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麻痹性贝类毒素检测的分析工具和生物检测方法比较。

Comparison of analytical tools and biological assays for detection of paralytic shellfish poisoning toxins.

机构信息

Australian Water Quality Centre, GPO Box 1751, Adelaide, SA, 5001, Australia.

出版信息

Anal Bioanal Chem. 2010 Jul;397(5):1655-71. doi: 10.1007/s00216-010-3459-4. Epub 2010 Jan 26.

DOI:10.1007/s00216-010-3459-4
PMID:20101494
Abstract

The paralytic shellfish poisoning toxins (PSTs) were, as their name suggests, discovered as a result of human poisoning after consumption of contaminated shellfish. More recently, however, the same toxins have been found to be produced by freshwater cyanobacteria. These organisms have worldwide distribution and are common in our sources of drinking water, thus presenting another route of potential human exposure. However, the regulatory limits for PSTs in drinking water are considerably lower than in shellfish. This has increased the need to find alternatives to the mouse bioassay, which, apart from being ethically questionable, does not have a limit of detection capable of detecting the PSTs in water at the regulated concentrations. Additionally, the number of naturally occurring PSTs has grown substantially since saxitoxin was first characterised, markedly increasing the analytical challenge of this group of compounds. This paper summarises the development of chromatographic, toxicity, and molecular sensor binding methodologies for detection of the PSTs in shellfish, cyanobacteria, and water contaminated by these toxins. It then summarises the advantages and disadvantages of their use for particular applications. Finally it recommends some future requirements that will contribute to their improvement for these applications.

摘要

麻痹性贝类毒素(PSTs)的名称表明,这些毒素是人类食用受污染贝类后中毒而被发现的。然而,最近同样的毒素也被发现是由淡水蓝藻产生的。这些生物体分布广泛,在我们的饮用水源中很常见,因此构成了另一种潜在的人类暴露途径。然而,饮用水中 PSTs 的监管限量远低于贝类中的限量。这增加了寻找替代小鼠生物测定的必要性,因为除了在伦理上存在问题外,这种方法的检测限无法检测到受监管浓度下水中的 PSTs。此外,自从首次对石房蛤毒素进行特征描述以来,天然存在的 PSTs 数量大幅增加,显著增加了对这组化合物的分析挑战。本文总结了用于检测贝类、蓝藻和受这些毒素污染的水中 PSTs 的色谱、毒性和分子传感器结合方法的发展。然后总结了它们在特定应用中的优缺点。最后,它建议了一些未来的需求,这将有助于提高它们在这些应用中的性能。

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