Virus detection using nanoparticles and deep neural network-enabled smartphone system

使用纳米粒子和深度神经网络智能手机系统检测病毒

阅读:6
作者:Mohamed S Draz, Anish Vasan, Aradana Muthupandian, Manoj Kumar Kanakasabapathy, Prudhvi Thirumalaraju, Aparna Sreeram, Sanchana Krishnakumar, Vinish Yogesh, Wenyu Lin, Xu G Yu, Raymond T Chung, Hadi Shafiee

Abstract

Emerging and reemerging infections present an ever-increasing challenge to global health. Here, we report a nanoparticle-enabled smartphone (NES) system for rapid and sensitive virus detection. The virus is captured on a microchip and labeled with specifically designed platinum nanoprobes to induce gas bubble formation in the presence of hydrogen peroxide. The formed bubbles are controlled to make distinct visual patterns, allowing simple and sensitive virus detection using a convolutional neural network (CNN)-enabled smartphone system and without using any optical hardware smartphone attachment. We evaluated the developed CNN-NES for testing viruses such as hepatitis B virus (HBV), HCV, and Zika virus (ZIKV). The CNN-NES was tested with 134 ZIKV- and HBV-spiked and ZIKV- and HCV-infected patient plasma/serum samples. The sensitivity of the system in qualitatively detecting viral-infected samples with a clinically relevant virus concentration threshold of 250 copies/ml was 98.97% with a confidence interval of 94.39 to 99.97%.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。