I-Corps: Multiplexed paper-based test for rapid diagnosis of early-stage Lyme Disease
I-Corps:用于快速诊断早期莱姆病的多重纸质测试
基本信息
- 批准号:2055749
- 负责人:
- 金额:$ 5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-03-01 至 2022-03-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The broader impact/commercialization potential of this I-Corps project is the development of a low-cost rapid diagnostic tool with the ability to measure multiple health markers in a single test. Development of this platform may benefit the healthcare system by improving the accessibility of diagnostic and prognostic testing that requires a panel of measurements. Specifically, there is potential for an early-stage Lyme disease (LD) diagnostic. Approximately 300,000 people contract LD each year, incurring a $1.3 billion cost to the medical system. Unfortunately, much of the societal and economic costs of LD stem from misdiagnosis due to the low sensitivity of the current Centers for Disease Control (CDC)-recommended testing protocol, which is typically 50% for the early-stage of disease, a time when many patients seek care. Therefore, the commercialization of an accurate and rapid test may provide a low-cost yet effective tool for identifying early-stage infection at the point-of-care, leading to faster and more effective treatment. The results from this potential I-Corps project also may benefit the diagnostics and medical community by demonstrating the value of a data-driven approach to multiplexed rapid testing.This I-Corps project is based on the development of a point-of-care diagnostic assay for early-stage Lyme disease (LD). Previously, the design and validation of a low-cost, paper-based vertical flow assay (VFA) was completed. Unlike standard rapid testing formats, the proposed VFA technology enables the point-of-care measurement (from patient blood) of a large panel of antibodies specific to the early-stage LD infection. A data-driven diagnostic algorithm is then used to infer a LD diagnosis from the panel of measurements, enabling sensitive and specific testing of suspected early-stage Lyme patients across a diverse population with varying immune responses. An initial peer-reviewed clinical study of the prototype assay and diagnostic algorithm reported sensitivity and specificity of 87.5% and 96.7%, respectively, when compared to a rigorous clinical gold standard using all early-stage LD samples. These initial technical results show potential for effective early-stage LD diagnosis at the point-of-care, enabling faster and more effective treatment that could mitigate debilitating late-stage symptoms and associated costs to the healthcare system.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.
I-Corps项目更广泛的影响/商业化潜力是开发一种低成本快速诊断工具,能够在一次测试中测量多种健康标志物。该平台的开发可以通过改善需要一组测量的诊断和预后测试的可访问性来使医疗保健系统受益。具体而言,有可能用于早期莱姆病(LD)诊断。每年约有30万人感染LD,给医疗系统带来13亿美元的成本。不幸的是,LD的大部分社会和经济成本源于误诊,这是由于目前疾病控制中心(CDC)推荐的检测方案的敏感性较低,对于疾病的早期阶段通常为50%,此时许多患者寻求护理。因此,商业化的准确和快速的测试可以提供一个低成本但有效的工具,用于识别早期感染的护理点,导致更快,更有效的治疗。这个潜在的I-Corps项目的结果也可能有利于诊断和医学界,通过展示数据驱动的多重快速检测方法的价值。这个I-Corps项目是基于早期莱姆病(LD)的即时诊断检测的开发。此前,已完成了低成本纸基垂直流检测(VFA)的设计和验证。与标准的快速检测格式不同,拟议的VFA技术能够对早期LD感染特异性抗体进行即时测量(从患者血液中)。然后使用数据驱动的诊断算法从测量组中推断LD诊断,从而能够在具有不同免疫反应的不同人群中对疑似早期莱姆病患者进行敏感和特异性检测。 原型检测和诊断算法的初步同行评审临床研究报告,与使用所有早期LD样本的严格临床金标准相比,灵敏度和特异性分别为87.5%和96.7%。这些初步的技术成果显示了在护理点进行有效的早期LD诊断的潜力,从而实现更快,更有效的治疗,从而减轻后期症状和医疗保健系统的相关成本。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。
项目成果
期刊论文数量(0)
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会议论文数量(0)
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Aydogan Ozcan其他文献
Deep Learning-designed Diffractive Materials for Optical Computing and Computational Imaging
用于光学计算和计算成像的深度学习设计的衍射材料
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
All-optical object classification through unknown phase diffusers using a single-pixel diffractive machine vision system
使用单像素衍射机器视觉系统通过未知相位漫射器进行全光学物体分类
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Yuhang Li;Bijie Bai;Yilin Luo;Ege Cetintas;Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
Volumetric fluorescence microscopy using convolutional recurrent neural networks
使用卷积循环神经网络的体积荧光显微镜
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Luzhe Huang;Yilin Luo;Y. Rivenson;Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
使用深度学习和金字塔采样对乳腺癌图像进行自动 HER2 评分
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Şahan Yoruç Selçuk;Xilin Yang;Bijie Bai;Yijie Zhang;Yuzhu Li;Musa Aydin;Aras Firat Unal;Aditya Gomatam;Zhen Guo;Morgan Angus Darrow;Goren Kolodney;Karine Atlan;T. Haran;N. Pillar;Aydogan Ozcan - 通讯作者:
Aydogan Ozcan
Super-Resolution Terahertz Imaging Through a Plasmonic Photoconductive Focal-Plane Array
通过等离子体光电导焦平面阵列进行超分辨率太赫兹成像
- DOI:
10.1364/cleo_si.2023.sm1n.2 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Xurong Li;Deniz Mengu;Aydogan Ozcan;M. Jarrahi - 通讯作者:
M. Jarrahi
Aydogan Ozcan的其他文献
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{{ truncateString('Aydogan Ozcan', 18)}}的其他基金
PFI-TT: A Rapid Multiplexed Diagnostic Tool for Serology of Tick-Borne Diseases
PFI-TT:蜱传疾病血清学快速多重诊断工具
- 批准号:
2345816 - 财政年份:2024
- 资助金额:
$ 5万 - 项目类别:
Continuing Grant
Biopsy-free, label-free 3D virtual histology of intact skin
完整皮肤的免活检、免标记 3D 虚拟组织学
- 批准号:
2141157 - 财政年份:2022
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensor
基于深度学习的血清学测试,使用纸基多重传感器对 COVID-19 免疫力进行即时分析
- 批准号:
2149551 - 财政年份:2022
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$ 5万 - 项目类别:
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EAGER: High-throughput early detection and analysis of COVID-19 plaque formation using time-lapse coherent imaging and deep learning
EAGER:使用延时相干成像和深度学习对 COVID-19 斑块形成进行高通量早期检测和分析
- 批准号:
2034234 - 财政年份:2020
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
EAGER: All-Optical Information Processing Device for Seeing Through Diffusers at the Speed of Light
EAGER:以光速透过漫射器的全光学信息处理装置
- 批准号:
2054102 - 财政年份:2020
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
NSF EAGER: DEEP LEARNING-BASED VIRTUAL HISTOLOGY STAINING OF TISSUE SAMPLES
NSF EAGER:基于深度学习的组织样本虚拟组织学染色
- 批准号:
1926371 - 财政年份:2019
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
PFI:BIC Human-Centered Smart-Integration of Mobile Imaging and Sensing Tools with Machine Learning for Ubiquitous Quantification of Waterborne and Airborne Nanoparticles
PFI:BIC 以人为中心的移动成像和传感工具与机器学习的智能集成,可实现水性和空气性纳米粒子的普遍定量
- 批准号:
1533983 - 财政年份:2015
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
EAGER: Mobile-phone based single molecule imaging of DNA and length quantification to analyze copy-number variations in genome
EAGER:基于手机的 DNA 单分子成像和长度定量分析基因组中的拷贝数变异
- 批准号:
1444240 - 财政年份:2014
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
EFRI-BioFlex: Cellphone-based Digital Immunoassay Platform for High-throughput Sensitive and Multiplexed Detection and Distributed Spatio-Temporal Analysis of Influenza
EFRI-BioFlex:基于手机的数字免疫分析平台,用于流感的高通量灵敏多重检测和分布式时空分析
- 批准号:
1332275 - 财政年份:2013
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
CAREER: A new Telemedicine Platform using Incoherent Lensfree Cell Holography and Microscopy On a Chip
事业:使用非相干无透镜细胞全息术和芯片显微镜的新型远程医疗平台
- 批准号:
0954482 - 财政年份:2010
- 资助金额:
$ 5万 - 项目类别:
Standard Grant
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