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用于多重检测的微流控芯片中光子晶体珠阵列的图像解码

Image decoding of photonic crystal beads array in the microfluidic chip for multiplex assays.

作者信息

Yuan Junjie, Zhao Xiangwei, Wang Xiaoxia, Gu Zhongze

机构信息

1] State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China [2] Laboratory of Environment and Biosafety Research Institute of Southeast University in Suzhou, Suzhou 215123, China.

出版信息

Sci Rep. 2014 Oct 24;4:6755. doi: 10.1038/srep06755.

Abstract

Along with the miniaturization and intellectualization of biomedical instruments, the increasing demand of health monitoring at anywhere and anytime elevates the need for the development of point of care testing (POCT). Photonic crystal beads (PCBs) as one kind of good encoded microcarriers can be integrated with microfluidic chips in order to realize cost-effective and high sensitive multiplex bioassays. However, there are difficulties in analyzing them towards automated analysis due to the characters of the PCBs and the unique detection manner. In this paper, we propose a strategy to take advantage of automated image processing for the color decoding of the PCBs array in the microfluidic chip for multiplex assays. By processing and alignment of two modal images of epi-fluorescence and epi-white light, every intact bead in the image is accurately extracted and decoded by PC colors, which stand for the target species. This method, which shows high robustness and accuracy under various configurations, eliminates the high hardware requirement of spectroscopy analysis and user-interaction software, and provides adequate supports for the general automated analysis of POCT based on PCBs array.

摘要

随着生物医学仪器的小型化和智能化,随时随地进行健康监测的需求不断增加,这提升了即时检验(POCT)发展的必要性。光子晶体珠(PCBs)作为一种良好的编码微载体,可以与微流控芯片集成,以实现经济高效且高灵敏度的多重生物检测。然而,由于PCBs的特性和独特的检测方式,对其进行自动化分析存在困难。在本文中,我们提出了一种策略,利用自动化图像处理对微流控芯片中用于多重检测的PCBs阵列进行颜色解码。通过对落射荧光和落射白光两种模态图像的处理和对齐,图像中的每个完整珠子都能被准确提取并用代表目标物种的PC颜色进行解码。该方法在各种配置下都具有很高的稳健性和准确性,消除了光谱分析对硬件的高要求以及用户交互软件的需求,并为基于PCBs阵列的POCT通用自动化分析提供了充分支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a6b/4208063/fc36e9a7c185/srep06755-f1.jpg

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