A Minimum Conditioning Protocol towards Transplantation Tolerance in NOD Mice by Mixed Hematopoietic Chimerism
Bibliographic record
Abstract
Abstract Stable mixed hematopoietic chimerism is a robust method for inducing donor specific tolerance. However, its clinical application is dampened by the toxicity of current recipient conditioning regimens. We previously showed an irradiation-free mixed chimerism protocol in diabetes prone NOD mice is achievable with antibodies to T cells and CD40L together with busulfan and high dose rapamycin. We sought to generate a more clinically feasible chimerism protocol and tested the hypothesis that more efficient recipient T cell depletion would eliminate the need for anti-CD40L (known to cause thromboembolism in humans) and rapamycin. We preconditioned NOD mice with donor specific transfusion from fully mismatched mice (d −10), cyclophosphamide (CYP) (d −8), antibodies against CD90 and/or CD4 + CD8 (d −6, −1, 4, 9, 14), busulfan (d −1) and donor bone marrow transplant (d 0). Flow cytometry was used to detect chimerism. Mixed chimerism was induced in 33/45 NOD mice. Stable chimerism with multilineage donor cells was maintained in 21/33 recipients. Loss of chimerism could be predicted by a lower early level of chimerism at d 4, 9 or 14. Inclusion of αCD90 mAb, busulfan and CYP was critical for chimerism induction. With anti-CD90 17/23 mice became stable chimeras. 5/5 chimeric mice accepted skin from bone marrow donors but rejected skin from MHC-matched and minor antigen mismatched donors. The loss of anti-donor Vβ11+ T cells and anti-recipient Vβ17+ T cells in stable chimeric mice indicated the establishment of chimerism involves clonal deletion. Conclusion A protocol causing rapid and robust recipient T cell depletion generated chimerism without the need for anti-CD40L or rapamycin in tolerance induction resistant NOD mice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".