IL-7 mediated protection against minor antigen-mismatched allograft rejection is associated with enhanced recovery of regulatory T cells
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Interleukin-7 (IL-7) has been studied for its possible immunorestorative capacities following stem cell transplantation and has been shown to enhance post-transplant immune recovery predominantly by peripheral T-cell expansion. A major concern of IL-7 is its possible aggravating effect on graft-versus-host and host-versus-graft reactivity. DESIGN AND METHODS: To study the effect of IL-7 on host-versus-graft reactivity, we applied IL-7 in an experimental transplantation model using RAG-1-/- mice supplied with B6 CD45.1 congenic T cells as recipients of T-cell depleted allogeneic bone marrow grafts. RESULTS: Rejection of minor antigen-mismatched bone marrow was significantly reduced in IL-7 treated recipients compared with PBS treated control mice. Rejection was observed in 2 out of 18 IL-7 treated mice compared with 9 out of 17 PBS treated mice (11% vs. 53%; p=0.012). IL-7 administration resulted in enhanced recovery of peripheral blood CD4+CD25+ regulatory T cells (Treg) with a concomitant increase in peripheral blood Foxp3 mRNA expression. IL-7Ra (CD127) was expressed by the vast majority of CD4+Foxp3+ T cells. The incidence of graft rejection following fully MHC mismatched bone marrow transplantation was not reduced nor enhanced by IL-7 administration. INTERPRETATION AND CONCLUSIONS: Post-transplant IL-7 administration protects against minor antigen-mismatched bone marrow rejection, which may be due to enhanced Treg recovery.
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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 | 0.000 |
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".