{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:14:45Z","timestamp":1792782885252,"version":"4.5.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"CONICET","award":["PIP GI 2021-2023-11220200102472CO."],"award-info":[{"award-number":["PIP GI 2021-2023-11220200102472CO."]}],"id":[{"id":"https:\/\/ror.org\/03cqe8w59","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Biomed Eng"],"DOI":"10.1007\/s10439-026-04411-3","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T22:48:03Z","timestamp":1792190883000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Abdominal Ultrasound Simulation from Semantic Labels Using Paired Label-to-Physics-Based Image Translation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4973-5342","authenticated-orcid":false,"given":"Santiago","family":"Vitale","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duilio","family":"Deangeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Larrabide","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Ignacio","family":"Orlando","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"4411_CR1","unstructured":"Rumack, C. M., S. R. Wilson, J. W. Charboneau, and D. Levine. Diagnostic Ultrasound, 5th edn. Elsevier Health Sciences, 2017."},{"issue":"1","key":"4411_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40560-016-0175-y","volume":"4","author":"T Kameda","year":"2016","unstructured":"Kameda, T., and N. Taniguchi. Overview of point-of-care abdominal ultrasound in emergency and critical care. J. Intensive Care. 4(1):53, 2016.","journal-title":"J. Intensive Care"},{"issue":"8","key":"4411_CR3","first-page":"1437","volume":"17","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Q. Yang, L. Drukker, A. Papageorghiou, Y. Hu, and J. A. Noble. Task model-specific operator skill assessment in routine fetal ultrasound scanning. IJCARS. 17(8):1437\u20131444, 2022.","journal-title":"IJCARS."},{"issue":"03","key":"4411_CR4","first-page":"240","volume":"42","author":"LJ Nayahangan","year":"2021","unstructured":"Nayahangan, L. J., C. F. Dietrich, and M. B. Nielsen. Simulation-based training in ultrasound-where are we now? UIM\/EJU. 42(03):240\u2013244, 2021.","journal-title":"UIM\/EJU."},{"issue":"2","key":"4411_CR5","doi-asserted-by":"publisher","first-page":"220","DOI":"10.11152\/mu-2599","volume":"22","author":"B Hoffmann","year":"2020","unstructured":"Hoffmann, B., M. Blaivas, J. Abramowicz, M. Bachmann, R. Badea, B. Braden, et al. Medical student ultrasound education, a WFUMB position paper, part II. A consensus statement of ultrasound societies. Med. Ultrason. 22(2):220\u2013229, 2020.","journal-title":"Med. Ultrason."},{"issue":"2","key":"4411_CR6","first-page":"413","volume":"35","author":"VA Dinh","year":"2016","unstructured":"Dinh, V. A., J. Y. Fu, S. Lu, A. Chiem, J. C. Fox, and M. Blaivas. Integration of ultrasound in medical education at United States medical schools: a national survey of directors\u2019 experiences. JUM. 35(2):413\u2013419, 2016.","journal-title":"JUM"},{"issue":"03","key":"4411_CR7","first-page":"253","volume":"37","author":"ML \u00d8stergaard","year":"2016","unstructured":"\u00d8stergaard, M. L., C. Ewertsen, L. Konge, E. Albrecht-Beste, and M. B. Nielsen. Simulation-based abdominal ultrasound training\u2014a systematic review. UIM\/EJU. 37(03):253\u2013261, 2016.","journal-title":"UIM\/EJU."},{"key":"4411_CR8","doi-asserted-by":"crossref","unstructured":"Shams, R., R. Hartley, and N. Navab. Real-time simulation of medical ultrasound from CT images. In: MICCAI. Springer, 2008, pp. 734\u2013741.","DOI":"10.1007\/978-3-540-85990-1_88"},{"issue":"3","key":"4411_CR9","first-page":"609","volume":"32","author":"B Burger","year":"2012","unstructured":"Burger, B., S. Bettinghausen, M. Radle, and J. Hesser. Real-time GPU-based ultrasound simulation using deformable mesh models. IEEE TMI. 32(3):609\u2013618, 2012.","journal-title":"IEEE TMI."},{"key":"4411_CR10","unstructured":"Cambet, T. P., S. Vitale, and I. Larrabide. High performance ultrasound simulation using Monte-Carlo simulation: a GPU ray-tracing implementation. In: SIPAIM, vol. 11583. SPIE, 2020, pp. 107\u2013115."},{"key":"4411_CR11","unstructured":"Wang, W., J. Bao, W. Zhou, D. Chen, D. Chen, L. Yuan, et\u00a0al. Semantic image synthesis via diffusion models. arXiv preprint, 2022. arXiv:2207.00050."},{"key":"4411_CR12","unstructured":"Ko, J., I. Kong, D. Park, and H. J. Kim. Stochastic conditional diffusion models for robust semantic image synthesis. arXiv preprint, 2024. arXiv:2402.16506"},{"key":"4411_CR13","volume":"79","author":"J Liang","year":"2022","unstructured":"Liang, J., X. Yang, Y. Huang, H. Li, S. He, X. Hu, et al. Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis. Media.79:102461, 2022.","journal-title":"Media"},{"key":"4411_CR14","doi-asserted-by":"crossref","unstructured":"Pigeau, G., L. Elbatarny, V. Wu, A. Schonewille, G. Fichtinger, and T. Ungi. Ultrasound image simulation with generative adversarial network. In: Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11315. SPIE, 2020, pp. 54\u201360.","DOI":"10.1117\/12.2549592"},{"key":"4411_CR15","unstructured":"Ho, J., A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. In: NeurIPS, vol. 33, 2020, pp. 6840\u20136851."},{"key":"4411_CR16","doi-asserted-by":"crossref","unstructured":"Stojanovski, D., U. Hermida, P. Lamata, A. Beqiri, and A. Gomez. Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation. In: ASMUS. Springer, 2023, pp. 34\u201343.","DOI":"10.1007\/978-3-031-44521-7_4"},{"key":"4411_CR17","doi-asserted-by":"crossref","unstructured":"Dom\u00ednguez, M., Y. Velikova, N. Navab, and M. F. Azampour. Diffusion as sound propagation: physics-inspired model for ultrasound image generation. In: MICCAI. Springer, 2024, pp. 613\u2013623.","DOI":"10.1007\/978-3-031-72083-3_57"},{"key":"4411_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, L., A. Rao, and M. Agrawala. Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 3836\u20133847.","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"4411_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103725","volume":"105","author":"Y Duan","year":"2025","unstructured":"Duan, Y., T. Tan, Z. Zhu, Y. Huang, et al. FetalFlex: anatomy-guided diffusion model for flexible control on fetal ultrasound image synthesis. Med. Image Anal.105:103725, 2025. https:\/\/doi.org\/10.1016\/j.media.2025.103725.","journal-title":"Med. Image Anal."},{"key":"4411_CR20","unstructured":"Freiche, B., A. El-Khoury, A. Nasiri-Sarvi, M. S. Hosseini, D. Garcia, A. Basarab, et\u00a0al. Ultrasound image generation using latent diffusion models. https:\/\/arxiv.org\/abs\/2502.08580."},{"key":"4411_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, L., T. Portenier, C. Paulus, and O. Goksel. Deep image translation for enhancing simulated ultrasound images. In: ASMUS 2020, PIPPI 2020. Springer, 2020, pp. 85\u201394.","DOI":"10.1007\/978-3-030-60334-2_9"},{"key":"4411_CR22","doi-asserted-by":"crossref","unstructured":"Tomar, D., L. Zhang, T. Portenier, and O. Goksel. Content-preserving unpaired translation from simulated to realistic ultrasound images. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, 27 September\u20131 October, 2021, Proceedings, Part VIII 24. Springer, 2021, pp. 659\u2013669.","DOI":"10.1007\/978-3-030-87237-3_63"},{"issue":"2","key":"4411_CR23","first-page":"183","volume":"15","author":"S Vitale","year":"2020","unstructured":"Vitale, S., J. I. Orlando, E. Iarussi, and I. Larrabide. Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs. IJCARS. 15(2):183\u2013192, 2020.","journal-title":"IJCARS."},{"key":"4411_CR24","doi-asserted-by":"crossref","unstructured":"Song, Y., and N. Y. Chong. S-cyclegan: Semantic segmentation enhanced CT-ultrasound image-to-image translation for robotic ultrasonography. In: 2024 IEEE International Conference on Cyborg and Bionic Systems (CBS). IEEE, 2024, pp. 115\u2013120.","DOI":"10.1109\/CBS61689.2024.10860598"},{"key":"4411_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108344","volume":"110","author":"X Wang","year":"2025","unstructured":"Wang, X., J. Xia, M. Wu, Y. Xu, and Y. Zhou. CT to ultrasound abdominal image synthesis using domain-constraint generative adversarial networks. Biomed. Signal Process. Control.110:108344, 2025.","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"4411_CR26","doi-asserted-by":"publisher","first-page":"4540","DOI":"10.1002\/mp.17801","volume":"52","author":"S Vitale","year":"2025","unstructured":"Vitale, S., J. I. Orlando, E. Iarussi, A. D\u00edaz, and I. Larrabide. Improving realism in abdominal ultrasound simulation combining a segmentation-guided loss and polar coordinates training. Med. Phys. 52(6):4540\u20134556, 2025.","journal-title":"Med. Phys."},{"issue":"11","key":"4411_CR27","first-page":"2459","volume":"35","author":"O Jimenez-del Toro","year":"2016","unstructured":"Jimenez-del Toro, O., H. M\u00fcller, M. Krenn, K. Gruenberg, A. A. Taha, M. Winterstein, et al. Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks. IEEE TMI. 35(11):2459\u20132475, 2016.","journal-title":"IEEE TMI."},{"issue":"5","key":"4411_CR28","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J., H. C. Breit, M. T. Meyer, M. Pradella, D. Hinck, A. W. Sauter, et al. Total segmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5):e230024, 2023.","journal-title":"Radiol. Artif. Intell."},{"key":"4411_CR29","unstructured":"Soler, L., A. Hostettler, V. Agnus, A. Charnoz, J. B. Fasquel, J. Moreau, et\u00a0al. 3D image reconstruction for comparison of algorithm database, 2010. https:\/\/www.ircad.fr\/research\/data-sets\/liver-segmentation-3d-ircadb-01\/."},{"key":"4411_CR30","doi-asserted-by":"crossref","unstructured":"Rubi, P., E. F. Vera, J. Larrabide, M. Calvo, J. D\u2019Amato, and I. Larrabide. Comparison of real-time ultrasound simulation models using abdominal CT images. In: SIPAIM. vol. 10160. SPIE, 2017, pp. 55\u201363.","DOI":"10.1117\/12.2255741"},{"key":"4411_CR31","doi-asserted-by":"crossref","unstructured":"Isola, P., J. Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In: IEEE CVPR, 2017, pp. 1125\u20131134.","DOI":"10.1109\/CVPR.2017.632"},{"key":"4411_CR32","unstructured":"Nichol, A. Q., and P. Dhariwal. Improved denoising diffusion probabilistic models. In: ICML. PMLR, 2021, pp. 8162\u20138171."},{"key":"4411_CR33","doi-asserted-by":"crossref","unstructured":"Park, T., M. Y. Liu, T. C. Wang,and J. Y. Zhu. Semantic image synthesis with spatially-adaptive normalization. In: IEE CVPR, 2019, pp. 2337\u20132346.","DOI":"10.1109\/CVPR.2019.00244"},{"key":"4411_CR34","doi-asserted-by":"crossref","unstructured":"Zhu, J. Y., T. Park, P. Isola, and A. A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE ICCV, 2017, pp. 2223\u20132232.","DOI":"10.1109\/ICCV.2017.244"},{"issue":"5","key":"4411_CR35","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s11548-021-02349-6","volume":"16","author":"L Zhang","year":"2021","unstructured":"Zhang, L., T. Portenier, and O. Goksel. Learning ultrasound rendering from cross-sectional model slices for simulated training. Int. J. Comput. Assist. Radiol. Surg. 16(5):721\u2013730, 2021.","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"2","key":"4411_CR36","doi-asserted-by":"publisher","first-page":"405","DOI":"10.3390\/biomedinformatics3020027","volume":"3","author":"S Katakis","year":"2023","unstructured":"Katakis, S., N. Barotsis, A. Kakotaritis, P. Tsiganos, G. Economou, E. Panagiotopoulos, et al. Generation of musculoskeletal ultrasound images with diffusion models. BioMedInformatics. 3(2):405\u2013421, 2023.","journal-title":"BioMedInformatics."},{"issue":"20","key":"4411_CR37","doi-asserted-by":"publisher","first-page":"4061","DOI":"10.3390\/electronics14204061","volume":"14","author":"J Peng","year":"2025","unstructured":"Peng, J., Z. Guo, X. Chen, and M. Zhou. Denoising degraded PCOS ultrasound images using an enhanced denoising diffusion probabilistic model. Electronics. 14(20):4061, 2025.","journal-title":"Electronics"},{"issue":"9","key":"4411_CR38","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1016\/j.ultrasmedbio.2026.05.005","volume":"52","author":"P Du","year":"2026","unstructured":"Du, P., F. Shen, Z. Lai, H. He, H. Shi, and F. Li. A synthetic data-augmented deep learning framework for robust segmentation and quantification of the carotid artery in ultrasound images. Ultrasound Med. Biol. 52(9):1879\u20131889, 2026.","journal-title":"Ultrasound Med. Biol."},{"key":"4411_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107642","volume":"240","author":"L Chen","year":"2023","unstructured":"Chen, L., H. Liao, W. Kong, D. Zhang, and F. Chen. Anatomy preserving GAN for realistic simulation of intraoperative liver ultrasound images. Comput. Methods Programs Biomed.240:107642, 2023.","journal-title":"Comput. Methods Programs Biomed."},{"key":"4411_CR40","unstructured":"Zhang, L., T. Portenier, and O. Goksel. Unpaired translation from semantic label maps to images by leveraging domain-specific simulations. arXiv preprint, 2023. arXiv:2302.10698."},{"key":"4411_CR41","doi-asserted-by":"crossref","unstructured":"Liang, J., X. Yang, Y. Huang, K. Liu, X. Zhou, X. Hu, et\u00a0al. Weakly-supervised high-fidelity ultrasound video synthesis with feature decoupling. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2022, pp. 310\u2013319.","DOI":"10.1007\/978-3-031-16440-8_30"},{"key":"4411_CR42","unstructured":"Obukhov, A., M. Seitzer, P. Wu, S. Zhydenko, J. Kyl, and E. Lin. High-Fidelity Performance Metrics for Generative Models in PyTorch. Zenodo"},{"key":"4411_CR43","unstructured":"Bushberg, J. T., J. A. Seibert, E. M. Leidholdt, and J. M. Boone. The Essential Physics of Medical Imaging, 3rd ed. Lippincott Williams & Wilkins, 2011."},{"issue":"2","key":"4411_CR44","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s00108-019-00728-5","volume":"61","author":"C H\u00f6ner Zu Siederdissen","year":"2020","unstructured":"H\u00f6ner Zu Siederdissen, C., and A. Potthoff. Sonographische Diagnostik bei Lebertumoren. Der Internist. 61(2):115\u2013122, 2020. https:\/\/doi.org\/10.1007\/s00108-019-00728-5.","journal-title":"Der Internist."},{"issue":"6","key":"4411_CR45","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1016\/j.jvir.2008.03.010","volume":"19","author":"RL Pande","year":"2008","unstructured":"Pande, R. L., and J. A. Beckman. Abdominal aortic aneurysm: populations at risk and how to screen. JVIR. 19(6):S2\u2013S8, 2008.","journal-title":"JVIR."},{"issue":"4","key":"4411_CR46","first-page":"52","volume":"13","author":"JC Simon","year":"2017","unstructured":"Simon, J. C., A. D. Maxwell, and M. R. Bailey. Some work on the diagnosis and management of kidney stones with ultrasound. Acoustics Today. 13(4):52, 2017.","journal-title":"Acoustics Today"},{"issue":"2","key":"4411_CR47","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s11938-005-0002-3","volume":"8","author":"KM Persley","year":"2005","unstructured":"Persley, K. M. Gallbladder polyps. Curr. Treat. Options Gastroenterol. 8(2):105\u2013108, 2005.","journal-title":"Curr. Treat. Options Gastroenterol."},{"key":"4411_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102846","volume":"88","author":"A Kazerouni","year":"2023","unstructured":"Kazerouni, A., E. K. Aghdam, M. Heidari, R. Azad, M. Fayyaz, I. Hacihaliloglu, et al. Diffusion models in medical imaging: a comprehensive survey. Med. Image Anal.88:102846, 2023. https:\/\/doi.org\/10.1016\/j.media.2023.102846.","journal-title":"Med. Image Anal."},{"issue":"3","key":"4411_CR49","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3390\/jimaging9030069","volume":"9","author":"Y Skandarani","year":"2023","unstructured":"Skandarani, Y., P. M. Jodoin, and A. Lalande. GANs for medical image synthesis: an empirical study. J. Imaging. 9(3):69, 2023. https:\/\/doi.org\/10.3390\/jimaging9030069.","journal-title":"J. Imaging"},{"key":"4411_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmir.2025.100028","author":"B Kocak","year":"2025","unstructured":"Kocak, B., et al. Evaluation metrics in medical imaging artificial intelligence. J. Med. Imaging Radiation Sci. 2025. https:\/\/doi.org\/10.1016\/j.jmir.2025.100028.","journal-title":"J. Med. Imaging Radiation Sci."},{"issue":"2","key":"4411_CR51","doi-asserted-by":"publisher","first-page":"160","DOI":"10.4103\/EUS-D-22-00151","volume":"12","author":"CF Dietrich","year":"2022","unstructured":"Dietrich, C. F., et al. The ultrasound use of simulators, current view, and perspectives. Endoscopic Ultrasound. 12(2):160\u2013168, 2022. https:\/\/doi.org\/10.4103\/EUS-D-22-00151.","journal-title":"Endoscopic Ultrasound."},{"issue":"3","key":"4411_CR52","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1097\/eus.0000000000000001","volume":"12","author":"C Lucius","year":"2023","unstructured":"Lucius, C., et al. The use of simulation in medical ultrasound: current view and perspectives. Endoscopic Ultrasound. 12(3):295\u2013303, 2023. https:\/\/doi.org\/10.1097\/eus.0000000000000001.","journal-title":"Endoscopic Ultrasound."}],"container-title":["Annals of Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-026-04411-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10439-026-04411-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-026-04411-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T22:48:07Z","timestamp":1792190887000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10439-026-04411-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,16]]},"references-count":52,"alternative-id":["4411"],"URL":"https:\/\/doi.org\/10.1007\/s10439-026-04411-3","relation":{},"ISSN":["0090-6964","1573-9686"],"issn-type":[{"value":"0090-6964","type":"print"},{"value":"1573-9686","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,16]]},"assertion":[{"value":"1 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Informed Consent","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","label":"Research Involving Human and Animal Rights","group":{"name":"EthicsHeading","label":"Declarations"}}]}}