Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensor

基于深度学习的血清学测试，使用纸基多重传感器对 COVID-19 免疫力进行即时分析

• 批准号: 2149551
• 负责人: Aydogan Ozcan
• 金额: $ 39.32万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149551&HistoricalAwards=false
• 关键词: Deep; learning; based; serological; test

Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensorAbstract: COVID-19, caused by the virus SARS-CoV-2, was declared a pandemic by the World Health Organization (WHO) on March 12, 2020. Diagnostic testing has been a critical focus of the response, with an urgent need to rapidly develop, scale, and distribute new tests. Despite all the successful testing methods developed for the direct detection of SARS-CoV-2 genetic material, there is still an urgent need to create new serological assays that can detect virus-specific antibodies as they can ascertain complementary information to direct detection methods by indicating previous exposure and potential immunity, especially important due to various emerging variants. In addition, as vaccines against new variants roll out, these serological tests can be used to evaluate the efficacy of vaccination campaigns, including the ability to elicit SARS-CoV-2 and variant antigen-specific antibodies across vaccinated and unvaccinated populations. In contrast to the current direct detection methods, serology tests that detect antibodies can be low-cost and conducive to a point-of-care (POC) setting, enabling broad screening efforts like widespread immunity testing to indicate individuals in need of vaccine boosters, qualify individuals for travel, return to work, and/or identify convalescent plasma donors. To serve this urgent need, this project will create a smartphone-based, cost-effective platform that can sense and measure the many different antibodies specific to SARS-CoV-2 a person may develop, in a testing format that is easy to use and can be completed within 15 min using an inexpensive paper-based test. The team of researchers will develop a multiplexed POC immunoassay and serodiagnostic algorithm that will infer the vaccination/immunity status from up to 10 unique immunoreactions to distinguish an array of SARS-CoV-2 antibodies. For this, the research team will create a multiplexed vertical flow assay (xVFA) to simultaneously detect IgA, IgM, and IgG antibodies to the S protein (as well as variants of the S protein, such as delta, lambda, and other emerging variants), with separate immunoreaction sites dedicated to S-1, S-2, and the receptor-binding domain (RBD) of the S-protein in the SARS-CoV-2 virus and its most recent variants. Using existing and de-identified human serum samples, with the xVFA platform, the research team will screen COVID-19-positive samples, including those resulting from common variants (confirmed through reverse transcriptase-Polymerase Chain Reaction and sequencing) along with vaccinated samples and pre-pandemic un-vaccinated negative control samples. A neural network will then be trained using quantitative information from the multiplexed immunoreactions and the ground-truth clinical state over a set of remnant human serum samples. This training phase will (1) create a serodiagnostic algorithm to identify a positive immune response to SARS-CoV-2 infection (including common variants) or vaccination status using the multiplexed antibody measurements, and (2) identify the key subset of antibody-antigen interactions that most accurately represent and quantify an immune response to SARS-CoV-2 infection or protection via vaccination. A blinded testing phase will benchmark the performance enhancement of the multiplexed and data-driven approach to rigorously validate the trained inference network's generalization. By validating a new multiplexed vertical flow assay and serodiagnosis algorithm for COVID-19 immune protection, the research team aims to determine the significant improvements in sensitivity and specificity gained through the multiple measurements and computational analysis, which come with little added cost or operational steps, or required sample volume. This project will also establish a complementary educational outreach program that will involve (1) public interviews and popular science articles in news media and the internet; (2) undergraduate research opportunities involving underrepresented students; and (3) graduate student training through the organization of workshops, seminars and conferences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

基于纸质的多重传感器提取：由病毒SARS-COV-2引起的COVID-19对COVID-19免疫的高度学习血清学测试，该系统被世界卫生组织（WHO）宣布为2020年3月12日的大流行。尽管开发了用于直接检测SARS-COV-2遗传物质的所有成功测试方法，但仍有迫切需要创建新的血清学测定方法，可以检测病毒特异性抗体，因为它们可以通过表明先前的暴露和潜在的免疫来确定互补信息以直接检测方法，尤其重要，由于各种出现的变体，因此尤其重要。此外，随着针对新变体的疫苗推出，这些血清学测试可用于评估疫苗接种运动的疗效，包括引起SARS-COV-2的能力和跨疫苗接种和未经疫苗的种群的变体抗原特异性抗体的能力。与当前的直接检测方法相反，检测抗体的血清学测试可能是低成本和有利于护理点（POC）设置的，从而实现了广泛的筛查工作，例如广泛的免疫测试，以表明需要疫苗的人，需要促进疫苗的人，可以进行旅行，返回工作，返回或识别回访帕拉斯马帕拉斯玛帕拉斯马plasma plasma plasma donors。为了满足这一迫切需求，该项目将创建一个基于智能手机的，具有成本效益的平台，该平台可以以易于使用的测试形式感知和测量一个人可能开发的许多不同抗体SARS-COV-2，并且可以在15分钟内使用廉价的纸质测试在15分钟内完成。研究人员团队将开发多重的POC免疫测定和血清诊断算法，该算法将从最高10个独特的免疫反应来推断疫苗接种/免疫状态，以区分一系列SARS-COV-2抗体。 For this, the research team will create a multiplexed vertical flow assay (xVFA) to simultaneously detect IgA, IgM, and IgG antibodies to the S protein (as well as variants of the S protein, such as delta, lambda, and other emerging variants), with separate immunoreaction sites dedicated to S-1, S-2, and the receptor-binding domain (RBD) SARS-COV-2病毒及其最新变体中的S蛋白。研究团队使用现有和去识别的人血清样品，使用XVFA平台，将筛选CoVID-19阳性样品，包括由常见变体产生的样品（通过逆转录酶 - 聚合酶链条确认的样品）以及疫苗接种的样品以及pervicential的样品以及预抗疫苗的未疫苗的阴性对照样品。然后，将使用来自多重免疫反应的定量信息和一组残留的人血清样品中的地面临床状态对神经网络进行训练。该训练阶段将（1）使用多重抗体测量值创建一种血清诊断算法，以鉴定对SARS-COV-2感染（包括常见变体）或疫苗接种状态的阳性免疫反应，并且（2）确定最准确地代表和量化SARS-COV-2感染的免疫反应的抗体 - 抗原相互作用的关键子集。盲型测试阶段将基准提高多路复用和数据驱动方法的性能，以严格验证受过训练的推理网络的概括。通过验证新的多路复用垂直流程测定和CoVID-19免疫保护的血清诊断算法，研究小组旨在确定通过多次测量和计算分析获得的敏感性和特异性的显着提高，而多个测量和计算分析所带来的敏感性和特异性很少，而这些敏感性和特异性却很少增加成本或操作步骤或所需的样品体积。该项目还将建立一个互补的教育外展计划，该计划将涉及（1）新闻媒体和互联网中的公众访谈和流行的科学文章； （2）涉及代表性不足的学生的本科研究机会； （3）通过组织研讨会，研讨会和会议的研究生培训。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。