Predictive toxicology of cobalt ferrite nanoparticles: comparative in-vitro study of different cellular models using methods of knowledge discovery from data.
Predictive toxicology of cobalt ferrite nanoparticles: comparative in-vitro study of different cellular models using methods of knowledge discovery from data.
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钴铁氧体纳米颗粒的预测毒理学：使用来自数据的知识发现方法对不同细胞模型进行比较体内研究。
DOI：
10.1186/1743-8977-10-32
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发表时间：
2013-07-29
影响因子：
10
通讯作者：
Korenstein R
中科院分区：
文献类型：
--
作者：
Horev-Azaria L;Baldi G;Beno D;Bonacchi D;Golla-Schindler U;Kirkpatrick JC;Kolle S;Landsiedel R;Maimon O;Marche PN;Ponti J;Romano R;Rossi F;Sommer D;Uboldi C;Unger RE;Villiers C;Korenstein R
钴铁氧体纳米粒子（Co-Fe NPs）在基于纳米技术的治疗中具有吸引力。因此，探索它们对代表人体不同器官的七种不同细胞系的生存能力的影响是非常重要的。通过体外暴露于A549和NCIH441细胞系（肺）、大鼠精密肺切片、HepG2细胞系（肝）、MDCK细胞系（肾）、Caco-2 TC7细胞系（肠）、TK6（淋巴母细胞）和小鼠原代树突状细胞，研究了Co-Fe NPs的毒理学效应。在0.05 -1.2 mM浓度范围内暴露于Co-Fe NPs 24和72小时后，采用Alamar蓝、MTT和中性红检测毒性。氧化应激的变化是通过基于二氯二氢荧光素的测定来确定的。采用基于决策树模型的数据发现方法对获得的数据集进行数据分析和预测建模（J48）。结果表明，不同浓度的Co-Fe NPs对不同细胞系的细胞活力有不同的影响。Co-Fe NPs诱导氧化应激增加，并与细胞类型有关。Co-Fe NPs的毒性与暴露于Co-Fe NPs后ROS生成程度呈高度线性相关（R2=0.97）。我们用于对观察到的毒性进行建模的算法属于一种监督分类器。根据排序参数的降低，决策树模型得出以下顺序：NP浓度（影响最大的参数）、细胞类型（细胞对活力降低的敏感性等级如下：TK6 >肺切片> NCIH441 > Caco-2 = MDCK > A549 > HepG2 =树突状）和暴露时间，其中排名最高的参数（NP浓度）提供了有关毒性的最高信息增益。所选择的决策树模型J48的有效性是通过产生比众所周知的“朴素贝叶斯”分类器更高的精度来建立的。观察到由Co-Fe NPs引起的氧化应激与不同细胞类型对毒性的敏感性等级之间的相关性，表明氧化应激可能是Co-Fe NPs毒性的一种机制。
Cobalt-ferrite nanoparticles (Co-Fe NPs) are attractive for nanotechnology-based therapies. Thus, exploring their effect on viability of seven different cell lines representing different organs of the human body is highly important. The toxicological effects of Co-Fe NPs were studied by in-vitro exposure of A549 and NCIH441 cell-lines (lung), precision-cut lung slices from rat, HepG2 cell-line (liver), MDCK cell-line (kidney), Caco-2 TC7 cell-line (intestine), TK6 (lymphoblasts) and primary mouse dendritic-cells. Toxicity was examined following exposure to Co-Fe NPs in the concentration range of 0.05 -1.2 mM for 24 and 72 h, using Alamar blue, MTT and neutral red assays. Changes in oxidative stress were determined by a dichlorodihydrofluorescein diacetate based assay. Data analysis and predictive modeling of the obtained data sets were executed by employing methods of Knowledge Discovery from Data with emphasis on a decision tree model (J48). Different dose–response curves of cell viability were obtained for each of the seven cell lines upon exposure to Co-Fe NPs. Increase of oxidative stress was induced by Co-Fe NPs and found to be dependent on the cell type. A high linear correlation (R2=0.97) was found between the toxicity of Co-Fe NPs and the extent of ROS generation following their exposure to Co-Fe NPs. The algorithm we applied to model the observed toxicity belongs to a type of supervised classifier. The decision tree model yielded the following order with decrease of the ranking parameter: NP concentrations (as the most influencing parameter), cell type (possessing the following hierarchy of cell sensitivity towards viability decrease: TK6 > Lung slices > NCIH441 > Caco-2 = MDCK > A549 > HepG2 = Dendritic) and time of exposure, where the highest-ranking parameter (NP concentration) provides the highest information gain with respect to toxicity. The validity of the chosen decision tree model J48 was established by yielding a higher accuracy than that of the well-known “naive bayes” classifier. The observed correlation between the oxidative stress, caused by the presence of the Co-Fe NPs, with the hierarchy of sensitivity of the different cell types towards toxicity, suggests that oxidative stress is one possible mechanism for the toxicity of Co-Fe NPs.
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影响因子：
17.1
作者：
Fourches D;Pu D;Tassa C;Weissleder R;Shaw SY;Mumper RJ;Tropsha A
通讯作者：
Tropsha A
影响因子：
7
作者：
Rivollier, A;Perrin-Cocon, L;Servet-Delprat, C
通讯作者：
Servet-Delprat, C
影响因子：
16.1
作者：
Shubayev VI;Pisanic TR 2nd;Jin S
通讯作者：
Jin S
DOI：
10.1007/0-387-25465-x_1
发表时间：
2005-01-01
期刊：
DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK
影响因子：
--
作者：
Maimon, Oded;Rokach, Lior
通讯作者：
Rokach, Lior
影响因子：
38.3
作者：
Puzyn, Tomasz;Rasulev, Bakhtiyor;Leszczynski, Jerzy
通讯作者：
Leszczynski, Jerzy