Boronate techniques for clean-up and concentration of the vic-diol-containing tetrodotoxins from shellfish
Bibliographic record
Abstract
Boronates bind reversibly to vic-diols, a common structural feature of algal toxins. This boronate–diol interaction can be exploited for selective toxin clean-up and concentration. Boric acid gel (BAG) solid phase extraction (SPE) was recently shown to eliminate interferences and matrix effects in LC-MS analyses of azaspiracids (AZAs) in mussel extracts. Here, we report a modified approach for cleanup of tetrodotoxin (TTX) and many of its congeners, which also contain vic-diols. The reaction between TTX and boronic acids was first investigated in solution to optimize conditions for TTX binding. Then, TTX-contaminated mussel extracts were applied to BAG SPE columns. TTXs were selectively bound and released from the BAG to yield very clean extracts containing TTX analogues and very little else. Potential interferences in LC-MS analyses, such as arginine and other amino acids, were completely eliminated, making this a promising approach for analytical sample preparation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".