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相关概念视频

T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
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Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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相关实验视频

Updated: Jun 7, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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通过细胞形态学检测和分类T细胞功能.

Xin Wang1, Stacey M Fernandes2, Jennifer R Brown2

  • 1Department of Biomedical Engineering, Columbia University, New York, NY.

BioMedInformatics
|November 11, 2024
PubMed
概括
此摘要是机器生成的。

细胞形态,特别是T细胞在弹性表面传播,可以表明免疫细胞的功能. 该方法将健康的T细胞与慢性淋巴细胞白血病 (CLL) 患者的健康T细胞区分开来,帮助免疫治疗.

关键词:
在 CLL CLL 中.在T细胞中,有机感知T细胞.T细胞形态学T细胞形态学免疫疗法 免疫疗法一个内在的状态.机器学习是机器学习.主要的人类T细胞.快速测量的快速测量周围环境 周围环境

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Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers
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科学领域:

  • 免疫学 免疫学 免疫学
  • 细胞生物学 细胞生物学
  • 生物物理学的生物物理.

背景情况:

  • 个体免疫细胞功能变异性对细胞免疫疗法提出了挑战.
  • 对有效的治疗策略来说,T细胞功能评估至关重要.

研究的目的:

  • 研究T细胞形态作为T细胞功能的可量化的指标.
  • 根据形态学来区分健康捐赠者和慢性淋巴细胞白血病 (CLL) 患者的T细胞.
  • 探索基质弹性的对T细胞行为的影响.

主要方法:

  • 量化T细胞在弹性表面的短期扩散,使用11个形态参数.
  • 分析了来自健康捐赠者和CLL患者的T细胞之间的形态变异.
  • 评估T细胞对具有不同弹性模块的基质的反应.
  • 采用机器学习算法 (决策树,随机森林) 来根据组合的形态特征对T细胞进行分类.

主要成果:

  • 鉴定出明显的形态特征,使T细胞与健康和CLL捐献者区别开来.
  • 观察到与基质弹性模量相关的T细胞扩散反应的差异.
  • 机器学习模型使用形态数据有效区分健康和CLLT细胞.

结论:

  • 在弹性表面上的T细胞形态作为T细胞功能的可靠指标.
  • 这种形态测量方法可以从健康和CLL个体中区分T细胞.
  • 进一步的开发可以实现快速的T细胞功能测定,以指导细胞免疫疗法.