Distinct patterns of dendritic cell cytokine release stimulated by beta-glucans and Toll-Like receptor agonists (98.33)
Bibliographic record
Abstract
Abstract β-glucans (βGs) derived from fungal cell walls have potential uses as immunomodulating agents and vaccine adjuvants. Yeast glucan particles (YGPs) are purified Saccharomyces cerevisiae cell walls treated so they are >80% β1,3-D-glucan and free of mannan and proteins. YGPs stimulated secretion of the proinflammatory cytokine TNF-α in wild type murine bone marrow-derived myeloid DCs (BMDCs), but did not stimulate IL-12p70 production. A purified soluble βG, scleroglucan, also stimulated TNF-α in BMDCs. These two βGs failed to stimulate TNF-α in dectin-1 (βG-receptor) knockout BMDCs. Co-stimulation of wild-type BMDCs with βGs and specific TLR ligands resulted in greatly enhanced TNF-α production but decreased IL-12p70 production compared with TLR ligands alone. The up-regulation of TNF-α and down-regulation IL-12p70 required dectin-1, but not IL-10. Similar patterns of cytokine regulation were observed in human monocyte-derived DCs (hDCs) co-stimulated with YGPs and, the TLR4 ligand, LPS. Finally, co-stimulation of BMDCs with the TLR9 ligand, CpG, and YGPs resulted in upregulated secretion of IL-1α and IL-10, downregulated IL-1β, IL-6 and IP-10, and had no significant effects on IL-12p40, KC, MCP-1 or MIP-α, compared with CpG alone. Thus, βGs have disparate effects on cytokine responses following DC stimulation with TLR ligands.
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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) | 0.002 | 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".