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Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis
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深度学习特拉赫兹单细胞代谢可行性研究

Ning Yang1, Qian Shi1, Mingji Wei1

  • 1School of Electrical Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.

ACS nano
|September 28, 2023
PubMed
概括

这项研究引入了一种使用太赫兹吸收光谱的新型细胞活力评估方法. 这种无标签,无接触的技术准确地测量单细胞代谢活动和细胞亡.

关键词:
吸收频谱中的吸收频谱.细胞亡是细胞的亡.细胞活力细胞的活力.深度学习是一种深度学习.机器学习是机器学习.频谱学是一种光谱学.太赫兹 (Terahertz) 是一个频率.

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科学领域:

  • 生物物理学的生物物理.
  • 细胞生物学 细胞生物学
  • 频谱学是一种光谱学.

背景情况:

  • 准确的细胞活力评估对于生物研究和药物开发至关重要.
  • 现有的方法往往缺乏精度或需要复杂的样本准备.

研究的目的:

  • 开发一种新的,准确的,非侵入性的方法来评估单细胞生存能力.
  • 为了将太赫兹吸收与细胞代谢状态和亡相关联.

主要方法:

  • 在没有培养基的情况下测量太赫兹激光束单细胞吸收率.
  • 使用卷积神经网络进行基于形态的细胞活力分类.
  • 建立一个量化模型,将太赫兹吸收率与细胞活力联系起来.

主要成果:

  • 太赫兹吸收测量准确地反映了单细胞代谢活动.
  • 太赫兹吸收率的变化与压力下的亡过程直接相关.
  • 提出了基于稳定的太赫兹吸收率的细胞生存能力的新定义.

结论:

  • 开发的太赫兹吸收光谱法为细胞活力评估提供了准确,无标签,无接触的方法.
  • 这种技术可以可视化细胞亡过程,使药物查和基础研究中的广泛应用成为可能.