基于深度学习的自动细分和分析由寨卡病毒和SARS-CoV-2造成的线粒体损伤
Brianda Alexia Agundis-Tinajero1,2, Miguel Ángel Coronado-Ipiña1,2, Ignacio Lara-Hernández2
1Department of Sciences, Autonomous University of San Luis Potosí (UASLP), San Luis Potosí 78295, Mexico.
Viruses
|September 27, 2025
概括
一个新的深度学习工具自动分析病毒感染细胞中的线粒体变化,准确预测免疫反应. 这种人工智能方法加快了研究速度,并有助于理解细胞对SARS-CoV-2等病原体的反应.
科学领域:
- 细胞生物学 细胞生物学
- 病毒学 病毒学
- 人工智能的人工智能
背景情况:
- 病毒感染引发了与免疫反应相关的线粒体形态变化.
- 分析这些变化为细胞免疫提供了洞察力,但它是劳动密集型的,容易产生偏见.
研究的目的:
- 开发一种深度学习 (DL) 方法,用于自动化线粒体识别,细分和病毒感染细胞中的分析.
- 为了比较基于DL的分析与手动方法的准确性和效率.
主要方法:
- 使用U-Net卷积神经网络架构进行图像分析.
- 应用DL模型传输电子显微镜图像感染SARS-CoV-2变种和寨卡病毒的细胞.
- 进行了手动对自动细分和形态指标的比较分析.
主要成果:
- 在自动化的线粒体细分和分析中达到超过85%的准确性.
- 在手动和自动化指标之间没有显示出统计学上显著的差异.
- 与手动分析相比,处理时间显著减少.
结论:
- 基于DL的工具准确有效地分析病毒感染中的线粒体超结构变化.
- 这种方法有助于通过检测完整和受损的线粒体来预测免疫反应.
- 该工具在研究新兴病原体和自动化细胞生物学研究方面具有潜在的应用.
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