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相关概念视频

Immunocytochemistry and Immunohistochemistry01:22

Immunocytochemistry and Immunohistochemistry

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Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
These...
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Immunofluorescence Microscopy01:12

Immunofluorescence Microscopy

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A fluorescence microscope uses fluorescent chromophores called fluorochromes, which can absorb energy from a light source and then emit this energy as visible light. Fluorochromes include naturally fluorescent substances (such as chlorophylls) and fluorescent stains that are added to the specimen to create contrast. Dyes such as Texas red and FITC are examples of fluorochromes. Other examples include the nucleic acid dyes 4’,6’-diamidino-2-phenylindole (DAPI), and acridine orange.
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相关实验视频

Updated: Jan 8, 2026

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
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Immunocto:一个庞大的免疫细胞数据库,自动生成用于组织病理学.

Mikaë El Simard1, Zhuoyan Shen1, Konstantin Bräutigam2

  • 1Department of Medical Physics and Biomedical Engineering, University College London, London, UK.

Medical image analysis
|December 16, 2025
PubMed
概括

我们开发了一个新的工作流程,自动创建Immunocto,这是从癌症组织图像中生成的免疫细胞的大数据库. 该数据库有助于研究瘤免疫微环境 (TIME) 并预测治疗反应.

关键词:
计算病理学计算病理学数据库数据库数据库是一个数据库.免疫治疗是一种免疫疗法.淋巴细胞 淋巴细胞巨细胞是一个巨细胞.分段任何东西,任何东西.

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科学领域:

  • 计算病理学计算病理学
  • 癌症免疫学 癌症免疫学
  • 生物信息学是一种生物信息学.

背景情况:

  • 研究瘤免疫微环境 (TIME) 对癌症预后和免疫治疗反应至关重要.
  • 传统的时间分析方法是劳动密集型的,需要专门的专业知识.
  • 组织病理学图像的自动化分析为 TIME 特性提供了一个可扩展的解决方案.

研究的目的:

  • 开发一个自动化工作流程,从H&E和多重免疫光 (IF) 染色组织部分生成单细胞轮和标签.
  • 创建Immunocto,一个大规模的,公开可用的人类细胞数据库,用于TIME研究.
  • 为了证明Immunocto在训练免疫细胞检测深度学习模型中的实用性.

主要方法:

  • 利用分段任何模型 (SAM) 进行自动细胞细分和标签.
  • 组合H&E染色幻灯片与多重IF标记器用于双染色组织段.
  • 创建了一个超过68万个细胞的数据库,包括220万个免疫细胞在四个亚型.

主要成果:

  • 创建了Immunocto,一个庞大的数据库,包含6,848,454个细胞,其中有2,282,818个免疫细胞 (CD4 +,CD8 +,CD20 +,巨细胞).
  • 每个单元格条目包括一个64x64像素的H&E图像,核面具和标签.
  • 在Immunocto上训练的深度学习模型在淋巴细胞检测方面取得了最先进的性能.

结论:

  • 开发的工作流程能够从例行组织病理学幻灯片中强大,自动生成单细胞数据.
  • Immunocto为计算病理学提供了宝贵的资源,促进了TIME研究和模型培训.
  • 该方法强调了整合匹配的H&E和IF数据的好处,以推进癌症研究.