Improved Outcomes in NOD Mice Treated with a Novel Th2 Cytokine-Biasing NKT Cell Activator
Bibliographic record
Abstract
Activation of CD1d-restricted invariant NKT (iNKT) cells by alpha-galactosylceramide (alphaGalCer) significantly suppresses development of diabetes in NOD mice. The mechanisms of this protective effect are complex, involving both Th1 and Th2 cytokines and a network of regulatory cells including tolerogenic dendritic cells. In the current study, we evaluated a newly described synthetic alphaGalCer analog (C20:2) that elicits a Th2-biased cytokine response for its impact on disease progression and immunopathology in NOD mice. Treatment of NOD mice with alphaGalCer C20:2 significantly delayed and reduced the incidence of diabetes. This was associated with significant suppression of the late progression of insulitis, reduced infiltration of islets by autoreactive CD8(+) T cells, and prevention of progressive disease-related changes in relative proportions of different subsets of dendritic cells in the draining pancreatic lymph nodes. Multiple favorable effects observed with alphaGalCer C20:2 were significantly more pronounced than those seen in direct comparisons with a closely related analog of alphaGalCer that stimulated a more mixed pattern of Th1 and Th2 cytokine secretion. Unlike a previously reported Th2-skewing murine iNKT cell agonist, the alphaGalCer C20:2 analog was strongly stimulatory for human iNKT cells and thus warrants further examination as a potential immunomodulatory agent for human disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".