Genetic Disassociation of Autoimmunity and Resistance to Costimulation Blockade-Induced Transplantation Tolerance in Nonobese Diabetic Mice
Bibliographic record
Abstract
Curing type 1 diabetes by islet transplantation requires overcoming both allorejection and recurrent autoimmunity. This has been achieved with systemic immunosuppression, but tolerance induction would be preferable. Most islet allotransplant tolerance induction protocols have been tested in nonobese diabetic (NOD) mice, and most have failed. Failure has been attributed to the underlying autoimmunity, assuming that autoimmunity and resistance to transplantation tolerance have a common basis. Out of concern that NOD biology could be misleading in this regard, we tested the hypothesis that autoimmunity and resistance to transplantation tolerance in NOD mice are distinct phenotypes. Unexpectedly, we observed that (NOD x C57BL/6)F(1) mice, which have no diabetes, nonetheless resist prolongation of skin allografts by costimulation blockade. Further analyses revealed that the F(1) mice shared the dendritic cell maturation defects and abnormal CD4(+) T cell responses of the NOD but had lost its defects in macrophage maturation and NK cell activity. We conclude that resistance to allograft tolerance induction in the NOD mouse is not a direct consequence of overt autoimmunity and that autoimmunity and resistance to costimulation blockade-induced transplantation tolerance phenotypes in NOD mice can be dissociated genetically. The outcomes of tolerance induction protocols tested in NOD mice may not accurately predict outcomes in human subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".