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相关概念视频

The Tumor Microenvironment02:17

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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
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相关实验视频

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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在OSCC中以人工智能驱动的空间转录学:绘制瘤微环境和个性化治疗.

Soundharya Manogaran1, Ramya Ramadoss2, Suganya Panneer Selvam2

  • 1Department of Oral Biology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India.

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概括

空间转录学 (ST) 揭示了口腔状细胞癌 (OSCC) 组织中的基因表达模式. 这项技术与人工智能相结合,增强了OSCC诊断和治疗策略.

关键词:
人工智能的人工智能是人工智能.口腔状细胞癌 (OSCC) 是一种精确瘤学是一门精确的专业.空间转录组学 空间转录组学瘤微环境是一个微环境.

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科学领域:

  • 在瘤学瘤学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 空间转录学 (ST) 在组织架构中提供高分辨率的基因表达数据.
  • 口腔状细胞癌 (OSCC) 的瘤微环境 (TME) 是复杂的,可以从空间分析中获益.
  • 像大量RNA测序这样的传统方法掩盖了关键的空间异质性.

研究的目的:

  • 探索ST在理解OSCC中的应用.
  • 在OSCC中识别空间上不同的基因特征和预后标记.
  • 调查ST与AI的协同作用,以改善OSCC分析.

主要方法:

  • 使用了各种ST平台 (例如Visium,Slide-seqV2,MERFISH,GeoMx DSP,CosMx SMI,Stereo-seq) 进行测试.
  • 在瘤结构的背景下分析基因表达模式.
  • 集成的ST数据与人工智能 (AI) 和机器学习算法.

主要成果:

  • 确定了特定于OSCC亚型的空间区别的基因特征.
  • 通过空间基因表达分析发现了新的预后标志物.
  • 人工智能集成使得自动功能提取,集群和预测建模成为可能.

结论:

  • ST为OSCC的空间组织和TME提供了前所未有的见解.
  • ST和AI的结合在OSCC中具有显著的精确病理学潜力.
  • 这种协同作用可以彻底改变OSCC诊断,风险评估和个性化治疗.