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CAREER: Rationale Design of Autonomous Biomimetic Wearable Sensor for Personalized Molecular Monitoring of Long COVID

CAREER: Rationale Design of Autonomous Biomimetic Wearable Sensor for Personalized Molecular Monitoring of Long COVID
职业:用于长新冠病毒个性化分子监测的自主仿生可穿戴传感器的基本原理设计
批准号:
2145802
负责人:
Wei Gao
金额:
$47.51万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-05-01 至 2027-04-30

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中文摘要
翻译
COVID-19大流行已经颠覆了数十亿人的生活,在全球蔓延至数亿病例。最近的研究表明,大量康复患者(即使是最初症状轻微或没有症状的患者)将出现长期的COVID后并发症(长期COVID)。据报道,我们体内的许多类型的生物标志物分子与这些后COVID状况密切相关。由于目前COVID-19大流行在世界各地仍然不受控制,迫切需要开发可穿戴传感器来监测个人在家的健康状况。目前,大多数医疗检查都是基于血液测试,需要侵入性抽血和物理诊所访问。人体汗液中含有丰富的化学物质,其中包括可以反映人体生理状态的化学物质。与血液测试相比,汗液分析可以作为日常生活中连续健康监测的非侵入性和更有吸引力的候选人。可穿戴汗液传感器可以允许个性化监测人们日常活动中与COVID长期相关的生物标志物。该项目旨在开发一种可穿戴的生物传感器平台,可以有效地提取和采样各种活动中的汗液,并对广谱生物标志物进行原位非侵入性分子分析,以实现对长期COVID的个性化分子监测。该项目将解决可穿戴传感器领域的多个研究挑战。 首先,为了实现与长期COVID相关的各种痕量水平汗液生物标志物的可穿戴监测,将基于人工生物受体开发独特的可穿戴分子传感策略。这种生物传感方法将优化计算和实验方法。其次,考虑到目前的可穿戴汗液传感器大多依赖于运动来获取汗液,不适合日常在家使用或久坐的个人,本项目将开发完全集成的微流体可穿戴系统,用于自主汗液感应,高效汗液采样和跨活动的原位多路复用分析。最后,将在长期COVID患者中进行可穿戴传感器的体内评估,以回顾性和前瞻性监测COVID后并发症。可穿戴汗液生物传感器持续收集的目前不可用的分子信息可能会对公共卫生产生深远的影响,并极大地促进我们对循环生物标志物在长期COVID中的作用的基本理解,以减少对后COVID并发症。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进行评估,被认为值得支持的搜索.
英文摘要
The COVID-19 pandemic has upended the lives of billions of people, spreading globally to hundreds of million cases. Recent studies show that a large number of recovered patients (even those with initially mild or no symptoms) will have prolonged post-COVID complications (long COVID). Many types of biomarker molecules in our body are reported to be closely linked to these post-COVID conditions. As the current COVID-19 pandemic remains uncontrolled around the world, there is a pressing need for developing wearable sensors to monitor an individual’s health status at home. Most of the medical examinations are currently based on blood tests that require invasive blood draws and physical clinic visits. Human sweat contains a wealth of chemicals including that can reflect the body’s physiological state. Compared with blood tests, sweat analyses could serve as a non-invasive and more attractive candidate for continuous health monitoring in daily life. Wearable sweat sensors could allow personalized monitoring of long-COVID-related biomarkers in people’s daily activities. Such technology could help people better understand post-COVID conditions and how to treat the patients with these longer-term effects.This project aims to develop a wearable biosensor platform that can efficiently extract and sample sweat across activities, and perform in situ, non-invasive molecular analysis of a broad spectrum of biomarkers toward personalized molecular monitoring of long COVID. This project will address multiple research challenges in the wearable sensor field. Firstly, to enable wearable monitoring of a variety of trace-level sweat biomarkers related to long COVID, a unique wearable molecular sensing strategy will be developed based on artificial bioreceptors. This biosensing approach will be optimized with both computational and experimental approaches. Secondly, considering that current wearable sweat sensors mostly rely on exercise to access sweat and are not suitable for daily at-home uses or for sedentary individuals, this project will develop the fully integrated microfluidic wearable system for autonomous sweat induction, efficient sweat sampling, and in situ multiplexed analysis across activities. Lastly, in vivo evaluation of the wearable senor will be performed in patients with long COVID for retrospective and prospective monitoring in post-COVID complications. The currently unavailable molecular information collected continuously by the wearable sweat biosensors could have a profound public health impact and greatly facilitate our fundamental understanding of the roles of circulating biomarkers in long COVID to reduce susceptibility to post-COVID complications.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.
期刊论文(14)
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会议论文
DOI: 10.1038/s41928-023-00996-y
发表时间: 2023-07-20
期刊: NATURE ELECTRONICS
影响因子: 34.3
作者: [Min, Jihong, Demchyshyn, Stepan, Gao, Wei]
通讯作者: Gao, Wei
DOI: 10.1038/s42256-023-00760-z
发表时间: 2023-12
期刊: Nature machine intelligence
影响因子: 23.8
作者: [Changhao Xu;Samuel A. Solomon;Wei Gao]
通讯作者: Changhao Xu;Samuel A. Solomon;Wei Gao
DOI: 10.1038/s41551-023-01059-5
发表时间: 2023-06
期刊: Nature Biomedical Engineering
影响因子: 28.1
作者: [J. Tu;Jihong Min;Yu Song;Changhao Xu;Jiahong Li;J. Moore;J. Hanson;Erin Hu;T. Parimon;T. Wang;Elham Davoodi;T. Chou;Peter Chen;J. Hsu;H. Rossiter;W. Gao]
通讯作者: J. Tu;Jihong Min;Yu Song;Changhao Xu;Jiahong Li;J. Moore;J. Hanson;Erin Hu;T. Parimon;T. Wang;Elham Davoodi;T. Chou;Peter Chen;J. Hsu;H. Rossiter;W. Gao
SCH: Bringing Intelligence to Pulmonology: New AI-Enabled Systems for Pulmonary Function Tests Anytime and Anywhere
  • 批准号:
    2205360
  • 项目类别:
    Standard Grant
  • 资助金额:
    $110.0万
  • 财政年份:
    2022
  • 负责人:
    Wei Gao
  • 依托单位:
Stress Modulated Phase Transition in 2D TMDC Materials
CAREER: Atomistic Investigation of Phase Transition in Nanostructured Silicon--Towards Convergent Understanding with Mechanics-Informed Machine Learning Potential
Collaborative Research: SHF: Small: Software Hardware Architecture Co-Design for Enabling True Virtual Reality on Mobile Devices
  • 批准号:
    2215042
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2022
  • 负责人:
    Wei Gao
  • 依托单位:
海外基金