Computational Toxicology最新文献

{"title":"Optimal experimental designs for big and small experiments in toxicology with applications to studying hormesis via metaheuristics","authors":"Brian P.H. Wu ,&nbsp;Ray-Bing Chen ,&nbsp;Weng Kee Wong","doi":"10.1016/j.comtox.2025.100345","DOIUrl":"10.1016/j.comtox.2025.100345","url":null,"abstract":"<div><div>There are theoretical methods for constructing model-based optimal designs for a given design criterion when the sample size is large. Some of these methods may work for certain models or design criteria and some may find the optimal designs only under a restrictive setting. When the sample size is small, the theory-based methods may become invalid and the optimal designs may also not be implementable. Our first goal is to introduce nature-inspired metaheuristics to efficiently find all types of model-based optimal designs. These metaheuristic algorithms, widely used in engineering, computer science, and artificial intelligence, are generally fast and free of stringent assumptions. For our second goal, we introduce an efficient rounding method to produce an implementable, exact design for small-sized experiments based on large-sample optimal designs. To provide toxicologists with easy access to a variety of model-based optimal designs for both large and small experiments, our third goal is to develop a web-based app. This app will generate different types of model-based optimal designs, allow comparisons, and evaluate the efficiency of any design. As an application, we focus on hormesis and find model-based designs for detecting the presence of hormesis, estimating model parameters and estimating the threshold dose. The methodology is not restricted to studying hormesis only and is broadly applicable for designing other studies in toxicology and beyond.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100345"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834957","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":"Validation of OECD QSAR Toolbox profilers for genotoxicity assessment of pesticides using the MultiCase genotoxicity database","authors":"Monika Kemény ,&nbsp;Colin M. North ,&nbsp;Felix M. Kluxen ,&nbsp;Markus Frericks ,&nbsp;Dragana Vukelic ,&nbsp;Sishuo Cao ,&nbsp;Roustem Saiakhov ,&nbsp;Mounika Girireddy","doi":"10.1016/j.comtox.2025.100356","DOIUrl":"10.1016/j.comtox.2025.100356","url":null,"abstract":"<div><div>Quantitative Structure Activity Relationship (QSAR) models are widely used for genotoxicity assessment in regulatory settings. <em>In silico</em> profilers are a special case of models capturing mechanistic insights specific to a particular toxicological endpoint or reflecting chemistry-related attributes that may not be directly associated with a defined mechanism of toxicity. This study explores the accuracy of using such profilers as a lower tier in genotoxicity assessment to inform regulatory concerns. Relevant profilers in the OECD QSAR Toolbox are investigated using an external validation dataset derived from the MultiCASE Genotoxicity database, which contains AMES mutagenicity and in vivo micronucleus (MNT) experimental results. The MNT dataset includes the commercial in vivo MNT dataset expanded with pesticide data from regulatory documents. This analysis incorporates the use of metabolism simulations by the OECD QSAR Toolbox to assess their influence on profiler performance. The present findings show that the absence of profiler alerts correlates well with experimentally negative outcomes. However, the calculated accuracy for the MNT-related and AMES-related profilers varies considerably (41%-78% for MNT-related profilers and 62%-88% for AMES-related profilers using the full set with and without consideration of metabolism). Incorporating metabolism simulations increases accuracy by 4–6% for the full AMES-dataset, and 4–16% for the full MNT-dataset. Together, genotoxicity assessment using the Toolbox profilers should include a critical evaluation of any triggered alerts, considering the overall performance statistics of the profilers presented within this work. Results from third-party QSAR models provide critical insights to complement the expert review of any profiler positive result, as profilers alone are not recommended to be used directly for prediction purpose.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100356"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089757","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":"An exploration of the use of hybrid fingerprints in Generalized Read-Across and their impact on predictive performance for selected in vivo toxicity outcomes","authors":"Aubrey Leary ,&nbsp;Imran Shah ,&nbsp;Grace Patlewicz","doi":"10.1016/j.comtox.2025.100349","DOIUrl":"10.1016/j.comtox.2025.100349","url":null,"abstract":"<div><div>Read-across is a cost-efficient means of generating information for hazard assessment. Approaches such as Generalized Read-Across (GenRA) facilitate objective and reproducible read-across for untested substances. GenRA is a web application, and its prediction engine is also available as a python package (genra-py). Recent updates permit source analogues to be identified using ‘hybrid’ fingerprints, i.e. analogues identified based on more than one type of similarity measure. Herein, the performance of hybrid fingerprints relative to Morgan chemical fingerprints was evaluated for a selection of acute and chronic <em>in vivo</em> toxicity outcomes. Grid search and cross-validation on a dataset of 5,830 chemicals with rodent acute oral toxicity (LD₅₀) values were used to tune the hybrid weight hyperparameter for up to four chemical fingerprints (Morgan, Torsion, ToxPrint and Analog Identification Methodology (AIM)). The optimal hybrid fingerprint derived (52.12% Morgan, 23.40% ToxPrint, 12.44% AIM, 12.04% Torsion) outperformed Morgan fingerprints across all 10 folds of a cross-validation procedure (mean test set coefficient of determination (R²) 0.517 (Morgan) vs. 0.557 (hybrid)). The hybrid fingerprint was then used to make toxicity predictions for 2 other datasets, a set of 3,266 chemicals with oral chronic human equivalent benchmark dose values (mean test set R² 0.445 vs. 0.417 for Morgan) and a set of 9,443 chemicals with acute mammalian oral hazard classifications (mean balanced accuracy (BA) 0.577 vs 0.553 for Morgan). Overall, performance improved when using the hybrid fingerprint tuned for the acute toxicity dataset. Using the custom hybrid option in GenRA results in improved read-across predictions relative to current defaults.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100349"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887946","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":"Evaluating marginal likelihood approximations of dose–response relationship models in Bayesian benchmark dose methods for risk assessment","authors":"Sota Minewaki ,&nbsp;Tomohiro Ohigashi ,&nbsp;Takashi Sozu","doi":"10.1016/j.comtox.2025.100347","DOIUrl":"10.1016/j.comtox.2025.100347","url":null,"abstract":"<div><div>Benchmark dose (BMD; a dose associated with a specified change in response) is used to determine the point of departure for the acceptable daily intake of substances for humans. Multiple dose–response relationship models are considered in the BMD method. The Bayesian model averaging (BMA) method is commonly used, where several models are averaged based on their posterior probabilities, which are determined by calculating the marginal likelihood (ML). Several ML approximation methods are employed in standard software packages, such as BBMD, <span>ToxicR</span>, and the EFSA platform for the BMD method, because the ML cannot be analytically calculated. Although ML values differ among approximation methods, resulting in BMD estimates, this phenomenon is neither widely recognized nor quantitatively evaluated. In this study, we evaluated the agreement of BMD estimates among five ML approximation methods in the BMA method. The five ML approximation methods are (1) maximum likelihood estimation (MLE)-based Schwarz criterion, (2) Markov chain Monte Carlo (MCMC)-based Schwarz criterion, (3) Laplace approximation, (4) density estimation, and (5) bridge sampling. We used eight dose–response relationship models and three prior distributions used in BBMD and <span>ToxicR</span> for 518 experimental datasets. The agreement among the approximation methods tended to be low in the non-informative prior distribution. Although the agreements tended to be high in the informative prior distribution, they were low in some approximation methods. Since the approximation method and the prior distribution affect the agreement, their selection should be carefully considered when implementing BMD methods.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100347"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947516","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":"The first report on chronic toxicity assessment of metals towards Ceriodaphnia dubia using QSTR technique: A step towards healthier and safer human health and eco-system","authors":"Ankur Kumar ,&nbsp;Joyita Roy ,&nbsp;Probir Kumar Ojha","doi":"10.1016/j.comtox.2025.100357","DOIUrl":"10.1016/j.comtox.2025.100357","url":null,"abstract":"<div><div>Exposure of humans and other living organisms to metals (including heavy metals) can lead to serious chronic and acute health effects, which may sometimes be life-threatening. As a result, assessing the toxicity of heavy metals is essential. However, experimental toxicity data for heavy metals is limited, and their toxicity estimation can be highly costly, lengthy analysis durations, and may require animal testing. Therefore, <em>in-silico</em> approaches such as quantitative structure–activity relationship (QSAR) are a suitable alternative. In this work, we have developed multi-endpoints MLR-QSAR models to assess the chronic toxicity of heavy metal towards <em>Ceriodaphnia dubia</em> using 48 data points and obeying the Organization for Economic Cooperation and Development (OECD) guidelines. Intra-endpoint uni-variate models were developed to fill the toxicity data gaps between the endpoints (acute to chronic). The statistical results of the developed models (individual models M1-M4; R² = 0.691–0.738, Q²_LOO = 0.542–0.578, Q²_F1 = 0.673–0.732, Q²_F2 = 0.552–0.580, MAE_95%data = 0.437–0.753; intra-endpoints models IEM1-IEM9; R² = 0.952–0.988, Q²_LOO = 0.907–0.987, Q²_F1 = 0.885–0.991, Q²_F2 = 0.979–0.991, MAE_95%data = 0.120–0.436) infer that the models are robust, reliable, reproducible, and predictive. The descriptors contributing to the development of the model imply that the release of electrons, formation of cations, higher electronegativity, and the presence of neutrons in the heavy metals significantly influence the toxicity caused by the metals. Thus, this study presents <em>in silico</em> models aimed at controlling the exposure of living organisms to toxic heavy metals. It assesses both acute and chronic toxicity, addresses gaps in toxicity data, and strives to create healthier and safer ecosystems by strictly following the principles of reduction, replacement, and refinement (the RRR framework).</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100357"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168106","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":"Consensus model for skin sensitization assessment using a rule-based model and LLNA and GPMT statistics-based models","authors":"Ryoichi Murakami ,&nbsp;Mika Imamura ,&nbsp;Masakazu Tateshita ,&nbsp;Hajime Kojima ,&nbsp;Yasushi Hikida","doi":"10.1016/j.comtox.2025.100348","DOIUrl":"10.1016/j.comtox.2025.100348","url":null,"abstract":"<div><div>The potential for skin sensitization has traditionally been assessed <em>in vivo</em>; however, animal welfare concerns, the trend toward restrictions, and the prohibition of the use of animals have led to a shift toward the use of non-animal alternatives such as <em>in vitro</em> and <em>in silico</em> tools. <em>In silico</em> tools mainly include rule-based and statistics-based models. Although the use of multiple computational methods is recommended, many tools consist of only one method. Furthermore, skin sensitization develops through multiple key event (KE)/adverse outcome (AO) pathways, but many <em>in silico</em> tools consist of only one KE/AO. We constructed a consensus model based on three different independent skin sensitization KE/AOs from a rule-based model, a local lymph node assay (LLNA) statistics-based model, and a guinea pig maximization test (GPMT) statistics-based model. The rule-based model is based on KE1 and considers the metabolism of pre- and pro-haptens. The LLNA and GPMT statistics-based models are based on KE4 and AO, respectively, and characterized by the use of approximately 2000 and 3000 chemicals in the training dataset, respectively. These models use larger datasets than those previously reported. The constructed consensus model was tested on chemicals labeled with human results from OECD Guideline 497. The results showed that the performance of the majority-voting model was the highest, with a balanced accuracy of 78%. The model combines a wide range of chemical spaces with high prediction accuracy.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100348"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874794","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":"Benchmark doses (BMD) extrapolated from in vitro cytotoxicity experiments in SH-SY5Y cells using the EFSA Bayesian BMD web app: The study case of imidacloprid","authors":"Lenin J. Ramirez-Cando ,&nbsp;Santiago J. Ballaz","doi":"10.1016/j.comtox.2025.100346","DOIUrl":"10.1016/j.comtox.2025.100346","url":null,"abstract":"<div><div>Identifying possible chemical hazards is critical to establish toxicological risk assessment related to non-cancer health effects. The benchmark dose (BMD) is an estimate of the hazardous toxic level (dose or concentration) that produces a predetermined variation in the response rate of an adverse effect (exposure-related risk endpoint). Our goal was to assess how well Bayesian weighted averaging models enhances the imidacloprid-treated SH-SY5Y cells’ dose-toxicological response in the MTT cytotoxicity assay. Notably, the Gelman-Rubin statistics for our models were constantly between 0.9 and 1.0, and the effective sample size was greater than 150, which guaranteed practical sufficiency. We used a weighted average of posteriorly fitted models to estimate the final BMD = 26.40 and the lower confidence limit (BMDL) = 13.10. Including uncertainty factors (UF) in conjunction with MTT data into our risk analysis, we assessed the population’s imidacloprid exposure. The Point of Departure (PoD) at 5th percentil (8.14) indicated adverse effects. Moreover, a similar link was observed between the target human dose for minimal impact (HDMI) and the HD₅₀ (dose hazardous to 50 % of people). The determined Reference Dose (RfD) of 0.0003 µM suggested a high toxicity risk associated with imidacloprid exposure. Summarizing, dose–response evaluations were enhanced by Bayesian model averaging, highlighting the significance of probabilistic modeling and toxicological understanding.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100346"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776894","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":"From blood to body tissues: a dynamic framework for estimating volatile organic compound exposure using Kalman filtering and physiological models","authors":"Laurent Simon","doi":"10.1016/j.comtox.2025.100362","DOIUrl":"10.1016/j.comtox.2025.100362","url":null,"abstract":"<div><div>Accurate quantification of volatile organic compound (VOC) concentrations in target tissues is critical for robust exposure assessment and toxicological risk analysis. Conventional methods that rely on blood measurements and partition behaviors often fail to consider the transient nature of real-time exposure. Physiologically-based pharmacokinetic (PBPK) models advance predictive capabilities by simulating absorption, distribution, metabolism, and excretion (ADME) processes. However, their accuracy is limited by measurement errors and parameter uncertainties. This study combines the Kalman Filter (KF) with a linear PBPK model (KF-PBPK) to dynamically refine VOC tissue concentration estimates and support real-time exposure assessment using blood measurements. The Kalman Filter is an algorithm that continuously updates model predictions based on new measurements. It filters out noise and improves the accuracy of estimates. The application of the KF-Expectation Maximization (KF-EM) approach to human m-xylene exposure data improved the signal-to-noise ratio (SNR) from 13.9 dB to 17.4 dB. The KF-PBPK scheme effectively captured the multi-compartment kinetics of VOC distribution across several compartments. Filtered estimates closely matched the experimental data, demonstrating the framework’s effectiveness in modeling and predicting human VOC exposure. This research suggests that the KF-PBPK is a reliable tool for improving VOC exposure assessments, with potential implications for environmental pollution monitoring, risk assessment and regulatory decision-making.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100362"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272478","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":"Drugs inhibition prediction in P-gp enzyme: a comparative study of machine learning and graph neural network","authors":"Maryam ,&nbsp;Mobeen Ur Rehman ,&nbsp;Kil to Chong ,&nbsp;Hilal Tayara","doi":"10.1016/j.comtox.2025.100344","DOIUrl":"10.1016/j.comtox.2025.100344","url":null,"abstract":"<div><div>Drug metabolism is a complex and highly regulated process that involves the safe breakdown and elimination of drugs from the body through chemical reactions. The P-glycoprotein (P-gp) plays a key role in drug metabolism, and interfere of drugs with its transport function leads to drug toxicity. Therefore, predicting P-gp inhibition is crucial to avoid adverse drug effects. To address this, machine learning and deep learning models offer a powerful approach to accurately predict the P-gp inhibition. In this study, we have utilized a publicly available P-gp dataset to develop classification models using various machine learning algorithms (SVM, RFC, HistGradient Boosting, AdaBoost) and graph neural networks. The dataset was transformed into molecular descriptors and graph feature vectors to explore the chemical space of metabolic enzymes. Our experimental results demonstrate that machine learning models outperform deep learning models in terms of accuracy and efficiency for independent datasets. Among all models, SVM exhibited superior predictive capabilities for the P-gp data set with an accuracy of 0.95 on independent datasets. Furthermore, the analysis of the importance of the characteristics of the best model highlighted the significant contributions of specific descriptors to the data set. Finally, our model outperformed previous studies when evaluated on an external dataset, emphasizing the efficacy of molecular features in providing more precise explanations of compound properties and biological activity.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100344"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808553","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 dual approach to flavonoid toxicity assessment: Bridging computational and experimental paradigms","authors":"Mriganka Das ,&nbsp;Sibashish Kityania ,&nbsp;Priyakshi Nath ,&nbsp;Rajat Nath ,&nbsp;Rashed N. Herqash ,&nbsp;Abdelaaty A. Shahat ,&nbsp;Deepa Nath ,&nbsp;Anupam Das Talukdar","doi":"10.1016/j.comtox.2025.100355","DOIUrl":"10.1016/j.comtox.2025.100355","url":null,"abstract":"<div><div>Flavonoids form a structurally diverse group of polyphenolic compounds with high ethnopharmacological relevance, primarily attributed to their antimicrobial and anticancer activity mediated by modulation of oxidative stress, induction of apoptosis, and regulation of the cell cycle. Their translatability to the clinic is critically hindered by multifaceted toxicities involving nephrotoxicity, cardiotoxicity, and respiratory issues often traceable to conserved structural motifs. In response, we adopted an integrative dual-methodological approach that linked thorough data mining across PubMed, Google Scholar, and PubChem for pharmacokinetic parameters and SMILES-based structural information to computational toxicity prediction using ProTox 3.0 and ADMET AI in order to unravel mechanistic endpoints of toxicity.Chemical drawing utilities like ChemSketch and ChemDraw supported the structural evaluations, and cross-referring DrugBank and <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> gave validation for clinical relevance. This computational model was further validated using in vitro and in vivo model systems, guaranteeing a comprehensive evaluation of flavonoid toxicity and therapeutic potential. Although flavonoids show great antimicrobial and anticancer potential, the translational roadblock arises from discrepancies between predictive models of toxicity and empirical validation, requiring sophisticated structure–activity relationship (SAR) analysis and integrative approaches to bridge computational-experimental gaps and enhance clinical relevance. This research highlights the need for a dual investigative approach, blending <em>in silico</em> and experimental paradigms, to maximize the predictive validity and translational potential of flavonoid-derived therapeutics.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100355"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942778","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}