Excellence in Research’: Mechanistic Modelling and Validation Approaches to Decontaminate (+) ssRNA Viruses using Ultra-Violet Technologies

卓越研究 –：使用紫外线技术净化 ( ) ssRNA 病毒的机​​械建模和验证方法

• 批准号: 2200683
• 负责人: Ankit Patras
• 金额: $ 45.34万
• 依托单位: Tennessee State University
• 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: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200683&HistoricalAwards=false
• 关键词: Excellence; Research; Mechanistic; Modelling; Validation

The ongoing COVID-19 pandemic has highlighted the need for efficient and cost effective technologies and solutions to mitigate and prevent the spread and transmission of pathogens with pandemic potential in the built environment including residential buildings, schools, and healthcare facilities. Many positive strand RNA (+ssRNA) viruses, which include coronaviruses and noroviruses, are pathogenic to humans and animals. These viruses can survive in air, aqueous dispersions, and on surfaces. Ultraviolet germicidal systems emitting UV-C radiation (200-280 nm in wavelength) have been proven effective at inactivating viral pathogens attached to various media and substrates including respiratory aerosols, aqueous dispersions, and non-porous surfaces. However, a fundamental understanding of the UV-C sensitivity of ssRNA viruses and their extents, rates, and mechanisms of inactivation by germicidal UV-C radiation has remained elusive. The overarching goal of this project is to develop and validate a genomic-based model that could be used to 1) estimate the UV-C sensitivity of pathogenic ssRNA viruses and 2) predict the UV-C doses and exposure times required to inactivate such viruses in aqueous dispersions and surfaces. The successful completion of this project will benefit society through the generation of fundamental knowledge and validated modeling tools to guide the design of more efficient UV-C disinfection systems and protocols to mitigate and prevent the spread and transmission of pathogenic ssRNA viruses in the built environment and in communities during future disease outbreaks and epidemics. Additional benefits to society will be achieved through outreach and educational activities including the mentoring of a post-doctoral research associate, two graduate students, and four undergraduate students at Tennessee State University and one graduate student at the University of Tennessee, Knoxville.The effectiveness of UV-C irradiation at inactivating viral pathogens in a given medium depends on several factors including viral concentration, radiation dose and exposure time, and the virus UV-C susceptibility (D90) defined as the required radiation dose (Joule/m2) to inactivate 90% of the viral pathogens. However, measured UV-C susceptibility data for many positive strand RNA (+ssRNA) viruses that are pathogenic to humans and animals are not available due to the unique challenges associated with the collection of experimental data on viral pathogens including the need for biosafety level-3 (BSL-3) facilities and a specialized and highly trained workforce. The goal of this project is to develop and validate a mathematical model (based on viral genomic parameters such as genome size and pyrimidine dinucleotide frequency) that could be used to predict the UV-C susceptibility of pathogenic +ssRNA viruses in air, aqueous dispersions, and onto surfaces at standard ambient conditions. The specific objectives of the research include: 1) Collection and/or generation of UV-C susceptibility data for a set of model ssRNA viruses for model calibration and validation; 2) Development and validation of quantitative structure activity relationships (QSARs) to predict wavelength specific UV-C susceptibility of +ssRNA viruses by analyzing the correlations between genome parameters (genome size, sequence-based pyrimidine dinucleotide frequency value, Trp and Tyr content) and the UV-C susceptibility data that were collected in Objective 1 ; and 3) Development and implementation of a Java computer interface that utilizes the QSARs developed in Objective 2 to estimate the UV-C radiation dose and exposure time required to inactive +ssRNA viruses in air, aqueous dispersions, and onto surfaces. The successful completion of this project has the potential for transformative impact through the generation of fundamental knowledge, data and modeling tools to guide the design of more efficient, and cost-effective UV-C viral inactivation and disinfection systems and protocols. To implement the education and outreach activities of the project, the Principal Investigators (PIs) plan to integrate the findings from this research into existing undergraduate and graduate courses at Tennessee State University, the University of Tennessee, Knoxville, and Meharry Medical College to provide students with new learning modules with a focus on technologies, solutions, and modeling tools that could guide the mitigation and prevention of the spread and transmission of viral pathogens in the built environment and the food industry.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.

持续的Covid-19大流行强调了需要高效且具有成本效益的技术和解决方案，以减轻和防止在建筑环境中具有大流行潜力的病原体的传播和传播，包括住宅建筑，学校和医疗设施。包括冠状病毒和诺病毒在内的许多阳性链RNA（+SSRNA）病毒对人类和动物都是致病性的。这些病毒可以在空气，水分散体和表面上生存。紫外线系统发射UV-C辐射（波长200-280 nm）已被证明有效地在各种培养基和底物上灭活病毒病原体，包括呼吸道气溶胶，水溶液，水性分散体和非孔表面。然而，对SSRNA病毒的UV-C敏感性的基本理解及其通过杀菌UV-C辐射失活的范围，速率和机制仍然难以捉摸。该项目的总体目标是开发和验证一个基于基因组的模型，该模型可用于1）估计致病性ssRNA病毒的UV-C敏感性，2）预测在水性分散体和表面中灭活此类病毒所需的UV-C剂量和暴露时间。该项目的成功完成将通过产生基本知识和经过验证的建模工具来使社会受益，以指导更有效的UV-C消毒系统和协议的设计，以减轻和防止在未来疾病爆发和流行病中，在建筑环境和社区中的致病性SSRNA病毒的传播和传播。将通过宣传和教育活动来实现社会的其他好处易感性（D90）被定义为所需的辐射剂量（Joule/M2），使其灭活90％的病毒病原体。但是，由于与对病毒病原体的实验数据相关的独特挑战，包括生物安全级别3（BSL-3）设施以及专业且训练有素的工作人员的需要，对人类和动物具有致病性的许多阳性链RNA（+SSRNA）病毒的测量UV-C易感性数据无法获得。该项目的目的是开发和验证一种数学模型（基于病毒基因组参数，例如基因组大小和嘧啶二核苷酸频率），可用于预测空气，水性分散体中的致病性 +ssRNA病毒在标准环境条件下的表面上的uv-c-c易感性。研究的具体目标包括：1）用于模型校准和验证的一组模型SSRNA病毒的UV-C易感性数据的收集和/或生成； 2）通过分析基因组参数（基因组大小，基于序列的基于序列的基于序列的嘧啶二核苷酸频率，TRP和TYR含量）和在目标1中收集的UV-C Commitibility Data的相关性，对定量结构活性关系（QSAR）的开发和验证以预测 +SSRNA病毒的波长特异性UV-C易感性。 3）使用目标2中开发的QSAR的Java计算机界面的开发和实施来估计空气，水分散体和表面上不活动 +SSRNA病毒所需的UV-C辐射剂量和暴露时间。通过基本知识，数据和建模工具的产生，该项目的成功完成可能会产生变革性影响，以指导设计更有效，具有成本效益的UV-C病毒灭活和消毒系统和协议的设计。为了实施该项目的教育和外展活动，首席研究人员（PIS）计划将这项研究的发现整合到田纳西州立大学，田纳西州立大学，田纳西大学，诺克斯维尔大学和梅哈里医学院的现有本科和研究生课程中该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准评估来反映NSF的法定任务。

项目成果

UV-C inactivation of microorganisms in droplets on food contact surfaces using UV-C light-emitting diode devices

使用 UV-C 发光二极管装置对食品接触表面液滴中的微生物进行 UV-C 灭活

• DOI: 10.3389/frfst.2023.1182765
• 发表时间: 2023
• 期刊: Frontiers in Food Science and Technology
• 作者: Sharma, Aakash;Mahmoud, Housyn;Pendyala, Brahmaiah;Balamurugan, Sampathkumar;Patras, Ankit
• 通讯作者: Patras, Ankit