Computational Toxicology最新文献

{"title":"Applying in silico approaches to nanotoxicology: Current status and future potential","authors":"Natalia Lidmar von Ranke ,&nbsp;Reinaldo Barros Geraldo ,&nbsp;André Lima dos Santos ,&nbsp;Victor G.O. Evangelho ,&nbsp;Flaminia Flammini ,&nbsp;Lucio Mendes Cabral ,&nbsp;Helena Carla Castro ,&nbsp;Carlos Rangel Rodrigues","doi":"10.1016/j.comtox.2022.100225","DOIUrl":"10.1016/j.comtox.2022.100225","url":null,"abstract":"<div><p>Nanomaterial development is one of the most significant technological advances of the 21st century, with considerable impact in several fields. However, nanomaterials can pose risks to human health and the environment. Therefore, it is imperative to perform toxicological tests; nonetheless, identification and analysis of all preparations is laborious. In this regard, <em>in silico</em> approaches facilitate nanotoxicity assessment at low cost and without involving animal testing. In this paper we review the use of computational approaches for nanotoxicology prediction. First, we present computational nanotoxicology in a regulatory context. Next, we discuss the primary computational methods used in toxicology, such as (quantitative) structure–activity relationship models, physiologically based pharmacokinetic models, and molecular modeling, and address the singularities of each method for nanomaterial analyses. Lastly, we describe several integrative approaches for computational nanotoxicology. Various database analyses combined with complementary computational approaches can lead to creative solutions for predicting toxicological effects during the design of new nanomaterials. Therefore, data-integration methods promote understanding of complex nanotoxicological events and can be used to develop successful precision models.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"22 ","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49518432","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":"Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches","authors":"Kevin M. Crofton ,&nbsp;Arianna Bassan ,&nbsp;Mamta Behl ,&nbsp;Yaroslav G. Chushak ,&nbsp;Ellen Fritsche ,&nbsp;Jeffery M. Gearhart ,&nbsp;Mary Sue Marty ,&nbsp;Moiz Mumtaz ,&nbsp;Manuela Pavan ,&nbsp;Patricia Ruiz ,&nbsp;Magdalini Sachana ,&nbsp;Rajamani Selvam ,&nbsp;Timothy J. Shafer ,&nbsp;Lidiya Stavitskaya ,&nbsp;David T. Szabo ,&nbsp;Steven T. Szabo ,&nbsp;Raymond R. Tice ,&nbsp;Dan Wilson ,&nbsp;David Woolley ,&nbsp;Glenn J. Myatt","doi":"10.1016/j.comtox.2022.100223","DOIUrl":"10.1016/j.comtox.2022.100223","url":null,"abstract":"<div><p>Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including <em>in silico</em> approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. <em>In silico</em> approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of <em>in silico</em> methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"22 ","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9748808","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":"Physiologically-based modeling of cholate disposition in beagle dog with and without treatment of the liver transporter inhibitor simeprevir","authors":"Shu-Wen Teng ,&nbsp;Michael Hafey ,&nbsp;Jeanine Ballard ,&nbsp;Xinjie Lin ,&nbsp;Changhong Yun ,&nbsp;Vijay More ,&nbsp;Robert Houle ,&nbsp;Ravi Katwaru ,&nbsp;Ann Thomas ,&nbsp;Grace Chan ,&nbsp;Kim Michel ,&nbsp;Yutai Li ,&nbsp;Kara Pearson ,&nbsp;Christopher Gibson","doi":"10.1016/j.comtox.2022.100224","DOIUrl":"10.1016/j.comtox.2022.100224","url":null,"abstract":"<div><p>BSEP inhibition is one risk factor for Drug-Induced Liver Injury (DILI). While in vitro screening of BSEP inhibition may prevent compounds with BSEP liability from progressing into the clinic, these in vitro data alone can result in false-positives and as such a specific in vivo biomarker would further enhance our BSEP inhibition de-risking strategy. Measurement of endogenous bile acids as biomarkers of BSEP inhibition in vivo is complicated by several factors, including drugs that inhibit BSEP can also inhibit other bile acid transporters such as NTCP. Here, we developed a novel translational framework, including an in vivo biomarker with a corresponding mechanistic model, and attempted to decouple the effect of liver sinusoidal uptake inhibition from efflux inhibition on bile acid disposition in the beagle dog. Specifically, we hypothesized that the change of a stable isotope-labeled (SIL) bile acid tracer’s exposure would yield a toxicodynamic signal that can provide insight into BSEP inhibition and ensuing bile salt accumulation. For this purpose we dosed the stable isotope-labeled cholic acid (¹³C-CA) and taurocholic acid (D4-TCA) as biomarker tracers in dogs, with and without the liver transporter inhibitor simeprevir, and determined the plasma and bile exposure of ¹³C-CA, ¹³C-TCA, D4-CA and D4-TCA in vivo. Key bile acid clearance and transporter inhibition parameters were determined in vitro. We developed a novel Physiologically Based Pharmacokinetic model (PBPK) to integrate the mechanistic physiological understanding, literature knowledge, and in vitro laboratory data to model bile acid disposition. Using modeling and simulation, we provided an increased mechanistic understanding of how to use plasma bile acid tracer data to inform on potential liver transporters inhibition and limitations to in vivo translation. The novel translational framework can enhance the future BSEP inhibition de-risking strategy, particularly if the experimental confounders to studying kinetics in dog hepatocytes in vitro models are solved.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"22 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111322000123/pdfft?md5=56a90a95a905ed980c1c5a2a975df2c8&pid=1-s2.0-S2468111322000123-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44313768","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":"In-silico profiling of SLC6A19, for identification of deleterious ns-SNPs to enhance the Hartnup disease diagnosis","authors":"Wahidah H. Al-Qahtani ,&nbsp;Dinakarkumar Yuvaraj ,&nbsp;Anjaneyulu Sai Ramesh ,&nbsp;Haryni Jayaradhika Raghuraman Rengarajan ,&nbsp;Muthusamy Karnan ,&nbsp;Jothiramalingam Rajabathar ,&nbsp;Arokiyaraj Charumathi ,&nbsp;Sayali Harishchandra Pangam ,&nbsp;Priyanka Kameswari Devarakonda ,&nbsp;Gouthami Nadiminti ,&nbsp;Prikshit Sharma","doi":"10.1016/j.comtox.2022.100215","DOIUrl":"10.1016/j.comtox.2022.100215","url":null,"abstract":"<div><p>The mutation in the solute carrier 6 (SLC6A19) gene causes the Hartnup disorder, affecting the absorption of non-polar amino acids. Recent DNA sequencing advances have increased the identification of single nucleotide polymorphisms (SNPs) in<!--> <!-->the SLC6A19 gene, but no further information regarding their deleterious probability is available. Hence, this study aims to comprehensively analyze and identify the potentially deleterious non-synonymous-SNPs of the SLC6A19 gene with a computational approach using openly accessible online software tools including SIFT, PolyPhen2, SAVES 5.0, SPIDER, <em>etc</em>. and also to determine effective lead compound for its treatment by docking. The SLC6A19 gene translates to B⁰AT1 tetramer protein, amongst chain A was taken into consideration. The analysis revealed mutation G490S (chain A) of the said protein as the candidate ns-SNP among the screened 539 missense mutations, retrieved from the National Centre for Biotechnology Information (NCBI). Moreover, the binding energy of the candidate ns-SNP had a higher affinity for benztropine over conventional drugs such as nicotinamide and niacin. Yet, clinical validation is required to support the above findings.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"22 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42470003","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":"Reflections of the QSAR2021 meeting","authors":"Grace Patlewicz","doi":"10.1016/j.comtox.2022.100221","DOIUrl":"10.1016/j.comtox.2022.100221","url":null,"abstract":"","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"22 ","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111322000093/pdfft?md5=ba9633c2cff408515290c4b03d2d1253&pid=1-s2.0-S2468111322000093-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85930743","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":"Toxicity prediction using locality-sensitive deep learner","authors":"Xiu Huan Yap ,&nbsp;Michael Raymer","doi":"10.1016/j.comtox.2021.100210","DOIUrl":"10.1016/j.comtox.2021.100210","url":null,"abstract":"<div><p>Toxicity prediction using linear QSAR models typically show good predictivity when trained on a small-scale, local level of similar chemicals, but not on a global level spanning a chemical library. We hypothesize that large chemical toxicity datasets generally have a <em>locally-linear data</em> structure, and propose the <em>locality-sensitive deep learner</em> (LSDL), a deep neural network with attention mechanism <span>[1]</span> and an optional instance-based feature weighting component, to tackle the challenges of heterogeneous classification space with locally-varying noise features. On carefully-constructed synthetic data with extremely unbalanced classes (10% positive), the locality-sensitive deep learner with learned feature weights retained high test performance (AUC &gt; 0.9) in the presence of 60% cluster-specific feature noise, while feed-forward neural network appeared to over-fit the data (AUC &lt; 0.6). For the Tox21 dataset <span>[2]</span>, locality-sensitive deep learner out-performed feed-forward neural network in 9 out of 12 labels. For acetylcholinesterase inhibition (AChEi) <span>[3]</span>, Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) <span>[4]</span>, and Acute Oral Toxicity (AOT) <span>[5]</span> datasets, we observed that the combination of locality-sensitive deep learner with feed-forward neural network showed improved test performance than individual models in almost all cases. Generalizing machine learning models to fit locally-linear data may potentially improve predictivity of chemical toxicity models. The proposed modeling approach could potentially complement and add diversity to the current suite of predictive toxicity algorithms for use in ensemble and/or consensus models.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47254022","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":"Prediction of mammalian maximal rates of metabolism and Michaelis constants for industrial and environmental compounds: Revisiting four quantitative structure activity relationship (QSAR) publications","authors":"Lisa M. Sweeney ,&nbsp;Teresa R. Sterner","doi":"10.1016/j.comtox.2022.100214","DOIUrl":"10.1016/j.comtox.2022.100214","url":null,"abstract":"<div><p>Traditional in vivo strategies for investigating toxicokinetics can be time consuming, expensive, and often do not directly address species of interest, e.g., humans. As such, conventional approaches for addressing emerging human health risk assessment concerns that rely on toxicokinetic information have been slow and suboptimal. Alternatives to rodent in vivo toxicokinetic studies include in vitro and in silico approaches for estimating toxicokinetic parameters. This paper focuses on quantitative structure-activity relationships (QSARs) that predict both maximal capacity for metabolism (Vmax) and KM (Michaelis constant, or half-maximal concentration for metabolism). The QSARs, identified from four publications, were evaluated using a previously published 10-step work flow. None of the evaluated QSARs in their published forms could be fully validated. Literature review, finding alternative sources of descriptors and identifiers, substitution of correlated descriptors, and use of graphical information allowed the deficiencies to be addressed for QSARs describing alkylbenzenes, volatile organic compounds (VOCs), and substrates of alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), cytochrome P450 (CYP), and flavin containing monooxygenases (FMO). Ultimately, reliable, well-documented, updated expressions for Vmax and KM (or Vmax/KM) were derived for each source/data set. The smaller data sets tended to have better predictivity, and Vmax was generally more accurately predicted than KM. Comparisons of the QSARs’ source chemicals found limited overlap in source chemicals, but substantial overlap in descriptor domains. In a feasibility case study, applicability of these QSARs to jet fuel components with limited toxicokinetic parameterization was assessed to determine the potential utility for investigation of mixture toxicokinetics. The VOC QSARs and alkylbenzene QSARs were identified as having greater potential to accurately predict in vivo toxicokinetics of the selected jet fuel components than the CYP QSARs, due to the physicochemical characteristics of the chemicals used in their development.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45275502","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":"Towards a qAOP framework for predictive toxicology - Linking data to decisions","authors":"Alicia Paini ,&nbsp;Ivana Campia ,&nbsp;Mark T.D. Cronin ,&nbsp;David Asturiol ,&nbsp;Lidia Ceriani ,&nbsp;Thomas E. Exner ,&nbsp;Wang Gao ,&nbsp;Caroline Gomes ,&nbsp;Johannes Kruisselbrink ,&nbsp;Marvin Martens ,&nbsp;M.E. Bette Meek ,&nbsp;David Pamies ,&nbsp;Julia Pletz ,&nbsp;Stefan Scholz ,&nbsp;Andreas Schüttler ,&nbsp;Nicoleta Spînu ,&nbsp;Daniel L. Villeneuve ,&nbsp;Clemens Wittwehr ,&nbsp;Andrew Worth ,&nbsp;Mirjam Luijten","doi":"10.1016/j.comtox.2021.100195","DOIUrl":"10.1016/j.comtox.2021.100195","url":null,"abstract":"<div><p>The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including <em>in silico</em>, <em>in vitro</em> and <em>in vivo</em> assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39959705","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":"Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network","authors":"Nicoleta Spînu ,&nbsp;Mark T.D. Cronin ,&nbsp;Junpeng Lao ,&nbsp;Anna Bal-Price ,&nbsp;Ivana Campia ,&nbsp;Steven J. Enoch ,&nbsp;Judith C. Madden ,&nbsp;Liadys Mora Lagares ,&nbsp;Marjana Novič ,&nbsp;David Pamies ,&nbsp;Stefan Scholz ,&nbsp;Daniel L. Villeneuve ,&nbsp;Andrew P. Worth","doi":"10.1016/j.comtox.2021.100206","DOIUrl":"10.1016/j.comtox.2021.100206","url":null,"abstract":"<div><p>In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as <em>in silico</em> and <em>in vitro</em> information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39959706","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":"Will qAOPs modernise toxicology?","authors":"Mark T.D. Cronin ,&nbsp;Nicoleta Spînu ,&nbsp;Andrew P. Worth","doi":"10.1016/j.comtox.2021.100199","DOIUrl":"10.1016/j.comtox.2021.100199","url":null,"abstract":"<div><p>In this editorial we reflect on the past decade of developments in predictive toxicology, and in particular on the evolution of the Adverse Outcome Pathway (AOP) paradigm. Starting out as a concept, AOPs have become the focal point of a community of scientists, regulators and decision-makers. AOPs provide the mechanistic knowledge underpinning the development of Integrated Approaches to Testing and Assessment (IATA), including computational models now referred to as quantitative AOPs (qAOPs). With reference to recent and related works on qAOPs, we take a brief historical perspective and ask what is the next stage in modernising chemical toxicology beyond animal testing.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111321000451/pdfft?md5=0927eedbfc1ef0325eeb1d7b1bf62ec9&pid=1-s2.0-S2468111321000451-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43063019","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}