Characterization of microcystins using in‐source collision‐induced dissociation
Bibliographic record
Abstract
The efficiency of the in-source collision-induced dissociation (in-source CID) technique for the structural characterization of microcystins (MCYSTs) was evaluated. Microcystins that did not contain arginine underwent facile fragmentation to produce characteristic product ions at relatively low cone voltage and could be fully characterized based on their mass spectra. On the other hand, cyclic peptides possessing arginine residues, such as MCYST-RR, -LR, -YR and nodularin, were considerably more stable under in-source CID conditions and required higher cone voltage to induce fragmentation. This behaviour is explained in terms of the mobile proton model for peptide fragmentation that can be used as an indication for the presence of arginine when unknown microcystins are analyzed. In-source CID was applied to the characterization of microcystins released into water from a Microcystis aeruginosa culture (UTCC299) (UTCC: University of Toronto Culture Collection of Algae and Cyanobacteria). Six microcystins were detected in extracts from UTCC299: I, [D-Asp(3)]MCYST-LR; II, MCYST-LR; III, isomer of MCYST-LR; IV, isomer of methyl MCYST-LR; V, [D-Asp(3), Glu(OCH(3))(6)]MCYST-LR; and VI, [D-Glu(OCH(3))(6)]MCYST-LR. In-source CID provided mass spectral patterns similar to those obtained by CID in the collision cell of the mass spectrometer but was more sensitive for the analysis of microcystins.
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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 | 0.001 |
| 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) | 0.001 | 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".