Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA.
Risk Anal. 2014 Mar;34(3):583-97. doi: 10.1111/risa.12109. Epub 2013 Sep 11.
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 N2 -BET-specific surface area decreased toxicity indicators.
本文提出了一种基于回归树的啮齿动物肺部毒性研究的荟萃分析，这些研究涉及未涂层、未功能化的碳纳米管（CNT）暴露。该分析提供了定量估计 CNT 属性（杂质、物理尺寸和聚集）对肺泡灌洗液中肺部毒性指标的贡献：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，这些模型相对不受数据缺陷的影响，这些数据缺陷会影响其他类型的回归分析：高维性、非线性、相关变量和大量缺失值。呈现了三种类型的分析：RT、随机森林（RF）和基于随机森林的剂量反应模型。RT 显示了所有数据支持的最佳单一模型，通常包含少量变量。RF 显示了数据集内的每个变量与减少的方差量之间的关系。剂量反应模型用于从 CNT 剂量中分离 CNT 属性的影响，显示出属性在测量的 CNT 剂量范围内引起的剂量反应变化。研究发现，对肺部毒性贡献最大的 CNT 属性是金属杂质（钴显著增加了观察到的毒性，而其他杂质的影响则混合）、CNT 长度（与大多数毒性指标呈负相关）、CNT 直径（与毒性呈显著正相关）和聚集尺寸（与细胞损伤指标呈负相关，与免疫反应指标呈正相关）。增加 CNT 的 N2-BET 比表面积会降低毒性指标。
