Project 1

项目1

• 批准号: 10349751
• 负责人: Erin S Baker
• 金额: $ 20.72万
• 依托单位: TEXAS A&M UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10349751
• 关键词: Address; Algorithms; Big Data; Characteristics; Chemical Exposure; Chemicals; Classification; Collaborations; Commerce; Complex; Computing Methodologies; Coupled; Data; Data Analyses; Databases; Defect; Development; Disasters; Emergency Situation; Environmental Engineering technology; Environmental Impact; Environmental Monitoring; Environmental Risk Factor; Environmental Science; Evaluation; Event; Exposure to; Funding; Grouping; Hazardous Chemicals; Hazardous Substances; Health; Human; In Vitro; Investigation; Libraries; Liquid Chromatography; Location; Machine Learning; Mass Spectrum Analysis; Measurement; Measures; Modeling; Molecular; Parents; Phase; Process; Property; Research; Resolution; Risk; Sampling; Sampling Studies; Science; Solid; Specific qualifier value; Spectrometry; Statistical Data Interpretation; Structure; Superfund; Testing; Time; Toxicology; Training; Translations; Water; Work; Xenobiotics; analytical method; base; chemical spill; cheminformatics; cloud based; community engagement; computerized data processing; data dissemination; data management; detection method; environmental chemical; experience; hands-on learning; in vivo; instrument; instrumentation; interest; ion mobility; machine learning algorithm; novel; organ on a chip; public database; response; screening; soil sampling; stem; tool; trend; volatile organic compound

Project 1 Abstract The comprehensive assessment of hazardous substances in complex environmental samples is essential in understanding the “environmental exposome” and identifying potential human health and environmental risks. Although targeted analyses are commonly used to measure between 10 and 100 specific substances per study, their precise parameters and limited coverage are not suitable for evaluating other potentially hazardous substances that may be present in the samples. This limitation has showcased the importance of untargeted measurements as hundreds of new chemicals are being introduced annually that need to be assessed. Since untargeted analyses can focus on all detected features, they are able to evaluate those with statistical significance between sample type and location, in addition to features with extremely high abundance. The information from the untargeted studies therefore provides the evaluation of novel and legacy hazardous substances in addition to their metabolites, intermediates and degradants which can be more hazardous than the parent compounds. However, untargeted measurements are greatly challenged by how to optimize instruments for broad characterization and then how to analyze all of the “big” data that are generated by the new analytical methods. Thus, both analytical and computational developments are necessary. By combining ion mobility spectrometry (IMS)-derived structural information, mass spectrometry (MS)-derived high-resolution m/z measurements and new data processing algorithms, we aim to create a uniform workflow for evaluation of complex environmental mixtures in the untargeted studies of samples obtained before, during and after environmental emergencies. To enable comprehensive analytical characterization, we will couple the multidimensional IMS-MS analyses with steps including sample concentration, extraction and liquid chromatography (LC) separations to allow an in-depth characterization of the mixtures. The information obtained from the untargeted IMS-MS and LC-IMS-MS studies will include molecular properties such as m/z, Kendrick Mass Defect (KMD), retention time (RT) and collision cross section (CCS). As these values have shown utility in targeted studies for molecular classification, they will be combined with our targeted library of >3,000 environmental chemicals from the past funding period and processed with cheminformatics and machine learning algorithms to annotate and classify the unknown features from the untargeted studies. We will also utilize both the targeted and untargeted studies to enable better disaster-related evaluation of potential chemical exposures by creating a list containing thousands of hazardous substances for rapid characterization with automated solid phase sample cleanup and IMS-MS. This automated SPE-IMS-MS platform will provide 10 s sample-to-sample throughput and when coupled with cloud-based data assessment, it will enable the rapid chemical analyses of complex environmental samples from disaster situations that may involve chemical spills.

项目1摘要 对复杂环境样品中有害物质的综合评估在 了解“环境暴露”，识别潜在的人类健康和环境风险。 尽管靶向分析通常用于每项研究测量10到100种特定物质， 它们精确的参数和有限的覆盖范围不适合于评估其他潜在的危险 样品中可能存在的物质。这一限制表明了无针对性的重要性 由于每年都有数以百计的新化学品被引入，需要对其进行评估。自.以来 无目标分析可以集中在所有检测到的要素上，他们能够评估那些具有统计意义的要素 除了具有极高丰度的特征外，样本类型和位置之间的重要性。这个 因此，来自非目标研究的信息提供了对新的和遗留的危险的评估 除了它们的代谢物、中间体和降解物之外的物质，这些物质可能比 母体化合物。然而，非目标测量受到如何优化的极大挑战 用于广泛表征的工具，然后如何分析由 新的分析方法。因此，分析和计算的发展都是必要的。通过组合 离子迁移率谱(IMS)衍生的结构信息，质谱学(MS)衍生的高分辨率 M/z测量和新的数据处理算法，我们的目标是创建一个统一的工作流程来评估 在对之前、期间和之后获得的样品进行非定向研究中的复杂环境混合物 环境紧急情况。为了实现全面的分析表征，我们将把 多维IMS-MS分析--包括样品浓缩、萃取和液体 层析(LC)分离，以便对混合物进行深入表征。所获得的信息 从非靶向的IMS-MS和LC-IMS-MS研究将包括分子性质，如m/z，Kendrick 质量缺陷(KMD)、保留时间(RT)和碰撞截面(CCS)。正如这些值在 分子分类的目标研究，它们将与我们的目标文库&>3,000结合在一起 过去资助期的环境化学品，用化学信息学和机器处理 学习算法对非目标研究中的未知特征进行标注和分类。我们还将 利用有针对性和无针对性的研究，更好地评估潜在化学品的灾害相关情况 通过创建包含数千种危险物质的列表来快速表征 自动固相样品净化和IMS-MS这个自动化的SPE-IMS-MS平台将为S提供10个 样本到样本吞吐量，当与基于云的数据评估相结合时，它将实现快速 对可能涉及化学品泄漏的灾害情况下的复杂环境样本进行化学分析。