Analysis of total microcystins and nodularins by oxidative cleavage of their ADMAdda, DMAdda, and Adda moieties
Bibliographic record
Abstract
Microcystins (MCs) and nodularins (NODs) exhibit high structural variability, including modifications of the Adda (3S-amino-9S-methoxy-2S,6,8S-trimethyl-10-phenyldeca-4E,6E-dienoic acid) moiety. Variations include 9-O-desmethylAdda (DMAdda) and 9-O-acetylDMAdda (ADMAdda) which, unless targeted, may go undetected. Therefore, reference standards were prepared of [ADMAdda5]MCs and [DMAdda5]MCs, which were analyzed using multiple approaches. The cross-reactivities of the [DMAdda5]- and [ADMAdda5]MC standards were similar to that of MC-LR when analyzed with a protein phosphatase 2A (PP2A) inhibition assay, but were <0.25% when analyzed with an Adda enzyme-linked immunosorbent assay (ELISA). Oxidative cleavage experiments identified compounds that could be used in the analysis of total MCs/NODs in a similar fashion to the 2R-methyl-3S-methoxy-4-phenylbutanoic acid (MMPB) technique. Products from oxidative cleavage of both the 4,5- and 6,7-ene of Adda, DMAdda and ADMAdda were observed, and three oxidation products, one from each Adda variant, were chosen for analysis and applied to three field samples and a Nostoc culture. Results from the oxidative cleavage method for total Adda, DMAdda, and ADMAdda were similar to those from the Adda-ELISA, PP2A inhibition, and LC-MS/MS analyses, except for the Nostoc culture where the Adda-ELISA greatly underestimated microcystin levels. This oxidative cleavage method can be used for routine analysis of field samples and to assess the presence of the rarely reported, but toxic, DMAdda/ADMAdda-containing MCs and NODs.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".