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多重复合的微环境响应式探针使得质母细胞瘤细胞系的快速分析成为可能.

Qian Wu1, Yi Ren2, Guoyang Zhang3

  • 1State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China.

Analytical chemistry
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概括

精确的质母细胞瘤 (GBM) 细胞识别通过使用新的光学传感平台得到了改进. 这项技术分析瘤微环境 (TME) 以区分GBM细胞表型,帮助临床诊断.

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科学领域:

  • 生物医学工程 生物医学工程
  • 癌症生物学 癌症生物学
  • 分析化学 分析化学

背景情况:

  • 精确的质母细胞瘤 (GBM) 细胞系表型为临床诊断至关重要,但由于瘤异质性和传统方法的局限性,具有挑战性.
  • 瘤微环境 (TME) 显著影响瘤的进展,需要对其进行分析以精确地描述癌症.
  • 现有的诊断方法往往无法捕捉出区分GBM表型所必需的功能细胞变异.

研究的目的:

  • 开发一个多重光学传感平台,用于区分GBM细胞系.
  • 为了利用TME内的代谢和物理化学异质性,以增强细胞识别.
  • 将多参数传感与深度学习集成在一起,以便准确地对GBM细胞系进行分类.

主要方法:

  • 开发一个多重光学传感平台,集成五个对微环境敏感的光探针 (HBTPB,CTCYS,BDPI,KLVIS,BIDOH).
  • 同时监测关键的TME参数:过氧化,囊,过氧化,粘度和pH值.
  • 应用基于ResNet的深度学习模型来分析来自传感平台的多参数数据.

主要成果:

  • 该平台成功地区分了六种细胞系,包括四种表型多样化的GBM细胞系,正常的人类星球细胞和中枢神经系统瘤细胞系.
  • 对TME属性的多参数分析为不同的细胞系提供了不同的特征.
  • 与ResNet模型的整合实现了对测试细胞系的高度准确的识别.

结论:

  • 开发的多重光学传感平台为准确的GBM细胞系识别提供了一个有希望的方法.
  • 利用TME异质性与深度学习相结合,可以克服传统诊断方法的局限性.
  • 这项技术有可能改善质母细胞瘤的手术内评估和临床诊断.