Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA.
Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, 39217, USA; RCMI Center for Environmental Health, Department of Biology, Jackson State University, Jackson, MS, 39217, USA.
Chemosphere. 2021 Feb;264(Pt 1):128428. doi: 10.1016/j.chemosphere.2020.128428. Epub 2020 Sep 25.
The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is ErO, GdO, CeO, CoO, MnO, CoO, FeO/WO (in descending order). The computed EC values from experimental data suggested that ErO and GdO were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1 and 2 generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.
采用标准生物测定法方案，实验评估了八种金属氧化物纳米粒子 (MONPs) 对大肠杆菌的毒性作用。根据这些研究中 MONPs 的细胞毒性排序为 ErO、GdO、CeO、CoO、MnO、CoO、FeO/WO（降序排列）。从实验数据计算得出的 EC 值表明，ErO 和 GdO 是对大肠杆菌毒性最严重的 MONPs。为了确定这 8 种 MONPs 以及我们之前研究中的 17 种其他 MONPs 的毒性机制，我们采用了七种分类和机器学习 (ML) 算法，包括线性判别分析 (LDA)、朴素贝叶斯 (NB)、多项逻辑回归 (MLogitR)、序贯最小优化 (SMO)、AdaBoost、J48 和随机森林 (RF)。我们还采用了由我们开发的（无需任何复杂的计算设施）第一和第二代元素周期表描述符以及实验分析的 Zeta 电位，来模拟这些 MONPs 的细胞毒性。根据定性验证指标，LDA 模型似乎是 7 种测试模型中最好的。由核心电子数与价电子数的比值和氧的电负性计数定义的金属核心环境对毒性有积极影响。确定的性质对于理解纳米毒性机制和预测与 MONPs 暴露相关的潜在环境风险非常重要。开发的模型可用于对未测试的 MONP 对大肠杆菌的环境风险评估，从而为设计和制备安全的纳米材料提供科学依据。
