{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:04:12Z","timestamp":1793131452106,"version":"4.5.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":33,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-42870-9","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T03:23:15Z","timestamp":1773372195000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spinal disease image segmentation technology integrating U-ResNet and shape-aware attention"],"prefix":"10.1038","volume":"16","author":[{"given":"Dexuan","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rujie","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijin","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoxin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qipeng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"42870_CR1","doi-asserted-by":"crossref","unstructured":"Degadwala, S., Krishnamurthy, V. N.\u00a0D. & Vyas, D. Deepspine: multi-class spine x-ray conditions classification using deep learning. In 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL), 8\u201313 (IEEE, 2024).","DOI":"10.1109\/ICSADL61749.2024.00008"},{"key":"42870_CR2","doi-asserted-by":"publisher","first-page":"105943","DOI":"10.1016\/j.bspc.2024.105943","volume":"91","author":"MU Saeed","year":"2024","unstructured":"Saeed, M. U., Bin, W., Sheng, J., Albarakati, H. M. & Dastgir, A. Msff: An automated multi-scale feature fusion deep learning model for spine fracture segmentation using MRI. Biomed. Signal Process. Control 91, 105943 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"42870_CR3","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1080\/14488388.2024.2307082","volume":"20","author":"D Gaikwad","year":"2024","unstructured":"Gaikwad, D., Sejal, A., Bagade, S., Ghodekar, N. & Labade, S. Identification of cervical spine fracture using deep learning. Austr. J. Multi-Discip. Eng. 20, 48\u201356 (2024).","journal-title":"Austr. J. Multi-Discip. Eng."},{"key":"42870_CR4","doi-asserted-by":"publisher","first-page":"111587","DOI":"10.1016\/j.ejrad.2024.111587","volume":"178","author":"M Hokamura","year":"2024","unstructured":"Hokamura, M. et al. Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging. Eur. J. Radiol. 178, 111587 (2024).","journal-title":"Eur. J. Radiol."},{"key":"42870_CR5","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1177\/02841851241279896","volume":"65","author":"Y Wan","year":"2024","unstructured":"Wan, Y. et al. Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors. Acta Radiol. 65, 1359\u20131367 (2024).","journal-title":"Acta Radiol."},{"key":"42870_CR6","doi-asserted-by":"crossref","unstructured":"Azad, R. et al. Medical image segmentation review: The success of u-net. IEEE Trans. Pattern Anal. Mach. Intell. (2024).","DOI":"10.1109\/TPAMI.2024.3435571"},{"key":"42870_CR7","first-page":"851","volume":"37","author":"E Goceri","year":"2024","unstructured":"Goceri, E. Polyp segmentation using a hybrid vision transformer and a hybrid loss function. J. Imag. Inf. Med. 37, 851\u2013863 (2024).","journal-title":"J. Imag. Inf. Med."},{"key":"42870_CR8","doi-asserted-by":"publisher","first-page":"108305","DOI":"10.1016\/j.compbiomed.2024.108305","volume":"172","author":"S Roshan","year":"2024","unstructured":"Roshan, S. et al. A deep ensemble medical image segmentation with novel sampling method and loss function. Comput. Biol. Med. 172, 108305 (2024).","journal-title":"Comput. Biol. Med."},{"key":"42870_CR9","volume-title":"Vm-unet: Vision mamba unet for medical image segmentation","author":"J Ruan","year":"2024","unstructured":"Ruan, J., Li, J. & Xiang, S. Vm-UNet: Vision mamba unet for medical image segmentation (ACM Transactions on Multimedia Computing, Communications and Applications, 2024)."},{"key":"42870_CR10","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, J.-Q., Zhang, Y., Cui, G. & Li, L. MAMBA-UNet: Unet-like pure visual mamba for medical image segmentation. arXiv preprint arXiv:2402.05079 (2024).","DOI":"10.2139\/ssrn.5097998"},{"key":"42870_CR11","doi-asserted-by":"crossref","unstructured":"Pham, T.-H., Li, X. & Nguyen, K.-D. seUNet-Trans: A simple yet effective UNet-transformer model for medical image segmentation. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3451304"},{"key":"42870_CR12","doi-asserted-by":"publisher","first-page":"e17005","DOI":"10.7717\/peerj.17005","volume":"12","author":"Y Dan","year":"2024","unstructured":"Dan, Y., Jin, W., Yue, X. & Wang, Z. Enhancing medical image segmentation with a multi-transformer U-Net. PeerJ 12, e17005 (2024).","journal-title":"PeerJ"},{"key":"42870_CR13","doi-asserted-by":"publisher","first-page":"12939","DOI":"10.1007\/s11042-018-6267-z","volume":"78","author":"I Bakkouri","year":"2019","unstructured":"Bakkouri, I. & Afdel, K. Multi-scale cnn based on region proposals for efficient breast abnormality recognition. Multimed. Tools Appl. 78, 12939\u201312960 (2019).","journal-title":"Multimedia Tools and Applications"},{"key":"42870_CR14","doi-asserted-by":"publisher","first-page":"20483","DOI":"10.1007\/s11042-019-07988-1","volume":"79","author":"I Bakkouri","year":"2020","unstructured":"Bakkouri, I. & Afdel, K. Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimedia Tools and Applications 79, 20483\u201320518 (2020).","journal-title":"Multimedia Tools and Applications"},{"key":"42870_CR15","doi-asserted-by":"publisher","first-page":"109204","DOI":"10.1016\/j.compbiomed.2024.109204","volume":"182","author":"Z Zhu","year":"2024","unstructured":"Zhu, Z. et al. Lightweight medical image segmentation network with multi-scale feature-guided fusion. Comput. Biol. Med. 182, 109204 (2024).","journal-title":"Comput. Biol. Med."},{"key":"42870_CR16","doi-asserted-by":"publisher","first-page":"107803","DOI":"10.1016\/j.compbiomed.2023.107803","volume":"168","author":"C Wang","year":"2024","unstructured":"Wang, C. et al. CFATransuNet: Channel-wise cross fusion attention and transformer for 2D medical image segmentation. Comput. Biol. Med. 168, 107803 (2024).","journal-title":"Comput. Biol. Med."},{"key":"42870_CR17","doi-asserted-by":"publisher","first-page":"111993","DOI":"10.1016\/j.knosys.2024.111993","volume":"299","author":"S Huang","year":"2024","unstructured":"Huang, S., Wang, L., Liao, J. & Liu, L. Multi-attentional causal intervention networks for medical image diagnosis. Knowl.-Based Syst. 299, 111993 (2024).","journal-title":"Knowl.-Based Syst."},{"key":"42870_CR18","doi-asserted-by":"publisher","first-page":"112203","DOI":"10.1016\/j.knosys.2024.112203","volume":"300","author":"C Ma","year":"2024","unstructured":"Ma, C. & Wang, Z. SEMI-MAMBA-Unet: Pixel-level contrastive and cross-supervised visual mamba-based unet for semi-supervised medical image segmentation. Knowl.-Based Syst. 300, 112203 (2024).","journal-title":"Knowl.-Based Syst."},{"key":"42870_CR19","unstructured":"Rahman, M.\u00a0M. & Marculescu, R. Multi-scale hierarchical vision transformer with cascaded attention decoding for medical image segmentation. In Medical Imaging with Deep Learning, 1526\u20131544 (PMLR, 2024)."},{"key":"42870_CR20","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s11548-023-03046-2","volume":"19","author":"N Ghamsarian","year":"2024","unstructured":"Ghamsarian, N., Wolf, S., Zinkernagel, M., Schoeffmann, K. & Sznitman, R. DeepPyramid+: medical image segmentation using pyramid view fusion and deformable pyramid reception. Int. J. Comput. Assist. Radiol. Surg. 19, 851\u2013859 (2024).","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"42870_CR21","doi-asserted-by":"publisher","first-page":"103295","DOI":"10.1016\/j.media.2024.103295","volume":"98","author":"T Liu","year":"2024","unstructured":"Liu, T., Bai, Q., Torigian, D. A., Tong, Y. & Udupa, J. K. VSmTrans: A hybrid paradigm integrating self-attention and convolution for 3D medical image segmentation. Med. Image Anal. 98, 103295 (2024).","journal-title":"Med. Image Anal."},{"key":"42870_CR22","doi-asserted-by":"publisher","first-page":"106704","DOI":"10.1016\/j.bspc.2024.106704","volume":"97","author":"S Zhu","year":"2024","unstructured":"Zhu, S. et al. SECA-Net: Squeezed-and-excitated contextual attention network for medical image segmentation. Biomed. Signal Process. Control 97, 106704 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"42870_CR23","doi-asserted-by":"publisher","first-page":"124698","DOI":"10.1016\/j.eswa.2024.124698","volume":"255","author":"L Wang","year":"2024","unstructured":"Wang, L., Xu, P., Cao, X., Nappi, M. & Wan, S. Label-aware attention network with multi-scale boosting for medical image segmentation. Expert Syst. Appl. 255, 124698 (2024).","journal-title":"Expert Syst. Appl."},{"key":"42870_CR24","doi-asserted-by":"crossref","unstructured":"Chen, T. et al. xLSTM-UNet can be an effective 2D & 3D medical image segmentation backbone with Vision-LSTM (ViL) better than its mamba counterpart. arXiv preprint arXiv:2407.01530 (2024).","DOI":"10.1109\/BHI62660.2024.10913659"},{"key":"42870_CR25","doi-asserted-by":"crossref","unstructured":"Tian, Q., Wang, Z. & Cui, X. Improved unet brain tumor image segmentation based on GSCONV module and ECA attention mechanism. arXiv preprint arXiv:2409.13626 (2024).","DOI":"10.54254\/2755-2721\/88\/20241740"},{"key":"42870_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, F. et al. SMAFormer: Synergistic multi-attention transformer for medical image segmentation. In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 4048\u20134053 (IEEE, 2024).","DOI":"10.1109\/BIBM62325.2024.10822736"},{"key":"42870_CR27","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1016\/j.neunet.2023.10.046","volume":"170","author":"E Song","year":"2024","unstructured":"Song, E., Zhan, B. & Liu, H. Combining external-latent attention for medical image segmentation. Neural Netw. 170, 468\u2013477 (2024).","journal-title":"Neural Netw."},{"key":"42870_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, Y., Wang, F. & Bao, Q. Improved unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism. In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), 393\u2013397 (IEEE, 2024).","DOI":"10.1109\/ICBASE63199.2024.10762617"},{"key":"42870_CR29","doi-asserted-by":"publisher","first-page":"112050","DOI":"10.1016\/j.knosys.2024.112050","volume":"299","author":"X Li","year":"2024","unstructured":"Li, X. et al. DMSA-UNet: Dual multi-scale attention makes unet more strong for medical image segmentation. Knowl.-Based Syst. 299, 112050 (2024).","journal-title":"Knowl.-Based Syst."},{"key":"42870_CR30","doi-asserted-by":"publisher","first-page":"100528","DOI":"10.1016\/j.eij.2024.100528","volume":"27","author":"TB Nguyen-Tat","year":"2024","unstructured":"Nguyen-Tat, T. B., Nguyen, T.-Q.T., Nguyen, H.-N. & Ngo, V. M. Enhancing brain tumor segmentation in MRI images: A hybrid approach using UNet, attention mechanisms, and transformers. Egypti. Inf. J. 27, 100528 (2024).","journal-title":"Egyptian Informatics Journal"},{"key":"42870_CR31","doi-asserted-by":"publisher","first-page":"108611","DOI":"10.1016\/j.cmpb.2025.108611","volume":"261","author":"J Zhong","year":"2025","unstructured":"Zhong, J. et al. PMFSNET: Polarized multi-scale feature self-attention network for lightweight medical image segmentation. Comput. Methods Programs Biomed. 261, 108611 (2025).","journal-title":"Comput. Methods Programs Biomed."},{"key":"42870_CR32","doi-asserted-by":"crossref","unstructured":"Bakkouri, I., Afdel, K., Benois-Pineau, J. & Initiative, G. C. F. t. A. D.\u00a0N. BG-3DM2F: Bidirectional gated 3d multi-scale feature fusion for alzheimer\u2019s disease diagnosis. Multimed. Tools Appl.81, 10743\u201310776 (2022).","DOI":"10.1007\/s11042-022-12242-2"},{"key":"42870_CR33","doi-asserted-by":"publisher","first-page":"108784","DOI":"10.1016\/j.compbiomed.2024.108784","volume":"178","author":"H Huang","year":"2024","unstructured":"Huang, H. et al. Channel prior convolutional attention for medical image segmentation. Comput. Biol. Med. 178, 108784 (2024).","journal-title":"Comput. Biol. Med."},{"key":"42870_CR34","doi-asserted-by":"publisher","first-page":"105889","DOI":"10.1016\/j.bspc.2023.105889","volume":"90","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Luan, Z., Ni, L., Qi, L. & Gong, X. Msdanet: A multi-scale dilation attention network for medical image segmentation. Biomed. Signal Process. Control 90, 105889 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"42870_CR35","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1007\/s11760-022-02325-w","volume":"17","author":"I Bakkouri","year":"2023","unstructured":"Bakkouri, I. & Afdel, K. MLCA2F: Multi-level context attentional feature fusion for covid-19 lesion segmentation from ct scans. SIViP 17, 1181\u20131188 (2023).","journal-title":"SIViP"},{"key":"42870_CR36","doi-asserted-by":"publisher","first-page":"107728","DOI":"10.1016\/j.compbiomed.2023.107728","volume":"168","author":"Y Zhao","year":"2024","unstructured":"Zhao, Y., Li, J., Ren, L. & Chen, Z. Dtan: Diffusion-based text attention network for medical image segmentation. Comput. Biol. Med. 168, 107728 (2024).","journal-title":"Comput. Biol. Med."},{"key":"42870_CR37","doi-asserted-by":"publisher","first-page":"108096","DOI":"10.1016\/j.compbiomed.2024.108096","volume":"170","author":"S Gangrade","year":"2024","unstructured":"Gangrade, S., Sharma, P. C., Sharma, A. K. & Singh, Y. P. Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation. Comput. Biol. Med. 170, 108096 (2024).","journal-title":"Comput. Biol. Med."},{"key":"42870_CR38","doi-asserted-by":"publisher","first-page":"105458","DOI":"10.1016\/j.bspc.2023.105458","volume":"87","author":"X Zhang","year":"2024","unstructured":"Zhang, X. et al. Smtf: Sparse transformer with multiscale contextual fusion for medical image segmentation. Biomed. Signal Process. Control 87, 105458 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"42870_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00586-023-07982-0","volume":"33","author":"J Vitale","year":"2024","unstructured":"Vitale, J., Sconfienza, L. M. & Galbusera, F. Cross-sectional area and fat infiltration of the lumbar spine muscles in patients with back disorders: A deep learning-based big data analysis. Eur. Spine J. 33, 1\u201310 (2024).","journal-title":"Eur. Spine J."},{"key":"42870_CR40","doi-asserted-by":"publisher","first-page":"102776","DOI":"10.1016\/j.artmed.2024.102776","volume":"148","author":"H Liu","year":"2024","unstructured":"Liu, H., Huang, J., Li, Q., Guan, X. & Tseng, M. A deep convolutional neural network for the automatic segmentation of glioblastoma brain tumor: Joint spatial pyramid module and attention mechanism network. Artif. Intell. Med. 148, 102776 (2024).","journal-title":"Artif. Intell. Med."},{"key":"42870_CR41","doi-asserted-by":"publisher","first-page":"106100","DOI":"10.1016\/j.bspc.2024.106100","volume":"92","author":"Y Chen","year":"2024","unstructured":"Chen, Y. et al. Icycle-gan: Improved cycle generative adversarial networks for liver medical image generation. Biomed. Signal Process. Control 92, 106100 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"42870_CR42","unstructured":"Xu, J. Hc-mamba: Vision mamba with hybrid convolutional techniques for medical image segmentation. arXiv preprint arXiv:2405.05007 (2024)."},{"key":"42870_CR43","doi-asserted-by":"publisher","first-page":"4652","DOI":"10.1609\/aaai.v39i5.32491","volume":"39","author":"C Li","year":"2025","unstructured":"Li, C. et al. U-kan makes strong backbone for medical image segmentation and generation. Proc. AAAI Conf. Artif. Intell.  39, 4652\u20134660 (2025).","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"42870_CR44","doi-asserted-by":"publisher","first-page":"103280","DOI":"10.1016\/j.media.2024.103280","volume":"97","author":"J Chen","year":"2024","unstructured":"Chen, J. et al. Transunet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med. Image Anal. 97, 103280 (2024).","journal-title":"Med. Image Anal."},{"key":"42870_CR45","unstructured":"Agrawal, A., Agrawal, A., Gupta, S. & Bagade, P. Kan-mamba fusionnet: Redefining medical image segmentation with non-linear modeling. arXiv preprint arXiv:2411.11926 (2024)."},{"key":"42870_CR46","doi-asserted-by":"crossref","unstructured":"Wu, L. et al. Cnn-transformer rectified collaborative learning for medical image segmentation. IEEE Trans. Circ. Syst. Video Technol. (2024).","DOI":"10.1109\/TCSVT.2024.3523316"},{"key":"42870_CR47","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","volume":"32","author":"H-Y Zhou","year":"2023","unstructured":"Zhou, H.-Y. et al. nnformer: Volumetric medical image segmentation via a 3d transformer. IEEE Trans. Image Process. 32, 4036\u20134045 (2023).","journal-title":"IEEE Trans. Image Process."},{"key":"42870_CR48","unstructured":"Zhu, J., Hamdi, A., Qi, Y., Jin, Y. & Wu, J. Medical sam 2: Segment medical images as video via segment anything model 2. arXiv preprint arXiv:2408.00874 (2024)."},{"key":"42870_CR49","doi-asserted-by":"publisher","first-page":"8938","DOI":"10.1038\/s41598-025-92994-7","volume":"15","author":"W Tang","year":"2025","unstructured":"Tang, W., Wu, Z., Wang, W., Pan, Y. & Gan, W. VM-UNet++ research on crack image segmentation based on improved VM-UNet. Sci. Rep. 15, 8938 (2025).","journal-title":"Sci. Rep."},{"key":"42870_CR50","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1007\/s00330-024-11155-y","volume":"35","author":"H M\u00f6ller","year":"2025","unstructured":"M\u00f6ller, H. et al. Spineps-automatic whole spine segmentation of t2-weighted mr images using a two-phase approach to multi-class semantic and instance segmentation. Eur. Radiol. 35, 1178\u20131189 (2025).","journal-title":"Eur. Radiol."},{"key":"42870_CR51","doi-asserted-by":"publisher","first-page":"109526","DOI":"10.1016\/j.compbiomed.2024.109526","volume":"185","author":"MU Saeed","year":"2025","unstructured":"Saeed, M. U., Bin, W., Sheng, J. & Saleem, S. 3d mfa: An automated 3d multi-feature attention based approach for spine segmentation using a multi-stage network pruning. Comput. Biol. Med. 185, 109526 (2025).","journal-title":"Comput. Biol. Med."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42870-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42870-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42870-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T05:16:34Z","timestamp":1776316594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-42870-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,13]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["42870"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-42870-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,13]]},"assertion":[{"value":"16 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":3,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12465"}}