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Fluorescence detection methods for microfluidic droplet platforms
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生物医学图像识别用于细胞封装的微流体液滴.

Xiao Zhou1, Yuanhang Mao1, Miao Gu1

  • 1Department of Automation, Tsinghua University, Beijing 100084, China.

Biosensors
|August 25, 2023
PubMed
概括

本研究介绍了一种微流体系统,使用弱监督的细胞计数网络 (WSCNet) 准确评估单细胞在滴滴中的封装. WSCNet系统有效地区分滴滴质量和细胞位置,以改进单细胞分析.

科学领域:

  • 生物技术是生物技术.
  • 微流体学 微流体学
  • 一个单细胞分析.

背景情况:

  • 微流体液滴对于单细胞分析至关重要,作为表型和基因型研究的独立微反应器.
  • 精确控制和监测每滴细胞的数量是一项挑战,特别是最大限度地减少多细胞封装.

研究的目的:

  • 开发和验证与弱监督细胞计数网络 (WSCNet) 集成的微流体系统,以评估微流体滴水质量.
  • 准确地识别滴体内封装细胞的位置,而不需要监督的位置数据.

主要方法:

  • 一个微流体系统的演示,其中包含WSCNet用于滴滴生成和质量评估.
  • 使用来自三个不同的微流体结构的微流体滴滴对WSCNet方法的系统验证.

主要成果:

  • 在区分滴滴封装 (F1分数>0.88) 和定位单个细胞 (精度>89%) 方面,WSCNet方法实现了高精度.
  • 该系统准确地预测了单细胞封装的概率,与被动方法有很强的一致性 (RSS < 0.5).

结论:

  • 开发的微流体系统为封装微流体液滴的定量评估提供了一个强大的平台.
  • 这种基于WSCNet的方法通过改善滴滴封装的控制和评估来提高单细胞分析的可靠性.
关键词:
卷积神经网络 (CNN) 是一种神经网络.滴滴微流体学 滴滴微流体学图像识别功能 图像识别功能一个单细胞封装封装.

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