EAGER: Compact Field Portable Biophotonics Instrument for Real-Time Automated Analysis and Identification of Blood Cells Impact Impacted by COVID-19

COVID-19大流行迅速使发达经济体的医疗资源不堪重负，造成自1918年西班牙流感以来未见的大规模全球死亡。本项目拟利用低成本的自动化野外便携式生物光子学仪器研究COVID-19病毒对人体红细胞的影响。这些研究可以更好地了解受影响的血细胞，并精确测量细胞异常，从而潜在地早期发现COVID-19。使用与移动设备连接的紧凑型现场便携式生物光子学仪器，从血细胞中准确、快速、低成本地分析和诊断COVID-19，将是朝着广泛测试、医疗诊断、早期发现、疾病预防和相关数据收集的重大进步，特别是在无法获得专用医疗设施的偏远地区。提出的跨学科项目是基于实时分析和疾病检测的变革性生物光子学传感方法，并提供了传统的劳动和资源密集型生物分子方法的替代方案。这种分析和能力将使医学研究人员能够研究并进一步了解COVID-19感染对血细胞的影响。拟议的方法可能提供一种快速可靠的测试机制，具有广泛部署的潜力，这对于应对COVID-19等具有高感染率和死亡率的大流行病至关重要。该方法的成功将允许对COVID-19对血细胞的影响进行低成本、快速和高度准确的自动化评估，这是目前使用传统方法无法实现的。提出的研究提供了新的能力和优势，包括实时传感和诊断；具有高准确性、特异性和敏感性的早期检测，以及在资源不足的卫生保健系统中低成本的现场便携式部署，用于实时监测大流行。研究COVID-19对血细胞的影响，并在亚微米尺度上详细实时测量COVID-19引起的血细胞变化和异常，将为抗击COVID-19和未来的大流行提供宝贵的研究见解。该方法采用亚微米尺度的计算多维传感和成像来分析血细胞的形态和运动。与移动设备集成专门的嵌入式算法，实时分析血细胞的光生物学特征，寻找COVID-19影响和存在的潜在线索，实现快速（实时）COVID分析和检测。对感染细胞的测量和分析将在亚微米级横向分辨率和纳米级纵向分辨率下进行。该项目在紧凑的3d打印平台上使用高分辨率自参考数字全息技术，高精度地定量研究血细胞形态和时间运动性。将研究血细胞的空间结构、折射率、刚度和动态时间行为等多维生物光学特征数据，以了解COVID-19对血细胞的影响。使用与分析COVID-19引起的血细胞异常相关的专用机器学习算法旨在产生准确的检测和分析。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。

COVID-19 pandemic quickly overwhelmed the healthcare resources in even advanced economies with large scale global fatalities not seen since the Spanish Flu of 1918. This project intends to investigate the impact of the COVID-19 virus on human red blood cells using an automated low-cost, field portable bio-photonics instrument. These studies can lead to better understanding of the impacted blood cells and precise measurement of cell anomalies for potential early detection of COVID-19. Accurate, rapid, and low-cost analysis and diagnosis of COVID-19 from blood cells with a compact field portable bio-photonics instrument interfaced with mobile devices will be a substantial advance toward widespread testing, medical diagnosis, early detection, disease prevention, and relevant data collection, particularly in remote areas without access to dedicated healthcare facilities. The proposed cross disciplinary project is based on a transformative biophotonics sensing approach for real-time analysis and disease detection and offers an alternative to conventional labor- and resource- intensive bio-molecular approaches. This analysis and capability would enable medical researchers to study and gain increased understanding of the effects of COVID-19 infections on blood cells. The proposed approach may provide a fast and reliable testing mechanism with the potential for widespread deployment, which is critical in dealing with pandemics, such as COVID-19, with high rates of infection and mortality. The success of the proposed approach would allow for automated low cost, rapid and highly accurate assessment of the impact of COVID-19 on blood cells, which is not currently possible using conventional methods. The proposed research provides new capabilities and benefits including real-time sensing and diagnosis; early detection with high accuracy, specificity, and sensitivity, and low cost field portable deployment in under resourced healthcare systems for real-time monitoring of pandemics.Investigating the impact of COVID-19 on blood cells and making detailed real-time measurements of the COVID-19 induced changes and anomalies of the blood cells at sub-micron scales would provide valuable research insights to fight COVID-19 and future pandemics. The proposed approach employs computational multi-dimensional sensing and imaging at sub-micron scales to analyze morphology and motility of blood cells. Specially embedded algorithms are integrated with mobile devices to analyze opto-biological signatures of blood cells in real time to find potential clues to the impact and presence of COVID-19 for rapid (real-time) COVID analysis and detection. The measurements and analysis of the infected cells will be performed at sub-micron scale lateral resolution and nano scale longitudinal resolution. The proposed project investigates blood cells morphology and temporal motility quantitatively with high precision using high resolution self-referencing digital holographic in compact 3D-printed platforms. Multidimensional bio-optical signature data, including spatial structure, refractive index, stiffness, and dynamic temporal behavior of the blood cells will be investigated to understand the influence of COVID-19 in blood cells. The use of dedicated machine learning algorithms associated with the analysis of anomalies in blood cells due to COVID-19 are intended to produce accurate detection and analysis.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.