Computational Toxicology最新文献

{"title":"Chronic and acute eco-toxicity modeling of carcinogenic and hazardous air pollutants toward humans for critical risk assessment and regulatory decision-making","authors":"Ankur Kumar ,&nbsp;Probir Kumar Ojha ,&nbsp;Kunal Roy","doi":"10.1016/j.comtox.2025.100358","DOIUrl":"10.1016/j.comtox.2025.100358","url":null,"abstract":"<div><div>Rapid and regular exposure to carcinogenic, toxic, and hazardous chemicals in humans and other living organisms can cause serious chronic (long-term) and acute (short-term) health issues. Since <em>in-vitro</em> and <em>in-vivo</em> toxicity testing requires a long time, a large number of animal experiments, and a high cost, in-silico toxicity testing is the best alternative supported by various regulatory organizations. In our current work, multiple regression-based Quantitative structure–activity relationship models (two chronic toxicity models, a QAAR (quantiative activity-activity relationship) model (chronic studies), and seven acute toxicity models) have been developed to assess the chronic and acute toxicities of carcinogenic chemicals toward humans rigorously following the OECD principles. Statistical validation metrics (R² = 0.604–0.990, Q²_LOO = 0.558––0.988, Q²_F1 = 0.580–0.990, Q²_F2 = 0.503–0.988, MAE_test = 0.103–0.766) demonstrated the robustness, reliability, reproducibility, and predictivity of the developed models. The developed models were utilized to screen the PPDB database, and their predictions were validated against real-world data to confirm their predictive accuracy and reliability. Thus, the present work will significantly aid in bridging the chronic and acute toxicity data gap, identifying carcinogenic chemicals, screening various chemical databases, and developing safer (from observed bio-marker), non-carcinogenic, and greener chemicals strictly obeying the reduction, refinement, and replacement (3Rs) guidelines.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100358"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196203","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":"Feasibility study on integrating morphological and transcriptomic data for identifying teratogenic molecular markers in zebrafish embryo toxicity testing","authors":"Matthias M. Wehr ,&nbsp;Hilda Witters ,&nbsp;Silvie Remy ,&nbsp;Bruce Schultz ,&nbsp;Marc Jacobs ,&nbsp;Sylvia E. Escher","doi":"10.1016/j.comtox.2025.100342","DOIUrl":"10.1016/j.comtox.2025.100342","url":null,"abstract":"<div><div>To create a resource for the integration of developmental toxicity new approach methodologies (NAM) data, zebrafish embryo toxicity test (ZET) data were curated and the zeTera database was created. To capture observations of the morphological alteration potential of chemicals, the zeTera database contains experimental study designs and morphological observation data from the literature. Observations of alterations recorded in zeTera were mapped to ontologies and terms were harmonized. In addition, public transcriptomics repositories were mined for data on zebrafish embryos under chemically induced stress. The re-analyzed datasets were compiled into the zetOmics database for the identification of biomarkers of teratogenicity. To identify data-rich compounds, an overlap of both databases was formed, and compounds were grouped based on structural similarities.</div><div>To identify the molecular drivers of teratogenic toxicants, Triadimefon was chosen as model compound for its well-documented teratogenic effects and known mode of action (MOA). We have compiled existing data about Triadimefon from zeTera and conducted additional testing using the ZET, with additional gene expression measurements for data gap filling. From the literature search we identified the adverse outcome pathway (AOP) of triadimefon leading to craniofacial malformations by disruption of retinoic acid metabolism.</div><div>Transcriptomic response in a concentration dependent manner was observed as early as 24 h post fertilization (hpf) with consistent, statistically significant, differential expression spanning the later timepoints. A set of 5 genes (<em>cyp26a1</em>, <em>dhrs3b</em>, <em>cyp26b1</em>, <em>cthrc1a</em>, and <em>cd248b</em>) were selected for their differential expression pattern across time and concentration. These biomarkers were further confirmed using read across approach including data from related structures.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100342"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738669","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":"Development of Nipah virus drugs from FDA-approved drugs: An integrated computational approach","authors":"Panneerselvam Theivendren ,&nbsp;Selvaraj Kunjiappan ,&nbsp;Parasuraman Pavadai ,&nbsp;Natarajan Kiruthiga ,&nbsp;Anusuya Murugavel ,&nbsp;Avinash Dayalan","doi":"10.1016/j.comtox.2025.100354","DOIUrl":"10.1016/j.comtox.2025.100354","url":null,"abstract":"<div><div>The Nipah virus (NiV) is a highly virulent zoonotic pathogen that presents a substantial risk to public health, as limited therapeutic interventions are available. The present study utilizes a computational methodology to discover pharmaceutical substances that have been received from the database consisting of 4344 U.S. Food and Drug Administration (FDA)-approved drugs and have the potential to be repurposed to treat NiV infection. We have used molecular docking and dynamics simulation to evaluate the binding affinity and stability of the drugs against the key viral target, Ephrin-B2. The findings of our study demonstrate the presence of numerous FDA-approved drugs that display favourable binding interactions with the target of Ephrin-B2. Within this FDA-approved data set of drugs, we have identified certain FDA-approved drugs, such as Guamecycline, Ergotamine, Sancycline, Entrectinib, and Atogepant, which showed considerably better binding scores. The dynamic behaviour of ligand–protein interaction was evaluated using molecular dynamics simulation, which offered valuable insights into drug-target complexes’ temporal stability and conformational alterations. The results of docking studies indicate to active ingredients Guamecycline, Ergotamine, Sancycline, Entrectinib and Atogepant having notable inhibition of the Ephrin-B2 protein. According to the findings from the MD simulation, it was noted that Guamecycline displayed significant interaction with the Ephrin-B2 protein. Therefore, Guamecycline shows potential as a suitable primary chemical for treating NiV. Further, the sub-structures of Guamecycline were used to optimize and substantiate the stability of Guamecycline; in this relation sub, structure <strong>ZINC000169368545</strong> was correlated with Guamecycline, and the observed result showed that the Guamecycline was better lead moiety to inhibit the target Ephrin-B2.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100354"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947515","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":"Can graph similarity metrics be helpful for analogue identification as part of a read-across approach?","authors":"Brett Hagan ,&nbsp;Imran Shah ,&nbsp;Grace Patlewicz","doi":"10.1016/j.comtox.2025.100353","DOIUrl":"10.1016/j.comtox.2025.100353","url":null,"abstract":"<div><div>Read-across is a technique used to fill data gaps for substances lacking specific hazard data. The technique relies on identifying source analogues with relevant data that are ‘similar’ to the substance of interest (target). Typically, source analogues are identified on the basis of structural similarity but the evaluation of their suitability for read-across depends on other contexts of similarity. This manuscript aimed to review the ways in which source analogues are identified for read-across using chemical fingerprint/scaffold approaches before describing graph-based approaches including; graph kernel, graph embedding, and deep learning. To demonstrate how these could be practically used for analogue identification, five different toxicity datasets of varying size and diversity were selected that had been the subject of previous read-across or QSAR analyses. One dataset was an analogue set whereas the other four datasets comprised substances evaluated for their skin sensitisation, skin irritation, fathead minnow aquatic toxicity and genotoxicity potential. The analogues and their associated similarities using the different graph based approaches were compared with the outcomes from two chemical fingerprint approaches (ToxPrints and Morgan). The results for each dataset are briefly described. Based on the examples evaluated, graph kernel approaches were found to have some promise, in contrast unsupervised whole graph embedding approaches were ineffective for all the datasets evaluated. Graph convolutional networks produced meaningful embeddings for the genotoxicity dataset evaluated. Depending on use case, availability and size of training data, graph similarity approaches have the potential to play a larger role in analogue identification and evaluation for read-across.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100353"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935958","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":"A comparative study of biostatistical pipelines for benchmark concentration modeling of in vitro screening assays","authors":"Kelly E. Carstens ,&nbsp;Arif Dönmez ,&nbsp;Jui-Hua Hsieh ,&nbsp;Kristina Bartmann ,&nbsp;Katie Paul Friedman ,&nbsp;Katharina Koch ,&nbsp;Martin Scholze ,&nbsp;Ellen Fritsche","doi":"10.1016/j.comtox.2025.100360","DOIUrl":"10.1016/j.comtox.2025.100360","url":null,"abstract":"<div><div>New approach methods (NAMs) have been prioritized to reduce the use of animals for chemical safety assessment while continuing to protect human health and the environment. A key challenge of generating toxicity data is the implementation of a standardized analysis approach for transparent and reproducible benchmark concentration (BMC) estimation and uncertainty quantification for assay developers, regulators, and other stakeholders. In this study, we compared the bioactivity results of 321 chemical samples from four established BMC analysis pipelines used for evaluation of developmental neurotoxicity (DNT) NAMs data: the ToxCast pipeline (tcpl), CRStats, DNT DIVER (Curvep and Hill pipelines). We found an overall activity hit call concordance of 77.2 % and highly correlated BMC estimations (r = 0.92 ± 0.02 SD), demonstrating generally good agreement across pipelines. Discordance appeared to be explained predominantly by noise within the data and borderline activity (activity occuring near the benchmark response level). Evaluation of the BMC confidence intervals indicated that pipeline selection may impact the estimation of the BMC lower bound. Consideration of biphasic models appeared important for capturing biologically-relevant changes in activity in the DNT battery. Lastly, different approaches to compute ‘selective’ bioactivity (activity below the threshold of cytotoxicity) were compared, identifying the CRstats classification model as more stringent for classifying selective activity. Overall, these findings indicated greater confidence in NAMs bioactivity results and emphasize the importance of understanding strengths and uncertainties of concentration–response modeling pipelines for informing biological interpretation and application decision making.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100360"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240741","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":"Predicting uptake and elimination kinetics of chemicals in invertebrates: A technical note on residual variance modeling","authors":"Henk J. van Lingen ,&nbsp;Edoardo Saccenti ,&nbsp;Maria Suarez-Diez ,&nbsp;Marta Baccaro ,&nbsp;Nico W. van den Brink","doi":"10.1016/j.comtox.2024.100337","DOIUrl":"10.1016/j.comtox.2024.100337","url":null,"abstract":"<div><div>Toxicokinetic models for predicting contents of nanomaterials and other toxic chemicals are often fitted without evaluation of the residual variance structure. The aim of the present study was to evaluate various residual variance structures, assuming either homoscedasticity or heteroscedasticity, when fitting non-linear toxicokinetic one-compartment models for predicting uptake, bioaccumulation and elimination of chemicals in invertebrate organisms. Data describing the exposure of several aquatic and terrestrial invertebrates to specific metal nanomaterials and other chemicals were available from real experiments for evaluating the residual variance functions for toxicokinetic models. As proof of concept, datasets of truly homoscedastic and heteroscedastic nature were simulated. Depending the dataset, applying models with different residuals variance assumption largely affected the residual plots and the error margins of parameters or the predicted content of a chemical. Consequently, selecting the most accurate residual variance functions for toxicokinetic modeling, either homoscedastic or heteroscedastic, improves the prediction of chemical contents in invertebrate organisms and the estimation of the associated uptake and elimination rates.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"33 ","pages":"Article 100337"},"PeriodicalIF":3.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147585","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":"A bioinformatics framework for human health risk assessment of externally applied dsRNA-based biopesticides","authors":"Upendra K. Devisetty ,&nbsp;Emma De Neef ,&nbsp;Eric R.L. Gordon ,&nbsp;Valeria Velásquez-Zapata ,&nbsp;Kenneth Narva ,&nbsp;Laurent Mézin ,&nbsp;Peter Mc Cahon ,&nbsp;Kenneth W. Witwer ,&nbsp;Krishnakumar Sridharan","doi":"10.1016/j.comtox.2024.100340","DOIUrl":"10.1016/j.comtox.2024.100340","url":null,"abstract":"<div><div>Current plant protection methods rely predominantly on conventional chemical pesticides that can have negative human health and environmental impacts. Consequently, there is a pressing need to develop sustainable crop protection solutions that have improved safety profiles for humans and other non-target organisms (NTOs). RNA interference (RNAi) is a natural defense mechanism against viruses found in eukaryotes that silences viral genes in a sequence-specific manner. Recently, RNAi has been utilized to specifically target essential genes of pests with a novel class of topical, sprayable biopesticides based on dsRNA (double-stranded RNA). A critical step in the regulatory approval of such externally applied dsRNA-based biopesticides is a robust bioinformatics analysis of potential off-target effects to humans and other organisms. However, no generally applicable guidelines are available for risk assessment of dsRNA-based biopesticides for humans. Here, we address this gap by describing a bioinformatics framework for risk assessment in humans, informed by peer-reviewed literature, that quantifies potential off-targets with a primary focus on externally applied dsRNA-based biopesticides. The framework comprises three main components: bioinformatics tools for predicting off-target effects in humans, a mismatch tolerance for sequence divergence between dsRNA and unintended targets to delineate potential human off-target effects, and siRNA criteria for quantifying the possibility of theoretical gene silencing in the presence of mismatches in humans. This bioinformatics framework represents the most comprehensive approach described to date and has been used successfully for evaluating the potential risks of the externally applied dsRNA-based biopesticide Calantha^TM to humans.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"33 ","pages":"Article 100340"},"PeriodicalIF":3.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147584","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":"The predictivity of QSARs for toxicity: Recommendations for improving model performance","authors":"Mark T.D. Cronin,&nbsp;Homa Basiri,&nbsp;Georgios Chrysochoou,&nbsp;Steven J. Enoch,&nbsp;James W. Firman,&nbsp;Nicoleta Spînu,&nbsp;Judith C. Madden","doi":"10.1016/j.comtox.2024.100338","DOIUrl":"10.1016/j.comtox.2024.100338","url":null,"abstract":"<div><div>Quantitative structure–activity relationships (QSARs) are invaluable computational tools for the prediction of the biological effects and physico-chemical properties of molecules. For chemical safety assessment they are used frequently to make predictions of toxic or adverse effects, as well as other activities related to toxicokinetics. QSARs and their predictions can be assessed against a number of criteria for their potential use as surrogates for animal, or other, tests. A recent exercise by the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan, assessed QSARs to predict the outcome of the Ames test. The predictive performance of models was scrutinised with full disclosure of results. The authors of this publication developed one such model, which had disappointing performance in this predictive exercise. In order to understand why the QSAR had poor performance metrics, this paper reflects on factors that affect a QSAR model. There is no one reason for poor performance of a QSAR model, rather it is likely to be a combination of factors. Reasons for poor performance included inadequate consideration of the underlying data quality, consistency and relevance; lack of appropriate descriptors relating to the endpoint and mechanism of action; not selecting a model correctly in terms of its structure (i.e., complexity) and number of descriptors; not addressing metabolism adequately in the modelling process; ill-defined assessment of the uncertainties within a model; and not ensuring predictions are within the applicability domain of the model. Whilst this paper draws on examples for the prediction of mutagenicity, the findings are applicable to all toxicological activities and physico-chemical properties.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"33 ","pages":"Article 100338"},"PeriodicalIF":3.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147586","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":"Augmented allometric scaling: Predicting drug clearance in farm animals with machine learning using body weight","authors":"David Inauen ,&nbsp;Leonie Sophie Lautz ,&nbsp;Aalbert Jan Hendriks ,&nbsp;Ronette Gehring","doi":"10.1016/j.comtox.2025.100341","DOIUrl":"10.1016/j.comtox.2025.100341","url":null,"abstract":"<div><div>In farm animals, kinetic data of exogenous chemicals, such as pharmaceuticals, environmental pollutants or feed contaminants, are scarce. To allow extrapolation across chemicals and species this study developed a machine learning approach that integrated allometric scaling and quantitative structure–activity relationships to predict total body clearance in farm animals. Using body weight and molecular descriptors of chemicals, the models applied both linear and non-linear machine learning methods such as random forest to predict clearance. Data for intravenously administered chemicals were collected from literature from a variety of species. Molecular descriptors of these chemicals were computed. Log-transformed clearances were predicted for five farm animal species—cattle, sheep, goat, swine, horse—as well as dogs and cats for comparative analysis. Two models using machine learning methods were developed: a purely extrapolative machine learning model, and an approach titled “augmented allometric scaling” which, similarly to simple allometric scaling, used pre-existing data in other species to predict a chemicals’ clearance in a target species. The extrapolative approach had large differences in training and test set metrics, while the latter approach demonstrated modestly improved predictive accuracy over simple allometric scaling in farm animals with up to 60.8% of predictions below a fold error of 2, compared to 51% given by allometry, with a difference of up to 0.5 fold errors. In dogs, the new approach performed comparably and worse in cats. This study highlights potentials and limits of machine learning in refining kinetic predictions in farm animals.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"33 ","pages":"Article 100341"},"PeriodicalIF":3.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580294","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":"Modeling chemical bioaccumulation in snakes, part 1: Model development","authors":"Xiaoyu Zhang,&nbsp;Zijian Li","doi":"10.1016/j.comtox.2024.100332","DOIUrl":"10.1016/j.comtox.2024.100332","url":null,"abstract":"<div><div>Environmental chemical emission influences ecological health to some extent. Predators (e.g., snakes) could bioaccumulate chemicals along the food chain, which also leaves potential health implications on their reproduction. For the difficulty of collecting related biomatrices for exposure assessment, part 1 of this study proposed a modeling method relying on physiologically based kinetic (PBK) theory to estimate snake chronic exposure to environmental chemicals. In the steady state, the biotransfer factors of chemicals produced by the PBK model can indicate a snake’s chronic internal exposure to environmental chemicals and their potential for bioaccumulation at this level of the food web. Specifically, 3074 organic chemicals were compelled into the dataset for PBK modeling (part 2 of the study). The modeling framework covered the physiological process of the skin to consider shed snakeskin as a potential biomarker for future study. The proposed modeling approach was integrated into a spreadsheet, enabling the modification of input values to simulate outcomes for a wide range of chemical and snake species. The proposed model can help assess the ecological risks of environmental chemicals and quantify their behavior in the food web.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"32 ","pages":"Article 100332"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432363","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}