There is still no epidemic surveillance system, implementing prevention and control measures is slow and inefficient. This study developed a rapid point-of-care (POC) testing device for detecting pathogens. Through multi-device networking, an early monitoring and warning system for major infectious diseases was developed. This study makes a contribution to the urgent need for a system to prevent and control the spread of epidemic diseases.
Early monitoring and warning arrangements are effective ways to distinguish infectious agents and control the spread of epidemic diseases. Current testing technologies, which cannot achieve rapid detection in the field, have a risk of slowing down the response time to the disease. In addition, there is still no epidemic surveillance system, implementing prevention and control measures is slow and inefficient. Motivated by these clinical needs, a sample-to-answer genetic diagnosis platform based on light-controlled capillary modified with a photocleavable linker is first developed, which could perform nucleic acid separation and release by light irradiation in less than 30 seconds. Then, on site polymerase chain reaction was performed in a handheld closed-loop convective system. Test reports are available within 20 min. Because this method is portable, rapid, and easy to operate, it has great potential for point-of-care testing. Additionally, through multiple device networking, a real-time artificial intelligence monitoring system for pathogens was developed on a cloud server. Through data reception, analysis, and visualization, the system can send early warning signals for disease control and prevention. Thus, anti-epidemic measures can be implemented effectively, and deploying and running this system can improve the capabilities for the prevention and control of infectious diseases.
Yu Fu†, Yan Liu†, Wenlu Song†, Delong Yang†, Wenjie Wu†, Jingyan Lin, Xiongtiao Yang, Jian Zeng, Lingzhi Rong, Jiaojiao Xia, Hongyi Lei*, Ronghua Yang*, Mingxia Zhang*, Yuhui Liao*
How to cite:
Y. Fu, Y. Liu, W. Song, D. Yang, W. Wu, J. Lin, X. Yang, J. Zeng, L. Rong, J. Xia, H. Lei, R. Yang, M. Zhang, Y. Liao, Exploration 2023, 20230028.