4.7 Article

Dual-mode detection of avian influenza virions (H9N2) by ICP-MS and fluorescence after quantum dot labeling with immuno-rolling circle amplification

期刊

ANALYTICA CHIMICA ACTA
卷 1096, 期 -, 页码 18-25

出版社

ELSEVIER
DOI: 10.1016/j.aca.2019.10.063

关键词

H9N2 avian influenza virus; Rolling circle amplification; Quantum dot; ICP-MS; Dual mode detection

资金

  1. National Natural Science Foundation of China, China [21575107, 21575113, 21675118]
  2. Science Fund for Creative Research Groups of NSFC, China [20921062]
  3. Large-Scale Instrument and Equipment Sharing Foundation of Wuhan University, China [LF201912362019]

向作者/读者索取更多资源

Avian influenza virus (AIVs), hosted in poultry, are the pathogens of many poultry diseases and human infections, which bring huge losses to the poultry breeding industry and huge panic to society. Therefore, it is of great significance to establish accurate and sensitive detection methods for AIVs. In this work, a dual-mode detection method based on immuno-rolling circle amplification (immuno-RCA) and quantum dots (QDs) labeling for inductively coupled plasma mass spectrometry (ICP-MS) and fluorescence detection of H9N2 AIV was developed. The dual-mode detection of the QDs by ICP-MS and fluorescence is used to achieve mutual verification within the analysis results, thus improving the accuracy of the method. With the immuno-RCA, the sensitivity of the method was increased by two orders of magnitude. The limit of detection of the proposed method is 17 ng L-1 and 61 ng L-1, and the linear range of the proposed method is 0.05-5 ng mL(-1) and 0.1-5 ng mL(-1) with ICP-MS and fluorescence detection, respectively. The relative standard deviation (n = 7) is 4.9% with ICP-MS detection and 3.1% with fluorescence detection. Furthermore, the proposed method was applied to the analysis of chicken serum samples, no significant different was found for two modes detection and the recoveries of the spiking experiments are acceptable, indicating that the method has good practical potential for real sample analysis. (C) 2019 Elsevier B.V. All rights reserved.

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