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Development of an imaging method for quantifying a large digital PCR droplet

机译:一种用于量化大型数字PCR液滴的成像方法的开发

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Portable devices have been recognized as the future linkage between end-users and lab-on-a-chip devices. It has a user friendly interface and provides apps to interface headphones, cameras, and communication duct, etc. In particular, the digital resolution of cameras installed in smartphones or pads already has a high imaging resolution with a high number of pixels. This unique feature has triggered researches to integrate optical fixtures with smartphone to provide microscopic imaging capabilities. In this paper, we report our study on developing a portable diagnostic tool based on the imaging system of a smartphone and a digital PCR biochip. A computational algorithm is developed to processing optical images taken from a digital PCR biochip with a smartphone in a black box. Each reaction droplet is recorded in pixels and is analyzed in a sRGB (red, green, and blue) color space. Multistep filtering algorithm and auto-threshold algorithm are adopted to minimize background noise contributed from ccd cameras and rule out false positive droplets, respectively. Finally, a size-filtering method is applied to identify the number of positive droplets to quantify target's concentration. Statistical analysis is then performed for diagnostic purpose. This process can be integrated in an app and can provide a user friendly interface without professional training.
机译:便携式设备已被识别为最终用户和实验室设备之间的未来联动。它具有用户友好的界面,并为接口耳机,相机和通信管道等提供应用。特别是,安装在智能手机或焊盘中的摄像机的数字分辨率已经具有具有大量像素的高成像分辨率。这种独特的功能触发了与智能手机集成光学夹具的研究,以提供微观成像功能。在本文中,我们报告了我们基于智能手机成像系统和数字PCR生物芯片开发便携式诊断工具的研究。开发了一种计算算法,用于处理从黑盒子中的智能手机从数字PCR Biochip拍摄的光学图像。每个反应液滴以像素的像素中记录,并以SRGB(红色,绿色和蓝色)颜色空间分析。采用多步骤过滤算法和自动阈值算法,以最大限度地减少从CCD摄像机贡献的背景噪声并分别排除误报液滴。最后,应用尺寸过滤方法以识别量化目标浓度的正液滴数。然后对诊断目的进行统计分析。此过程可以集成在应用中,可以提供用户友好的界面,无需专业培训。

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