The Toll‐like receptor 7/8‐ligand resiquimod (R‐848) primes human neutrophils for leukotriene B <sub>4</sub> , prostaglandin E <sub>2</sub> and platelet‐activating factor biosynthesis
Bibliographic record
Abstract
Toll-like receptors (TLR) recognize pathogen-associated molecular patterns and play important roles in the innate immune system. While single-stranded viral RNA is the natural ligand of TLR7/TLR8, the imidazoquinoline resiquimod (R-848) is recognized as a potent synthetic agonist of TLR7/TLR8. We investigated the effects of TLR7/8 activation on lipid mediator production in polymorphonuclear leukocytes exposed to R-848. Although R-848 had minimal effects by itself, it strongly enhanced leukotriene B4 formation on subsequent stimulation by fMLP, platelet-activating factor, and the ionophore A23187. R-848 acted via TLR8 but not TLR7 as shown by the lack of effect of the TLR7-specific ligand imiquimod. Priming with R-848 also resulted in enhanced arachidonic acid release and platelet-activating factor formation following fMLP stimulation, as well as enhanced prostaglandin E2 synthesis following the addition of arachidonic acid. Western blot analysis demonstrated that R-848 induced the phosphorylation of the cytosolic phospholipase A2alpha, promoted 5-lipoxygenase translocation and potently stimulated the expression of the type 2 cyclooxygenase. Bafilomycin A1, an inhibitor of endosomal acidification, efficiently inhibited all R-848-induced effects. These studies demonstrate that TLR8 signaling strongly promotes inflammatory lipid mediator biosynthesis and provide novel insights on innate immune response to viral infections.
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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 | 0.001 |
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".