{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T19:48:55Z","timestamp":1778528935672,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003382","name":"Core Research for Evolutional Science and Technology","doi-asserted-by":"publisher","award":["JPMJCR21M2"],"award-info":[{"award-number":["JPMJCR21M2"]}],"id":[{"id":"10.13039\/501100003382","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01015-y","type":"journal-article","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T18:02:02Z","timestamp":1707847322000},"page":"1217-1227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Synthesis of Hybrid Data Consisting of Chest Radiographs and Tabular Clinical Records Using Dual Generative Models for COVID-19 Positive Cases"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4222-4569","authenticated-orcid":false,"given":"Tomohiro","family":"Kikuchi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shouhei","family":"Hanaoka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takahiro","family":"Nakao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomomi","family":"Takenaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukihiro","family":"Nomura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harushi","family":"Mori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takeharu","family":"Yoshikawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"key":"1015_CR1","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","volume":"37","author":"G Chartrand","year":"2017","unstructured":"Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A: Deep learning: a primer for radiologists. Radiographics 37:2113\u20132131, 2017","journal-title":"Radiographics"},{"key":"1015_CR2","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1148\/rg.2021200210","volume":"41","author":"PM Cheng","year":"2021","unstructured":"Cheng PM, Montagnon E, Yamashita R, Pan I, Cadrin-Ch\u00eanevert A, Perdig\u00f3n Romero F, Chartrand G, Kadoury S, Tang A: Deep learning: an update for radiologists. Radiographics 41:1427\u20131445, 2021","journal-title":"Radiographics"},{"key":"1015_CR3","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.1007\/s00330-021-07892-z","volume":"31","author":"KG van Leeuwen","year":"2021","unstructured":"van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M: Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 31:3797\u20133804, 2021","journal-title":"Eur Radiol"},{"key":"1015_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321\u2013331, 2018","journal-title":"Neurocomputing"},{"key":"1015_CR5","doi-asserted-by":"publisher","first-page":"6051939","DOI":"10.1155\/2019\/6051939","volume":"2019","author":"Y Onishi","year":"2019","unstructured":"Onishi Y, Teramoto A, Tsujimoto M, Tsukamoto T, Saito K, Toyama H, Imaizumi K, Fujita H: Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. Biomed Res Int 2019:6051939, 2019","journal-title":"Biomed Res Int"},{"key":"1015_CR6","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.1002\/jmri.26544","volume":"49","author":"M Gadermayr","year":"2019","unstructured":"Gadermayr M, Li K, M\u00fcller M, Truhn D, Kr\u00e4mer N, Merhof D, Gess B: Domain-specific data augmentation for segmenting MR images of fatty infiltrated human thighs with neural networks. J Magn Reson Imaging 49:1676\u20131683, 2019","journal-title":"J Magn Reson Imaging"},{"key":"1015_CR7","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1007\/s11548-019-02042-9","volume":"14","author":"T Russ","year":"2019","unstructured":"Russ T, Goerttler S, Schnurr A-K, Bauer DF, Hatamikia S, Schad LR, Z\u00f6llner FG, Chung K: Synthesis of CT images from digital body phantoms using CycleGAN. Int J Comput Assist Radiol Surg 14:1741\u20131750, 2019","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"1015_CR8","doi-asserted-by":"publisher","first-page":"12098","DOI":"10.1038\/s41598-023-39278-0","volume":"13","author":"G M\u00fcller-Franzes","year":"2023","unstructured":"M\u00fcller-Franzes G, Niehues JM, Khader F, Arasteh ST, Haarburger C, Kuhl C, Wang T, Han T, Nolte T, Nebelung S, Kather JN: A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis. Sci Rep 13:12098, 2023","journal-title":"Sci Rep"},{"key":"1015_CR9","doi-asserted-by":"publisher","unstructured":"Lee H, Park S, Lee J, Choi E: Unconditional image-text pair generation with multimodal cross quantizer. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.2204.07537 (October 14, 2022)","DOI":"10.48550\/arXiv.2204.07537"},{"key":"1015_CR10","doi-asserted-by":"publisher","unstructured":"Hu M, Zheng C, Zheng H, Cham T-J, Wang C, Yang Z, Tao D, Suganthan PN: Unified discrete diffusion for simultaneous vision-language generation. arXiv prepirnt, https:\/\/doi.org\/10.48550\/arXiv.2211.14842 (November 27, 2022)","DOI":"10.48550\/arXiv.2211.14842"},{"key":"1015_CR11","doi-asserted-by":"publisher","unstructured":"Chambon P, Bluethgen C, Delbrouck J-B, Van der Sluijs R, Po\u0142acin M, Chaves JMZ, Abraham TM, Purohit S, Langlotz CP, Chaudhari A: RoentGen: vision-language foundation model for chest X-ray generation. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.2211.12737 (November 23, 2022)","DOI":"10.48550\/arXiv.2211.12737"},{"key":"1015_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-023-00927-3","volume":"6","author":"M Giuffr\u00e8","year":"2023","unstructured":"Giuffr\u00e8 M, Shung DL: Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. Npj Digital Medicine 6:1\u20138, 2023","journal-title":"Npj Digital Medicine"},{"key":"1015_CR13","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","volume":"5","author":"RJ Chen","year":"2021","unstructured":"Chen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F: Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng 5:493\u2013497, 2021","journal-title":"Nat Biomed Eng"},{"key":"1015_CR14","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/s12874-020-00977-1","volume":"20","author":"A Goncalves","year":"2020","unstructured":"Goncalves A, Ray P, Soper B, Stevens J, Coyle L, Sales AP: Generation and evaluation of synthetic patient data. BMC Med Res Methodol 20:108, 2020","journal-title":"BMC Med Res Methodol"},{"key":"1015_CR15","doi-asserted-by":"publisher","unstructured":"Rodriguez-Almeida AJ, Fabelo H, Ortega S, Deniz A, Balea-Fernandez FJ, Quevedo E, Soguero-Ruiz C, Wagner AM, Callico GM: Synthetic patient data generation and evaluation in disease prediction using small and imbalanced datasets. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2022.3196697, 2022","DOI":"10.1109\/JBHI.2022.3196697"},{"key":"1015_CR16","doi-asserted-by":"publisher","unstructured":"Wang J, Yan X, Liu L, Li L, Yu Y: CTTGAN: traffic data synthesizing scheme based on conditional GAN. Sensors. https:\/\/doi.org\/10.3390\/s22145243, 2022","DOI":"10.3390\/s22145243"},{"key":"1015_CR17","doi-asserted-by":"publisher","unstructured":"Kotelnikov A, Baranchuk D, Rubachev I, Babenko A: TabDDPM: modelling tabular data with diffusion models. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.2209.15421 (September 30, 2022)","DOI":"10.48550\/arXiv.2209.15421"},{"key":"1015_CR18","doi-asserted-by":"publisher","unstructured":"Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K: Modeling tabular data using conditional GAN. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.1907.00503 (October 28, 2019)","DOI":"10.48550\/arXiv.1907.00503"},{"key":"1015_CR19","doi-asserted-by":"publisher","first-page":"375","DOI":"10.3390\/info12090375","volume":"12","author":"S Bourou","year":"2021","unstructured":"Bourou S, El Saer A, Velivassaki T-H, Voulkidis A, Zahariadis T: A review of tabular data synthesis using GANs on an IDS dataset. Information 12:375, 2021","journal-title":"Information"},{"key":"1015_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106077","volume":"150","author":"MAB Hameed","year":"2022","unstructured":"Hameed MAB, Alamgir Z: Improving mortality prediction in acute pancreatitis by machine learning and data augmentation. Comput Biol Med 150:106077, 2022","journal-title":"Comput Biol Med"},{"key":"1015_CR21","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1186\/s40537-023-00792-7","volume":"10","author":"J Fonseca","year":"2023","unstructured":"Fonseca J, Bacao F: Tabular and latent space synthetic data generation: a literature review. J Big Data 10:115, 2023","journal-title":"J Big Data"},{"key":"1015_CR22","unstructured":"The cancer imaging archive. Available at https:\/\/doi.org\/10.7937\/TCIA.BBAG-2923. Accessed February 8, 2024"},{"key":"1015_CR23","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045\u20131057, 2013","journal-title":"J Digit Imaging"},{"key":"1015_CR24","doi-asserted-by":"crossref","unstructured":"Shih G, Wu CC, Halabi SS, Kohli MD, Prevedello LM, Cook TS, Sharma A, Amorosa JK, Arteaga V, Galperin-Aizenberg M, Gill RR, Godoy MC, Hobbs S, Jeudy J, Laroia A, Shah PN, Vummidi D, Yaddanapudi K, Stein A: Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. Radiol Artif Intell 1:e180041, 2019","DOI":"10.1148\/ryai.2019180041"},{"key":"1015_CR25","doi-asserted-by":"crossref","unstructured":"Marlapalli K, Bandlamudi RSBP, Busi R, Pranav V, Madhavrao B: A review on image compression techniques, Singapore: Springer Singapore, 2021","DOI":"10.1007\/978-981-15-5397-4_29"},{"key":"1015_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108346","volume":"191","author":"D Mishra","year":"2022","unstructured":"Mishra D, Singh SK, Singh RK: Deep architectures for image compression: A critical review. Signal Processing 191:108346, 2022","journal-title":"Signal Processing"},{"key":"1015_CR27","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.procs.2017.06.017","volume":"111","author":"SC Ng","year":"2017","unstructured":"Ng SC: Principal component analysis to reduce dimension on digital image. Procedia Comput Sci 111:113\u2013119, 2017","journal-title":"Procedia Comput Sci"},{"key":"1015_CR28","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/978-3-031-24628-9_16","volume-title":"Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook","author":"D Bank","year":"2023","unstructured":"Bank D, Koenigstein N, Giryes R: Autoencoders. In: Rokach L, Maimon O, Shmueli E (eds) Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Springer International Publishing, Cham, 353\u2013374, 2023"},{"key":"1015_CR29","doi-asserted-by":"publisher","unstructured":"Rosca M, Lakshminarayanan B, Warde-Farley D, Mohamed S: Variational approaches for auto-encoding generative adversarial networks. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.1706.04987 (October 21, 2017)","DOI":"10.48550\/arXiv.1706.04987"},{"key":"1015_CR30","first-page":"918","volume":"11","author":"RG Deshpande","year":"2018","unstructured":"Deshpande RG, Ragha LL, Sharma SK: Video quality assessment through PSNR estimation for different compression standards. Indones J Electr Eng Comput Sci 11:918\u2013924, 2018","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"1015_CR31","unstructured":"Wang Z, Simoncelli EP, Bovik AC: Multiscale structural similarity for image quality assessment. Proc. 37th IEEE Asilomar Conference on Signals, Systems and Computers, 2003."},{"key":"1015_CR32","first-page":"1","volume":"28","author":"J S\u00f8gaard","year":"2016","unstructured":"S\u00f8gaard J, Krasula L, Shahid M, Temel D, Brunnstr\u00f6m K, Razaak M: Applicability of existing objective metrics of perceptual quality for adaptive video streaming. IS&T Int Symp Electron Imaging 28:1\u20137, 2016","journal-title":"IS&T Int Symp Electron Imaging"},{"key":"1015_CR33","doi-asserted-by":"publisher","unstructured":"van den Oord A, Vinyals O, Kavukcuoglu K: Neural discrete representation learning. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.1711.00937 (May 30, 2018)","DOI":"10.48550\/arXiv.1711.00937"},{"key":"1015_CR34","doi-asserted-by":"publisher","unstructured":"He K, Chen X, Xie S, Li Y, Doll\u00e1r P, Girshick R: Masked autoencoders are scalable vision learners. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.2111.06377 (December 19, 2021)","DOI":"10.48550\/arXiv.2111.06377"},{"key":"1015_CR35","doi-asserted-by":"crossref","unstructured":"Esser P, Rombach R, Ommer B: Taming transformers for high-resolution image synthesis. Proc. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"1015_CR36","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1007\/s10278-020-00413-2","volume":"34","author":"T Nakao","year":"2021","unstructured":"Nakao T, Hanaoka S, Nomura Y, Murata M, Takenaga T, Miki S, Watadani T, Yoshikawa T, Hayashi N, Abe O: Unsupervised deep anomaly detection in chest radiographs. J Digit Imaging 34:418\u2013427, 2021","journal-title":"J Digit Imaging"},{"key":"1015_CR37","doi-asserted-by":"crossref","unstructured":"Bhagat V, Bhaumik S: Data augmentation using generative adversarial networks for pneumonia classification in chest Xrays. Proc. Fifth International Conference on Image Information Processing (ICIIP), 2019","DOI":"10.1109\/ICIIP47207.2019.8985892"},{"key":"1015_CR38","doi-asserted-by":"crossref","unstructured":"Osuala R, Kushibar K, Garrucho L, Linardos A, Szafranowska Z, Klein S, Glocker B, Diaz O, Lekadir K: Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging. Med Image Anal 84:102704, 2022","DOI":"10.1016\/j.media.2022.102704"},{"key":"1015_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103046","volume":"92","author":"S Dayarathna","year":"2023","unstructured":"Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z: Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal 92:103046, 2023","journal-title":"Med Image Anal"},{"key":"1015_CR40","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1146\/annurev-publhealth-040119-094437","volume":"41","author":"TL Wiemken","year":"2020","unstructured":"Wiemken TL, Kelley RR: Machine learning in epidemiology and health outcomes research. Annu Rev Public Health 41:21\u201336, 2020","journal-title":"Annu Rev Public Health"},{"key":"1015_CR41","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1038\/s41374-022-00801-y","volume":"102","author":"Q Yin","year":"2022","unstructured":"Yin Q, Chen W, Zhang C, Wei Z: A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection. Lab Invest 102:1064\u20131074, 2022","journal-title":"Lab Invest"},{"key":"1015_CR42","doi-asserted-by":"publisher","first-page":"2303","DOI":"10.3390\/life13122303","volume":"13","author":"T Kikuchi","year":"2023","unstructured":"Kikuchi T, Hanaoka S, Nakao T, Nomura Y, Yoshikawa T, Alam MA, Mori H, Hayashi N: Relationship between thyroid CT density, volume, and future TSH elevation: A 5-year follow-up study. Life 13:2303, 2023","journal-title":"Life"},{"key":"1015_CR43","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","volume":"28","author":"JN Acosta","year":"2022","unstructured":"Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ: Multimodal biomedical AI. Nat Med 28:1773\u20131784, 2022","journal-title":"Nat Med"},{"key":"1015_CR44","doi-asserted-by":"publisher","unstructured":"Koh JY, Fried D, Salakhutdinov R: Generating images with multimodal language models. arXiv preprint, https:\/\/doi.org\/10.48550\/arXiv.2305.17216 (October 13, 2023)","DOI":"10.48550\/arXiv.2305.17216"},{"key":"1015_CR45","doi-asserted-by":"publisher","first-page":"5164970","DOI":"10.1155\/2022\/5164970","volume":"2022","author":"S Hussain","year":"2022","unstructured":"Hussain S, Mubeen I, Ullah N, Shah SSUD, Khan BA, Zahoor M, Ullah R, Khan FA, Sultan MA: Modern diagnostic imaging technique applications and risk factors in the medical field: a review. Biomed Res Int 2022:5164970, 2022","journal-title":"Biomed Res Int"},{"key":"1015_CR46","unstructured":"Dwork C. Differential privacy: A survey of results. In International conference on theory and applications of models of computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008"},{"key":"1015_CR47","doi-asserted-by":"publisher","first-page":"13524","DOI":"10.1038\/s41598-021-93030-0","volume":"11","author":"A Ziller","year":"2021","unstructured":"Ziller A, Usynin D, Braren R, Makowski M, Rueckert D, Kaissis G: Medical imaging deep learning with differential privacy. Sci Rep 11:13524, 2021","journal-title":"Sci Rep"},{"key":"1015_CR48","doi-asserted-by":"crossref","unstructured":"Fang ML, Dhami DS, Kersting K: DP-CTGAN: Differentially private medical data generation using CTGANs. Proc. 20th International Conference on Artificial Intelligence in Medicine (AIME 2022), 2022","DOI":"10.1007\/978-3-031-09342-5_17"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01015-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01015-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01015-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T12:20:07Z","timestamp":1718194807000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01015-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,13]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["1015"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01015-y","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,13]]},"assertion":[{"value":"26 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the work described herein did not involve experimentation with humans or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}