Mixed chimerism and split tolerance: Mechanisms and clinical correlations
Bibliographic record
Abstract
Establishing hematopoietic mixed chimerism can lead to donor-specific tolerance to transplanted organs and may eliminate the need for long-term immunosuppressive therapy, while also preventing chronic rejection. In this review, we discuss central and peripheral mechanisms of chimerism induced tolerance. However, even in the long-lasting presence of a donor organ or donor hematopoietic cells, some allogeneic tissues from the same donor can be rejected; a phenomenon known as split tolerance. With the current goal of creating mixed chimeras using clinically feasible amounts of donor bone marrow and with minimal conditioning, split tolerance may become more prevalent and its mechanisms need to be explored. Some predisposing factors that may increase the likelihood of split tolerance are immunogenicity of the graft, certain donor-recipient combinations, prior sensitization, location and type of graft and minimal conditioning chimerism induction protocols. Additionally, split tolerance may occur due to a differential susceptibility of various types of tissues to rejection. The mechanisms involved in a tissue's differential susceptibility to rejection include the presence of polymorphic tissue-specific antigens and variable sensitivity to indirect pathway effector mechanisms. Finally, we review the clinical attempts at allograft tolerance through the induction of chimerism; studies that are revealing the complex relationship between chimerism and tolerance. This relationship often displays split tolerance, and further research into its mechanisms is warranted.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".