Computational Toxicology最新文献_第3页

In silico predictions of sub-chronic effects: Read-across using metabolic relationships between parents and transformation products 亚慢性效应的硅学预测：利用亲本和转化产物之间的代谢关系进行交叉阅读

Computational Toxicology Pub Date : 2024-05-09 DOI: 10.1016/j.comtox.2024.100314

Darina G. Yordanova , Chanita D. Kuseva , Hristiana Ivanova , Terry W. Schultz , Vanessa Rocha , Andreas Natsch , Heike Laue , Ovanes G. Mekenyan

{"title":"In silico predictions of sub-chronic effects: Read-across using metabolic relationships between parents and transformation products","authors":"Darina G. Yordanova , Chanita D. Kuseva , Hristiana Ivanova , Terry W. Schultz , Vanessa Rocha , Andreas Natsch , Heike Laue , Ovanes G. Mekenyan","doi":"10.1016/j.comtox.2024.100314","DOIUrl":"10.1016/j.comtox.2024.100314","url":null,"abstract":"<div><p>Justifying read-across predictions for subchronic effects, such as no observed adverse effect levels (NOAEL), is challenging. The scarcity of suitable experimental data hampers such predictions, such that a conservative approach is often employed where the structural similarity between target and the tested source substances is very high. A less stringent interpretation of structural similarity may be used to expand data gap-filling by read-across if other types of similarity (e.g., toxicokinetic and toxicodynamic consideration) are factored into the justification. Herein, qualitative and quantitative <em>in silico</em>-assisted procedures are described and demonstrated for those instances where no structurally similar analogues are identified. In the qualitative approach, the toxicity classification of the most toxic metabolite is assigned directly to the target compound. While simple, this approach may lead to an over-classification of the target compound and a false positive result. In contrast, the quantitative approach is more complicated. In addition to identifying those metabolites causing toxicity, it examines the quantitative information for the amount of the most toxic metabolite. The maximum dose of the parent chemical is estimated which will not result in the generation of toxic metabolites sufficient to cause harmful effects. This quantitative approach permits a calculation of the margin of exposure, is noteworthy for industrial assessment purposes.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MoS-TEC: A toxicogenomics database based on model selection for time-expression curves MoS-TEC：基于时间表达曲线模型选择的毒物基因组学数据库

Computational Toxicology Pub Date : 2024-05-08 DOI: 10.1016/j.comtox.2024.100313

Franziska Kappenberg, Benedikt Küthe, Jörg Rahnenführer

{"title":"MoS-TEC: A toxicogenomics database based on model selection for time-expression curves","authors":"Franziska Kappenberg, Benedikt Küthe, Jörg Rahnenführer","doi":"10.1016/j.comtox.2024.100313","DOIUrl":"10.1016/j.comtox.2024.100313","url":null,"abstract":"<div><p>MoS-TEC is a newly developed toxicogenomics database for time-expression curves fitted with a statistical model selection approach. Toxicogenomic data provide information on the response of the genome to compounds, often measured in terms of gene expression values. When such experimental data are available for different exposure times, the functional relationships between the exposure time and the expression values of genes might be of interest. The TG-GATEs (Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System) database provides such information for genomewide gene expression data for 170 compounds. We performed extensive model selection using MCP-Mod on these data. Specifically, gene expression data measured for eight time points from in vivo experiments on rat liver for 120 compounds with complete datasets were considered. MCP-Mod is a two-step approach, including a multiple comparison procedure (MCP) and a modelling (Mod) approach. The results are estimated time-expression curves that model the relationship between exposure time and gene expression values for all combinations of genes and compounds. We present an appropriate data normalization approach and report which models were selected per compound and in total. For high-quality model fits with a large value for the explained variance, the sigEmax model was most frequently selected. The new R Shiny application MoS-TEC provides easy access for researchers to the best curve fit for all genes individually for all compounds. It can be used online without installing additional software.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246811132400015X/pdfft?md5=20beae1e82472af8cce2948be44f93a1&pid=1-s2.0-S246811132400015X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141056037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simplified toxicity assessment in pharmaceutical and pesticide mixtures: Leveraging interpretable structural parameters 简化药物和农药混合物的毒性评估：利用可解释的结构参数

Computational Toxicology Pub Date : 2024-04-24 DOI: 10.1016/j.comtox.2024.100312

Mohammad Hossein Keshavarz, Zeinab Shirazi, Zeinab Davoodi

{"title":"Simplified toxicity assessment in pharmaceutical and pesticide mixtures: Leveraging interpretable structural parameters","authors":"Mohammad Hossein Keshavarz, Zeinab Shirazi, Zeinab Davoodi","doi":"10.1016/j.comtox.2024.100312","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100312","url":null,"abstract":"<div><p>The potential toxicity arising from antibiotics and pesticides poses a significant risk to the preservation of groundwater. This study investigates the effects of binary mixtures of pharmaceuticals and pesticides by assessing their log <em>EC<sub>50</sub></em>, log <em>EC<sub>30</sub></em>, and log <em>EC<sub>10</sub></em> values in relation to <em>Vibrio fischeri</em> bacteria. Based on a comprehensive dataset of 459 observations, this work identifies suitable simple descriptors. Rigorous statistical analysis confirms the models’ reliability, accuracy, precision, and favorable goodness-of-fit. Notably, the ratios of coefficient of determination (R<sup>2</sup>) for the novel models compared to the best comparative models exceed 1.0: 0.8618/0.8085 for log <em>EC<sub>50</sub></em>, 0.8856/0.8422 for log <em>EC<sub>30</sub></em>, and 0.8973/0.8556 for log <em>EC<sub>10</sub></em>. Additionally, the ratios of root mean square error (RMSE) for the new models relative to their counterparts are all below 1.0: 0.159/0.191 for log <em>EC<sub>50</sub></em>, 0.131/0.169 for log <em>EC<sub>30</sub></em>, and 0.182/0.215 for log <em>EC<sub>10</sub></em>.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140649318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

S-COPHY: A deep learning model for predicting the chemical class of compounds as cosmetics or pharmaceuticals based on single 3D molecular images S-COPHY：基于单个三维分子图像预测化妆品或药品化合物化学类别的深度学习模型

Computational Toxicology Pub Date : 2024-04-22 DOI: 10.1016/j.comtox.2024.100311

Tomoka Hisaki , Koki Yoshida , Takumi Nukaga , Shinya Iwanaga , Masaaki Mori , Yoshihiro Uesawa , Shuichi Sekine , Akiko Tamura

{"title":"S-COPHY: A deep learning model for predicting the chemical class of compounds as cosmetics or pharmaceuticals based on single 3D molecular images","authors":"Tomoka Hisaki , Koki Yoshida , Takumi Nukaga , Shinya Iwanaga , Masaaki Mori , Yoshihiro Uesawa , Shuichi Sekine , Akiko Tamura","doi":"10.1016/j.comtox.2024.100311","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100311","url":null,"abstract":"<div><p>Non-animal-based <em>in vitro</em> and <em>in silico</em> approaches for the safety assessment of cosmetic ingredients, recently referred to as Next Generation Risk Assessment (NGRA)/New Approach Methodologies (NAMs), are evolving rapidly as approaches to provide a basis for the regulatory acceptance of new materials. However, predictive models should be applied only to chemicals within the chemical space defined by the dataset used in generating the model. Thus, only predictions for new molecules that are relatively similar to the modeling set can considered reliable with strong confidence. In this study, we developed the S-COPHY model, which employs deep learning to classify new compounds based on their structural similarity to a large collection of pharmaceutical and cosmetic compounds. S-COPHY shows high predictive accuracy both internally and externally, and in particular, there were only a few instances where pharmaceuticals were incorrectly predicted as cosmetics. The use of deep learning enabled the automatic generation of input data from SMILES (Simplified Molecular Input Line Entry System) information, resulting in more consistent model outcomes. Furthermore, GRAD-CAM (Gradient-weighted Class Activation Map) analysis provided insights into the specific structures that contribute to the model's predictions. The potentiality of S-COPHY to identify characteristic structures associated with pharmaceutical-like activity indicates its potential value in supporting safety assessments of cosmetic ingredients. Our results indicate that the S-COPHY model is a promising approach to support decision-making in large chemical spaces, thereby contributing to the safety evaluation of cosmetic ingredients. Expansion of the model to other categories, such as pesticides, could further extend its applicability.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140645313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New approach methods in chemicals safety decision-making – Are we on the brink of transformative policy-making and regulatory change? 化学品安全决策中的新方法--我们是否正处于变革性决策和监管变化的边缘？

Computational Toxicology Pub Date : 2024-04-04 DOI: 10.1016/j.comtox.2024.100310

Camilla Alexander-White

{"title":"New approach methods in chemicals safety decision-making – Are we on the brink of transformative policy-making and regulatory change?","authors":"Camilla Alexander-White","doi":"10.1016/j.comtox.2024.100310","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100310","url":null,"abstract":"<div><p>Decision-making on the use and management of chemicals in society is on the brink of a scientific and technological revolution. At the same time world politics is focusing more on chemicals, waste and pollution prevention, alongside climate change and biodiversity loss. To enable effective decision-making, policy makers and regulators will need to draw upon the best scientific evidence available on the real-life causation and consequences of adverse effects of chemical and waste exposures affecting humans, wildlife and the environment. New Approach Method (NAM) data from modern day multidisciplinary science and technology is becoming more available using cheminformatics, computational prediction algorithms using AI, transcriptomics, genomics, proteomics, mathematical modelling, epidemiology, biological monitoring, and clinical science. Current chemical regulation has been shaped by the animal models of the 20th century. NAMs and Next Generation Risk Assessment (NGRA) have the potential to better support innovations in chemicals and materials through science-informed decision making that is more species-relevant and protective of adverse outcomes; this will require future-proofed regulatory transformation. Capacity building and skills development in computational and in vitro NAMs will be key to this transformation.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140347492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity 评估含咪唑和吡啶离子液体毒性的新 QSTR 模型

Computational Toxicology Pub Date : 2024-03-22 DOI: 10.1016/j.comtox.2024.100309

Ivan Semenyuta, Vasyl Kovalishyn, Diana Hodyna, Yuliia Startseva, Sergiy Rogalsky, Larysa Metelytsia

{"title":"New QSTR models to evaluation of imidazolium- and pyridinium-contained ionic liquids toxicity","authors":"Ivan Semenyuta, Vasyl Kovalishyn, Diana Hodyna, Yuliia Startseva, Sergiy Rogalsky, Larysa Metelytsia","doi":"10.1016/j.comtox.2024.100309","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100309","url":null,"abstract":"<div><p>We present machine learning studies devoted to the creation of predictive models for toxicity evaluation of imidazolium- and pyridinium-containing ionic liquids. New created predictive models were developed using the OCHEM. The predictive ability of the models was tested by cross-validation, giving a coefficient of determination q<sup>2</sup> = 0.77–0.82. The models were applied to screen a virtual chemical library to the toxicity of ILs in Danio rerio and Daphnia magna bioassays. Models were used to predict toxicity for 25 ILs, which were then synthesized and tested in vivo. The in vivo toxicity studies found that D. magna is a more sensitive aquatic test organism than D. rerio – 67 % of the studied ILs are classified as extremely toxic with an LC<sub>50</sub> range from 0.005 to 0.01 mg/l. At the same time, only one IL 1-dodecylpyridinium bromide with an LC<sub>50</sub> of 0.08 mg/l is classified as extremely toxic, and 76 % are classified as slightly and moderately toxic compounds using D. rerio as a test organism. The most toxic ILs 5 and 19 were docked into the human AChE active center and demonstrated calculated binding energy values −9.5 and −9.3 kcal/mol that is comparable with the complexation of the human AChE inhibitor Donepezil, which provides insight into the potential molecular mechanisms of ILs toxicity. The created QSTR models are a successful tool for the toxicity analysis of new promising ILs. QSTR models demonstrated not only high predictive indicators but also a high percentage of correctly predicted toxicity values in vivo studies.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140195734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models AOPWIKI-ExPLORER：利用大型语言模型的基于图的交互式查询引擎

Computational Toxicology Pub Date : 2024-03-21 DOI: 10.1016/j.comtox.2024.100308

Saurav Kumar , Deepika Deepika , Karin Slater , Vikas Kumar

{"title":"AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models","authors":"Saurav Kumar , Deepika Deepika , Karin Slater , Vikas Kumar","doi":"10.1016/j.comtox.2024.100308","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100308","url":null,"abstract":"<div><p>Adverse Outcome Pathways (AOPs) provide a basis for non-animal testing, by outlining the cascade of molecular and cellular events initiated upon stressor exposure, leading to adverse effects. In recent years, the scientific community has shown interest in developing AOPs through crowdsourcing, with the results archived in the AOP-Wiki: a centralized repository coordinated by the OECD, hosting nearly 512 AOPs (April, 2023). However, the AOP-Wiki platform currently lacks a versatile querying system, which hinders developers' exploration of the AOP network and impedes its practical use in risk assessment. This work proposes to unleash the full potential of the AOP-Wiki archive by adapting its data into a Labelled Property Graph (LPG) schema. Additionally, the tool offers a visual network query interface for both database-specific and natural language queries, facilitating the retrieval and analysis of graph data. The multi-query interface allows non-technical users to construct flexible queries, thereby enhancing the potential for AOP exploration. By reducing the time and technical requirements, the present query engine enhances the practical utilization of the valuable data within AOP-Wiki. To evaluate the platform, a case study is presented with three levels of use-case scenarios (simple, moderate, and complex queries). AOPWIKI-EXPLORER is freely available on GitHub (https://github.com/Crispae/AOPWiki_Explorer) for wider community reach and further enhancement.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111324000100/pdfft?md5=542059b7f2c1ba3e8e43c9fa101d3325&pid=1-s2.0-S2468111324000100-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140309180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Replicate Number and Sequencing Depth in Toxicology Dose-Response RNA-seq 评估毒理学剂量反应 RNA-seq 中的重复数量和测序深度

Computational Toxicology Pub Date : 2024-03-19 DOI: 10.1016/j.comtox.2024.100307

A. Rasim Barutcu

{"title":"Evaluation of Replicate Number and Sequencing Depth in Toxicology Dose-Response RNA-seq","authors":"A. Rasim Barutcu","doi":"10.1016/j.comtox.2024.100307","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100307","url":null,"abstract":"<div><p>Sequencing depth and biological replication represent key experimental design considerations in toxicogenomics and risk assessment. However, their relative impacts on differential gene expression analysis remain unclear. Using an 8-dose chemical (Prochloraz) perturbation RNA-seq dataset in A549 cells, we systematically subsampled sequencing depth (5–100 %) and replicates (2–4) to evaluate effects on number of differentially expressed genes. While dose was the primary variance driver, replication had a greater influence than depth for optimizing detection power. With only 2 replicates, over 80% of the ∼2000 differential genes were unique to specific depths, indicating high variability. Increasing to 4 replicates substantially improved reproducibility, with over 550 genes consistently identified across most depths, representing 30% of the total differential genes. Higher replicates also increased the rate of overlap of benchmark dose pathways and precision of median benchmark dose estimates. However, key gene ontology pathways related to DNA replication, cell cycle, and division were consistently captured even at lower replicates. Thus, replication enhanced confidence but did not fundamentally expand biological findings. Our study delineates key trade-offs between sequencing depth and replication for toxicogenomic experimental design. While additional replicates fundamentally improve reproducibility, gains from depth exhibit diminishing returns. Prioritizing biological replication over depth provides a cost-effective approach to enhance interpretation without sacrificing detection of core gene expression patterns. Altogether, this study provides important insights into the experimental design of toxicogenomics experiments.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A framework to support the application of the OECD guidance documents on (Q)SAR model validation and prediction assessment for regulatory decisions 支持在监管决策中应用经合组织 (OECD) 关于 (Q)SAR 模型验证和预测评估的指导文件的框架

Computational Toxicology Pub Date : 2024-03-16 DOI: 10.1016/j.comtox.2024.100305

Christopher Barber, Crina Heghes, Laura Johnston

引用次数: 0

Identification of potential human targets of glyphosate using in silico target fishing 利用硅学靶标钓法确定草甘膦的潜在人体靶标

Computational Toxicology Pub Date : 2024-03-15 DOI: 10.1016/j.comtox.2024.100306

Alejandro Gómez, Andrés Alarcón, Wilson Acosta, Andrés Malagón

{"title":"Identification of potential human targets of glyphosate using in silico target fishing","authors":"Alejandro Gómez, Andrés Alarcón, Wilson Acosta, Andrés Malagón","doi":"10.1016/j.comtox.2024.100306","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100306","url":null,"abstract":"<div><p>Glyphosate is a widely used herbicide known for its effectiveness in weed control; and it is an inhibitor of the plant enzyme 5-enolpyruvylshikimate-3-phosphate synthase. Currently, it is one of the most extensively used non-specific herbicides in agroindustry. However, toxic effects of glyphosate have recently been reported, including endocrine disruption, metabolic alterations, teratogenic, tumorigenic, and hepatorenal effects. Additionally, there are environmental concerns related to possible interactions with proteins from microorganisms, aquatic organisms, and mammals.</p><p>Research on the description of these interactions has gained interest, primarily with the aim of generating recommendations in terms of its use and possible regulations. On the other hand, computational methods have emerged to identify potential targets or unintended targets among numerous possible receptors. Several programs, online services, and databases are available for use in these methods.</p><p>In this study, we employed a set of online tools for computational target fishing to identify receptors of glyphosate. A set of thirteen targets were selected using six fishing tools. Furthermore, docking procedures were performed to investigate the expected interactions and binding energies. Certain associations with diseases are also reported.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140145179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0