Weak Value Amplification Based Optical Sensor for High Throughput Real-Time Immunoassay of SARS-CoV-2 Spike Protein
Autor: | Xiaonan Zhang, Lizhong Zhang, Han Li, Yang Xu, Lingqin Meng, Gengyu Liang, Bei Wang, Le Liu, Tian Guan, Cuixia Guo, Yonghong He |
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Jazyk: | angličtina |
Rok vydání: | 2024 |
Předmět: | |
Zdroj: | Biosensors, Vol 14, Iss 7, p 332 (2024) |
Druh dokumentu: | article |
ISSN: | 14070332 2079-6374 |
DOI: | 10.3390/bios14070332 |
Popis: | The demand for accurate and efficient immunoassays calls for the development of precise, high-throughput analysis methods. This paper introduces a novel approach utilizing a weak measurement interface sensor for immunoassays, offering a solution for high throughput analysis. Weak measurement is a precise quantum measurement method that amplifies the weak value of a system in the weak interaction through appropriate pre- and post-selection states. To facilitate the simultaneous analysis of multiple samples, we have developed a chip with six flow channels capable of conducting six immunoassays concurrently. We can perform real-time immunoassay to determine the binding characteristics of spike protein and antibody through real-time analysis of the flow channel images and calculating the relative intensity. The proposed method boasts a simple structure, eliminating the need for intricate nano processes. The spike protein concentration and relative intensity curve were fitted using the Log-Log fitting regression equation, and R2 was 0.91. Utilizing a pre-transformation approach to account for slight variations in detection sensitivity across different flow channels, the present method achieves an impressive limit of detection(LOD) of 0.85 ng/mL for the SARS-CoV-2 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, with a system standard deviation of 5.61. Furthermore, this method has been successfully verified for monitoring molecular-specific binding processes and differentiating binding capacities. |
Databáze: | Directory of Open Access Journals |
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