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Rapid and Quantitative Detection of Avian Influenza A(H7N9) Virions in Complex Matrices Based on Combined Magnetic Capture and Quantum Dot Labeling

Avian influenza A(H7N9) virus, which emerged in China in the spring of 2013, has infected hundreds of people and resulted in many deaths. Herein, a rapid and quantitative assay is proposed for the one‐step detection of H7N9 virions. Immunomagnetic nanospheres (IMNs) and antibody‐conjugated quantum dots (Ab‐QDs) are simultaneously employed to capture and identify the target virus, leading to a high efficiency, good specificity, and strong anti‐interference ability. Moreover, this reliable detection assay, which combines the efficient magnetic enrichment and the unique photophysical properties of QDs, can achieve a high sensitivity for a low detection limit. At the same time, this detection strategy shows great flexibility for employment in a variety of fluorescence detectors, including fluorescence spectrometry, microscope assays, and handheld UV lamp tests. Furthermore, our one‐step detection strategy induces very little change in the integrity of the vulnerable virions, which enables additional genotyping testing following the fluorescence detection. The present study, thus, reports a rapid and quantitative approach for the detection of H7N9 virions based on simultaneous magnetic capture and QD labeling, thereby providing a higher probability for detection and therefore faster diagnosis of H7N9‐infected patients.

A rapid, one‐step detection strategy for avian influenza A(H7N9) virions simultaneously uses magnetic nanospheres and quantum dots. This strategy shows a high efficiency, good specificity, excellent quantitative capacity, and strong anti‐interference ability. It demonstrates a flexibility for use with different fluorescence detection methods, including fluorescence spectrometry, microscope assays, and handheld UV lamps, possessing great application potential in point‐of‐care detection.

Authors:   Min Wu, Zhi‐Ling Zhang, Gang Chen, Cong‐Ying Wen, Ling‐Ling Wu, Jiao Hu, Chao‐Chao Xiong, Jian‐Jun Chen, Dai‐Wen Pang
Journal:   Small
Year:   2015
Pages:   n/a
DOI:   10.1002/smll.201403746
Publication date:   17-Aug-2015
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