Nonobese Diabetic Natural Killer Cells: A Barrier to Allogeneic Chimerism That Can be Reduced by Rapamycin
Bibliographic record
Abstract
BACKGROUND: Induction of allogeneic hematopoietic chimerism is a promising strategy to induce tolerance to donor islets for treating type 1 diabetes. Successful induction of chimerism requires overcoming host alloimmunity. In diabetes-prone nonobese diabetic (NOD) mice, this is challenging due to their general tolerance resistance. Although the adaptive alloimmunity of NOD mice is a known barrier to allogeneic chimerism, whether NOD natural killer (NK) cells are an additional barrier has not been examined. Because NOD NK cells exhibit functional defects, they may not inhibit chimerism generation. METHODS: Antibody depletion of NK cells in vivo, or transplantation of F1 hybrid donor cells to eliminate the "missing-self" trigger of NK cells, was preformed to test the NK-mediated rejection of donor bone marrow cells. We also studied the capacity of rapamycin to block the NK cell response against allogeneic cells in vivo. RESULTS: Depleting NK cells or rendering them unresponsive to the donor greatly improved the level of chimerism obtained in NOD mice. Rapamycin significantly reduced the resistance to allogeneic chimerism mounted by NOD NK cells; however, it was much less effective than NK cell depletion by antibodies. CONCLUSIONS: Contrary to the view that NOD NK cells are defective, we found these cells to be a substantial barrier to allogeneic chimerism in the presence or absence of adaptive immunity. Moreover, rapamycin will need to be combined with other approaches to fully overcome the NK cell barrier.
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) | 0.001 | 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) | 0.002 | 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".