Development and validation of a multiclass LC–MS/MS method for the analysis of cyanotoxins
Bibliographic record
Abstract
Abstract Cyanobacteria are prokaryotic organisms that can form large monospecific blooms, which pose a risk to human and animal health as some species produce toxic secondary metabolites called cyanotoxins. Multiclass cyanotoxin analysis is challenging due to varying chemical and physical properties between classes, as well as potentially large numbers of analogues within each class. Incorporating anatoxins (ATXs) into multiclass methods can be particularly challenging due to their small molecular size, potential interferences, polarity, and a lack of chemical standards for most analogues. Here, we present the development of a multiclass LC–MS/MS method and a quantitative calibration solution for aetokthonotoxin (AETX), an emerging cyanotoxin linked to mass mortalities of bald eagles in the Eastern United States. The developed method is capable of detecting 17 microcystins (MCs), nodularin-R, three cylindrospermopsins (CYNs), AETX, and 17 ATXs, including recently tentatively identified 10-hydroxy analogues. Analytes were identified by retention time and product ion ratio matching with available standards. The method was evaluated with respect to limits of detection (LODs), linear range, accuracy, and precision using neat and matrix matched standards. LODs in wet cyanobacterial biofilms ranged from 0.14 ng/g for CYN to 2.8 ng/g for [Dha 7 ]MC-LR with accuracies ranging from 65% for [Leu 1 ]MC-LY to 116% for CYN. Finally, the method’s application was demonstrated through analysis of cyanobacterial field samples, a dietary supplement matrix reference material, and passive sampler extracts to assess versatility within different matrices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".