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A sensitive and quantitative immunochromatographic assay for HBsAg based on novel red silica nanoparticles

Abstract

An immunochromatographic assay (ICA) based on red silica nanoparticles (red-SiNPs) was developed for quantitative detection of hepatitis B surface antigen (HBsAg) in serum. The red SiNPs have unique optical properties and remarkable stability compared to the red latex microspheres. Red-SiNPs functionalized with anti-HBsAg mAb1 were employed as detection immuno-probes. The sandwich-type immunoreaction structures were performed on the ICA strips, and the accumulation of red SiNPs-mAb1 on the test line produced the characteristic red color bands, enabling visual detection of HBsAg without instrumentation. The quantitative detection was performed by researching the relationship between the HBsAg concentration and the corresponding peak area of gray value of test line with a camera and image analysis software Image J. Under optimal conditions, the visual limit of detection (vLOD) of the strip for the qualitative detection of HBsAg is 1 ng/mL, which is lower than that of the routinely-used red latex microspheres-based ICA. In the quantitative detection, the peak area of gray value of the test line and the HBsAg concentration show a good linearity when the HBsAg concentration is in the range of 5~500 ng/mL. The calibration equation was y = 370.6118 + 9.6673x (R2 = 0.9962) and the limit of detection (LOD) of the method was 0.97 ng/mL (S/N = 3). Our proposed method provides promising and versatile opportunities for the rapid and quantitative screening of other proteins at relatively sensitive levels by combining the colored label with ICA.

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Supplementary files

Publication details

The article was received on 26 Oct 2018, accepted on 29 Nov 2018 and first published on 30 Nov 2018


Article type: Paper
DOI: 10.1039/C8AY02348D
Citation: Anal. Methods, 2018, Accepted Manuscript
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    A sensitive and quantitative immunochromatographic assay for HBsAg based on novel red silica nanoparticles

    Q. Yu, J. Liu, W. Dou, G. Zhao, S. Yang, D. Zhu, Y. Zhao and L. Li, Anal. Methods, 2018, Accepted Manuscript , DOI: 10.1039/C8AY02348D

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