permissive immunosuppression may be a viable therapeutic approach to modulate rejection in LT.
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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) | 0.001 | 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".