Computational Toxicology最新文献

{"title":"PBK models to predict internal and external dose levels following oral exposure of rats to imidacloprid and carbendazim","authors":"Bohan Hu,&nbsp;Hans J.H.J. van den Berg,&nbsp;Ivonne M.C.M. Rietjens,&nbsp;Nico W. van den Brink","doi":"10.1016/j.comtox.2024.100321","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100321","url":null,"abstract":"<div><p>Monitoring oral exposure to pesticides in wildlife is crucial for assessing environmental risks and preventing adverse effects on non-target species. Traditionally, this requires invasive tissue sampling, raising ethical, regulatory, and economic concerns. To address this gap, our study aims to develop a method for assessing external oral dose levels in rats using physiologically-based kinetic (PBK) modeling based on blood concentration levels of two pesticides, imidacloprid and carbendazim, and one of their primary metabolites. We utilized <em>in vitro</em> metabolic kinetic data from hepatic microsomal and S9 incubations to inform our models. These models were then evaluated by comparing their predictions with existing <em>in vivo</em> experimental data from the literature. Our results demonstrate that the models provide accurate predictions, presenting a novel <em>in vitro</em> and <em>in silico</em> approach for environmental exposure and risk assessment of pesticides. This methodology has the potential for application in wildlife species, advancing the frontier of knowledge in non-invasive pesticide exposure assessment.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"31 ","pages":"Article 100321"},"PeriodicalIF":3.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111324000239/pdfft?md5=da6bee24a4252857f106dd35f4ca3b45&pid=1-s2.0-S2468111324000239-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542890","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}

{"title":"Structuring expert review using AOPs: Enabling robust weight-of-evidence assessments for carcinogenicity under ICH S1B(R1)","authors":"Susanne A. Stalford,&nbsp;Alex N. Cayley,&nbsp;Adrian Fowkes,&nbsp;Antonio Anax F. de Oliveira,&nbsp;Ioannis Xanthis,&nbsp;Christopher G. Barber","doi":"10.1016/j.comtox.2024.100320","DOIUrl":"10.1016/j.comtox.2024.100320","url":null,"abstract":"<div><p>There is widespread acceptance that non-animal studies can be used to assess chemical safety in humans. These New Approach Methodologies (NAMs) typically integrate data from multiple sources including <em>in silico</em> and <em>in vitro</em> models. Regulatory guidelines are being updated to recognise that these scientific advances are allowing animal studies to be replaced without compromising human safety. One such regulation, ICH S1B(R1), was updated in 2022 to include the provision for a weight-of-evidence assessment for carcinogenicity, using six factors to determine if there was sufficient evidence to waive the need to run a rat carcinogenicity assay. The volume of data and evidence, however, can be hard to organise and interpret into a cohesive evaluation. To aid such assessments, software has been developed that combines adverse outcome pathways (AOPs) and reasoning, to organise and contextualise knowledge, and provide an outcome based on the data available. Using this framework, a workflow has been developed to assess the initial outcome and structure expert review to investigate the factors, and potential biological mechanisms which could contribute to a compound’s carcinogenic potential (or lack thereof). The framework was used to structure expert review of three examples of differing activity and levels of supporting evidence. This highlighted where AOPs supported expert review by showing 1) the value in using AOPs to analyse data, 2) the importance of expert review to strengthen confidence in outcomes, and 3) how this approach can accurately predict experimental results. Therefore, using this approach to assess evidence for ICH S1B(R1) will give transparent, scientifically robust, and reproducible calls, and thus reduce the need for rat carcinogenicity studies.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"31 ","pages":"Article 100320"},"PeriodicalIF":3.1,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141404408","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}

{"title":"Cross-species molecular docking method to support predictions of species susceptibility to chemical effects","authors":"Peter G. Schumann ,&nbsp;Daniel T. Chang ,&nbsp;Sally A. Mayasich ,&nbsp;Sara M.F. Vliet ,&nbsp;Terry N. Brown ,&nbsp;Carlie A. LaLone","doi":"10.1016/j.comtox.2024.100319","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100319","url":null,"abstract":"<div><p>The advancement of protein structural prediction tools, exemplified by AlphaFold and Iterative Threading ASSEmbly Refinement, has enabled the prediction of protein structures across species based on available protein sequence and structural data. In this study, we introduce an innovative molecular docking method that capitalizes on this wealth of structural data to enhance predictions of chemical susceptibility across species. We demonstrated this method using the androgen receptor as a pertinent modulator of endocrine function. By using protein structures, this method contextualizes species susceptibility within a functional framework and helps to integrate molecular docking into the repertoire of New Approach Methodologies (NAMs) that support the Next-Generation Risk Assessment (NGRA) paradigm through the novel integration of various open-source tools.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"30 ","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141240662","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}

{"title":"Pobody’s Nerfect: (Q)SAR works well for predicting bacterial mutagenicity of pesticides and their metabolites, but predictions for clastogenicity in vitro have room for improvement","authors":"Benjamin Christian Fischer ,&nbsp;Daniel Harrison Foil ,&nbsp;Asya Kadic,&nbsp;Carsten Kneuer,&nbsp;Jeannette König,&nbsp;Kristin Herrmann","doi":"10.1016/j.comtox.2024.100318","DOIUrl":"10.1016/j.comtox.2024.100318","url":null,"abstract":"<div><p>Genotoxicity assessment is a key component of regulatory decision-making in pesticide authorization and biocide approval. Conventionally, these genotoxicity requirements are addressed with OECD test guideline-compliant <em>in vitro</em> tests. In recent years, <em>in silico</em> approaches, such as (Q)SAR, have matured sufficiently so that they may be suitable to support, complement or even replace <em>in vitro</em> testing as a first tier of genotoxicity assessment. Among the different endpoints for genotoxicity, a high reliability is expected for <em>in silico</em> predictions of the endpoint bacterial mutagenicity. For other endpoints predictive performance is either unclarified or seems to be comparably lower. Herein, we describe the evaluation of several commercial and freely available (Q)SAR models and complementary combinations thereof with respect to the endpoints bacterial mutagenicity and chromosome damage <em>in vitro</em>. We used curated in-house test sets derived from OECD test guideline-compliant studies, gathered from submissions for the regulatory approval of biocides and plant protection products. The data set comprises active substances, metabolites and impurities. In line with previous publications we show that (Q)SAR models for bacterial mutagenicity generally performed well for compounds of the pesticide domain. Model combinations significantly increased the respective sensitivity. Models for chromosome damage still need to improve prior to their stand-alone use in regulatory decision-making, either strongly leaning towards sensitivity, at the expense of specificity or vice versa. Similar to the endpoint bacterial mutagenicity, combinations of models for chromosome damage increase sensitivity when compared to the individual models alone.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"30 ","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111324000203/pdfft?md5=2b32ca412b49bd2ffffd17dd0634acc0&pid=1-s2.0-S2468111324000203-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132258","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}

{"title":"Revealing an adverse outcome pathway network for reproductive toxicity induced by atrazine, via oxidative stress","authors":"Leonardo Vieira ,&nbsp;Matheus Alves ,&nbsp;Terezinha Souza ,&nbsp;Davi Farias","doi":"10.1016/j.comtox.2024.100317","DOIUrl":"https://doi.org/10.1016/j.comtox.2024.100317","url":null,"abstract":"<div><p>Adverse outcome pathway AOPs are conceptual frameworks that organize scientific knowledge about how stressors disrupt specific biological targets, pathways. AOP network consists of two or more AOPs that share common key events (KEs), including crucial events like molecular initiating events (MIEs) and adverse outcomes (AOs), which offer the opportunity to link toxicological pathways. Thus, to better understand the sequential series of KEs involved in the AOP 492 (<span><u>https://aopwiki.org/aops/492</u></span><svg><path></path></svg>), which is triggered by atrazine (ATZ), we first generated a Reproductive Toxicity via Oxidative Stress (RTOS) AOP network from individual AOPs published in the AOP-Wiki database, using this AOP as a seed. The KEs “Increased, Reactive oxygen species” and “Apoptosis” were considered the most common/highly connected KE within this network and an important point of divergence. Furthermore, “Increased, DNA damage and mutations” is a critical KE within the network, as it is highly connected and central, and represents a point of divergence. This suggests that these three KEs have a high predictive value and could, for example, serve as a basis for the development/selection of <em>in vitro</em> assays to assess reproductive toxicity. The <em>in silico</em> analyses revealed that the pivotal target proteins for ATZ-induced infertility via oxidative stress in humans are Tp53, Bcl2, Esr1, and Nos3, which interact indirectly with ATZ via intermediary factors such as Mapk3, Mapk1, and Cyp19a1. Further, the gene enrichment analyses indicate that these entities are involved in several biological processes and pathways directly associated with oxidative stress, DNA damage and apoptosis, further reinforcing the developed network.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"30 ","pages":"Article 100317"},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091002","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}

{"title":"NNI nanoinformatics conference 2023: Movement toward a common infrastructure for federal nanoEHS data computational toxicology: Short communication","authors":"Holly M. Mortensen ,&nbsp;Jaleesia D. Amos ,&nbsp;Thomas E. Exner ,&nbsp;Kenneth Flores ,&nbsp;Stacey Harper ,&nbsp;Annie M. Jarabek ,&nbsp;Fred Klaessig ,&nbsp;Vladimir Lobaskin ,&nbsp;Iseult Lynch ,&nbsp;Christopher S. Marcum ,&nbsp;Marvin Martens ,&nbsp;Branden Brough ,&nbsp;Quinn Spadola ,&nbsp;Rhema Bjorkland","doi":"10.1016/j.comtox.2024.100316","DOIUrl":"10.1016/j.comtox.2024.100316","url":null,"abstract":"<div><p>The National Nanotechnology Initiative organized a Nanoinformatics Conference in the 2023 Biden-Harris Administration’s Year of Open Science, which included interested U.S. and EU stakeholders, and preceded the U.S.-EU COR meeting on November 15th, 2023 in Washington, D.C. Progress in the development of a common nanoinformatics infrastructure in the European Union and United States were discussed. Development of contributing, individual database projects, and their strengths and weaknesses, were highlighted. Recommendations and next steps for a U.S. nanoEHS common infrastructure were discussed in light of the pending update of the National Nanotechnology Initiative (NNI)’s Environmental, Health and Safety Research Strategy, and U.S. efforts to curate and house nano Environmental Health and Safety (nanoEHS) data from U.S. federal stakeholder groups. Improved data standards, for reporting and storage have been identified as areas where concerted efforts could most benefit initially. Areas that were not addressed at the conference, but that are critical to progress of the U.S. federal consortium effort are the evaluation of data formats according to use and sustainability measures; modeler and end user, including risk-assessor and regulator perspectives; a need for a community forum or shared data location that is not hosted by any individual U.S. federal agency, and is accessible to the public; as well as emerging needs for integration with new data types such as micro and nano plastics, and interoperability with other data and meta-data, such as adverse outcome pathway information. Future progress will depend on continued interaction of the U.S. and EU CORs, stakeholders and partners in the continued development goals for shared or interoperable infrastructure for nanoEHS.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"30 ","pages":"Article 100316"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141056374","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}

{"title":"Assessment of abiotic reduction rates of organic compounds by interpretable structural factors and experimental conditions in anoxic water environments","authors":"Mohammad Hossein Keshavarz,&nbsp;Zeinab Shirazi,&nbsp;Mohammad Jafari,&nbsp;Arezoo Rajabi","doi":"10.1016/j.comtox.2024.100315","DOIUrl":"10.1016/j.comtox.2024.100315","url":null,"abstract":"<div><p>For organic contaminants in lake sediments, aquifers, and anaerobic bioreactors, their reduction is one of the primary transformation paths in these anoxic water environments. A simple model is introduced to predict pseudo-first order rate constants (<em>k_obs</em>) for the abiotic reduction of organic compounds featuring diverse reducible functional groups. It utilizes the largest experimental dataset of –log <em>k_obs</em>, encompassing 59 organic compounds (278 data points). Unlike available complex quantitative structure–activity relationship (QSAR) methods, the novel approach requires both experimental conditions and structural parameters. In comparison to one of the available general QSAR methods, the new model demonstrates favorable performance. The average absolute deviation (AAD), absolute maximum deviation (AD_max), average absolute relative deviation (AARD%), and R-squared (R²) values of the estimated outputs for 54/5 training/test data sets of the new model are 0.641/1.761, 1.761/1.417, 20.52/83.87, and 0.797/0.949, respectively. On the other hand, the available general comparative QSAR method shows the AAD: 1.311/2.301, AD_max: 3.795/3.732, AARD%: 641.0/821.2, and R²: 0.003/0.447. For the test set, AAD, AARD%, AD_max, and R² values for the new/comparative models are 0.649/2.403, 62.20/190.5, 1.215/3.732 and 0.974/0.789, respectively. In summary, the new model offers a straightforward approach for the manual calculation of –log <em>k_obs</em>, demonstrating excellent goodness-of-fit, reliability, precision, and accuracy.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"30 ","pages":"Article 100315"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141050365","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}

{"title":"In silico predictions of sub-chronic effects: Read-across using metabolic relationships between parents and transformation products","authors":"Darina G. Yordanova ,&nbsp;Chanita D. Kuseva ,&nbsp;Hristiana Ivanova ,&nbsp;Terry W. Schultz ,&nbsp;Vanessa Rocha ,&nbsp;Andreas Natsch ,&nbsp;Heike Laue ,&nbsp;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":"30 ","pages":"Article 100314"},"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}

{"title":"MoS-TEC: A toxicogenomics database based on model selection for time-expression curves","authors":"Franziska Kappenberg,&nbsp;Benedikt Küthe,&nbsp;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":"30 ","pages":"Article 100313"},"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}

{"title":"Simplified toxicity assessment in pharmaceutical and pesticide mixtures: Leveraging interpretable structural parameters","authors":"Mohammad Hossein Keshavarz,&nbsp;Zeinab Shirazi,&nbsp;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>EC₅₀</em>, log <em>EC₃₀</em>, and log <em>EC₁₀</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 (R²) for the novel models compared to the best comparative models exceed 1.0: 0.8618/0.8085 for log <em>EC₅₀</em>, 0.8856/0.8422 for log <em>EC₃₀</em>, and 0.8973/0.8556 for log <em>EC₁₀</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>EC₅₀</em>, 0.131/0.169 for log <em>EC₃₀</em>, and 0.182/0.215 for log <em>EC₁₀</em>.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"30 ","pages":"Article 100312"},"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}