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Enregistrement W6888474497 · doi:10.19663/j.issn2095-9869.20230613004

Technological Advances in Preparing Raw Materials of Paralytic Shellfish Toxin Matrix Standards by Mixed Exposure to Multiple Toxic Dinoflagellates

2024· article· en· W6888474497 sur OpenAlexaboutno aff

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueMarine Toxins and Detection Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMarine toxinShellfishFood safetyContaminationSafeguardAlgal bloomShellfish poisoningRaw material

Résumé

récupéré en direct d'OpenAlex

Paralytic shellfish toxins (PSTs) pose significant ecological and food safety hazards in China and the world. Consequently, there is a global consensus to intensify safety monitoring of these toxins to safeguard consumers. In recent years, the occurrence of harmful algal blooms (known as "red tides") has increased due to human activities and climate change. The contamination risk posed by PSTs is a pressing concern. This problem poses significant ecological and food safety risks in China and the world, leading to a global consensus on strengthening the safety monitoring of PSTs to ensure consumer safety. Several countries and regions, including the European Union, the United States, and Canada, have established strict regulatory limits (800 μg STXeq/kg) for PSTs and implemented monitoring programs. Currently, the main methods for detecting and monitoring PSTs include enzyme-linked immunosorbent assay, mouse bioassay, high-performance liquid chromatography, and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Among them, LC-MS/MS has gained international attention due to its high throughput and sensitivity, allowing detailed analysis of toxin profiles in contaminated shellfish. However, the complexity of aquatic food matrices often introduces interference during detection, affecting the accuracy and precision of the results. Using matrix reference materials as quality controls during the analysis can effectively mitigate the errors caused by matrix effects. In recent years, research has been conducted in China on the preparation techniques of matrix reference materials. However, existing techniques have limitations, such as unstable raw materials, limited toxin types, and lack of commercial production. There is still a lack of PST standard materials in China that possess traceability, certification, and physical specimens. To meet the quality control and validation needs for PST detection and analysis, and to enhance the regulation of shellfish quality and safety, the preparation of various PST matrix reference materials has become a top priority.In this study, six PST-producing algae species from four major Chinese coastal areas were compared as the raw material for toxin production: Alexandrium catenella (GY-H25), Gymnodinium catenatum (GY-H65), Alexandrium tamarense (GY-H31 and AT5-3), Alexandrium pacificum (TI0989), and Alexandrium minutum (GY-H46). Through three-step extended culture, all strains achieved mean cell densities ranging from 4.0 × 106 to 4.0 × 107 cells/L. Single cell toxin producing capacity was ordered from highest to lowest as GY-H46, GY-H31, AT5-3, GY-H25, GY-H65, and TI0989. After comparing the toxin production per cell, algal cell density, and toxin composition of the six strains, GY-H46, GY-H31, GY-H25, and GY-H65 were selected for the exposure experiments. These four species of toxic algae (up to 1.0 × 105 cells) were fed to mussels (Mytilus galloprovincialis) for 7 days under controlled conditions. The results demonstrated that the toxin composition of contaminated mussels clustered with those of the causative algae and may separate from one another significantly during the different accumulation phases. GY-H46, GY-H31, and GY-H25 showed consistent toxin profiles with those found in mussels, but varied in the proportions of individual toxin components. GY-H65 exhibited significant differences in toxin composition and proportions in mussels. We also detected a new conversion component (dcSTX) with a maximum proportion of 29.6%. The proportion of dcNEO increased from 1.9% to 13.2% in GY-H65 and mussels. In addition, a high proportion of conversion from C2 to C1 was observed in mussels fed GY-H25 and GY-H65. The mean accumulation rates of toxins from the four toxic algae species in mussels ranged from 39.7% to 83.1%, with the highest toxicity reaching (1, 151.0 ± 7.6) μg STXeq/kg in mussels. Individual exposure experiments with the four toxic algae species demonstrated that mussels are capable of accumulating multiple components and high levels of PSTs. Based on the above observations, the feeding ratios of the low- and high-density groups in the mixed exposure experiment were determined as 1∶1∶1 ∶4 and 1∶1.6∶2.4∶8 for GY-H25, GY-H65, GY-H31, and GY-H46, respectively. The total algae feeding amount per mussel per day was 3.5 × 104 cells in the low-density group and 2.6 × 105 cells in the high-density group. When examining the PST content in mussels from the two mixed exposure groups, it was observed that the mean accumulation conversion rates were comparable at 41.4% and 44.6%, which were lower than the theoretical rates of 48.7% and 47.5%, respectively. The overall toxicity of the low- and high-density groups was (535.0±5.6) and (2, 636.0±12.4) μg STXeq/kg, respectively. After mixed exposure, both experimental groups of mussels accumulated a total of 12 PST components (NEO, dcSTX, dcNEO, GTX1, GTX4, GTX2, GTX3, GTX5, dcGTX2, dcGTX3, C1, and C2).This study demonstrates that mixed exposure of mussels to four toxin-producing algae species, namely A. catenella, A. tamarense, G. catenatum, and A. minutum, can result in the stable and manageable presence of 12 PST components. This provides technical support for the preparation of matrix reference materials that can be used in industrial regulation and industry services. The development of a preparation technology for matrix reference materials with multiple PST components, which do not exceed international limits, holds promise for water quality monitoring and the assessment of marine pollution risks, and provides reference materials for theoretical studies on PSTs risk formation mechanisms.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,071
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,002
Science ouverte0,0020,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0200,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,074
Tête enseignante GPT0,502
Écart entre enseignants0,428 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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