Studies on azaspiracid biotoxins. III. Instrumental validation for rapid quantification of AZA 1 in complex biological matrices
Bibliographic record
Abstract
Azaspiracids are neurotoxins produced by marine algae that have been detected in harvested mussels since 1995. They pose a significant threat to human health through the consumption of contaminated shellfish, and negatively impact the economy of areas where shellfish are harvested and processed. Regulatory agencies are beginning to advocate instrumental assays over traditional mouse bioassay methods. The development and validation of an assay method for AZA 1, the predominant azaspiracid toxin, and the production of a calibration standard and reference material will therefore be vital for quality control in monitoring laboratories worldwide. This report demonstrates a rapid and reproducible liquid chromatography/mass spectrometry (LC/MS) method for separation of all twelve known azaspiracids. Using a triple-quadrupole mass spectrometer, ultra-high sensitivity was obtained at the low-femtogram level injected on-column. At the same time, a linear response of three orders of magnitude was observed. We compared the results with those measured on an ion-trap mass spectrometer. The triple-quadrupole instrument was more sensitive, reliable and reproducible than the ion-trap instrument. The detection limit obtained on the ion-trap mass spectrometer was ten times higher than that obtained on the triple quadrupole. During the study, a new azaspiracid analog (AZA 7c) was discovered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".