Waldstein Kody A, Yi Jirong, Cho Michael Myung, Mudumbai Raghu, Wu Xiaodong, Varga Steven M, Xu Weiyu
Interdisciplinary Graduate Program in Immunology, University of Iowa, Iowa City, IA 52242, USA.
Department of Microbiology and Immunology, University of Iowa, Iowa City, IA 52242, USA.
medRxiv. 2021 Aug 10:2021.08.09.21261669. doi: 10.1101/2021.08.09.21261669.
The rapid spread of SARS-CoV-2 has placed a significant burden on public health systems to provide rapid and accurate diagnostic testing highlighting the critical need for innovative testing approaches for future pandemics. In this study, we present a novel sample pooling procedure based on compressed sensing theory to accurately identify virally infected patients at high prevalence rates utilizing an innovative viral RNA extraction process to minimize sample dilution. At prevalence rates ranging from 0-14.3%, the number of tests required to identify the infection status of all patients was reduced by 75.6% as compared to conventional testing in primary human SARS-CoV-2 nasopharyngeal swabs and a coronavirus model system. Additionally, our modified pooling and RNA extraction process minimized sample dilution which remained constant as pool sizes increased. Our use of compressed sensing can be adapted to a wide variety of diagnostic testing applications to increase throughput for routine laboratory testing as well as a means to increase testing throughput to combat future pandemics.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的迅速传播给公共卫生系统带来了巨大负担，需要提供快速准确的诊断检测，这凸显了未来应对大流行时创新检测方法的迫切需求。在本研究中，我们基于压缩感知理论提出了一种新型样本合并程序，利用创新的病毒RNA提取过程，在高流行率下准确识别病毒感染患者，以尽量减少样本稀释。在0-14.3%的流行率范围内，与传统检测相比，在原发性人类SARS-CoV-2鼻咽拭子和冠状病毒模型系统中，确定所有患者感染状态所需的检测次数减少了75.6%。此外，我们改进的合并和RNA提取过程将样本稀释降至最低，且随着合并样本量的增加，稀释度保持恒定。我们对压缩感知的应用可适用于各种诊断检测应用，以提高常规实验室检测的通量，并作为提高检测通量以应对未来大流行的一种手段。
