SBIR Phase I: Combating Pathogens, Helios-1 Onsite Universal Detection

SBIR 第一阶段：对抗病原体，Helios-1 现场通用检测

• 批准号: 2304483
• 负责人: Darrell Marshall
• 金额: $ 27.5万
• 依托单位: TOTAL ANALYSIS L.L.C.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304483&HistoricalAwards=false
• 关键词: SBIR; Phase; Combating; Pathogens; Helios

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is protection against pathogen-related infections. Currently, detection tests for pathological agents are laborious, time-consuming, expensive, and require advanced technical expertise to conduct. The proposed portable, onsite pathogen detector will allow for fast, specific, sensitive, and cost-effective pathogen tests that can be conducted with minimal personnel training and equipment. The solution is intended to be used at healthcare centers, transport nodes, defense facilities, and any other site where the spread of infectious diseases is a possibility. This technology will benefit the population’s health and welfare, by facilitating the implementation of pathogen detection routines that reduce the risk of large-scale infections. Such infections disproportionally affect under-represented groups. The solution will also improve the national defense against bioterrorism, since the proposed technology could be as standard as a typical metal detector used in large, populated venues, on the battlefield protecting troops, or at airports to keep the traveling public safe. The nation’s economic competitiveness may also improve, since the proposed solution could mitigate and even avoid the economic consequences of a health crisis.The proposed project seeks to prove that Matrix Assisted Ionization can be coupled with Ion-Mobility Spectrometry (MAI-IMS) for pathogen detection and identification. The recent pandemic outbreak has demonstrated the necessity of rapid, on-site, and accurate pathogen detection devices. The proposed method is to use the existing IMS technology and modify it to detect pathogens by fabricating a Matrix assisted ionization vault (Helios-1) that overcomes the biomolecule volatility restriction of all current ion mobility spectrometers. A crucial technical hurdle is finding the device's optimal ionization and operational environment. To overcome this challenge, the most similar conditions to mass spectrometry must be found, which will involve experimental tests to determine the adequate environmental conditions and the engineering modifications of the MAI extension chamber to adapt IMS for non-volatile biomolecule detection. Standardize organism sample conditions and protocols are also needed. This challenge represents a critical step to prevent variation caused by the extraction of the sampling procedure. This challenge will be tackled by testing different extraction procedures until they meet the criteria for satisfactory performance. Additionally, machine learning algorithms will be employed for pathogen recognition. All of the above will help prove the feasibility of the proposed MAI-IMS-based pathogen detection and identification platform.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.

这项小型企业创新研究（SBIR）I期项目的广播/商业影响是保护与病原体相关感染的保护。当前，病理学药物的检测测试是实验室，耗时，昂贵的，并且需要进行高级技术专业知识。拟议的便携式病原体检测器将允许快速，特定，敏感且具有成本效益的病原体测试，可以通过最少的人员培训和设备进行。该解决方案旨在用于医疗保健中心，运输节点，国防设施以及任何其他传染病可能性传播的地点。这项技术将通过支持减少大规模感染风险的病原体检测程序的实施，从而使人口的健康和福利受益。这种感染会影响不足的人群。该解决方案还将改善国防对生物恐怖主义的防御，因为拟议的技术可能是在大型，人口稠密的场地，在战场上保护部队的典型金属探测器，或者在机场保护公众安全。国家的经济竞争力也可能会提高，因为拟议的解决方案可以减轻甚至避免健康危机的经济后果。拟议的项目试图证明，矩阵辅助电离可以与离子摩托车光谱法（MAI-IMS）相结合，以进行病原体检测和识别。最近的大流行暴发表明了快速，现场和准确的病原体检测装置的必要性。提出的方法是使用现有的IMS技术并通过制造矩阵辅助电离库（HELIOS-1）来修改它以检测病原体，从而克服了所有当前离子迁移率的生物分子挥发性限制。一个关键的技术障碍是找到设备的最佳电离和操作环境。为了克服这一挑战，必须找到与质谱法的最相似条件，这将涉及实验测试，以确定MAI扩展室的适当环境条件和工程修饰，以适应非挥发性生物分子检测。还需要标准化生物样品条件和方案。这项挑战代表了防止通过抽样程序提取引起的变化的关键步骤。该挑战将通过测试不同的提取程序来应对，直到符合满意度的标准为止。此外，将采用机器学习算法进行病原体识别。以上所有这些都将有助于证明拟议的基于MAIIM的病原体检测和识别平台的可行性。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来评估被认为是宝贵的支持。