{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:04:18Z","timestamp":1776679458940,"version":"3.51.2"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s11517-020-02299-2","type":"journal-article","created":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T15:12:30Z","timestamp":1616080350000},"page":"825-839","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["Transfer learning\u2013based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data"],"prefix":"10.1007","volume":"59","author":[{"given":"Mukul","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shrey","family":"Bansal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sakshi","family":"Ahuja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1662-3718","authenticated-orcid":false,"given":"Rahul Kumar","family":"Dubey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bijaya Ketan","family":"Panigrahi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nilanjan","family":"Dey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,18]]},"reference":[{"key":"2299_CR1","unstructured":"WebMD Lung Diseases Overview (2020) https:\/\/www.webmd.com\/lung\/lung-diseases-overview#1, Last accessed date 22nd May"},{"key":"2299_CR2","unstructured":"Novel Coronavirus World Health Organization (WHO) (2020) https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019, Last accessed date 22nd May"},{"key":"2299_CR3","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/s10916-020-01582-x","volume":"44","author":"RA Hamid","year":"2020","unstructured":"Hamid RA, Alwan JK, Albahri AS (2020) Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (covid-19): a systematic review. J Med Syst 44:122. https:\/\/doi.org\/10.1007\/s10916-020-01582-x","journal-title":"J Med Syst"},{"key":"2299_CR4","unstructured":"Worldometer Global COVID-19 Case Counter (2020) https:\/\/www.worldometers.info\/coronavirus, Last accessed date 22nd May"},{"key":"2299_CR5","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1080\/14737159.2020.1757437","volume":"20","author":"A Tahamtana","year":"2020","unstructured":"Tahamtana A, Ardebili A (2020) Real-time rt-pcr in covid-19 detection: issues affecting the results. Expert review of molecular diagnostics 20:453\u2013454. https:\/\/dx.doi.org\/10.1080%2F14737159.2020.1757437","journal-title":"Expert review of molecular diagnostics"},{"issue":"2","key":"2299_CR6","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"A Tao","year":"2020","unstructured":"Tao A, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L (2020) Correlation of chest ct and rt-pcr testing for coronavirus disease 2019 (covid-19) in china: A report of 1014 cases. Radiology 296(2):E32\u2013E40. https:\/\/doi.org\/10.1148\/radiol.2020200642. https:\/\/doi.org\/10.1148\/radiol.2020200642. PMID: 32101510","journal-title":"Radiology"},{"key":"2299_CR7","doi-asserted-by":"publisher","first-page":"105617","DOI":"10.1016\/j.cmpb.2020.105617","volume":"196","author":"OS Albahri","year":"2020","unstructured":"Albahri OS, Al-Obaidi JR, Zaidan AA, et al. (2020) Helping doctors hasten covid-19 treatment: towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel mcdm methods. Computer Methods and Programs in Biomedicine 196:105617. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105617. ISSN 0169-2607.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"05","key":"2299_CR8","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1142\/S0219622020500285","volume":"19","author":"AS Albahri","year":"2020","unstructured":"Albahri AS, Al-Obaidi JR, Zaidan AA, et al. (2020) Multi-biological laboratory examination framework for the prioritization of patients with covid-19 based on integrated ahp and group vikor methods. International Journal of Information Technology & Decision Making 19(05):1247\u20131269. https:\/\/doi.org\/10.1142\/S0219622020500285. https:\/\/doi.org\/10.1142\/S0219622020500285","journal-title":"International Journal of Information Technology & Decision Making"},{"key":"2299_CR9","doi-asserted-by":"publisher","first-page":"4381","DOI":"10.1007\/s00330-020-06801-0","volume":"30","author":"Z Ye","year":"2020","unstructured":"Ye Z, Zhang Y, Wang Y, Huang Z, Song B (2020) Chest ct manifestations of new coronavirus disease 2019(covid-19): a pictorial review. European Radiology 30:4381\u20134389. https:\/\/doi.org\/10.1007\/s00330-020-06801-0","journal-title":"European Radiology"},{"issue":"10","key":"2299_CR10","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1016\/j.jiph.2020.06.028","volume":"13","author":"OS Albahri","year":"2020","unstructured":"Albahri OS, Zaidan AA, et al. (2020) Systematic review of artificial intelligence techniques in the detection and classification of covid-19 medical images in terms of evaluation and benchmarking: taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health 13(10):1381\u20131396. https:\/\/doi.org\/10.1016\/j.jiph.2020.06.028. ISSN 1876-0341","journal-title":"Journal of Infection and Public Health"},{"key":"2299_CR11","doi-asserted-by":"publisher","unstructured":"Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S, Shukla PK Deep transfer learning based classification model for covid-19 disease. IRBM, 2020. ISSN 1959-0318. https:\/\/doi.org\/10.1016\/j.irbm.2020.05.003","DOI":"10.1016\/j.irbm.2020.05.003"},{"key":"2299_CR12","doi-asserted-by":"crossref","unstructured":"Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, Khan MS, Iqbal A, Al-Emadi N, et al. (2020) Can ai help in screening viral and covid-19 pneumonia?. arXiv:2003.13145","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"2299_CR13","unstructured":"Hall LO, Paul R, Goldgof DB, Goldgof GM (2020) Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv:2004.02060"},{"key":"2299_CR14","unstructured":"Cohen JP, Morrison P, Dao L (2020) Covid-19 image data collection. arXiv:2003.11597"},{"key":"2299_CR15","unstructured":"Zhao J, Zhang Y, He X, Xie P (2020) Covid-ct-dataset: a ct scan dataset about covid-19. arXiv:2003.13865"},{"key":"2299_CR16","doi-asserted-by":"publisher","unstructured":"Zhang R, Guo Z, et al. Covid19xraynet: a two-step transfer learning model for the covid-19 detecting problem based on a limited number of chest x-ray images. 12(4):555\u2013565, Dec 2020. ISSN 1867-1462. https:\/\/doi.org\/10.1007\/s12539-020-00393-5","DOI":"10.1007\/s12539-020-00393-5"},{"key":"2299_CR17","unstructured":"Luz E, Silva PL, Silva R, Silva L, Moreira G, Menotti D (2020) Towards an effective and efficient deep learning model for covid-19 patterns detection in x-ray images. arXiv:2004.05717"},{"issue":"1","key":"2299_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A (2020) A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports 10(1):1\u201312","journal-title":"Scientific Reports"},{"key":"2299_CR19","unstructured":"Mangal A, Kalia S, Rajgopal H, Rangarajan K, Namboodiri V, Banerjee S, Arora C (2020) Covidaid: Covid-19 detection using chest x-ray. arXiv preprint arXiv:2004.09803"},{"key":"2299_CR20","doi-asserted-by":"crossref","unstructured":"Basu S, Mitra S, Saha N (2020) Deep learning for screening covid-19 using chest x-ray images. arXiv:2004.10507","DOI":"10.1101\/2020.05.04.20090423"},{"key":"2299_CR21","unstructured":"Ilyas M, Rehman H, Nait-ali A (2020) Detection of covid-19 from chest x-ray images using artificial intelligence: an early review. arXiv:2004.05436"},{"key":"2299_CR22","doi-asserted-by":"crossref","unstructured":"Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ (2020) Deep-covid: predicting covid-19 from chest x-ray images using deep transfer learning. arXiv:2004.09363","DOI":"10.1016\/j.media.2020.101794"},{"key":"2299_CR23","unstructured":"Yu-Huan W, Gao S-H, Mei J, Jun X, Fan D-P, Zhao C-W, Cheng M-M (2020) Jcs: an explainable covid-19 diagnosis system by joint classification and segmentation. arXiv:2004.07054"},{"key":"2299_CR24","unstructured":"Italian Society of Medical and Interventional Radiology (2020) https:\/\/www.sirm.org\/en\/category\/articles\/covid-19-database\/, Last accessed date 7th May"},{"key":"2299_CR25","unstructured":"Sajid N (2020) Covid-19 patients lungs x ray images 10000. https:\/\/www.kaggle.com\/nabeelsajid917\/covid-19-x-ray-10000-images"},{"key":"2299_CR26","doi-asserted-by":"publisher","unstructured":"Jaiswal A, Tiwari P, Rathi V, Qian J, Pandey H, Albuquerque VHC (2020) Covidpen: a novel covid-19 detection model using chest x-rays and ct scans. 07. https:\/\/doi.org\/10.1101\/2020.07.08.20149161","DOI":"10.1101\/2020.07.08.20149161"},{"key":"2299_CR27","unstructured":"Mobiny A, Cicalese PA, Zare S, Yuan P, Abavisani M, Wu CC, Ahuja J, de Groot PM, Van Nguyen H (2020) Radiologist-level covid-19 detection using ct scans with detail-oriented capsule networks. arXiv:2004.07407"},{"key":"2299_CR28","doi-asserted-by":"publisher","unstructured":"Narayan Das N, Kumar N, Kaur M, Kumar V, Singh D (2020) Automated deep transfer learning-based approach for detection of covid-19 infection in chest x-rays. IRBM. ISSN 1959-0318. https:\/\/doi.org\/10.1016\/j.irbm.2020.07.001. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1959031820301172","DOI":"10.1016\/j.irbm.2020.07.001"},{"issue":"7","key":"2299_CR29","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s10096-020-03901-z","volume":"39","author":"D Singh","year":"2020","unstructured":"Singh D, Kumar V, Vaishali, Kaur M (2020) Classification of covid-19 patients from chest ct images using multi-objective differential evolution-based convolutional neural networks. European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology 39(7):1379\u20131389. ISSN 1435-4373. https:\/\/doi.org\/10.1007\/s10096-020-03901-z","journal-title":"European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology"},{"key":"2299_CR30","doi-asserted-by":"publisher","first-page":"108941","DOI":"10.1016\/j.ejrad.2020.108941","volume":"126","author":"K-C Liu","year":"2020","unstructured":"Liu K-C, Xu P, Lv W-F, Qiu X-H, Yao J-L, Gu J-F, Wei W (2020) Ct manifestations of coronavirus disease-2019: a retrospective analysis of 73 cases by disease severity. European Journal of Radiology 126:108941. ISSN 0720-048X. https:\/\/doi.org\/10.1016\/j.ejrad.2020.108941. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0720048X20301303","journal-title":"European Journal of Radiology"},{"key":"2299_CR31","unstructured":"Gozes O, Frid-Adar M, Sagie N, Zhang H, Ji W, Greenspan H (2020) Coronavirus detection and analysis on chest ct with deep learning. arXiv:2004.02640"},{"issue":"2","key":"2299_CR32","doi-asserted-by":"publisher","first-page":"E113","DOI":"10.1148\/radiol.2020200527","volume":"296","author":"J Kanne","year":"2020","unstructured":"Kanne J, et al. (2020) Essentials for radiologists on covid-19: an update\u2014radiology scientific expert panel. Radiology 296(2):E113\u2013E114. https:\/\/doi.org\/10.1148\/radiol.2020200527. https:\/\/doi.org\/10.1148\/radiol.2020200527","journal-title":"Radiology"},{"key":"2299_CR33","first-page":"12","volume":"12","author":"AR Zubair","year":"2018","unstructured":"Zubair AR, Busari H (2018) Robustness of median filter for suppression of salt and pepper noise (spn) and random valued impulse noise (rvin). IJIP 12:12\u201327","journal-title":"IJIP"},{"key":"2299_CR34","doi-asserted-by":"publisher","unstructured":"Jamil N, Sembok TMT, Bakar ZA Noise removal and enhancement of binary images using morphological operations. 4:1\u20136, 2008. https:\/\/doi.org\/10.1109\/ITSIM.2008.4631954","DOI":"10.1109\/ITSIM.2008.4631954"},{"issue":"1","key":"2299_CR35","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. Journal of Big Data 6(1):60. ISSN 2196-1115. https:\/\/doi.org\/10.1186\/s40537-019-0197-0. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"Journal of Big Data"},{"key":"2299_CR36","doi-asserted-by":"publisher","first-page":"100427","DOI":"10.1016\/j.imu.2020.100427","volume":"20","author":"P Silva","year":"2020","unstructured":"Silva P, Luz E, Silva G, Moreira G, Silva R, Lucio D, Menotti D (2020) Covid-19 detection in ct images with deep learning: a voting-based scheme and cross-datasets analysis. Informatics in Medicine Unlocked 20:100427. ISSN 2352-9148. https:\/\/doi.org\/10.1016\/j.imu.2020.100427. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352914820305773","journal-title":"Informatics in Medicine Unlocked"},{"key":"2299_CR37","doi-asserted-by":"publisher","first-page":"149808","DOI":"10.1109\/ACCESS.2020.3016780","volume":"8","author":"MJ Horry","year":"2020","unstructured":"Horry MJ, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N (2020) Covid-19 detection through transfer learning using multimodal imaging data. IEEE Access 8:149808\u2013149824. https:\/\/doi.org\/10.1109\/ACCESS.2020.3016780","journal-title":"IEEE Access"},{"key":"2299_CR38","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition"},{"issue":"3","key":"2299_CR39","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s13246-020-00888-x","volume":"43","author":"D Das","year":"2020","unstructured":"Das D, Santosh KC, Pal U (2020) Truncated inception net: Covid-19 outbreak screening using chest x-rays. Physical and Engineering Sciences in Medicine 43(3):915\u2013925. ISSN 2662-4737. https:\/\/doi.org\/10.1007\/s13246-020-00888-x. https:\/\/doi.org\/10.1007\/s13246-020-00888-x","journal-title":"Physical and Engineering Sciences in Medicine"},{"key":"2299_CR40","doi-asserted-by":"publisher","first-page":"149808","DOI":"10.1109\/ACCESS.2020.3016780","volume":"8","author":"MJ Horry","year":"2020","unstructured":"Horry MJ, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N (2020) Covid-19 detection through transfer learning using multimodal imaging data. IEEE Access 8:149808\u2013149824. https:\/\/doi.org\/10.1109\/ACCESS.2020.3016780","journal-title":"IEEE Access"},{"key":"2299_CR41","doi-asserted-by":"publisher","unstructured":"Shaltout N, Moustafa M, Rafea A, Moustafa A, ElHefnawi M (2015) Comparing pca to information gain as a feature selection method for influenza-a classification. In: 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pages 279\u2013283. https:\/\/doi.org\/10.1109\/ICIIBMS.2015.7439550","DOI":"10.1109\/ICIIBMS.2015.7439550"},{"key":"2299_CR42","doi-asserted-by":"publisher","first-page":"156053","DOI":"10.1109\/ACCESS.2020.3019095","volume":"8","author":"M Ceci","year":"2020","unstructured":"Ceci M, Corizzo R, Japkowicz N, Mignone P, Pio G (2020) Echad: embedding-based change detection from multivariate time series in smart grids. IEEE Access 8:156053\u2013156066. https:\/\/doi.org\/10.1109\/ACCESS.2020.3019095","journal-title":"IEEE Access"},{"key":"2299_CR43","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv:1512.03385"},{"issue":"1","key":"2299_CR44","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501. ISSN 0925-2312. https:\/\/doi.org\/10.1016\/j.neucom.2005.12.126. Neural Networks","journal-title":"Neurocomputing"},{"key":"2299_CR45","unstructured":"Rajpal S, Rajpal A, Lakhyani N, Kumar N (2020) Cov-elm classifier: an extreme learning machine based identification of covid-19 using chest x-ray images. arXiv:2007.08637"},{"issue":"6","key":"2299_CR46","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"N Liang","year":"2006","unstructured":"Liang N, Huang G, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 17(6):1411\u20131423. https:\/\/doi.org\/10.1109\/TNN.2006.880583","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"5","key":"2299_CR47","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1093\/bioinformatics\/btz781 10.1093\/bioinformatics\/btz781","volume":"36","author":"P Mignone","year":"2019","unstructured":"Mignone P, Pio G, D\u2019Elia D, Ceci M (2019) Exploiting transfer learning for the reconstruction of the human gene regulatory network. Bioinformatics 36(5):1553\u20131561. ISSN 1367-4803. https:\/\/doi.org\/10.1093\/bioinformatics\/btz781. https:\/\/doi.org\/10.1093\/bioinformatics\/btz781","journal-title":"Bioinformatics"},{"key":"2299_CR48","doi-asserted-by":"publisher","unstructured":"Krzysztof A, Cyran JK, Kawulok M, Stawarz M, Michalak M, Pietrowska M, Wid\u0142ak P, Pola\u0144ska J Support Vector Machines in Biomedical and Biometrical Applications, pages 379\u2013417. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013. ISBN 978-3-642-28699-5. https:\/\/doi.org\/10.1007\/978-3-642-28699-5_15.","DOI":"10.1007\/978-3-642-28699-5_15"},{"key":"2299_CR49","unstructured":"Covid-19 chest ct scans Radiopedia (2020) https:\/\/radiopaedia.org\/search?utf8=%E2%9C%93&q=covid&scope=all&lang=us.. Last accessed date 7th May 2020"},{"key":"2299_CR50","unstructured":"NIH (2020) Nih chest x-ray dataset. https:\/\/www.kaggle.com\/nih-chest-xrays\/data. Last accessed date 7th May 2020"},{"key":"2299_CR51","doi-asserted-by":"publisher","unstructured":"He X, Yang X, Zhang S, Zhao J, Zhang Y, Xing E, Xie P (2020) Sample-efficient deep learning for covid-19 diagnosis based on ct scans. medRxiv. https:\/\/doi.org\/10.1101\/2020.04.13.20063941. https:\/\/www.medrxiv.org\/content\/early\/2020\/04\/17\/2020.04.13.20063941","DOI":"10.1101\/2020.04.13.20063941"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02299-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-020-02299-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02299-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T22:27:00Z","timestamp":1618871220000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-020-02299-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,18]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["2299"],"URL":"https:\/\/doi.org\/10.1007\/s11517-020-02299-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-32493\/v1","asserted-by":"object"}]},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,18]]},"assertion":[{"value":"14 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}