Preparation and characterization of an immunoaffinity column for the selective extraction of azaspiracids
Bibliographic record
Abstract
The presence of azaspiracids (AZAs) in shellfish may cause food poisoning in humans. AZAs can accumulate in shellfish filtering seawater that contains marine dinoflagellates such as Azadinium and Amphidoma spp. More than 60 AZA analogues have been identified, of which AZA1, AZA2 and AZA3 are regulated in Europe. Shellfish matrices may complicate quantitation by ELISA and LC-MS methods. Polyclonal antibodies have been developed that bind specifically to the C-26-C-40 domain of the AZA structure and could potentially be used for selectively extracting compounds containing this substructure. This includes almost all known analogues of AZAs, including AZA1, AZA2 and AZA3. Here we report preparation of immunoaffinity chromatography (IAC) columns for clean-up and concentration of AZAs. The IAC columns were prepared by coupling polyclonal anti-AZA IgG to CNBr-activated sepharose. The columns were evaluated using shellfish extracts, and the resulting fractions were analyzed by ELISA and LC-MS. The columns selectively bound over 300 ng AZAs per mL of gel without significant leakage, and did not retain the okadaic acid, cyclic imine, pectenotoxin and yessotoxin analogues that were present in the applied samples. Furthermore, 90-92% of the AZAs were recovered by elution with 90% MeOH, and the columns could be re-used without significant loss of performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".