金属氧化物纳米颗粒诱导巨噬细胞毒性定量预测的机器学习模型。

IF 8.1 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Tianqin Wang , Yang Huang , Hongwu Zhang , Xuehua Li , Fei Li
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引用次数: 0

摘要

随着纳米技术的进步,金属氧化物纳米颗粒(MeONPs)越来越多地与人类接触。吸入的MeONPs不能被纤毛或肺粘液有效清除。在过去的十年中,由于肺巨噬细胞是清洁吸入外源颗粒的主要途径,MeONPs暴露引起的潜在免疫毒性一直备受争议。然而,由于巨噬细胞反应的复杂性和MeONPs的复杂性质,它们对肺巨噬细胞的毒性很少在计算机上定量预测。本研究采用机器学习(ML)方法建立模型,定量预测MeONPs对巨噬细胞的毒性。获得了包含240个数据点的多维数据集,涵盖了30种MeONPs的致死率、生化行为和理化性质。通过解决训练过程中的类不平衡问题,构建了基于不同算法的预测精度较高的机器学习模型。通过10倍交叉验证和外部验证对模型进行验证。在10倍交叉验证集和外部检验集中,表现最好的模型的R2分别为0.85和0.90;10倍交叉验证和测试集的Q2分别为0.88和0.90。确定了5个影响毒性的参数,并通过ML分析阐明了其毒性机制。预测结果可用于填补纳米材料风险评估中的数据空白。该框架为设计和利用安全的纳米粒子以及帮助旨在保护环境和公众健康的决策过程提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles
As nanotechnology advances, metal oxide nanoparticles (MeONPs) increasingly come into contact with humans. The inhaled MeONPs cannot be effectively cleared by cilia or lung mucus. In the last decade, potential immune toxicity arising from exposure to MeONPs has been extensively debated, as lung macrophage is the main pathway for cleaning inhaled exogenous particles. However, their toxicity on lung macrophages has rarely been quantitatively predicted in silico due to the complexity of responses in macrophages and the intricate properties of MeONPs. Here, machine learning (ML) methods were used to establish models for quantitatively predicting the toxicity of MeONPs in macrophages. A multidimensional dataset including 240 data points covering the lethality, biochemical behaviors, and physicochemical properties of 30 MeONPs was obtained. ML models based on different algorithms with high prediction accuracy were constructed by addressing the issue of class imbalance during the training process. The models were verified by 10-fold cross-validation and external validation. The best-performed model has an R2 of 0.85 and 0.90 in the 10-fold cross-validation and external test set, respectively; and Q2 of 0.88 and 0.90 in the 10-fold cross-validation and test set, respectively. Five parameters that impact toxicity were identified and the toxicity mechanisms were elucidated by ML analysis. The prediction results can be used to fill the data gap in the risk assessment of nanomaterials. The framework offers valuable insights for designing and utilizing safe nanoparticles, as well as aiding in decision-making processes aimed at protecting the environment and public health.
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来源期刊
Chemosphere
Chemosphere 环境科学-环境科学
CiteScore
15.80
自引率
8.00%
发文量
4975
审稿时长
3.4 months
期刊介绍: Chemosphere, being an international multidisciplinary journal, is dedicated to publishing original communications and review articles on chemicals in the environment. The scope covers a wide range of topics, including the identification, quantification, behavior, fate, toxicology, treatment, and remediation of chemicals in the bio-, hydro-, litho-, and atmosphere, ensuring the broad dissemination of research in this field.
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