在相互作用和深度神经网络建模用于甲基甲硫酸盐的毒性概况
Ömer Can Pehlivan1, Kültiğin Cavuşoğlu2, Emine Yalçin3
1Department of Biology, Institute of Science, Giresun University, Giresun, Türkiye.
Environmental science and pollution research international
|October 24, 2023
概括
甲基甲硫酸盐 (MMS) 诱导植物细胞的多功能毒性,影响生理学,细胞遗传学和生物化学参数. 深度神经网络 (DNN) 模型有效地预测了MMS诱导的基因毒性,证明了它对化学化合物评估的价值.
科学领域:
- 环境毒理学环境毒理学
- 植物科学 植物科学
- 计算生物学 计算生物学
背景情况:
- 甲基甲硫酸盐 (MMS) 等化剂对环境构成风险.
- 了解植物中的MMS毒性机制对于风险评估至关重要.
- Allium cepa L. (洋) 是一种敏感的模型生物体,用于基因毒性测试.
研究的目的:
- 为了调查甲基甲硫酸盐 (MMS) 在Allium cepa L.中的毒性.
- 评估深度神经网络 (DNN) 模型在预测MMS诱导的基因毒性的有效性.
- 探索各种毒性指标与MMS度之间的关系.
主要方法:
- (Allium cepa L.) 的球泡暴露于不同度的MMS (100,500,4000微米).
- 使用发芽,根延长,线粒指数 (MI),微核 (MN) 和染色体异常 (CA) 试验,DNA碎片化彗星试验以及生物化学分析 (MDA,SOD,CAT) 来评估毒性.
- 在Matlab中开发了一个深度神经网络 (DNN) 模型来预测毒性终点,并使用相关性/PCA来分析参数关系.
主要成果:
- 接触MMS导致生理学参数 (发芽,根延长,体重) 降低,细胞遗传学 (MN,CA) 和生物化学 (MDA) 参数增加.
- 抗氧化酶活性 (SOD,CAT) 增加至500μM MMS,然后在4000μM下降.
- 彗星测定和分子对接证实了DNA碎片化增加和强大的DNA-MMS相互作用与MMS度上升;DNN模型在预测毒性方面显示出高准确性.
结论:
- 在Allium cepa L.根系细胞中,MMS诱导显著和多功能毒性.
- 开发的DNN模型是预测基因毒性和评估化学化合物风险的宝贵工具.
- 研究的生理学,细胞遗传学和生物化学参数是MMS诱导毒性的有效指标.
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