Establishment of nonmyeloablative bone marrow chimerism by double negative Treg cells through inducing T cell clonal deletion and suppressing NK cell function (126.1)
Bibliographic record
Abstract
Abstract In bone marrow transplantation, T cells and NK cells play important role for graft rejection. In addition, graft-versus-host-disease and establishment of stable chimeric states without complete marrow ablation remain as major obstacles post bone marrow transplantation. In this study, we aimed to establish mixed chimerism in a non-irradiation condition. Our data indicate that adoptive transfer of donor-derived TCRαβ+CD3+CD4-CD8-NK1.1-(double negative, DN) Treg cells prior to C57BL/6 to BALB/c bone marrow transplantation, in combination with cyclophosphamide but not cyclosporine, FK506, or rapamycin, established stable mixed chimerism that led to acceptance of C57BL/6 skin allografts and rejection of 3rd party C3H skin grafts. Adoptive transfer of CD4+ and CD8+ T cells, but not DN-Treg cells, induced graft-versus-host diseases in this regimen. The recipient T cell alloreacitve responsiveness was reduced in the DN-Treg cell-treated group and T cell receptor Vβ2, Vβ7 and Vβ8 clonal deletions were observed in both CD4+ and CD8+ T cells. Furthermore, DN-Treg cell treatment suppressed NK cell-mediated donor bone marrow rejection in perforin-dependent manner. Taken together, our results suggest that adoptive transfer of DN-Treg cells can control both adoptive and innate immunity and promote a stable mixed chimerism and donor-specific tolerance in the non-irradiation regimen.
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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".