Development of ELISAs for detecting domoic acid, okadaic acid, and saxitoxin and their applicability for the detection of marine toxins in samples collected in Belgium
Bibliographic record
Abstract
Okadaic acid, a diarrhetic shellfish poison, domoic acid, an amnesic shellfish poison, and saxitoxin, a paralytic shellfish poison, are three of the best-known marine biotoxins. The mouse bioassay is the method most widely used to detect many of these toxins in shellfish samples, but animal welfare concerns have prompted researchers to seek alternative methods of detection. In this study, three direct competitive enzyme-linked immunosorbent assays (ELISAs), each based on antibodies raised in rabbits against a conjugate of the analyte of interest, were developed for marine biotoxin detection in mussel, oyster, and scallop. One assay was for okadaic acid, one for saxitoxin, and one for domoic acid usually detected and quantified by high-performance liquid chromatography-ultraviolet light (HPLC-UV). All three compounds and a number of related toxins were extracted quickly and simply from the shellfish matrices with a 9 : 1 mixture of ethanol and water before analysis. The detection capabilities (CCbeta values) of the developed ELISAs were 150 microg kg(-1) for okadaic acid, 50 microg kg(-1) for domoic acid, and 5 microg kg(-1) or less for saxitoxin. The assays proved satisfactory when used over a 4-month period for the analysis of 110 real samples collected in Belgium.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".