Yeo Seon-Ju, Choi Kyunghan, Cuc Bui Thi, Hong Nguyen Ngoc, Bao Duong Tuan, Ngoc Nguyen Minh, Le Mai Quynh, Hang Nguyen Le Khanh, Thach Nguyen Co, Mallik Shyam Kumar, Kim Hak Sung, Chong Chom-Kyu, Choi Hak Soo, Sung Haan Woo, Yu Kyoungsik, Park Hyun
1. Zoonosis Research Center, Department of Infection Biology, School of Medicine, Wonkwang University, Iksan, 570-749, Republic of Korea.
2. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 305-338, Republic of Korea.
Theranostics. 2016 Jan 1;6(2):231-42. doi: 10.7150/thno.14023. eCollection 2016.
Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflective light collection module using a coumarin-derived dendrimer-based fluorescent lateral flow immunoassay. By application of an optimized bioconjugate, a smartphone-based diagnostic device had a two-fold higher detectability as compared to that of the table-top fluorescence strip reader for three different AI subtypes (H5N3, H7N1, and H9N2). Additionally, in a clinical study of H5N1-confirmed patients, the smartphone-based diagnostic device showed a sensitivity of 96.55% (28/29) [95% confidence interval (CI): 82.24 to 99.91] and a specificity of 98.55% (68/69) (95% CI: 92.19 to 99.96). The measurement results from the distributed individual smartphones were wirelessly transmitted via short messaging service and collected by a centralized database system for further information processing and data mining. Smartphone-based diagnosis provided highly sensitive measurement results for H5N1 detection within 15 minutes. Because of its high sensitivity, portability and automatic reporting feature, the proposed device will enable agile identification of patients and efficient control of AI dissemination.
禽流感(AI)的现场诊断工具对于高致病性AI相关疾病的预防和控制管理至关重要。需要更准确、快速且联网的现场监测,以便高灵敏度地检测此类AI病毒,并掌握其地理传播的最新信息。在这项工作中,我们使用基于香豆素的树枝状大分子荧光侧向流动免疫分析法,评估了一款配备高效反射光收集模块的基于智能手机的荧光诊断设备在临床和现场层面的性能。通过应用优化的生物共轭物,对于三种不同的AI亚型(H5N3、H7N1和H9N2),基于智能手机的诊断设备的可检测性比台式荧光条读取器高出两倍。此外,在一项针对H5N1确诊患者的临床研究中,基于智能手机的诊断设备显示出96.55%(28/29)的灵敏度[95%置信区间(CI):82.24至99.91]和98.55%(68/69)的特异性(95%CI:92.19至99.96)。分布式个体智能手机的测量结果通过短消息服务进行无线传输,并由集中式数据库系统收集,以进行进一步的信息处理和数据挖掘。基于智能手机的诊断在15分钟内为H5N1检测提供了高灵敏度测量结果。由于其高灵敏度、便携性和自动报告功能,所提出的设备将能够快速识别患者并有效控制AI传播。