Relating nano-particle properties to biological outcomes in exposure escalation experiments

被引:11
作者
Patel, T. [1 ]
Telesca, D. [1 ,2 ]
Low-Kam, C. [1 ,2 ]
Ji, Z. X. [2 ]
Zhang, H. Y. [2 ]
Xia, T. [2 ,3 ]
Zinc, J. I. [2 ,4 ]
Nel, A. E. [2 ,3 ]
机构
[1] Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA USA
[2] Univ Calif Los Angeles, Calif Nanosyst Inst, Los Angeles, CA USA
[3] Univ Calif Los Angeles, Div Nanomed, Los Angeles, CA USA
[4] Univ Calif Los Angeles, Dept Chem & Biochem, Los Angeles, CA 90024 USA
基金
美国国家科学基金会;
关键词
dose-response models; model selection; nano-informatics; smoothing splines; DOSE-RESPONSE; VARIABLE SELECTION; BAYESIAN-ANALYSIS; OXIDATIVE STRESS; MODEL; OXIDE; NANOPARTICLES; DISTRIBUTIONS; TOXICITY;
D O I
10.1002/env.2246
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a library of nano-materials. We discuss a modeling strategy that relates the outcome of an exposure escalation experiment to nano-particle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physicochemical descriptors. The proposed inferential framework results in summaries that are easily interpretable as simple probability statements. We present the application of the proposed method to a dataset on 24 metal oxides nano-particles, characterized in relation to their electrical, crystal and dissolution properties. Copyright (c) 2013 John Wiley & Sons, Ltd.
引用
收藏
页码:57 / 68
页数:12
相关论文
共 45 条
[1]   BAYESIAN-ANALYSIS OF BINARY AND POLYCHOTOMOUS RESPONSE DATA [J].
ALBERT, JH ;
CHIB, S .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1993, 88 (422) :669-679
[2]  
Berger J.O., 1985, Statistical decision theory and Bayesian analysis, V2nd
[3]   Bayesian analysis of agricultural field experiments [J].
Besag, J ;
Higdon, D .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1999, 61 :691-717
[4]  
Besag J, 1995, BIOMETRIKA, V82, P733, DOI 10.2307/2337341
[5]   MIXTURE-MODELS FOR CONTINUOUS DATA IN DOSE-RESPONSE STUDIES WHEN SOME ANIMALS ARE UNAFFECTED BY TREATMENT [J].
BOOS, DD ;
BROWNIE, C .
BIOMETRICS, 1991, 47 (04) :1489-1504
[6]   Generalized structured additive regression based on Bayesian P-splines [J].
Brezger, A ;
Lang, S .
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2006, 50 (04) :967-991
[7]   QSAR modeling of nanomaterials [J].
Burello, Enrico ;
Worth, Andrew P. .
WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY, 2011, 3 (03) :298-306
[8]  
Carlin BP, 2000, C&H TEXT STAT SCI
[9]  
Clayton DG., 1996, Markov Chain Monte Carlo in Practice, P275
[10]   Dose-response analyses using restricted cubic spline functions in public health research [J].
Desquilbet, Loic ;
Mariotti, Francois .
STATISTICS IN MEDICINE, 2010, 29 (09) :1037-1057