IL-2 complex therapy prolongs fully MHC-mismatched murine cardiac allograft survival
Bibliographic record
Abstract
Regulatory T cell (Treg)-biased IL-2/anti-IL-2 monoclonal antibody complexes (IL-2c) can preferentially deliver IL-2 to CD25+ Tregs, causing proliferation of Tregs that is potentially advantageous in transplantation. We tested the ability of IL-2c to prolong murine cardiac allograft survival. C57BL/6 (H-2b) mice received fully major histocompatibility complex-mismatched BALB/c (H-2d) or hemi-allogeneic B6 × BALB/c (H-2b/d) F1 allografts. Recipients were treated prior to transplantation with IL-2c or control. Graft survival, anti-donor T cell priming, and donor-specific antibody production were measured. High-dimensional flow cytometry and transcriptomic analyses were used to characterize IL-2c-induced modulation of the alloimmune response. IL-2c treatment prolonged BALB/c allograft survival to 14 d (vs. 7 in control; P < 0.0001), and F1 allograft survival to 22 d (vs. 13 in control; P = 0.0018). Donor-specific T cell priming and antibody production were significantly reduced by IL-2c. Increased frequencies of CD4+CD25+FOXP3+ Tregs expressing high levels of ICOS, GITR, CD73, and CTLA-4 were identified in both the spleen and allograft in IL-2c-treated recipients. Reduced infiltration of F4/80+ cells into allografts and a marked reduction in intragraft myeloid activity were observed in IL-2c-treated recipients. When combined with a transient 21-d course of perioperative tacrolimus therapy, median survival time was extended to 48 d, and some recipients experienced indefinite allograft survival without ongoing immunosuppressive therapy. IL-2c increases Treg populations in priming and effector sites, is associated with downregulation of both the early adaptive immune response and myeloid response to cardiac allografts, and can synergize with tacrolimus to enable long-term graft survival.
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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) | 0.001 | 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".