Paeonol reduces IL-β production by inhibiting the activation of nucleotide oligomerization domain-like receptor protein-3 inflammasome and nuclear factor-κB in macrophages
Bibliographic record
Abstract
Interleukin-1β, a key cytokine in gouty inflammation, is precisely regulated by the NLRP3 inflammasome and NF-κB. Our previous study demonstrated that paeonol suppressed IL-1β production in rats with monosodium urate (MSU)-induced arthritis. Whether NLRP3 inflammasome or NF-κB is responsible for the anti-inflammatory effect of paeonol remains unclear. In this study, J774A.1 cells induced by lipopolysaccharide (LPS) plus MSU, was used to investigate the effect of paeonol on NLRP3 inflammasome activation, and J774A.1 cells induced by LPS alone were used to investigate the effect of paeonol on NF-κB activation. In J774A.1 cells induced by LPS plus MSU, paeonol decreased the levels of IL-1β and caspase-1 and reduced the MSU-induced interaction of pro-caspase-1 and apoptosis-associated speck-like protein containing caspase recruitment domain (ASC), but did not affect the levels of pro-IL-1β and pro-caspase-1. In J774A.1 cells induced by LPS alone, paeonol reduced the levels of IL-1β, NLRP3, p-IKK, p-IκBα, and p-p65, but did not affect ASC levels. Paeonol also promoted the content of IκBα and retained more p65 in the cytoplasm. Furthermore, paeonol reduced the DNA-binding activity of p65 and lowered the levels of p-JNK, p-ERK, and p-p38. These results suggest that paeonol inhibits IL-1β production by inhibiting the activation of NLRP3 inflammasome, NF-κB, and MAPK signaling pathways.
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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) | 0.001 | 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".