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Record W4415879108 · doi:10.1021/acsomega.5c05500

Stability and Recovery of Palytoxin and Ovatoxin-a

2025· article· en· W4415879108 on OpenAlexafffund
Elizabeth Mudge, Christopher O. Miles, Valentina Miele, Carmela Dell’Aversano, Pearse McCarron

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaUniversità degli Studi di Napoli Federico II
KeywordsPalytoxinSolubilityAnalyteSolventBenzenesulfonic acidBromoform

Abstract

fetched live from OpenAlex

spp., respectively. Several documented events have resulted in human exposure to aerosolized toxins that led to significant respiratory distress. It has been reported that processing of samples containing palytoxin and ovatoxin during analysis can lead to significant analyte recovery issues due to a variety of parameters. In this study, systematically designed experiments, monitored by LC-MS/MS, were used to evaluate palytoxin and ovatoxin-a stability and recovery, and the effects of pH, solvent composition, and vial contact surface. Significant losses of palytoxin and ovatoxin-a were observed when drying highly aqueous solutions in glass, which were reduced with the use of a polypropylene contact surface and the addition of bovine serum albumin and phosphate-buffered saline. The results showed that palytoxin analogues should be maintained in solutions containing greater than 50% organic solvent, such as methanol, and in a pH range of 5-8 in order to minimize losses or degradation. The recovery of ovatoxin-a was lower than for palytoxin in several experiments, indicating that the structural differences between these analogues may affect solubility or stability. This work provides insight into palytoxin and ovatoxin-a handling, and will help improve analytical measurements, handling during toxicology studies, and minimize losses during isolation protocols for the development of reference materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.257
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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