Superior Protection against Malaria and Melanoma Metastases by a C-glycoside Analogue of the Natural Killer T Cell Ligand α-Galactosylceramide
Bibliographic record
Abstract
alpha-Galactosylceramide (alpha-GalCer) is a glycolipid that stimulates natural killer T cells to produce both T helper (Th) 1 and Th2 cytokines. This property enables alpha-GalCer to ameliorate a wide variety of infectious, neoplastic, and autoimmune diseases; however, its effectiveness against any one disease is limited by the opposing activities of the induced Th1 and Th2 cytokines. Here, we report that a synthetic C-glycoside analogue of alpha-GalCer, alpha-C-galactosylceramide (alpha-C-GalCer), acts as natural killer T cell ligand in vivo, and stimulates an enhanced Th1-type response in mice. In two disease models requiring Th1-type responses for control, namely malaria and melanoma metastases, alpha-C-GalCer exhibited a 1,000-fold more potent antimalaria activity and a 100-fold more potent antimetastatic activity than alpha-GalCer. Moreover, alpha-C-GalCer consistently stimulated prolonged production of the Th1 cytokines interferon-gamma and interleukin (IL)-12, and decreased production of the Th2 cytokine IL-4 compared with alpha-GalCer. Finally, alpha-C-GalCer's enhanced therapeutic activity required the presence of IL-12, which was needed to stimulate natural killer cells for optimal interferon-gamma production, but did not affect IL-4. Overall, our results suggest that alpha-C-GalCer may one day be an excellent therapeutic option for diseases resolved by Th1-type responses.
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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 | 0.000 |
| 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".