Reductive Amination for LC–MS Signal Enhancement and Confirmation of the Presence of Caribbean Ciguatoxin-1 in Fish
Bibliographic record
Abstract
Ciguatera poisoning is a global health concern caused by the consumption of seafood containing ciguatoxins (CTXs). Detection of CTXs poses significant analytical challenges due to their low abundance even in highly toxic fish, the diverse and in-part unclarified structures of many CTX congeners, and the lack of reference standards. Selective detection of CTXs requires methods such as liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) or high-resolution MS (LC-HRMS). While HRMS data can provide greatly improved resolution, it is typically less sensitive than targeted LC-MS/MS and does not reliably comply with the FDA guidance level of 0.1 µg/kg CTXs in fish tissue that was established for Caribbean CTX-1 (C-CTX-1). In this study, we provide a new chemical derivatization approach employing a fast and simple one-pot derivatization with Girard's reagent T (GRT) that tags the C-56-ketone intermediate of the two equilibrating C-56 epimers of C-CTX-1 with a quaternary ammonium moiety. This derivatization improved the LC-MS/MS and LC-HRMS responses to C-CTX-1 by approximately 40- and 17-fold on average, respectively. These improvements in sensitivity to the GRT-derivative of C-CTX-1 are attributable to: the improved ionization efficiency caused by insertion of a quaternary ammonium ion; the absence of adduct-ions and water-loss peaks for the GRT derivative in the mass spectrometer, and; the prevention of on-column epimerization (at C-56 of C-CTX-1) by GRT derivatization, leading to much better chromatographic peak shapes. This C-CTX-1-GRT derivatization strategy mitigates many of the shortcomings of current LC-MS analyses for C-CTX-1 by improving instrument sensitivity, while at the same time adding selectivity due to the reactivity of GRT with ketones and aldehydes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".