Protective effect of Tremella fuciformis Berk extract on LPS-induced acute inflammation via inhibition of the NF-κB and MAPK pathways
Bibliographic record
Abstract
Tremella fuciformis Berk (TFB) has long been used as a traditional medicine in Asia. Although TFB exhibits antioxidant and anti-inflammatory effects, the mechanisms of action responsible have remained unknown. We confirmed the anti-inflammatory effects of Tremella fuciformis Berk extract (TFE) in RAW 264.7 cells and observed significantly suppressed LPS-induced iNOS/NO and COX-2/PGE2 production. TFE also suppressed LPS-induced IKK, IkB, and p65 phosphorylation, as well as LPS-induced translocation of p65 from the cytosol. Additionally, TFE inhibited LPS-induced phosphorylation of MAPKs. In an acute inflammation study, oral administration of TFE significantly inhibited LPS-induced IL-1β, IL-6 and TNF-α production and iNOS and COX-2 expression. The major bioactive compounds from TFB extract were identified as gentisic acid, protocatechuic acid, 4-hydroxybenzoic acid, and coumaric acid. Among these compounds, protocatechuic acid showed the strongest inhibitory effects on LPS-induced NO production in RAW 264.7 cells. Overall, these results suggest that TFE is a promising anti-inflammatory agent that suppresses iNOS/NO and COX-2/PGE2 expression, as well as the 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) | 0.001 | 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 | 0.000 |
| 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".