Marvin Hans J P, Bouzembrak Yamine, Janssen Esmée M, van der Zande Meike, Murphy Finbarr, Sheehan Barry, Mullins Martin, Bouwmeester Hans
a Wageningen University and Research, RIKILT , Wageningen , the Netherlands.
b Kemmy Business School, University of Limerick , Ireland.
Nanotoxicology. 2017 Feb;11(1):123-133. doi: 10.1080/17435390.2016.1278481.
In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO, SiO, Ag, CeO, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.
在本研究中，开发了一种贝叶斯网络（BN），用于预测潜在危害和生物效应，重点关注金属及金属氧化物纳米材料，以支持人类健康风险评估。所开发的贝叶斯网络以整体方式捕捉暴露途径、纳米材料物理化学性质与最终生物效应之间的（相互）关系，其构建基于国际专家咨询和科学文献（如体外/体内数据）。该贝叶斯网络通过从已发表研究中提取的独立数据进行验证，纳米材料潜在危害预测的准确率分别为72%，生物效应预测的准确率为71%。通过对TiO、SiO、Ag、CeO、ZnO纳米材料的情景研究展示了贝叶斯网络的应用。结果表明，不同利益相关者可在风险评估的多个阶段使用该贝叶斯网络，以预测信息匮乏的纳米材料的某些性质，或对纳米材料进行优先级排序以便进一步筛选。
