In Vitro Metabolism of Azaspiracids 1–3 with a Hepatopancreatic Fraction from Blue Mussels (<i>Mytilus edulis</i>)
Bibliographic record
Abstract
spp. that can accumulate in shellfish and cause food poisoning in humans. Of the 60 AZAs identified, levels of AZA1, AZA2, and AZA3 are regulated in shellfish as a food safety measure based on occurrence and toxicity. Information about the metabolism of AZAs in shellfish is limited. Therefore, a fraction of blue mussel hepatopancreas was made to study the metabolism of AZA1-3 in vitro. A range of AZA metabolites were detected by liquid chromatography-high-resolution tandem mass spectrometry analysis, most notably the novel 22α-hydroxymethylAZAs AZA65 and AZA66, which were also detected in naturally contaminated mussels. These appear to be the first intermediates in the metabolic conversion of AZA1 and AZA2 to their corresponding 22α-carboxyAZAs (AZA17 and AZA19). α-Hydroxylation at C-23 was also a prominent metabolic pathway, producing AZA8, AZA12, and AZA5 as major metabolites of AZA1-3, respectively, and AZA67 and AZA68 as minor metabolites via double-hydroxylation of AZA1 and AZA2, but only low levels of 3β-hydroxylation were observed in this study. In vitro generation of algal toxin metabolites, such as AZA3, AZA5, AZA6, AZA8, AZA12, AZA17, AZA19, AZA65, and AZA66 that would otherwise have to be laboriously purified from shellfish, has the potential to be used for the production of standards for analytical and toxicological studies.
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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".