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用于农产品基质中快速检测的全自动测试平台的开发与评估/验证

Development and Evaluation/Verification of a Fully Automated Test Platform for the Rapid Detection of in Produce Matrices.

作者信息

Zhu Hui, Kim Beum Jun, Spizz Gwendolyn, Rothrock Derek, Yasmin Rubina, Arida Joseph, Grocholl John, Montagna Richard, Schwartz Brooke, Trujillo Socrates, Almeria Sonia

机构信息

Rheonix, Inc., Ithaca, NY 14850, USA.

Division of Virulence Assessment, Office of Applied Research and Safety Assessment, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, Laurel, MD 20708, USA.

出版信息

Microorganisms. 2023 Nov 19;11(11):2805. doi: 10.3390/microorganisms11112805.

Abstract

Cyclosporiasis, caused by the coccidian parasite , has emerged as an increasing global public health concern, with the incidence of laboratory-confirmed domestically acquired cases in the US exceeding 10,000 since 2018. A recently published qPCR assay (Mit1C) based on a mitochondrial target gene showed high specificity and good sensitivity for the detection of in fresh produce. The present study shows the integration and verification of the same mitochondrial target into a fully automated and streamlined platform that performs DNA isolation, PCR, hybridization, results visualization, and reporting of results to simplify and reduce hands-on time for the detection of this parasite. By using the same primer sets for both the target of interest (i.e., Mit1C) and the internal assay control (IAC), we were able to rapidly migrate the previously developed Mit1C qPCR assay into the more streamlined and automated format Rheonix C. cayetanensis Assay. Once the best conditions for detection were optimized and the migration to the fully automated format was completed, we compared the performance of the automated platform against the original "bench top" Mit1C qPCR assay. The automated Rheonix C. cayetanensis Assay achieved equivalent performance characteristics as the original assay, including the same performance for both inclusion and exclusion panels, and it was able to detect as low as 5 oocysts in fresh produce while significantly reducing hands-on time. We expect that the streamlined assay can be used as a tool for outbreak and/or surveillance activities to detect the presence of in produce samples.

摘要

由球虫寄生虫引起的环孢子虫病已成为全球日益关注的公共卫生问题,自2018年以来,美国实验室确诊的本土获得性病例发病率超过10000例。最近发表的一种基于线粒体靶基因的qPCR检测方法(Mit1C)对新鲜农产品中环孢子虫的检测具有高特异性和良好的敏感性。本研究展示了将相同的线粒体靶标整合并验证到一个全自动、简化的平台中,该平台可进行DNA提取、PCR、杂交、结果可视化以及结果报告,以简化并减少检测这种寄生虫的实际操作时间。通过对目标物(即Mit1C)和内部检测对照(IAC)使用相同的引物组,我们能够将先前开发的Mit1C qPCR检测方法快速迁移到更简化、自动化的Rheonix C. cayetanensis检测方法中。一旦优化了最佳检测条件并完成向全自动形式的迁移,我们将自动化平台的性能与原始的“台式”Mit1C qPCR检测方法进行了比较。自动化的Rheonix C. cayetanensis检测方法实现了与原始检测方法相当的性能特征,包括对包含和排除样本组的相同性能,并且能够在新鲜农产品中检测低至5个卵囊,同时显著减少实际操作时间。我们预计,这种简化的检测方法可作为一种工具,用于疫情爆发和/或监测活动,以检测农产品样本中环孢子虫的存在。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0c7/10673183/a494bcbe2d23/microorganisms-11-02805-g001.jpg

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