{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:25:02Z","timestamp":1773933902351,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s10586-025-05862-4","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:25:08Z","timestamp":1767014708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual attention and multi-scale pyramid network (DAMP): a novel approach to chromosome classification"],"prefix":"10.1007","volume":"29","author":[{"given":"Neelam","family":"Umbreen","sequence":"first","affiliation":[]},{"given":"Sara","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Hasan","family":"Sajid","sequence":"additional","affiliation":[]},{"given":"Yasar","family":"Ayaz","sequence":"additional","affiliation":[]},{"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Jamel","family":"Baili","sequence":"additional","affiliation":[]},{"given":"Fatimah","family":"Alhayan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"5862_CR1","unstructured":"Karyotyping for chromosomal abnormalities, Nature Scitable. [Online]. Available: https:\/\/www.nature.com\/scitable\/topicpage\/karyotyping-for-chromosomal-abnormalities-298\/. Accessed: Sep. 24, (2025)"},{"issue":"3","key":"5862_CR2","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TCBB.2020.3003445","volume":"19","author":"C Lin","year":"2022","unstructured":"Lin, C., Zhao, G., Yang, Z., Yin, A., Wang, X., Guo, L., Chen, H., Ma, Z., Zhao, L., Luo, H., et al.: CIR-net: automatic classification of human chromosome based on Inception-ResNet architecture. IEEE\/ACM Transactions on Computational Biology and Bioinformatics 19(3), 1285\u20131293 (2022)","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"5862_CR3","doi-asserted-by":"publisher","unstructured":"Sharma, M., Saha,\u00a0O., Sriramana,\u00a0A.: Crowdsourcing for chromosome segmentation and deep classification. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), pp. 34\u201341. (2017)\u00a0https:\/\/doi.org\/10.1109\/CVPRW.2017.109.","DOI":"10.1109\/CVPRW.2017.109"},{"key":"5862_CR4","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.jbi.2008.05.004","volume":"42","author":"X Wang","year":"2009","unstructured":"Wang, X., Zheng, B., Li, S.: Automated identification of human chromosomes as an exercise in building intelligent image recognition systems. J. Biomed. Inform. 42, 22\u201331 (2009)","journal-title":"J. Biomed. Inform."},{"issue":"6","key":"5862_CR5","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1109\/TBME.2010.2040279","volume":"57","author":"A Khmelinskii","year":"2010","unstructured":"Khmelinskii, A., Ventura, R., Sanches, J.M.: A novel metric for bone-marrow cell chromosome pairing. IEEE Trans. Biomed. Eng. 57(6), 1420\u20131429 (2010)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"5862_CR6","doi-asserted-by":"crossref","unstructured":"Slayton,\u00a0R.L., and Kantaputra P.N.: Congenital genetic disorders and syndromes. In Pediatric Dentistry, Elsevier, pp. 244\u2013258. (2019)","DOI":"10.1016\/B978-0-323-60826-8.00017-1"},{"issue":"10","key":"5862_CR7","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1016\/S0031-3203(01)00178-9","volume":"35","author":"M Egmont-Petersen","year":"2002","unstructured":"Egmont-Petersen, M., de Ridr, D., Handels, H.: Image processing with neural networks\u2014a review. Pattern Recognit. 35(10), 2279\u20132301 (2002)","journal-title":"Pattern Recognit."},{"key":"5862_CR8","doi-asserted-by":"crossref","unstructured":"Jindal, S., Gupta,\u00a0G., Yadav,\u00a0M., Sharma,\u00a0M., Vig,\u00a0L.: Siamese networks for chromosome classification. In Proc. IEEE Int. Conf. Comput. Vis. Workshops (ICCVW), pp. 72\u201381.\u00a0(2017)","DOI":"10.1109\/ICCVW.2017.17"},{"key":"5862_CR9","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.eswa.2017.05.070","volume":"86","author":"AO Kusakci","year":"2017","unstructured":"Kusakci, A.O., Ayvaz, B., Karakaya, E.: Towards an autonomous human chromosome classification system using competitive support vector machines teams (CSVMT). Expert Syst. Appl. 86, 224\u2013234 (2017)","journal-title":"Expert Syst. Appl."},{"key":"5862_CR10","doi-asserted-by":"crossref","unstructured":"Sharma,\u00a0M., Vig,\u00a0L., et al.: Automatic chromosome classification using deep attention-based sequence learning of chromosome bands, In Proc. Int. Joint Conf. Neural Netw. (IJCNN), pp. 1\u20138. (2018)","DOI":"10.1109\/IJCNN.2018.8489321"},{"key":"5862_CR11","doi-asserted-by":"crossref","unstructured":"Zhang,\u00a0W., Song,\u00a0S., Bai,\u00a0T., Zhao,\u00a0Y., Ma,\u00a0F., Su, J., Yu,\u00a0L.: Chromosome classification with convolutional neural network-based deep learning. In Proc. 11th Int. Congr. Image Signal Process., Biomed. Eng. Inform. (CISP-BMEI), pp. 1\u20135. (2018)","DOI":"10.1109\/CISP-BMEI.2018.8633228"},{"key":"5862_CR12","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1089\/cmb.2018.0212","volume":"26","author":"X Hu","year":"2019","unstructured":"Hu, X., Yi, W., Jiang, L., Wu, S., Zhang, Y., Du, J., Ma, T., Wang, T., Wu, X.: Classification of metaphase chromosomes using deep convolutional neural network. J. Comput. Biol. 26, 473\u2013484 (2019)","journal-title":"J. Comput. Biol."},{"key":"5862_CR13","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1109\/TMI.2019.2905841","volume":"38","author":"Y Qin","year":"2019","unstructured":"Qin, Y., Wen, J., Zheng, H., Huang, X., Yang, J., Song, N., Zhu, Y.M., Wu, L., Yang, G.Z.: Varifocal-net: a chromosome classification approach using deep convolutional networks. IEEE Trans. Med. Imaging 38, 2569\u20132581 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5862_CR14","doi-asserted-by":"crossref","unstructured":"Remya,\u00a0R., Hariharan,\u00a0S., Vinod,\u00a0V., Fernandez,\u00a0D.J.W., Ajmal,\u00a0N.M., Gopakumar,\u00a0C.: A comprehensive study on convolutional neural networks for chromosome classification. In Proc. 2020 Adv. Comput. Commun. Technol. High Perform. Appl. (ACCTHPA), pp. 287\u2013292. (2020)","DOI":"10.1109\/ACCTHPA49271.2020.9213238"},{"key":"5862_CR15","doi-asserted-by":"crossref","unstructured":"Lin,\u00a0C., Zhao,\u00a0G., Yin,\u00a0A., Ding,\u00a0B., Guo,\u00a0L., Chen,\u00a0H.: A multi-stages chromosome segmentation and mixed classification method for chromosome automatic karyotyping. In Proc. Int. Conf. Web Inf. Syst. Appl., pp. 365\u2013376. (2020)","DOI":"10.1007\/978-3-030-60029-7_34"},{"key":"5862_CR16","doi-asserted-by":"crossref","unstructured":"Xiao,\u00a0L., and Luo,\u00a0C.: DeepACC: Automate chromosome classification based on metaphase images using deep learning framework fused with priori knowledge. In Proc. IEEE 18th Int. Symp. Biomed. Imaging (ISBI), pp. 607\u2013610. (2021)","DOI":"10.1109\/ISBI48211.2021.9433943"},{"key":"5862_CR17","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud,\u00a0P.H., Riegler,\u00a0M.A., Halvorsen, P., de Lange,\u00a0T., Johansen,\u00a0D., Johansen,\u00a0H.D.: Kvasir-SEG: A segmented polyp dataset. In Proc. Int. Conf. Multimedia Modeling, pp. 451\u2013462. (2020)","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"5862_CR18","doi-asserted-by":"crossref","unstructured":"Fan,\u00a0D.-P., Ji,\u00a0G.-P., Zhou,\u00a0T., Chen,\u00a0G., Fu,\u00a0H., Shen,\u00a0J., Shao,\u00a0L.: PraNet: Parallel reverse attention network for polyp segmentation. In Proc. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), pp. 263\u2013273. (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"5862_CR19","doi-asserted-by":"crossref","unstructured":"Zhou,\u00a0Z., Siddiquee, M.M.R., Tajbakhsh,\u00a0N., Liang,\u00a0J.: UNet++: A nested U-Net architecture for medical image segmentation. In Deep Learn. Med. Image Anal. Multimodal Learn. Clin. Decision Support, pp. 3\u201311. (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"5862_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger,\u00a0O., Fischer,\u00a0P., Brox,\u00a0T.: U-Net: Convolutional networks for biomedical image segmentation. In Proc. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), pp. 234\u2013241. (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"5862_CR21","doi-asserted-by":"crossref","unstructured":"Jha,\u00a0D., Smedsrud,\u00a0P.H., Riegler,\u00a0M.A., Johansen,\u00a0D., De Lange,\u00a0T., Halvorsen, P., Johansen,\u00a0H.D.: ResUNet++: An advanced architecture for medical image segmentation. In Proc. IEEE Int. Symp. Multimedia (ISM), pp. 225\u2013230. (2019)","DOI":"10.1109\/ISM46123.2019.00049"},{"issue":"3","key":"5862_CR22","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1109\/TCBB.2019.2939522","volume":"18","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Huang, W., Dong, P., Xia, Y., Wang, S.: D-unet: a dimension-fusion U-shape network for chronic stroke lesion segmentation. IEEE\/ACM Trans. Comput. Biol. Bioinf. 18(3), 940\u2013950 (2021)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"5862_CR23","unstructured":"Tan,\u00a0M., and Le,\u00a0Q.V.: EfficientNet: Rethinking model scaling for convolutional neural networks. In Proc. 36th Int. Conf. Mach. Learn. (ICML), pp. 6105\u20136114. (2019)"},{"key":"5862_CR24","doi-asserted-by":"crossref","unstructured":"de Santana Correia,\u00a0A., and Colombini E.L.: Attention, please! A survey of neural attention models in deep learning. arXiv:2103.16775, (2021)","DOI":"10.1007\/s10462-022-10148-x"},{"key":"5862_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2894399","author":"X Liu","year":"2019","unstructured":"Liu, X., Zhou, Y., Zhao, J., Yao, R., Liu, B., Zheng, Y.: Siamese convolutional neural networks for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters (2019). https:\/\/doi.org\/10.1109\/LGRS.2019.2894399","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"5862_CR26","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2017.07.060","volume":"273","author":"S Berlemont","year":"2018","unstructured":"Berlemont, S., Lefebvre, G., Duffner, S., Garcia, C.: Class-balanced Siamese neural networks. Neurocomputing 273, 47\u201356 (2018)","journal-title":"Neurocomputing"},{"issue":"24","key":"5862_CR27","doi-asserted-by":"publisher","first-page":"34325","DOI":"10.1007\/s11042-022-12792-5","volume":"81","author":"P Cao","year":"2022","unstructured":"Cao, P., Xie, F., Zhang, S., Zhang, Z., Zhang, J.: MSANet: multi-scale attention networks for image classification. Multimedia Tools and Applications 81(24), 34325\u201334344 (2022)","journal-title":"Multimedia Tools and Applications"},{"key":"5862_CR28","doi-asserted-by":"crossref","unstructured":"Woo,\u00a0S., Park,\u00a0J., Lee,\u00a0J.-Y., Kweon,\u00a0I.S.: CBAM: Convolutional block attention module.\u00a0arXiv:1807.06521, (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"5862_CR29","doi-asserted-by":"crossref","unstructured":"Wang,\u00a0F., Jiang,\u00a0M., Qian,\u00a0C., Yang,\u00a0S., Li,\u00a0C., Zhang, H., Wang,\u00a0X., Tang,\u00a0X.: Residual attention network for image classification. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 3156\u20133164. (2017)","DOI":"10.1109\/CVPR.2017.683"},{"key":"5862_CR30","doi-asserted-by":"crossref","unstructured":"Qi,\u00a0K., Yang,\u00a0H., Li,\u00a0C., Liu,\u00a0Z., Wang,\u00a0M., Liu,\u00a0Q., Wang,\u00a0S.: X-Net: Brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies. In Proc. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), pp. 247\u2013255. (2019)","DOI":"10.1007\/978-3-030-32248-9_28"},{"key":"5862_CR31","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., Heng, P.-A.: H-denseunet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37, 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5862_CR32","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters 15, 749\u2013753 (2018)","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"5862_CR33","doi-asserted-by":"crossref","unstructured":"Chen, L.-C.., Zhu,\u00a0Y., Papandreou,\u00a0G., Schroff,\u00a0F., Adam,\u00a0H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 801\u2013818. (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"5862_CR34","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122\u20131131 (2018)","journal-title":"Cell"},{"key":"5862_CR35","doi-asserted-by":"publisher","first-page":"3079","DOI":"10.3390\/s19143079","volume":"19","author":"A Reiss","year":"2019","unstructured":"Reiss, A., Indlekofer, I., Schmidt, P., Van Laerhoven, K.: Deep PPG: large-scale heart rate estimation with convolutional neural networks. Sensors 19, 3079 (2019)","journal-title":"Sensors"},{"issue":"6","key":"5862_CR36","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1109\/TCBB.2022.3150232","volume":"19","author":"R Huang","year":"2022","unstructured":"Huang, R., Lin, C., Yin, A., Chen, H., Guo, L., Zhao, G., Fan, X., Li, S., Yang, J.: A clinical dataset and various baselines for chromosome instance segmentation. IEEE\/ACM Trans. Comput. Biol. Bioinf. 19(6), 3586\u20133597 (2022)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"6","key":"5862_CR37","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1007\/s11684-024-1098-y","volume":"18","author":"C Xia","year":"2024","unstructured":"Xia, C., Wang, J., You, X., Fan, Y., Chen, B., Chen, S., Yang, J.: ChromTR: chromosome detection in raw metaphase cell images via deformable transformers. Front. Med. 18(6), 1100\u20131114 (2024). https:\/\/doi.org\/10.1007\/s11684-024-1098-y","journal-title":"Front. Med."},{"key":"5862_CR38","doi-asserted-by":"crossref","unstructured":"Liu,\u00a0X., Fu,\u00a0L., Lin,\u00a0J.C., Liu,\u00a0S.: SRAS-Net: Low-resolution chromosome image classification based on deep learning. IET Syst. Biol., (2022)","DOI":"10.1049\/syb2.12042"},{"key":"5862_CR39","doi-asserted-by":"publisher","first-page":"157727","DOI":"10.1109\/ACCESS.2020.3019937","volume":"8","author":"MS Al-Kharraz","year":"2020","unstructured":"Al-Kharraz, M.S., Elrefaei, L.A., Fadel, M.A.: Automated system for chromosome karyotyping to recognize the most common numerical abnormalities using deep learning. IEEE Access 8, 157727\u2013157747 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3019937","journal-title":"IEEE Access"},{"key":"5862_CR40","doi-asserted-by":"publisher","first-page":"179445","DOI":"10.1109\/ACCESS.2019.2951723","volume":"7","author":"N Xie","year":"2019","unstructured":"Xie, N., Li, X., Li, K., Yang, Y., Shen, H.T.: Statistical karyotype analysis using CNN and geometric optimization. IEEE Access 7, 179445\u2013179453 (2019)","journal-title":"IEEE Access"},{"key":"5862_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s11045-022-00819-x","author":"D Menaka","year":"2022","unstructured":"Menaka, D., Vaidyanathan, S.G.: Chromenet: a CNN architecture with comparison of optimizers for classification of human chromosome images. Multidimensional Systems and Signal Processing (2022). https:\/\/doi.org\/10.1007\/s11045-022-00819-x","journal-title":"Multidimensional Systems and Signal Processing"},{"key":"5862_CR42","doi-asserted-by":"publisher","first-page":"3920","DOI":"10.1109\/TMI.2020.3007642","volume":"39","author":"L Xiao","year":"2020","unstructured":"Xiao, L., Li, H., Zhao, Y., Zhang, Y., Luo, C.: DeepACEv2: automated chromosome enumeration in metaphase cell images using deep convolutional neural networks. IEEE Trans. Med. Imaging 39, 3920\u20133932 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5862_CR43","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/s41597-023-02003-7","volume":"10","author":"J-J Tseng","year":"2023","unstructured":"Tseng, J.-J., Lu, C.-H., Li, J.-Z., Lai, H.-Y., Chen, M.-H., Cheng, F.-Y., Kuo, C.-E.: An open dataset of annotated metaphase cell images for chromosome identification. Sci. Data 10, 104 (2023). https:\/\/doi.org\/10.1038\/s41597-023-02003-7","journal-title":"Sci. Data"},{"key":"5862_CR44","unstructured":"Chromosomal mutation. Shutterstock. [Online]. Available: https:\/\/www.shutterstock.com\/search\/chromosomal-mutation?image_type=illustration. Accessed: Sep. 24, (2025)"},{"key":"5862_CR45","unstructured":"Chromosome abnormalities fact sheet. National Human Genome Research Institute, Genome.gov. [Online]. Available: https:\/\/www.genome.gov\/about-genomics\/fact-sheets\/Chromosome-Abnormalities-Fact-Sheet. Accessed: Mar. 5, (2025)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05862-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05862-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05862-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:07:18Z","timestamp":1773925638000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05862-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["5862"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05862-4","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]},"assertion":[{"value":"9 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2025","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"67"}}