机器学习辅助的"收缩限制"SERS策略对环境纳米塑料诱导的细胞死亡的分类
Ruili Li1, Xiaotong Sun1, Yuyang Hu1
1College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
Environmental science & technology
|December 13, 2024
概括
环境纳米塑料 (NP) 显示出显著的生物毒性",NP载体效应"加剧了危害. 一个机器学习辅助的SERS策略监测了细胞变化,揭示了改变的细胞死亡途径和代谢漏洞.
科学领域:
- 环境科学 环境科学
- 生物技术是生物技术.
- 毒理学 毒理学 毒理学
背景情况:
- 纳米塑料 (NP) 构成新兴的环境风险.
- 对NP的生物毒性和载体效应的研究不足.
- 目前的细胞死亡分析缺乏时间分辨率和整体分子洞察力.
研究的目的:
- 开发一种用于监测NP暴露后细胞分泌物变化的新方法.
- 调查环境NP的毒性和NP载体效应.
- 为了比较不同类型细胞对NP暴露的代谢脆弱性.
主要方法:
- 开发了一种机器学习辅助的"收缩限制"表面增强拉曼光谱 (SERS) 策略 (SRSS).
- 三维 (3D) Ag@hydrogel基板被用于活性分子向.
- 机器学习分析了光谱形状,生化特征和依赖时间的细胞变化.
主要成果:
- 环境来源的NP对BEAS-2B和L02细胞具有更高的毒性.
- "NP载体效应" (污染物吸附) 显著增加了NP的危害.
- 暴露于NP将BEAS-2B细胞死亡从亡/铁亡转移到主要是铁亡.
- L02细胞对NP表现出更大的代谢脆弱性,特别是在载体效应下.
结论:
- 通过ML辅助的SRSS方法,可以全面监测NP诱导的细胞和分子变化.
- 环境NP及其载体效应带来重大风险,改变细胞死亡途径和新陈代谢.
- 这项研究促进了对塑料污染对细胞健康的影响的理解.
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