Evaluation of ELISA-based method for total anabaenopeptins determination and comparative analysis with on-line SPE-UHPLC-HRMS in freshwater cyanobacterial blooms
Bibliographic record
Abstract
Anabaenopeptins (APs) are bioactive cyanopeptides of emerging concern produced by cyanobacteria. The research for analytical development has recently gained in importance due to their abundance in toxic cyanobacterial blooms. A new commercial enzyme-linked immunosorbent assay kit for the determination of total APs (APtot ELISA) has been released promising a rapid response with good cost efficiency for the routine monitoring of uncommon cyanopeptides. The present study explores the suitability of this new kit in comparison with a validated quantitative analytical method based on liquid chromatography coupled to mass spectrometry (LC-MS). The validation results were comparable with both methods for accuracy, precision, and calibration. Method detection limits were more sensitive using LC-MS specifically evaluated at 0.011 and 0.013 μg L−1 for AP-A and B respectively, compared to APtot ELISA evaluated at 0.10 μg L−1 for total of the two. For APtot ELISA, results were independent from the matrix; however, a systematic signal response was measured in blanks, requiring a blank subtraction in data treatment. Cross-reactivity of APtot ELISA was investigated by analyzing ten cyanopeptides selected for their abundance and diversity. Cyanopeptolin A (CP-A), nodularin-R (NOD), microcystin (MC)-RR, [Asp3]RR, and HilR showed cross-reactivity with an average overestimation going from 25 to 66%. Considering the contribution of cross-reactive cyanopeptides, thirteen lake samples out of fifteen showed higher concentrations using APtot ELISA with overestimation values up to 2261% compared to LC-MS. In light of this study results, LC-MS should still be preconized for the study and monitoring of APs when sensitivity and specificity are needed.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".