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在磁性纳米粒子处理的巨细胞中对形态表征的无监督变异自编码分析

Su-Yeon Hwang¹, Tae-Il Kang², Hyeon-Seo Kim¹

¹Graduate School of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea.

Bioengineering (Basel, Switzerland)

|January 28, 2026

概括

磁纳米颗粒 (MNP) 导致巨细胞形状发生显著变化. 无监督的机器学习有效地量化了这些微妙的形态变化,揭示了细胞对纳米粒子暴露的反应.

科学领域:

生物医学工程生物医学工程
细胞生物学细胞生物学
人工智能在医学中的应用

背景情况:

磁纳米粒子 (MNP) 在生物医学中至关重要,用于诸如药物输送和生物成像等应用.
巨细胞,关键的免疫细胞,通过细胞分裂与MNP显著相互作用,导致细胞变化.
之前对MNP-巨细胞相互作用的研究主要集中在吸收和毒性上,忽视了详细的形态评估.

研究的目的:

使用先进的计算方法系统量化MNP治疗引起的巨细胞形态变化.
评估基于无监督变异自编码器 (VAE) 的框架在检测微妙细胞变化的有效性.

主要方法:

分析了MNP治疗前和后的巨细胞相对照显微镜图像.
使用无监督的VAE框架 (β-VAE,β-总相关性VAE,多编码器VAE) 来提取细胞形态的潜在表示.
进行了定量分析,包括效果大小,核密度估计,潜在穿越和差异映射.

主要成果:

用MNP处理的巨体显示出显著的形态变化,包括膜扩张,中心密度变化和形状扭曲.
VAE框架成功地提取和可视化了这些微妙的结构变化.
定量评估证实了观察到的形态变化的明显性质.

结论:

无监督的基于VAE的学习提供了一种强大而稳健的方法,用于检测暴露在纳米颗粒中的巨细胞中微妙的形态反应.
这种方法对于分析不同细胞类型,治疗方法和成像方式的细胞形态具有广泛的适用性.

关键词:

巨细胞是什么?巨细胞是什么?磁性纳米粒子是一种磁性纳米粒子.形态分析分析形态分析没有监督的学习学习.变量自动编码器变量自动编码器

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