DN-Treg cells induce long-term cardiac allograft survival in fully MHC mismatched recipients but do not eliminate T memory cells (145.13)
Bibliographic record
Abstract
Abstract Establishment of immune tolerance is a major goal in transplantation. Regulatory T (Treg) cells play an important role in the regulation of immune responses. Our previous studies have shown that TCRαβ+CD3+CD4-CD8-NK1.1- (double negative, DN) Treg cells suppress anti-donor T cell responses and prolong allograft survival in a single MHC-mismatched mouse model. In this study, we tested whether DN-Treg cells can induce long-term graft survival in the stringent BALB/c to C57BL/6 heart transplant model. Adoptive transfer of 107 DN-Treg cells in combination with rapamycin (RAPA) treatment (day 1- 9) induced long-term graft survival (101 vs. 39 days RAPA alone, p<0.01). DN-Treg treated recipients had few graft-infiltrating CD4+ and CD8+ T cells on day 40 compared to massive infiltration in RAPA treated controls. Memory T (Tm) cells (CD3+CD44+) were found to be resistant to DN-Treg cell-mediated suppression in vitro. The effect of DN-Treg on Tm in vivo was tested in B6-Rag1-/- mice. DN-Treg cells along with CD44+ Tm cells modestly prolonged BALB/c skin graft survival in contrast to DN-Treg with CD44- T cells (26 vs 43 days, P<0.001). In conclusion, DN-Treg cells combined with short term immunosuppression, can prolong allograft survival in the absence of tolerance. Furthermore, this may be related to the resistance of Tm cells to DN-Treg cell suppression, suggesting alternative strategies will be needed to control Tm cells and establish tolerance after transplantation.
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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) | 0.002 | 0.001 |
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".