Immunoinformatics (Amsterdam, Netherlands)最新文献

{"title":"Structural pre-training improves physical accuracy of antibody structure prediction using deep learning.","authors":"Jarosław Kończak,&nbsp;Bartosz Janusz,&nbsp;Jakub Młokosiewicz,&nbsp;Tadeusz Satława,&nbsp;Sonia Wróbel,&nbsp;Paweł Dudzic,&nbsp;Konrad Krawczyk","doi":"10.1016/j.immuno.2023.100028","DOIUrl":"https://doi.org/10.1016/j.immuno.2023.100028","url":null,"abstract":"<div><p>Protein folding problem obtained a practical solution recently, owing to advances in deep learning. There are classes of proteins though, such as antibodies, that are structurally unique, where the general solution still lacks. In particular, the prediction of the CDR-H3 loop, which is an instrumental part of an antibody in its antigen recognition abilities, remains a challenge. Antibody-specific deep learning frameworks were proposed to tackle this problem noting great progress, both on accuracy and speed fronts. Oftentimes though, the original networks produce physically implausible bond geometries that then need to undergo a time-consuming energy minimization process. Here we hypothesized that pre-training the network on a large, augmented set of models with correct physical geometries, rather than a small set of real antibody X-ray structures, would allow the network to learn better bond geometries. We show that fine-tuning such a pre-trained network on a task of shape prediction on real X-ray structures improves the number of correct peptide bond distances, abstracted as the Cα distances. We further demonstrate that pre-training allows the network to produce physically plausible shapes on an artificial set of CDR-H3s, showing the ability to generalize to the vast antibody sequence space. We hope that our strategy will benefit the development of deep learning antibody models that rapidly generate physically plausible geometries, without the burden of time-consuming energy minimization.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"11 ","pages":"Article 100028"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49865553","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":"Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interaction predictions","authors":"Ceder Dens,&nbsp;Wout Bittremieux,&nbsp;Fabio Affaticati,&nbsp;Kris Laukens,&nbsp;Pieter Meysman","doi":"10.1016/j.immuno.2023.100027","DOIUrl":"10.1016/j.immuno.2023.100027","url":null,"abstract":"<div><p>The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR–epitope pair is still a challenging task, especially for novel epitopes, because the underlying patterns are largely unknown to domain experts and machine learning models. To achieve a deeper understanding of TCR–epitope interactions, we have used interpretable deep learning techniques to gain insights into the performance of TCR–epitope binding machine learning models. We demonstrate how interpretable AI techniques can be linked to the three-dimensional structure of molecules to offer novel insights into the factors that determine TCR affinity on a molecular level. Additionally, our results show the importance of using interpretability techniques to verify the predictions of machine learning models for challenging molecular biology problems where small hard-to-detect problems can accumulate to inaccurate results.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"11 ","pages":"Article 100027"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45830738","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":"AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets","authors":"William D. Lees ,&nbsp;Scott Christley ,&nbsp;Ayelet Peres ,&nbsp;Justin T. Kos ,&nbsp;Brian Corrie ,&nbsp;Duncan Ralph ,&nbsp;Felix Breden ,&nbsp;Lindsay G. Cowell ,&nbsp;Gur Yaari ,&nbsp;Martin Corcoran ,&nbsp;Gunilla B. Karlsson Hedestam ,&nbsp;Mats Ohlin ,&nbsp;Andrew M. Collins ,&nbsp;Corey T. Watson ,&nbsp;Christian E. Busse ,&nbsp;The AIRR Community","doi":"10.1016/j.immuno.2023.100025","DOIUrl":"10.1016/j.immuno.2023.100025","url":null,"abstract":"<div><p>Analysis of an individual's immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes. We describe interoperability standards for germline sets, and an approach to transparency based on principles of findability, accessibility, interoperability, and reusability.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"10 ","pages":"Article 100025"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10310305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9734901","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":"Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report","authors":"Pieter Meysman ,&nbsp;Justin Barton ,&nbsp;Barbara Bravi ,&nbsp;Liel Cohen-Lavi ,&nbsp;Vadim Karnaukhov ,&nbsp;Elias Lilleskov ,&nbsp;Alessandro Montemurro ,&nbsp;Morten Nielsen ,&nbsp;Thierry Mora ,&nbsp;Paul Pereira ,&nbsp;Anna Postovskaya ,&nbsp;María Rodríguez Martínez ,&nbsp;Jorge Fernandez-de-Cossio-Diaz ,&nbsp;Alexandra Vujkovic ,&nbsp;Aleksandra M. Walczak ,&nbsp;Anna Weber ,&nbsp;Rose Yin ,&nbsp;Anne Eugster ,&nbsp;Virag Sharma","doi":"10.1016/j.immuno.2023.100024","DOIUrl":"https://doi.org/10.1016/j.immuno.2023.100024","url":null,"abstract":"<div><p>Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"9 ","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49858643","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 race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions","authors":"Sonia Gazeau ,&nbsp;Xiaoyan Deng ,&nbsp;Hsu Kiang Ooi ,&nbsp;Fatima Mostefai ,&nbsp;Julie Hussin ,&nbsp;Jane Heffernan ,&nbsp;Adrianne L. Jenner ,&nbsp;Morgan Craig","doi":"10.1016/j.immuno.2023.100021","DOIUrl":"10.1016/j.immuno.2023.100021","url":null,"abstract":"<div><p>The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"9 ","pages":"Article 100021"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9826539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10741906","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":"Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes","authors":"Christian A Balbin ,&nbsp;Janelle Nunez-Castilla ,&nbsp;Vitalii Stebliankin ,&nbsp;Prabin Baral ,&nbsp;Masrur Sobhan ,&nbsp;Trevor Cickovski ,&nbsp;Ananda Mohan Mondal ,&nbsp;Giri Narasimhan ,&nbsp;Prem Chapagain ,&nbsp;Kalai Mathee ,&nbsp;Jessica Siltberg-Liberles","doi":"10.1016/j.immuno.2023.100023","DOIUrl":"https://doi.org/10.1016/j.immuno.2023.100023","url":null,"abstract":"<div><p>Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope Database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"9 ","pages":"Article 100023"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49858642","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":"Modeling interaction of Glioma cells and CAR T-cells considering multiple CAR T-cells bindings","authors":"Runpeng Li ,&nbsp;Prativa Sahoo ,&nbsp;Dongrui Wang ,&nbsp;Qixuan Wang ,&nbsp;Christine E. Brown ,&nbsp;Russell C. Rockne ,&nbsp;Heyrim Cho","doi":"10.1016/j.immuno.2023.100022","DOIUrl":"10.1016/j.immuno.2023.100022","url":null,"abstract":"<div><p>Chimeric antigen receptor (CAR) T-cell based immunotherapy has shown its potential in treating blood cancers, and its application to solid tumors is currently being extensively investigated. For glioma brain tumors, various CAR T-cell targets include IL13R<span><math><mi>α</mi></math></span>2, EGFRvIII, HER2, EphA2, GD2, B7-H3, and chlorotoxin. In this work, we are interested in developing a mathematical model of IL13R<span><math><mi>α</mi></math></span>2 targeting CAR T-cells for treating glioma. We focus on extending the work of Kuznetsov et al. (1994) by considering binding of multiple CAR T-cells to a single glioma cell, and the dynamics of these multi-cellular conjugates. Our model more accurately describes experimentally observed CAR T-cell killing assay data than the models which do not consider multi-cellular conjugates. Moreover, we derive conditions in the CAR T-cell expansion rate that determines treatment success or failure. Finally, we show that our model captures distinct CAR T-cell killing dynamics from low to high antigen receptor densities in patient-derived brain tumor cells.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"9 ","pages":"Article 100022"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9c/44/nihms-1874038.PMC9983577.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10838740","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 design and evaluation of a multi-epitope and multi-antigenic African swine fever vaccine","authors":"Ara Karizza G. Buan,&nbsp;Nico Alexander L. Reyes,&nbsp;Ryan Nikkole B. Pineda,&nbsp;Paul Mark B. Medina","doi":"10.1016/j.immuno.2022.100019","DOIUrl":"10.1016/j.immuno.2022.100019","url":null,"abstract":"<div><p>African Swine Fever (ASF) is caused by a highly contagious and fatal hemorrhagic virus, which in 2019 alone in Asia, has killed 8 million pigs with a devastating estimated economic loss amounting to $130 billion. There were attempts to control ASFV transmission; however, developed vaccines failed to produce lasting immunity. Currently, no vaccine has been approved yet. This study designed a novel multi-epitope and multi-antigenic vaccine using open-access bioinformatics tools. B-cell, helper-T and cytotoxic T-cell epitopes were predicted using consensus sequences from ASFV genotypes of antigens p12, p17, p22, p54, p72, and CD2v, and combined with adjuvants and linkers to form the ASF vaccine. Analyses revealed that the ASF vaccine is stable, antigenic, non-allergenic, and not cross-reactive. Docking of SLA-1 to CTL-HTL regions of the developed vaccine revealed that it effectively binds to SLA-1, a vital process in priming an effective immune response. Immune simulations demonstrated that the designed ASF vaccine can elicit primary and secondary immune responses, and stimulate the production of effector immune cells and cytokines. Overall, these results revealed that the designed multi-epitope and multi-antigenic ASF vaccine is potentially effective and warrants further <em>in vitro</em> and <em>in vivo</em> studies to confirm its protective function against ASFV infection.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"8 ","pages":"Article 100019"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119022000118/pdfft?md5=2f7cb00692592699861bd6c1f4854437&pid=1-s2.0-S2667119022000118-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46800839","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":"SARS-CoV-2 Omicron (BA.1 and BA.2) specific novel CD8+ and CD4+ T cell epitopes targeting spike protein","authors":"Simone Parn ,&nbsp;Kush Savsani ,&nbsp;Sivanesan Dakshanamurthy","doi":"10.1016/j.immuno.2022.100020","DOIUrl":"10.1016/j.immuno.2022.100020","url":null,"abstract":"<div><p>The Omicron (BA.1/B.1.1.529) variant of SARS-CoV-2 harbors an alarming 37 mutations on its spike protein, reducing the efficacy of current COVID-19 vaccines. In this study, we identified CD8+ and CD4+ T cell epitopes from SARS-CoV-2 S protein mutants. To identify the highest quality CD8 and CD4 epitopes from the Omicron variant, we selected epitopes with a high binding affinity towards both MHC I and MHC II molecules. We applied other clinical checkpoint predictors, including immunogenicity, antigenicity, allergenicity, instability and toxicity. Subsequently, we found eight Omicron (BA.1/B.1.1.529) specific CD8+ and eleven CD4+ T cell epitopes with a world population coverage of 76.16% and 97.46%, respectively. Additionally, we identified common epitopes across Omicron BA.1 and BA.2 lineages that target mutations critical to SARS-CoV-2 virulence. Further, we identified common epitopes across B.1.1.529 and other circulating SARS-CoV-2 variants, such as B.1.617.2 (Delta). We predicted CD8 epitopes’ binding affinity to murine MHC alleles to test the vaccine candidates in preclinical models. The CD8 epitopes were further validated using our previously developed software tool PCOptim. We then modeled the three-dimensional structures of our top CD8 epitopes to investigate the binding interaction between peptide-MHC and peptide-MHC-TCR complexes. Notably, our identified epitopes are targeting the mutations on the RNA-binding domain and the fusion sites of S protein. This could potentially eliminate viral infections and form long-term immune responses compared to relatively short-lived mRNA vaccines and maximize the efficacy of vaccine candidates against the current pandemic and potential future variants.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"8 ","pages":"Article 100020"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9624113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40468488","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":"Machine-learning-based analytics for risk forecasting of anaphylaxis during general anesthesia","authors":"Shuang Liu ,&nbsp;Yasuyuki Suzuki ,&nbsp;Toshihiro Yorozuya ,&nbsp;Masaki Mogi","doi":"10.1016/j.immuno.2022.100018","DOIUrl":"10.1016/j.immuno.2022.100018","url":null,"abstract":"<div><p>Perioperative anaphylaxis has a risk of mortality and compromised quality of patient care. It is difficult to design an evaluation system for risk of anaphylaxis using preoperative tests available in clinical practice. To develop a personalized risk forecast platform for general anesthesia-related anaphylaxis, as a first step, we aimed to investigate the feasibility of machine-learning-based classification using clinical features of patients for risk prediction of anesthesia-related anaphylaxis. After data pre-processing, the performance of five classification methods: Logistic Regression Analysis, Support Vector Machine, Random Forest, Linear Discriminant Analysis, and Naïve Bayes), which were integrated with four feature selection methods (Recursive Feature Elimination, Chi-Squared Method, Correlation-based Feature Selection, and Information Gain Ratio), was evaluated using two-layer cross-validation. Seventy-four features, which were defined from 225 participants, were applied for model fitting. Linear Discriminant Analysis in conjunction with Recursive Feature Elimination showed good performance, with accuracy of 0.867 and Matthews correlation coefficient (MCC) of 0.558 with 25 features used in the classification. Logistic Regression in conjunction with Recursive Feature Elimination model also showed adequate performance, with accuracy of 0.858 and MCC of 0.541 with six features used in the classification. This study presents initial proof of the capability of a machine-learning-based strategy for forecasting low-prevalence anesthesia-related anaphylaxis from a clinical perspective. It could provide a basis for establishing an effective risk-scoring and predictive system for perioperative anaphylaxis that would help identify preoperatively whether anaphylaxis will occur and could be used to predict unstable patient states preceding anaphylactic shock.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"8 ","pages":"Article 100018"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119022000106/pdfft?md5=f7650e92c7467a93c664c6e740e93243&pid=1-s2.0-S2667119022000106-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48382690","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}