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采用基于效应的分析方法对医院废水中的化学物质进行可疑筛选,作为优先排序策略。

Suspect Screening of Chemicals in Hospital Wastewaters Using Effect-Directed Analysis Approach as Prioritization Strategy.

机构信息

Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940 Leioa, Basque Country, Spain.

Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), 48620 Plentzia, Basque Country, Spain.

出版信息

Molecules. 2023 Jan 26;28(3):1212. doi: 10.3390/molecules28031212.

DOI:10.3390/molecules28031212
PMID:36770879
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9921743/
Abstract

The increasing number of contaminants in the environment has pushed water monitoring programs to find out the most hazardous known and unknown chemicals in the environment. Sample treatment-simplification methods and non-target screening approaches can help researchers to not overlook potential chemicals present in complex aqueous samples. In this work, an effect-directed analysis (EDA) protocol using the sea urchin embryo test (SET) as a toxicological in vivo bioassay was used as simplified strategy to identify potential unknown chemicals present in a very complex aqueous matrix such as hospital effluent. The SET bioassay was used for the first time here to evaluate potential toxic fractions in hospital effluent, which were obtained after a two-step fractionation using C and aminopropyl chromatographic semi-preparative columns. The unknown compounds present in the toxic fractions were identified by means of liquid chromatography coupled to a Q Exactive Orbitrap high-resolution mass spectrometer (LC-HRMS) and using a suspect analysis approach. The results were complemented by gas chromatography-mass spectrometry analysis (GC-MS) in order to identify the widest range of chemical compounds present in the sample and the toxic fractions. Using EDA as sample treatment simplification method, the number of unknown chemicals (>446 features) detected in the raw sample was narrowed down to 94 potential toxic candidates identified in the significantly toxic fractions. Among them, the presence of 25 compounds was confirmed with available chemical standards including 14 pharmaceuticals, a personal care product, six pesticides and four industrial products. The observations found in this work emphasize the difficulties in identifying potential toxicity drivers in complex water samples, as in the case of hospital wastewater.

摘要

环境中污染物数量的增加促使水监测计划发现环境中最危险的已知和未知化学物质。样品处理简化方法和非靶向筛选方法可以帮助研究人员不忽视复杂水样中存在的潜在化学物质。在这项工作中,使用海胆胚胎测试 (SET) 作为毒理学体内生物测定的定向效应分析 (EDA) 方案被用作简化策略,以识别非常复杂的水性基质(如医院废水)中存在的潜在未知化学物质。SET 生物测定法首次用于评估医院废水中的潜在有毒馏分,这些馏分是使用 C 和氨丙基色谱半制备柱进行两步分级后获得的。通过液相色谱与 Q Exactive Orbitrap 高分辨率质谱联用 (LC-HRMS) 并使用可疑分析方法鉴定有毒馏分中存在的未知化合物。通过气相色谱-质谱分析 (GC-MS) 补充结果,以鉴定样品和有毒馏分中存在的最广泛的化学化合物。使用 EDA 作为样品处理简化方法,在原始样品中检测到的未知化学物质数量(>446 个特征)减少到在显著有毒馏分中鉴定出的 94 个潜在有毒候选物。其中,25 种化合物的存在得到了可用化学标准品的证实,包括 14 种药物、一种个人护理产品、六种农药和四种工业产品。这项工作中的观察结果强调了在复杂水样中识别潜在毒性驱动因素的困难,就像在医院废水的情况下一样。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/de0621c4f8cb/molecules-28-01212-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/6a7a1edf0c62/molecules-28-01212-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/64749eb70d05/molecules-28-01212-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/bafc3e6c5a5c/molecules-28-01212-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/de0621c4f8cb/molecules-28-01212-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/6a7a1edf0c62/molecules-28-01212-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/64749eb70d05/molecules-28-01212-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/bafc3e6c5a5c/molecules-28-01212-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a347/9921743/de0621c4f8cb/molecules-28-01212-g004.jpg

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SETApp: A machine learning and image analysis based application to automate the sea urchin embryo test.SETApp:一种基于机器学习和图像分析的应用程序,用于自动化海胆胚胎测试。
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