Targeting Cells Causing Split Tolerance Allows Fully Allogeneic Islet Survival With Minimal Conditioning in NOD Mixed Chimeras
Bibliographic record
Abstract
Donor-specific tolerance induced by mixed chimerism is one approach that may eliminate the need for long-term immunosuppressive therapy, while preventing chronic rejection of an islet transplant. However, even in the presence of chimerism it is possible for certain donor tissues or cells to be rejected whereas others from the same donor are accepted (split tolerance). We previously developed a nonmyeloablative protocol that generated mixed chimerism across full major histocompatability complex plus minor mismatches in NOD (nonobese diabetic) mice, however, these chimeras demonstrated split tolerance. In this study, we used radiation chimeras and found that the radiosensitive component of NOD has a greater role in the split tolerance NOD mice develop. We then show that split tolerance is mediated primarily by preexisting NOD lymphocytes and have identified T cells, but not NK cells or B cells, as cells that both resist chimerism induction and mediate split tolerance. Finally, after recognizing the barrier that preexisting T cells impose on the generation of fully tolerant chimeras, the chimerism induction protocol was refined to include nonmyeloablative recipient NOD T cell depletion which generated long-term mixed chimerism across fully allogeneic barriers. Furthermore, these chimeric NOD mice are immunocompetent, diabetes free and accept donor islet allografts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".