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

2024· article· en· W6888474497 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMarine toxinShellfishFood safetyContaminationSafeguardAlgal bloomShellfish poisoningRaw material

Abstract

fetched live from 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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.000

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 head0.074
GPT teacher head0.502
Teacher spread0.428 · 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.

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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