CpG ODN induced NK cell IFNγ production is positively regulated by IL-12 producing neutrophils and suppressed by IL-10 producing B cells (108.8)
Bibliographic record
Abstract
Abstract CpG-containing oligodeoxynucleotides (CpG ODN) stimulates spleen cells in vitro and induces IFNγ secretion mostly from NK cells, whereas purified NK cells are not stimulated by CpG ODN. IL-12 neutralization inhibits NK cell IFNγ production, and IL-12 knockout mouse spleen cells stimulated with CpG ODN produce very little IFNγ, indicating that IL-12 is required. Depletion of macrophages and DC has no effect, whereas neutrophil depletion dramatically decreases both IL-12 and IFNγ production by spleen cells, indicating that neutrophils are a key source of IL-12. NK cells in B cell-deficient mouse spleen secrete higher levels of IFNγ than wild type spleen cells, and B cell depletion enhances NK cell IFNγ production, indicating that B cells are suppressive. CD5+ B cells secrete IL-10 in response to CpG ODN. The neutralization of IL-10 increases IFNγ and IL-12 production by CpG ODN-stimulated spleen cells, and B cell-deficient splenocytes produce more IL-12 than WT splenocytes. Furthermore, exogenously added IL-10 effectively inhibits CpG ODN-induced IL-12 production by purified neutrophils, and IL-12/IL-18 induced stimulation of purified NK cells. Therefore, CD5+ B cells produce IL-10 and suppress CpG ODN-mediated NK cell IFNγ production by inhibiting neutrophil IL-12 production as well as by directly acting on activated NK cells themselves.
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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) | 0.000 | 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) | 0.004 | 0.001 |
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".