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Record W4410941502 · doi:10.1021/acs.jafc.5c01142

Comparison of Different Solid-Phase Cleanup Methods Prior to the Detection of Ciguatoxins in Fish by Cell-Based Assay and LC-MS/MS

2025· article· en· W4410941502 on OpenAlexaff
Andrés Sanchez-Henao, F. Real, Yefermin Darias-Dágfeel, Natalia García‐Álvarez, Jorge Diogène, Maria Rambla-Alegre

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsContinental (Canada)
FundersH2020 European Research CouncilAgencia Estatal de InvestigaciónAgencia Canaria de Investigación, Innovación y Sociedad de la InformaciónUniversidad de Las Palmas de Gran CanariaEuropean Food Safety Authority
KeywordsCiguatoxinChromatographyCiguateraFish <Actinopterygii>ChemistryBiologyFishery

Abstract

fetched live from OpenAlex

Ciguatera poisoning (CP) is the most reported food poisoning associated with fish consumption. Ciguatoxins (CTXs) are produced by microalgae and metabolized in fish; even low levels of these toxins in fish can lead to CP. To date, there is no unique validated methodology for their study, and demonstrating their presence in fish tissues is an analytical challenge. The main techniques used are cell-based assay and liquid chromatography, which may present different matrix effect interferences; thus, purification protocols are necessary. Six cleanup strategies for fish extracts, assessing the principal analogues found in fish in different parts of the world (CTX1B/CTX3C/C-CTX1), are compared here. Cleaned-up extracts are evaluated by cell-based assay and chromatography. All protocols are suitable for recovering the analogues of CTXs. Two of them, those that used polystyrene-divinylbenzene and silica cartridges, achieve the most adequate results showing toxicity in their fractions over 53% and chromatography efficiencies over 79% for CTX1B/CTX3C, proving to be the most versatile clean-ups for the study of the different CTX analogues.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.238

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 head0.012
GPT teacher head0.322
Teacher spread0.310 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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