Increasing Donor Chimerism and Inducing Tolerance to Islet Allografts by Post-Transplant Donor Lymphocyte Infusion
Bibliographic record
Abstract
Inducing donor chimerism is the most consistently successful approach to achieve transplant tolerance. We found that a low level of donor chimerism, which was induced by a relatively non-toxic approach, induced donor-specific tolerance to islet allografts in chemically induced diabetic mice. However, a similar level of donor chimerism could not protect donor islet allografts in non-obese diabetic (NOD) mice that spontaneously developed autoimmune diabetes. Rejection of donor islet allografts in diabetic NOD mice with a low level of donor chimerism was mediated by recurrent autoimmunity. We used post-transplant donor lymphocyte infusion (DLI) to increase donor chimerism and to induce tolerance to islet allografts. DLI significantly increased donor chimerism and promoted donor-specific tolerance to islet allografts in diabetic NOD mice. Self-tolerance to islet autoantigens was restored and restoring self-tolerance is mediated by immunoregulation. Thus, our data showed that adoptive immunotherapy with post-transplant DLI after establishing a low level of donor chimerism as a platform enhances donor chimerism, induces donor-specific tolerance to islet allografts and restores self-tolerance in the setting of autoimmune diabetes. Our data also showed that central tolerance is not sufficient to induce tolerance and peripheral tolerance through immunoregulation for restoring self-tolerance is required in the setting of autoimmune diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".