SBIR Phase I: Using patient specific DNA methylation to predict COVID-19 clinical prognosis

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现场临床测试，以筛选可能感染COVID-19的入院患者，并根据预测的感染严重程度和治疗反应优先考虑医院资源和人员。一项能够预测COVID-19感染严重程度的测试的好处是巨大的。除了数百万人感染和数十万人死亡之外，医院平均每天要为每位住院病人花费约2500美元。该项目将开发一种COVID-19筛查测试，以准确识别有风险的患者。这项小企业创新研究（SBIR）第一阶段项目旨在利用DNA甲基化模式对COVID-19进行个性化筛查和治疗。COVID-19进展的症状和结果的变化使卫生保健工作者难以准确分诊。开发基于DNA甲基化的测试来预测COVID-19感染的严重程度将有助于控制大流行。该项目将：1)生成COVID-19感染患者白细胞DNA甲基化模式、健康史和临床数据的综合数据集；2)建立COVID-19感染严重程度和治疗反应预测模型。该项目的预期技术成果是基于患者独特的DNA甲基化模式预测感染严重程度和治疗反应的测试方法和计算机算法。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 the development of an onsite, clinical test to screen incoming patients potentially infected with COVID-19 and prioritize hospital resources and personnel based on a predicted infection severity and treatment response. The benefits of a test that can predict COVID-19 infection severity are enormous. In addition to the millions of infections and hundreds of thousands of dealths, it costs hospitals an average of roughly $2,500 per day per patient for inpatient care. This project will develop a test for COVID-19 screening to accurately identify patients at risk. This Small Business Innovation Research (SBIR) Phase I project is establishing the use of DNA methylation patterns for personalized screening and treatment for COVID-19. The variation in symptoms and outcomes for COVID-19 progression make it challenging for healthcare workers to triage accurately. The development of a DNA methylation-based test to predict the severity of COVID-19 infection will help manage the pandemic. This project will: 1) generate a comprehensive dataset of white blood cell DNA methylation patterns, health history, and clinical data for patients infected with COVID-19; 2) generate a predictive model for COVID-19 infection severity and treatment response. The anticipated technical results of this project are a testing method and a computer algorithm for predicting infection severity and treatment response based on a patient’s unique DNA methylation pattern.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.