Evaluation of the efficiency of cyclodextrin polymers as sustainable sampling material for catching palytoxin-like compounds in seawater
Bibliographic record
Abstract
Palytoxin-like compounds, including ovatoxins, are potent emerging toxins responsible for human respiratory poisonings following inhalation of contaminated marine aerosols. Periodic massive proliferations of the ovatoxin-producing organism (Ostreopsis cf. ovata) worldwide, particularly in the Mediterranean, have caused severe toxic outbreaks, drawing the attention of health authorities. At present, an efficient and sustainable sampling system for monitoring ovatoxins in seawater remains unavailable. Herein, different cyclodextrin (CD) polymers were investigated as a green and effective alternative to conventional and low-performing resins to detect ovatoxins in seawater. Spiking experiments using different concentrations of palytoxin or ovatoxins (namely 200 and 3.3 ng PLTX/mL or 200 ng OVTX-a/mL) were conducted and LC-HRMS was used to evaluate the suitability of CD polymers in capturing palytoxin-like compounds. Several conditions were tested for extracting polymer materials, including different extraction times (1.5 to 4 h), various solvent mixtures (acidic or alkaline), and organic modifiers (methanol or acetonitrile) at different ratios. Among the tested polymers, γ-CD-hexamethylene diisocyanate (HDI) resulted to be the most promising one, providing ovatoxin recoveries in the range 82-108% at a spiking level of 200 ng OVTX-a per mL. The best extracting condition was alkaline pH methanol:water 8:2 mixture, which showed the best palytoxin recovery in both high and low concentration spiking experiments. Finally, a time-dependent increase in the amount of ovatoxins captured by γ-CD-HDI disks deployed in O. cf. ovata cultures was observed. These findings provide valuable insights on the efficiency of passive sampling using CD polymers for capturing ovatoxins during O. cf. ovata bloom events.
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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) | 0.000 | 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 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".