Multiplex Sensor Platform for Biological Monitoring

用于生物监测的多重传感器平台

• 批准号: 9761919
• 负责人: Yuehe Lin
• 金额: $ 56.24万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9761919
• 关键词: Multiplex; Sensor; Platform; Biological; Monitoring

Project Abstract This research project directly support NIOSH research objectives focused on the “development of methods for measuring early markers of adverse health effects”, with a specific focus on novel technologies for quantitative non-invasive biomonitoring. More specifically, this project directly targets the needs of the Agricultural Industry Sector where NIOSH has identified pesticide exposure as a high research priority. In addition, the proposal specifically addresses the Cross Sector- Exposure Assessment Strategic Goal #2 that is focused on, the development and/or improvement of specific methods and tools to assess worker exposures to critical occupational agents and stressors. These NIOSH research objectives are also fully consistent with the vision of the National Research Council (NRC) which noted that exposure science will play a critical role in a new risk-based toxicity assessment framework. In this context, biomonitoring is a key component that quantitatively associates an internal dose with a measurable effect. It has also been suggested that epidemiology studies which accurately assess chemical exposures along with biological effects will have the most meaningful interpretation and thus maximal impact. However, a major impediment in conducting quantitative biomonitoring within epidemiology studies is the lack of rapid, sensitive, field deployable, quantitative technologies that measure chemical exposure/response biomarkers using minimally invasive biological fluids (i.e. saliva or drop of blood). Human exposure is rarely to single chemical agents but rather to complex chemical mixtures. This is particularly true in agriculture where multiple pesticides are utilized in combination and/or sequential application to crops. Due to the complex nature of occupational exposures and biological systems, measurement of a single biomarker may not provide adequate quantitative assessment of exposure. Hence, this project will undertake development, validation, and refinement of a novel multiplex sensor platform capable of quantifying multiple biomarkers associated with pesticide exposures (dose), utilizing saliva, blood or urine for biomonitoring. In addition, pharmacokinetic evaluations will be exploited to facilitate quantifying dosimetry. Hence, it is hypothesized that a multiplex biomarker sensor can be used to quantify mixed pesticide exposures (i.e. systemic dose) by measuring parent metabolites and cholinesterase (ChE) in biological samples. This project will specifically focus on the development of a multiplex sensor array for those pesticides and their metabolites that are known to be readily excreted in saliva and correlate with blood concentrations. Short-term outcomes of this research will include publication and presentation of research results; whereas, intermediate outcomes will focus on development/testing of a sensor platform with an eventual end outcome (beyond scope of current project) of using the sensor system for measuring and subsequently reducing worker exposure to pesticides. The development, validation and subsequent deployment of a multiplex sensor array platform as a quantitative tool to measure mixed pesticide occupational exposures is fully consistent with the goals of the NIOSH Research to Practice (r2P) initiative.

项目摘要 该研究项目直接支持NIOSH研究目标，该目标的重点是“发展 测量不良健康影响早期标记的方法”，特别关注 定量非侵入性生物监测的新技术。更具体地说，这个项目 直接针对NIOSH已确定的农业行业的需求 农药暴露是很高的研究重点。此外，该提案专门解决 跨部门 - 集中于 开发和/或改进特定方法和工具，以评估工人的暴露 关键的占用代理和压力源。这些NIOSH研究目标也完全 与国家研究委员会（NRC）的愿景一致，该委员会指出暴露 科学将在新的基于风险的毒性评估框架中发挥关键作用。在这个 上下文，生物监测是一个关键组件，将内部剂量定量关联 可测量的效果。还有人提出了精确的流行病学研究 评估化学暴露以及生物学作用将具有最有意义的 解释，从而最大程度地影响。但是，进行的主要障碍 流行病学研究中的定量生物监测是缺乏快速，敏感的领域 可部署的定量技术测量化学曝光/响应生物标志物 使用微创生物学流体（即唾液或血液滴）。人类暴露很少 用于单个化学剂，而不是复杂的化学混合物。在 在组合和/或顺序应用中使用多种农药的农业 农作物。由于职业暴露和生物系统的复杂性， 单个生物标志物的测量可能无法提供足够的定量评估 接触。因此，该项目将对小说进行开发，验证和完善 多重传感器平台能够量化与农药相关的多种生物标志物 暴露（剂量），使用唾液，血液或尿液进行生物监测。另外，药代动力学 将探索评估以促进量化剂量法。因此，假设 多重生物标志物传感器可用于量化混合农药暴露（即 全身剂量）通过测量生物学中的母体代谢产物和胆碱酯酶（CHE） 样品。该项目将特别关注多路复用传感器阵列的开发 这些农药及其代谢产物在唾液和 与血液浓度相关。这项研究的短期结果将包括 研究结果的出版和呈现；而中级结果将重点放在 具有最终最终结果的传感器平台的开发/测试（超出了 当前的项目）使用传感器系统测量和随后减少工人 暴露于农药。多路复用的开发，验证和随后的部署 传感器阵列平台作为测量混合农药职业暴露的定量工具 与NIOSH研究实践（R2P）倡议的目标完全一致。

项目成果

Translating nanoparticle dosimetry from conventional in vitro systems to occupational inhalation exposures.

• DOI: 10.1016/j.jaerosci.2021.105771
• 发表时间: 2021-06
• 期刊: JOURNAL OF AEROSOL SCIENCE
• 影响因子: 4.5
• 作者: Smith, Jordan Ned;Skinner, Andrew W.
• 通讯作者: Skinner, Andrew W.

Physiologically Based Pharmacokinetic Modeling of Salivary Concentrations for Noninvasive Biomonitoring of 2,4-Dichlorophenoxyacetic Acid (2,4-D).

基于生理学的唾液浓度药代动力学模型，用于 2,4-二氯苯氧基乙酸 (2,4-D) 的无创生物监测。

• DOI: 10.1093/toxsci/kfz206
• 发表时间: 2019
• 期刊: Toxicological sciences : an official journal of the Society of Toxicology
• 作者: Han,AliceA;Timchalk,Charles;Carver,ZanaA;Weber,ThomasJ;Tyrrell,KimberlyJ;Sontag,RyanL;Gibbins,Teresa;Chrisler,WilliamB;Weitz,KarlK;Du,Dan;Lin,Yuehe;Smith,JordanN
• 通讯作者: Smith,JordanN

Aptasensor based on fluorophore-quencher nano-pair and smartphone spectrum reader for on-site quantification of multi-pesticides.

• DOI: 10.1016/j.bios.2018.06.002
• 发表时间: 2018-10-15
• 期刊: Biosensors & bioelectronics
• 影响因子: 12.6
• 作者: Cheng N;Song Y;Fu Q;Du D;Luo Y;Wang Y;Xu W;Lin Y
• 通讯作者: Lin Y

A 3D-Printed, Portable, Optical-Sensing Platform for Smartphones Capable of Detecting the Herbicide 2,4-Dichlorophenoxyacetic Acid.

• DOI: 10.1021/acs.analchem.7b02139
• 发表时间: 2017-09-05
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: Wang Y;Zeinhom MMA;Yang M;Sun R;Wang S;Smith JN;Timchalk C;Li L;Lin Y;Du D
• 通讯作者: Du D

Nanozyme-Mediated Dual Immunoassay Integrated with Smartphone for Use in Simultaneous Detection of Pathogens.

• DOI: 10.1021/acsami.7b12734
• 发表时间: 2017-11-22
• 期刊: ACS applied materials & interfaces
• 影响因子: 9.5
• 作者: Cheng N;Song Y;Zeinhom MMA;Chang YC;Sheng L;Li H;Du D;Li L;Zhu MJ;Luo Y;Xu W;Lin Y
• 通讯作者: Lin Y