SBIR Phase I: Rapid, pre-symptomatic detection of COVID-19 and unknown viruses using a novel biosensor chip, computer vision and machine learning.

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是开发一种新型设备，用于在症状前环境中检测COVID-19及其衍生株和新的未知病毒。目前的方法依赖于了解病毒结构和/或产生针对特定已知病毒蛋白的抗体。该项目将提供症状前病毒检测，使用标准咽拭子或鼻拭子进行快速即时护理（20分钟），以先发制人地检测已知和未知来源的新发病毒感染。该项目将机器学习与新型设备相结合。这可能会缓解人们对社交距离的担忧，特别是在养老院和学校等特殊环境中。这个小企业创新研究（SBIR）一期项目采用细胞表型来检测细胞的病毒感染。细胞的表型是特定细胞的形态、功能和其他可观察到的特征，这些特征是由其当前基因组和蛋白质表达的特定组合产生的。该项目将一种新的计算机视觉算法与光刻制备的高度表征的微芯片基板结合起来，向细胞传递特定的、可量化的、物理的和化学的线索。这使得在体外观察他们的反应，定量表征细胞表型和检测有意义的细胞行为偏差或异常，由于细胞病毒感染的发作。该项目将展示：1)机器视觉图像分析算法提取最相关数据集的能力；2)检测感染发生时有意义的特征向量的能力。预期的技术成果是通过体外研究表征细胞表型，证明使用感染细胞表型特征来诊断传统光学显微镜下的病毒感染，并描述感染sars - cov -2样冠状病毒的细胞系的表型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a novel device to detect COVID-19, its derivative strains, and new, unknown viruses in pre-symptomatic environments. Current methods depend on knowing the viral structure and/or producing antibodies to specific, known viral proteins. This project will provide pre-symptomatic virus detection using a standard throat or nasal swab in a rapid point-of-care format (20 minutes) for preemptive detection of emerging viral infections of both known and unknown origin. The project integrates machine learning with novel devices. This could potentially alleviate social distancing concerns, particularly in special environments, such as nursing homes and schools. This Small Business Innovation Research (SBIR) Phase I project employs cellular phenotyping to detect viral infection of cells. A cell’s phenotype is the particular cell’s morphology, functionality and otherwise observable characteristics which result from the specific combination of its present genomic and protein expression. This project combines a novel computer vision algorithm with lithographically-prepared, highly-characterized microchip substrates that impart specific, quantifiable, physical, and chemical cues to cells. This enables in-vitro observation of their responses and to quantitatively characterize cell phenotype and detection of meaningful cell behavior deviations or anomalies due to the onset of cellular viral infection. This project will demonstrate: 1) the capability of a machine vision image analysis algorithm to extract the most relevant datasets; and 2) the ability to detect meaningful feature vectors due to infection onset. The anticipated technical results are to characterize cell phenotype through in-vitro studies, demonstrate the use of infected cell phenotype features to diagnose viral infections with traditional modes of light microscopy, and describe the phenotyping of cell lines infected with a SARS-CoV-2-like coronavirus.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.