Asperflavin, an Anti-Inflammatory Compound Produced by a Marine-Derived Fungus, Eurotium amstelodami
Bibliographic record
Abstract
In the present study, 16 marine-derived fungi were isolated from four types of marine materials including float, algae, animals and drift woods along with the coast of Jeju Island, Korea and evaluated for anti-inflammatory effects in lipopolysaccharide (LPS)-stimulated RAW 24.7 cells. The broth and mycelium extracts from the 16 fungi were prepared and the broth extract (BE) of Eurotium amstelodami (015-2) inhibited nitric oxide (NO) production in LPS-stimulated RAW 264.7 cells without cytotoxicity. By further bioassay-guided isolation, three compounds including asperflavin, neoechinulin A and preechinulin were successfully isolated from the BE of E. amstelodami. It was revealed that asperflavin showed no cytotoxicity up to 200 μM and significantly inhibited LPS-induced NO and PGE2 production in a dose-dependent manner. In the western blot results, asperflavin suppressed only inducible NOS (iNOS), but COX-2 were slightly down-regulated. Asperflavin was also observed to inhibit the production of pro-inflammatory cytokines including TNF-α, IL-1β, and IL-6. In conclusion, this study reports a potential use of asperflavin isolated from a marine fungus, E. amstelodami as an anti-inflammatory agent via suppression of iNOS and pro-inflammatory cytokines as well as no cytotoxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".