Regulatory Natural Killer T Cells Protect against Spontaneous and Recurrent Type 1 Diabetes
Bibliographic record
Abstract
Autoimmune diseases, especially type 1 diabetes (T1D), may be caused by dysregulation of the immune system, which leads to hyporesponsiveness of regulatory T helper 2 (Th2) cells and promotion of autoimmune Th1 cells. Natural killer T (NKT) cells, which comprise a minor subpopulation of T cells, play a critical role in immunoregulation as a result of a rapid burst of IL-4 and IFN-gamma secretion. These cells are functionally and numerically deficient in individuals at risk of T1D, as well as in nonobese diabetic (NOD) mice. It is conceivable that protection from T1D may be achieved by correction of this deficiency. Alpha-galactosylceramide (alpha-GalCer) specifically binds to NKT cells in a CD1-dependent manner and stimulates these cells to proliferate and to produce various cytokines, including IFN-gamma, IL-4, and IL-10. In this review, we present evidence that a multiple-dose alpha-GalCer treatment regimen, which is known to promote a dominant Th2 environment, can prevent the onset of spontaneous and cyclophosphamide (CY)-accelerated T1D. This protection is associated with elevated IL-4 and IL-10 in the spleen and pancreas of protected female NOD mice. Concomitantly, IFN-gamma levels are reduced in both tissues. More importantly, the protective effect of gamma-GalCer in CY-accelerated T1D is abrogated by the in vivo blockade of IL-10 activity. We also show that alpha-GalCer treatment significantly prolongs syngeneic islet graft survival in recipient diabetic NOD mice. These findings raise the possibility that alpha-GalCer treatment may be used therapeutically to prevent the onset and recurrence of human T1D.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".