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相关概念视频

Oogenesis02:07

Oogenesis

64.3K
In human women, oogenesis produces one mature egg cell or ovum for every precursor cell that enters meiosis. This process differs in two unique ways from the equivalent procedure of spermatogenesis in males. First, meiotic divisions during oogenesis are asymmetric, meaning that a large oocyte (containing most of the cytoplasm) and minor polar body are produced as a result of meiosis I, and again following meiosis II. Since only oocytes will go on to form embryos if fertilized, this unequal...
64.3K

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相关实验视频

Updated: Sep 8, 2025

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

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机器学习方法来描述小鼠和人类卵子细胞的特征.

Gaëlle Letort1

  • 1Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, Paris, France. gaelle.letort@pasteur.fr.

Methods in molecular biology (Clifton, N.J.)
|July 30, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种机器学习管道,用于使用传输光成像来非侵入性表型化卵子. 该系统通过机器学习算法提取形态和动态特征来表征卵细胞.

关键词:
功能 功能 功能 功能机器学习 机器学习形态学 形态学 形态学卵细胞是卵细胞中的一个.分段化 分段化 分段化 分段化

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Using Mouse Oocytes to Assess Human Gene Function During Meiosis I
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Using Mouse Oocytes to Assess Human Gene Function During Meiosis I

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Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse
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Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse

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相关实验视频

Last Updated: Sep 8, 2025

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

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Using Mouse Oocytes to Assess Human Gene Function During Meiosis I
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Using Mouse Oocytes to Assess Human Gene Function During Meiosis I

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Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse
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Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse

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科学领域:

  • 生殖生物学 生殖生物学
  • 计算生物学 计算生物学
  • 生物医学成像技术 生物医学成像技术

背景情况:

  • 卵细胞质量评估对于繁殖成功至关重要.
  • 目前的表型化方法可能具有侵入性或缺乏详细的特征提取.
  • 需要自动化,非侵入性的方法来有效地表征卵细胞.

研究的目的:

  • 开发和验证用于非侵入性卵细胞表型化的机器学习管道.
  • 从卵细胞图像/电影中提取全面的形态和动态特征.
  • 使用基于特征的机器学习算法来表征卵细胞.

主要方法:

  • 使用非侵入式传输光显微镜获取卵细胞图像/电影.
  • 图像细分以划分卵细胞轮.
  • 数以百计的形态和动态特征的提取.
  • 基于特征的机器学习算法的应用,用于卵细胞的表征.

主要成果:

  • 成功实施了用于卵细胞表型化的机器学习管道.
  • 提取各种描述卵细胞形态和动态的定量特征.
  • 基于特征的机器学习用于卵细胞表征的演示.

结论:

  • 开发的管道提供了一种非侵入性,数据驱动的卵细胞表型化方法.
  • 这种方法可以根据提取的特征对个体卵细胞进行详细的表征.
  • 机器学习有助于客观,高效的卵子质量评估.