Saliva-based COVID-19 detection: A rapid antigen test of SARS-CoV-2 nucleocapsid protein using an electrical-double-layer gated field-effect transistor-based biosensing system

被引:39
作者
Chen, Pin-Hsuan [1 ]
Huang, Chih-Cheng [2 ]
Wu, Chia-Che [2 ]
Chen, Po-Hsuan [2 ]
Tripathi, Adarsh [3 ]
Wang, Yu-Lin [1 ,2 ]
机构
[1] Natl Tsing Hua Univ, Dept Power Mech Engn, Hsinchu 300044, Taiwan
[2] Natl Tsing Hua Univ, Inst Nanoengn & Microsyst, Hsinchu 300044, Taiwan
[3] Natl Tsing Hua Univ, Inst Mol Med, Hsinchu 300044, Taiwan
关键词
Covid-19; SARS-CoV-2; Electrical double layer; Field-effect transistor-based biosensors; Rapid antigen tests;
D O I
10.1016/j.snb.2022.131415
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Facing the unstopped surges of COVID-19, an insufficient capacity of diagnostic testing jeopardizes the control of disease spread. Due to a centralized setting and a long turnaround, real-time reverse transcription polymerase chain reaction (real-time RT-PCR), the gold standard of viral detection, has fallen short in timely reflecting the epidemic status quo during an urgent outbreak. As such, a rapid screening tool is necessitated to help contain the spread of COVID-19 amid the countries where the vaccine implementations have not been widely deployed. In this work, we propose a saliva-based COVID-19 antigen test using the electrical double layer (EDL)-gated field-effect transistor-based biosensor (BioFET). The detection of SARS-CoV-2 nucleocapsid (N) protein is validated with limits of detection (LoDs) of 0.34 ng/mL (7.44 pM) and 0.14 ng/mL (2.96 pM) in 1x PBS and artificial saliva, respectively. The specificity is inspected with types of antigens, exhibiting low cross-reactivity among MERS-CoV, Influenza A virus, and Influenza B virus. This portable system is embedded with Bluetooth communication and user-friendly interfaces that are fully compatible with digital health, feasibly leading to an on-site turnaround, an effective management, and a proactive response taken by medical providers and frontline health workers.
引用
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页数:10
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