Rapid quantitative screening of cyanobacteria for production of anatoxins using direct analysis in real time high‐resolution mass spectrometry
Bibliographic record
Abstract
RATIONALE: Anatoxins (ATXs) are a potent class of cyanobacterial neurotoxins that are increasingly problematic in drinking water reservoirs and recreational water bodies worldwide. Because of their high polarity and low molecular weight, analysis of ATXs is challenging and they can be considered underreported compared with other classes of cyanobacterial toxins. Improved screening methods are therefore needed to effectively assess their occurrence and concentrations in the environment. METHODS: A rapid screening method was developed for ATXs in cyanobacteria using direct analysis in real time combined with high-resolution mass spectrometry (DART-HRMS), requiring less than 2 min per sample for triplicate analysis. The developed method was evaluated for its quantitative capabilities, applied to the screening of 30 cyanobacterial culture samples for the presence of anatoxin-a, homoanatoxin-a and dihydroanatoxin-a, and compared with a more typical liquid chromatography (LC)/HRMS method. RESULTS: Excellent linearity was observed in the analysis of a matrix-matched calibration curve using DART-HRMS, with ionization suppression of about 50% and relative standard deviations between replicate analyses of approximately 30%. Limits of detection for both anatoxin-a and homoanatoxin-a were estimated as 1 ng/mL. Excellent agreement was observed between DART-HRMS and LC/HRMS with all ATX-producing cultures correctly identified and only one false positive culture by DART-HRMS. CONCLUSIONS: DART-HRMS shows excellent promise for the rapid, quantitative screening of ATXs in cyanobacteria and could be expanded in the future to include the analysis of field samples and drinking water, as well as additional ATX analogues.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".