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Record W3090283078 · doi:10.1016/j.toxcx.2020.100059

Fatty acid esters of azaspiracids identified in mussels (Mytilus edulis) using liquid chromatography-high resolution mass spectrometry

2020· article· en· W3090283078 on OpenAlexaff
Elizabeth Mudge, Christopher O. Miles, W. R. Hardstaff, Pearse McCarron

Bibliographic record

VenueToxicon X · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsNational Research Council Canada
FundersMarine Institute
KeywordsMytilusChemistryChromatographyShellfishMusselEicosapentaenoic acidMarine toxinFatty acidMass spectrometryBlue musselBiochemistryPolyunsaturated fatty acidBiologyFisheryFish <Actinopterygii>Aquatic animal

Abstract

fetched live from OpenAlex

Azaspiracids (AZAs) are lipophilic polyether toxins produced by Azadinium and Amphidoma species of marine microalgae. The main dinoflagellate precursors AZA1 and AZA2 are metabolized by shellfish to produce an array of AZA analogues. Many marine toxins undergo fatty acid esterification in shellfish, therefore mussel tissues contaminated with AZAs were screened for intact fatty acid esters of AZAs using liquid chromatography-high resolution mass spectrometry. Acyl esters were primarily observed for AZAs containing hydroxy groups at C-3 with 3-O-palmitoylAZA4 identified as the most abundant acyl ester, while other fatty acid esters including 18:1, 16:1, 17:0, 20:2 and 18:0 acyl esters were detected. The structures of these acyl derivatives were determined through LC-MS/MS experiments, and supported by periodate cleavage reactions and semi-synthesis of palmitate esters of the AZAs. Esters of the hydroxy groups at C-20 or C-21 were not observed in mussel tissue. The relative proportion of the most abundant AZA ester was less than 3% of the sum of the major free AZA analogues. These findings reveal an additional metabolic pathway for AZAs in shellfish.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.269
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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