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相关实验视频

Updated: May 8, 2025

Manual Construction of a Tissue Microarray using the Tape Method and a Handheld Microarrayer
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人工智能辅助高通量组织微阵列工作流程

Konrad Kurowski1,2,3, Sylvia Timme1, Melanie Christine Föll1

  • 1Institute for Surgical Pathology, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany.

Methods and protocols
|December 27, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种高效的,人工智能辅助的工作流程,用于对大型患者队列的免疫组织化学 (IHC) 生物标志物研究. 高通量方法显著减少了瘤特征的时间和资源消耗.

关键词:
在这里,我们可以看到AIAIAI.FFPE组织组织.免疫组织化学 免疫组织化学组织微阵列.工作流的工作流.

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相关实验视频

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A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies
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科学领域:

  • 在瘤学瘤学.
  • 病理学 病理学 病理学
  • 生物标志物发现发现

背景情况:

  • 免疫组织化学 (IHC) 对于瘤瘤的特征和生物标志物识别至关重要.
  • 由于劳动密集型IHC工艺,存档的甲固定嵌入式 (FFPE) 样品未得到充分利用.
  • 需要高通量方法来分析大患者队列.

研究的目的:

  • 使用现代技术开发IHC生物标志物研究的高通量工作流程.
  • 为了促进大型患者群体和存档的FFPE样本的高效分析.
  • 为了将抗体表达与临床病理学数据和患者存活率相关联.

主要方法:

  • 从两个瘤患者队列中构建半自动组织微阵列 (TMA).
  • 针对七种抗体 (AB) 的免疫组织化学染色.
  • 用人工智能支持的图像分析 (QuPath) 来量化AB表达和与临床病理学数据的相关性,使用R-script.

主要成果:

  • 与整个幻灯片研究相比,工作流减少了92.3%的劳动时间.
  • 显著减少人力和物质资源的消耗.
  • 成功地将AB表达与患者整体生存率和临床病理学数据相关联,生成准备出版的报告.

结论:

  • 人工智能辅助的高通量TMA工作流简化了他的病理学研究.
  • 与传统的全幻灯片研究相比,这种方法提供了成本和时间的效率.
  • 它非常适合高通量研究中心和协作,保持研究质量,同时减少资源使用.