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Article

A Digital Microfluidic RT-qPCR Platform for Multiple Detections of Respiratory Pathogens

1
Zhuhai Center for Disease Control and Prevention, Zhuhai 519087, China
2
Digifluidic Biotech Ltd., Zhuhai 519000, China
3
Guangzhou Nansha IT Park Postdoctoral Programme, Guangzhou 511466, China
4
State Key Laboratory of Analog and Mixed-Signal VLSI, University of Macau, Macao 999078, China
5
College of Information Science and Technology, Jinan University, Guangzhou 510632, China
6
School of Intelligent Systems Science and Engineering/JNU-Industry School of Artificial Intelligence, Jinan University, Zhuhai 519000, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Micromachines 2022, 13(10), 1650; https://doi.org/10.3390/mi13101650
Submission received: 24 August 2022 / Revised: 24 September 2022 / Accepted: 27 September 2022 / Published: 30 September 2022

Abstract

The coronavirus disease 2019 pandemic has spread worldwide and caused more than six million deaths globally. Therefore, a timely and accurate diagnosis method is of pivotal importance for controlling the dissemination and expansions. Nucleic acid detection by the reverse transcription-polymerase chain reaction (RT-PCR) method generally requires centralized diagnosis laboratories and skilled operators, significantly restricting its use in rural areas and field settings. The digital microfluidic (DMF) technique provides a better option for simultaneous detections of multiple pathogens with fewer specimens and easy operation. In this study, we developed a novel digital microfluidic RT-qPCR platform for multiple detections of respiratory pathogens. This method can simultaneously detect eleven respiratory pathogens, namely, mycoplasma pneumoniae (MP), chlamydophila pneumoniae (CP), streptococcus pneumoniae (SP), human respiratory syncytial virus A (RSVA), human adenovirus (ADV), human coronavirus (HKU1), human coronavirus 229E (HCoV-229E), human metapneumovirus (HMPV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (FLUA) and influenza B virus (FLUB). The diagnostic performance was evaluated using positive plasmids samples and clinical specimens compared with off-chip individual RT-PCR testing. The results showed that the limit of detections was around 12 to 150 copies per test. The true positive rate, true negative rate, positive predictive value, negative predictive value, and accuracy of DMF on-chip method were 93.33%, 100%, 100%, 99.56%, and 99.85%, respectively, as validated by the off-chip RT-qPCR counterpart. Collectively, this study reported a cost-effective, high sensitivity and specificity on-chip DMF RT-qPCR system for detecting multiple respiratory pathogens, which will greatly contribute to timely and effective clinical management of respiratory infections in medical resource-limited settings.
Keywords: respiratory pathogens; RT-qPCR; digital microfluidic; on-chip respiratory pathogens; RT-qPCR; digital microfluidic; on-chip

Share and Cite

MDPI and ACS Style

Huang, H.; Huang, K.; Sun, Y.; Luo, D.; Wang, M.; Chen, T.; Li, M.; Duan, J.; Huang, L.; Dong, C. A Digital Microfluidic RT-qPCR Platform for Multiple Detections of Respiratory Pathogens. Micromachines 2022, 13, 1650. https://doi.org/10.3390/mi13101650

AMA Style

Huang H, Huang K, Sun Y, Luo D, Wang M, Chen T, Li M, Duan J, Huang L, Dong C. A Digital Microfluidic RT-qPCR Platform for Multiple Detections of Respiratory Pathogens. Micromachines. 2022; 13(10):1650. https://doi.org/10.3390/mi13101650

Chicago/Turabian Style

Huang, Huitao, Kaisong Huang, Yun Sun, Dasheng Luo, Min Wang, Tianlan Chen, Mingzhong Li, Junwei Duan, Liqun Huang, and Cheng Dong. 2022. "A Digital Microfluidic RT-qPCR Platform for Multiple Detections of Respiratory Pathogens" Micromachines 13, no. 10: 1650. https://doi.org/10.3390/mi13101650

APA Style

Huang, H., Huang, K., Sun, Y., Luo, D., Wang, M., Chen, T., Li, M., Duan, J., Huang, L., & Dong, C. (2022). A Digital Microfluidic RT-qPCR Platform for Multiple Detections of Respiratory Pathogens. Micromachines, 13(10), 1650. https://doi.org/10.3390/mi13101650

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