Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Gene expression dynamics in wound healing: Comparative analysis between the wound edge and center.

PloS one·2026
Same author

Towards adaptive bioelectronic wound therapy with integrated real-time diagnostics and machine learning-driven closed-loop control.

npj biomedical innovations·2026
Same author

A multi therapy bioelectronic wound dressing.

npj biomedical innovations·2026
Same author

Correction: A data-driven approach to establishing cell motility patterns as predictors of macrophage subtypes and their relation to cell morphology.

PloS one·2026
Same author

A Flow Cytometry Workflow for Identifying Myeloid Subsets in Porcine Wound Tissue.

Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society·2026
Same author

Characterization of cutaneous wound healing in swine.

JID innovations : skin science from molecules to population health·2026
Same journal

A mechanistic study on the enhanced antihypertensive effects of a <i>Dendrobium officinale</i> compound combined with Western antihypertensive drugs in spontaneously hypertensive rats based on metabolomics and gut microbiota analysis.

Frontiers in cell and developmental biology·2026
Same journal

From pathophysiology to therapy: molecular mechanisms of stem cell and extracellular vesicle-mediated repair in diabetic peripheral neuropathy.

Frontiers in cell and developmental biology·2026
Same journal

The gut-brain axis: mechanisms linking intestinal dysbiosis with stroke.

Frontiers in cell and developmental biology·2026
Same journal

Towards a bioengineered airway: advances in tracheal tissue engineering and biofabrication.

Frontiers in cell and developmental biology·2026
Same journal

Targeting nuclear receptors in muscular dystrophies and regenerative myogenesis.

Frontiers in cell and developmental biology·2026
Same journal

Risk-centered benchmarking of large language models for AI-enabled counseling in chronic autoimmune thyroid eye disease.

Frontiers in cell and developmental biology·2026
查看所有相关文章

相关实验视频

Updated: Jul 2, 2025

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry
10:43

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry

Published on: June 22, 2017

24.0K

通过细胞迁移模式分析,对巨细胞亚型进行深度学习分类.

Manasa Kesapragada1, Yao-Hui Sun2, Ksenia Zlobina1

  • 1Department of Applied Mathematics, Baskin School of Engineering, University of California, Santa Cruz, Santa Cruz, CA, United States.

Frontiers in cell and developmental biology
|February 22, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个深度学习模型,该模型仅使用它们的运动轨迹来分类巨细胞亚型 (M0,M1,M2). 这种方法绕过了详细细胞形态的需要,为免疫细胞分析提供了一种新的方法.

关键词:
对巨细胞轨迹模式的分析.使用迁移模式对巨细胞亚型进行分类.细胞形状和轨迹之间的相关性.深度学习用于分类巨细胞亚型.巨细胞的两极分化

更多相关视频

Simultaneous Study of the Recruitment of Monocyte Subpopulations Under Flow In Vitro
09:16

Simultaneous Study of the Recruitment of Monocyte Subpopulations Under Flow In Vitro

Published on: November 26, 2018

7.0K
Polarization and Characterization of M1 and M2 Human Monocyte-Derived Macrophages on Implant Surfaces
08:37

Polarization and Characterization of M1 and M2 Human Monocyte-Derived Macrophages on Implant Surfaces

Published on: December 6, 2024

737

相关实验视频

Last Updated: Jul 2, 2025

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry
10:43

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry

Published on: June 22, 2017

24.0K
Simultaneous Study of the Recruitment of Monocyte Subpopulations Under Flow In Vitro
09:16

Simultaneous Study of the Recruitment of Monocyte Subpopulations Under Flow In Vitro

Published on: November 26, 2018

7.0K
Polarization and Characterization of M1 and M2 Human Monocyte-Derived Macrophages on Implant Surfaces
08:37

Polarization and Characterization of M1 and M2 Human Monocyte-Derived Macrophages on Implant Surfaces

Published on: December 6, 2024

737

科学领域:

  • 免疫学和细胞生物学
  • 计算生物学和生物信息学

背景情况:

  • 巨细胞是重要的免疫细胞,具有多种功能,包括由其激活状态决定的亲炎性和亲修复性作用.
  • 巨细胞激活状态与它们的动态行为和运动性有关,但仅基于运动来区分亚型是具有挑战性的.
  • 以前的方法利用形态学进行分类,但这项研究探索了一种基于轨迹的新方法.

研究的目的:

  • 开发和评估深度学习模型,使用原始轨迹数据对巨细胞亚型 (M0,M1,M2) 进行分类.
  • 调查轨迹模式的潜力,包括x-y坐标,行驶距离和净位移,用于亚型识别.
  • 探索基于形态的分类的替代方案,可能减少对高质量的成像的依赖.

主要方法:

  • 从巨细胞轨迹的时间序列x-y坐标数据上训练的深度学习模型的开发.
  • 包括运动指标,如行驶距离和净位移作为分类模型的特征.
  • 对巨细胞迁移模式的分析,以了解亚型之间的固有动态差异.

主要成果:

  • 深度学习模型成功地根据它们的轨迹模式分类了三种巨细胞亚型 (M0,M1,M2).
  • 细胞轨迹数据揭示了每个巨细胞亚型的复杂和独特的迁移动态.
  • 该模型证明了在不依赖形态特征的情况下识别巨细胞亚型的潜力.

结论:

  • 通过使用深度学习模型分析原始细胞轨迹数据,可以实现巨亚型的分类.
  • 这种基于轨迹的方法为依赖形态的方法提供了一个有希望的替代方案,可能简化实验要求.
  • 这些发现表明,在未来,即使使用较低质量的成像,也可以进行强大的巨亚型分析.