Stimulation of a shorter duration in the state of anergy by an invariant natural killer T cell agonist enhances its efficiency of protection from type 1 diabetes
Bibliographic record
Abstract
We have reported previously that treatment of non-obese diabetic (NOD) mice with the invariant natural killer T (iNK T) cell agonist α-galactosylceramide C26:0 (α-GalCer) or its T helper type 2 (Th2)-biasing derivative α-GalCer C20:2 (C20:2) protects against type 1 diabetes (T1D), with C20:2 yielding greater protection. After an initial response to α-GalCer, iNK T cells become anergic upon restimulation. While such anergic iNK T cells can induce tolerogenic dendritic cells (DCs) that mediate protection from T1D, chronic administration of α-GalCer also results in long-lasting anergy accompanied by significantly reduced iNK T cell frequencies, which raises concerns about its long-term therapeutic use. In this study, our objective was to understand more clearly the roles of anergy and induction of tolerogenic DCs in iNK T cell-mediated protection from T1D and to circumvent potential complications associated with α-GalCer. We demonstrate that NOD iNK T cells activated during multi-dose (MD) treatment in vivo with C20:2 enter into and exit from anergy more rapidly than after activation by α-GalCer. Importantly, this shorter duration of iNK T cells in the anergic state promotes the more rapid induction of tolerogenic DCs and reduced iNK T cell death, and enables C20:2 stimulated iNK T cells to elicit enhanced protection from T1D. Our findings further that suggest C20:2 is a more effective therapeutic drug than α-GalCer for protection from T1D. Moreover, the characteristics of C20:2 provide a basis of selection of next-generation iNK T cell agonists for the prevention of T1D.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".