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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是防止病原体相关感染。目前，对病理因子的检测测试是费力的、耗时的、昂贵的，并且需要先进的技术专长来进行。拟议的便携式现场病原体检测器将允许进行快速、特异、灵敏和具有成本效益的病原体检测，只需最少的人员培训和设备。该解决方案旨在用于医疗中心，运输节点，国防设施以及任何其他可能传播传染病的场所。这项技术将有利于人民的健康和福利，促进执行病原体检测程序，减少大规模感染的风险。 这种感染对代表性不足的群体产生了不良影响。 该解决方案还将改善国家对生物恐怖主义的防御，因为拟议的技术可能与大型人口密集场所，战场保护部队或机场保护旅行公众安全的典型金属探测器一样标准。 国家的经济竞争力也可能得到提高，因为所提出的解决方案可以减轻甚至避免健康危机的经济后果。拟议的项目旨在证明基质辅助电离可以与离子迁移谱（MAI-IMS）结合用于病原体检测和鉴定。最近的大流行爆发表明了快速、现场和准确的病原体检测设备的必要性。所提出的方法是使用现有的IMS技术，并通过制造基质辅助电离室（Helios-1）对其进行修改，以检测病原体，该电离室克服了所有当前离子迁移谱仪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，必须找到与质谱法最相似的条件，这将涉及实验测试以确定适当的环境条件和MAI扩展室的工程修改，以使IMS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。该挑战是防止取样程序提取引起变异的关键步骤。将通过测试不同的浸提程序来应对这一挑战，直到它们符合令人满意的性能标准。此外，机器学习算法将用于病原体识别。以上所有内容将有助于证明拟议的基于MAI-IMS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。