Hyperforin, an Anti-Inflammatory Constituent from St. John's Wort, Inhibits Microsomal Prostaglandin E2 Synthase-1 and Suppresses Prostaglandin E2 Formation in vivo
Bibliographic record
Abstract
The acylphloroglucinol hyperforin (Hyp) from St. John's wort possesses anti-inflammatory and anti-carcinogenic properties which were ascribed among others to the inhibition of 5-lipoxygenase. Here, we investigated whether Hyp also interferes with prostanoid generation in biological systems, particularly with key enzymes participating in prostaglandin (PG)E(2) biosynthesis, i.e., cyclooxygenases (COX)-1/2 and microsomal PGE(2) synthase (mPGES)-1 which play key roles in inflammation and tumorigenesis. Similar to the mPGES-1 inhibitors MK-886 and MD-52, Hyp significantly suppressed PGE(2) formation in whole blood assays starting at 0.03-1 μM, whereas the concomitant generation of COX-derived 12(S)-hydroxy-5-cis-8,10-trans-heptadecatrienoic acid, thromboxane B(2), and 6-keto PGF(1α) was not significantly suppressed up to 30 μM. In cell-free assays, Hyp efficiently blocked the conversion of PGH(2) to PGE(2) mediated by mPGES-1 (IC(50) = 1 μM), and isolated COX enzymes were not (COX-2) or hardly (COX-1) suppressed. Intraperitoneal (i.p.) administration of Hyp (4 mg kg(-1)) to rats impaired exudate volume and leukocyte numbers in carrageenan-induced pleurisy associated with reduced PGE(2) levels, and Hyp (given i.p.) inhibited carrageenan-induced mouse paw edema formation (ED(50) = 1 mg kg(-1)) being superior over indomethacin (ED(50) = 5 mg kg(-1)). We conclude that the suppression of PGE(2) biosynthesis in vitro and in vivo by acting on mPGES-1 critically contributes to the anti-inflammatory efficiency of Hyp.
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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) | 0.001 | 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".