Stability of Chimerism in Non-Obese Diabetic Mice Achieved By Rapid T Cell Depletion Is Associated With High Levels of Donor Cells Very Early After Transplant
Bibliographic record
Abstract
Stable mixed hematopoietic chimerism is a robust method for inducing donor-specific tolerance with the potential to prevent rejection of donor islets in recipients with autoimmune type-1 diabetes. However, with reduced intensity conditioning, fully allogeneic chimerism in a tolerance resistant autoimmune-prone non-obese diabetic (NOD) recipient has rarely been successful. In this setting, successful multilineage chimerism has required either partial major histocompatability complex matching, mega doses of bone marrow, or conditioning approaches that are not currently clinically feasible. Irradiation free protocols with moderate bone marrow doses have not generated full tolerance; donor skin grafts were rejected. We tested whether more efficient recipient T cell depletion would generate a more robust tolerance. We show that a combination of donor-specific transfusion-cyclophosphamide and multiple T cell depleting antibodies could induce stable high levels of fully allogeneic chimerism in NOD recipients. Less effective T cell depletion was associated with instability of chimerism. Stable chimeras appeared fully donor-specific tolerant, with clonal deletion of allospecific T cells and acceptance of donor skin grafts, while recovering substantial immunocompetence. The loss of chimerism months after transplant was significantly associated with a lower level of chimerism and donor T cells within the first 2 weeks after transplant. Thus, rapid and robust recipient T cell depletion allows for stable high levels of fully allogeneic chimerism and robust donor-specific tolerance in the stringent NOD model while using a clinically feasible protocol. In addition, these findings open the possibility of identifying recipients whose chimerism will later fail, stratifying patients for early intervention.
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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) | 0.000 | 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) | 0.001 | 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".