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A Meta-Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes

机译：碳纳米管肺毒性研究的荟萃分析—物理尺寸和杂质如何影响碳纳米管的毒性

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摘要

This article presents a regression-tree-based meta-analysis of rodent pulmonary toxicity studies of uncoated, nonfunctionalized carbon nanotube (CNT) exposure. The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random-forest-based dose-response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose-response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose-response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). Increasing CNT N_2-BET-specific surface area decreased toxicity indicators.
机译：本文提供了基于回归树的未涂覆，未官能化碳纳米管（CNT）暴露的啮齿动物肺毒性研究的荟萃分析。结果分析提供了碳纳米管属性（杂质，物理尺寸和聚集）对支气管肺泡灌洗液中肺毒性指标的贡献的定量估计：中性粒细胞和巨噬细胞计数，以及乳酸脱氢酶和总蛋白浓度。该方法采用分类和回归树（CART）模型，这些技术对损害其他类型的回归分析的数据缺陷相对不敏感：高维，非线性，相关变量和大量缺失值。提出了三种类型的分析：RT，随机森林（RF）和基于随机森林的剂量反应模型。 RT显示所有数据支持的最佳单一模型，并且通常包含少量变量。 RF显示与数据集中的每个变量相关的方差减少量。剂量反应模型用于从CNT剂量中分离出CNT属性的影响，显示了在整个CNT剂量范围内，由该属性引起的剂量反应的变化。发现对肺部毒性影响最大的CNT属性是金属杂质（钴显着增加了观察到的毒性，而其他杂质具有混合效应），CNT长度（与大多数毒性指标负相关），CNT直径（与毒性）和聚集体大小（与细胞损伤指标负相关，与免疫反应指标正相关）。 CNT N_2-BET比表面积的增加降低了毒性指标。

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