Issue 21, 2022

Accurate, rapid and highly sensitive detection of African swine fever virus via graphene oxide-based accelerated strand exchange amplification

Abstract

African swine fever is an acute, severe and highly contagious infectious disease caused by African swine fever virus (ASFV), posing a huge threat to the global swine industry. Rapid and accurate diagnostic methods are of great significance for the effective prevention and control of ASFV transmission. In this work, we established and evaluated a graphene oxide-based accelerated strand exchange amplification (GO-ASEA) method for rapid, highly sensitive, and quantitative detection of ASFV. The use of GO provided a novel solution reference for improving the specificity of strand exchange amplification and solving the potential false positive problem caused by primer dimers. The detection limit of the GO-ASEA assay was 5.8 × 10−1 copies per μL of ASFV (equal to 2.9 copies per reaction) or 5.8 × 100 copies per μL of ASFV in spiked swine nasal swabs. The selectivity of the GO-ASEA assay was supported by the ASFV DNA reference material and another seven porcine-derived viruses with similar clinical symptoms. The GO-ASEA assay took only about 29 minutes and was validated with 6 inactivated specimens and 52 swine nasal swabs, showing excellent clinical applicability. The novel assay is an accurate and practical method for rapid, highly sensitive detection of ASFV, and can potentially serve as a robust tool in epidemic prevention and point-of-care diagnosis.

Graphical abstract: Accurate, rapid and highly sensitive detection of African swine fever virus via graphene oxide-based accelerated strand exchange amplification

Article information

Article type
Paper
Submitted
11 Apr 2022
Accepted
08 May 2022
First published
09 May 2022

Anal. Methods, 2022,14, 2072-2082

Accurate, rapid and highly sensitive detection of African swine fever virus via graphene oxide-based accelerated strand exchange amplification

L. Zhuang, J. Yang, C. Song, L. Sun, B. Zhao, Q. Shen, X. Ren, H. Shi, Y. Zhang and M. Zhu, Anal. Methods, 2022, 14, 2072 DOI: 10.1039/D2AY00610C

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