{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:06:55Z","timestamp":1771258015585,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB0804202"],"award-info":[{"award-number":["2018YFB0804202"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFB0804203"],"award-info":[{"award-number":["2018YFB0804203"]}]},{"name":"Regional Joint Fund of NSFC","award":["U19A2057"],"award-info":[{"award-number":["U19A2057"]}]},{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876070"],"award-info":[{"award-number":["61876070"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jilin University \u201cInterdisciplinary Integration and Innovation\u201d Young Scholars Free Exploration Project","award":["JLUXKJC2021QZ01"],"award-info":[{"award-number":["JLUXKJC2021QZ01"]}]},{"name":"Science and Technology Development Plan Project","award":["20190303134SF"],"award-info":[{"award-number":["20190303134SF"]}]},{"name":"Anhui University Collaborative Innovation Project Subproject","award":["GXXT-2021-008"],"award-info":[{"award-number":["GXXT-2021-008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16805-9","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T07:02:05Z","timestamp":1695625325000},"page":"33713-33730","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MCA-Net: multi-cascade attention network for polyp segmentation"],"prefix":"10.1007","volume":"83","author":[{"given":"Yitong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanjing","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-6692","authenticated-orcid":false,"given":"Yingda","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"issue":"3","key":"16805_CR1","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/17517575.2018.1557256","volume":"13","author":"UA Bhatti","year":"2019","unstructured":"Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterp Inf Syst 13(3):329\u2013351","journal-title":"Enterp Inf Syst"},{"issue":"10","key":"16805_CR2","doi-asserted-by":"publisher","first-page":"0256971","DOI":"10.1371\/journal.pone.0256971","volume":"16","author":"SA Nawaz","year":"2021","unstructured":"Nawaz SA, Li J, Bhatti UA, Bazai SU, Zafar A, Bhatti MA, Mehmood A, Ain Qu, Shoukat MU (2021) A hybrid approach to forecast the covid-19 epidemic trend. Plos One 16(10):0256971","journal-title":"Plos One"},{"issue":"4","key":"16805_CR3","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.3233\/IDA-205388","volume":"25","author":"Z Zeeshan","year":"2021","unstructured":"Zeeshan Z, Bhatti UA, Memon WH, Ali S, Nawaz SA, Nizamani MM, Mehmood A, Bhatti MA, Shoukat MU et al (2021) Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation. Intell Data Analysis 25(4):1013\u20131029","journal-title":"Intell Data Analysis"},{"issue":"1","key":"16805_CR4","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1166\/jmihi.2021.3313","volume":"11","author":"UA Bhatti","year":"2021","unstructured":"Bhatti UA, Yuan L, Yu Z, Nawaz SA, Mehmood A, Bhatti MA, Nizamani MM, Xiao S et al (2021) Predictive data modeling using sp-knn for risk factor evaluation in urban demographical healthcare data. J Med Imaging Health Informatics 11(1):7\u201314","journal-title":"J Med Imaging Health Informatics"},{"key":"16805_CR5","doi-asserted-by":"crossref","unstructured":"Ahmad RM, Yao X, Nawaz SA, Bhatti UA, Mehmood A, Bhatti MA, Shaukat MU (2020) Robust image watermarking method in wavelet domain based on sift features. In: Proceedings of the 2020 3rd international conference on artificial intelligence and pattern recognition, pp 180\u2013185","DOI":"10.1145\/3430199.3430243"},{"key":"16805_CR6","doi-asserted-by":"publisher","first-page":"41019","DOI":"10.1109\/ACCESS.2021.3060744","volume":"9","author":"UA Bhatti","year":"2021","unstructured":"Bhatti UA, Yan Y, Zhou M, Ali S, Hussain A, Qingsong H, Yu Z, Yuan L (2021) Time series analysis and forecasting of air pollution particulate matter (pm 2.5): an sarima and factor analysis approach. IEEE Access 9:41019\u201341031","journal-title":"IEEE Access"},{"key":"16805_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2021.132569","volume":"288","author":"UA Bhatti","year":"2022","unstructured":"Bhatti UA, Zeeshan Z, Nizamani MM, Bazai S, Yu Z, Yuan L (2022) Assessing the change of ambient air quality patterns in jiangsu province of china pre-to post-covid-19. Chemosphere 288:132569","journal-title":"Chemosphere"},{"issue":"8","key":"16805_CR8","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1002\/ijc.29800","volume":"138","author":"KE Vinson","year":"2016","unstructured":"Vinson KE, George DC, Fender AW, Bertrand FE, Sigounas G (2016) The n otch pathway in colorectal cancer. Int J Cancer 138(8):1835\u20131842. https:\/\/doi.org\/10.1002\/ijc.29800","journal-title":"Int J Cancer"},{"issue":"1","key":"16805_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-017-3336-z","volume":"17","author":"BA Magaji","year":"2017","unstructured":"Magaji BA, Moy FM, Roslani AC, Law CW (2017) Survival rates and predictors of survival among colorectal cancer patients in a malaysian tertiary hospital. BMC Cancer 17(1):1\u20138. https:\/\/doi.org\/10.1186\/s12885-017-3336-z","journal-title":"BMC Cancer"},{"key":"16805_CR10","doi-asserted-by":"publisher","unstructured":"Cheng M, Kong Z, Song G, Tian Y, Liang Y, Chen J (2021) Learnable oriented-derivative network for polyp segmentation. In: Medical image computing and computer assisted intervention\u2014 MICCAI 2021, pp 720\u2013730 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-87193-2_68","DOI":"10.1007\/978-3-030-87193-2_68"},{"key":"16805_CR11","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention \u2014 MICCAI 2015, pp 234\u2013241 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"16805_CR12","doi-asserted-by":"crossref","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. Preprint at https:\/\/arxiv.org\/abs\/1807.10165","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"9","key":"16805_CR13","doi-asserted-by":"publisher","first-page":"7209","DOI":"10.1109\/TGRS.2019.2912301","volume":"57","author":"X Yang","year":"2019","unstructured":"Yang X, Li X, Ye Y, Lau RYK, Zhang X, Huang X (2019) Road detection and centerline extraction via deep recurrent convolutional neural network u-net. IEEE Trans Geosci Remote Sens 57(9):7209\u20137220. https:\/\/doi.org\/10.1109\/TGRS.2019.2912301","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"16805_CR14","doi-asserted-by":"publisher","unstructured":"Fang Y, Chen C, Yuan Y, K-y Tong, (2019) Selective feature aggregation network with area-boundary constraints for polyp segmentation.In: Medical image computing and computer assisted intervention - MICCAI, (2019) pp 302\u2013310 Springer. Cham. https:\/\/doi.org\/10.1007\/978-3-030-32239-7_34","DOI":"10.1007\/978-3-030-32239-7_34"},{"key":"16805_CR15","doi-asserted-by":"publisher","unstructured":"Fan D-P, Ji G-P, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: Parallel reverse attention network for polyp segmentation. In: Medical image computing and computer assisted intervention \u2013 MICCAI 2020, pp 263\u2013273 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-59725-2_26","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"16805_CR16","unstructured":"Huang C, Wu H, Lin Y (2021) Hardnet-mseg: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 FPS. Preprint at arXiv:2101.07172"},{"issue":"2","key":"16805_CR17","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva J, Histace A, Romain O, Dray X, Granado B (2014) Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int J CARS 9(2):283\u2013293. https:\/\/doi.org\/10.1007\/s11548-013-0926-3","journal-title":"Int J CARS"},{"key":"16805_CR18","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, Gil D, Rodr\u00edguez C, Vilari\u00f1o F (2015) Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99\u2013111. https:\/\/doi.org\/10.1016\/j.compmedimag.2015.02.007","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"16805_CR19","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TMI.2015.2487997","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Gurudu SR, Liang J (2016) Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans Med Imaging (TMI) 35(2):630\u2013644. https:\/\/doi.org\/10.1109\/TMI.2015.2487997","journal-title":"IEEE Trans Med Imaging (TMI)"},{"key":"16805_CR20","doi-asserted-by":"publisher","unstructured":"V\u00e1zquez D, Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, L\u00f3pez AM, Romero A, Drozdzal M, Courville A (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. Journal of healthcare engineering 2017. https:\/\/doi.org\/10.1155\/2017\/4037190","DOI":"10.1155\/2017\/4037190"},{"key":"16805_CR21","doi-asserted-by":"publisher","unstructured":"Jha D, Smedsrud PH, Riegler MA, Halvorsen P, de Lange T, Johansen D, Johansen HD (2020) Kvasir-seg: A segmented polyp dataset. In: MultiMedia Modeling, pp 451\u2013462. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-37734-2_37","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"16805_CR22","doi-asserted-by":"publisher","unstructured":"Jha D, Smedsrud PH, Riegler MA, Johansen D, Lange TD, Halvorsen P, Johansen HD (2019) Resunet++: An advanced architecture for medical image segmentation. In: 2019 IEEE international symposium on multimedia (ISM), pp 225\u20132255. https:\/\/doi.org\/10.1109\/ISM46123.2019.00049","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"16805_CR23","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman MS (2020) Multiresunet\u202f: Rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw 121:74\u201387. https:\/\/doi.org\/10.1016\/j.neunet.2019.08.025","journal-title":"Neural Netw"},{"key":"16805_CR24","doi-asserted-by":"publisher","unstructured":"Valanarasu JMJ, Sindagi VA, Hacihaliloglu I, Patel VM (2020) Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations. In: Medical image computing and computer assisted intervention \u2013 MICCAI 2020, pp 363\u2013373 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-59719-1_36","DOI":"10.1007\/978-3-030-59719-1_36"},{"key":"16805_CR25","doi-asserted-by":"publisher","unstructured":"Zhang R, Li G, Li Z, Cui S, Qian D, Yu Y (2020) Adaptive context selection for polyp segmentation. In: Medical image computing and computer assisted intervention \u2013 MICCAI 2020, pp 253\u2013262 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-59725-2_25","DOI":"10.1007\/978-3-030-59725-2_25"},{"key":"16805_CR26","doi-asserted-by":"publisher","unstructured":"Yin Z, Liang K, Ma Z, Guo J (2022) Duplex contextual relation network for polyp segmentation. In: 2022 IEEE 19th international symposium on biomedical imaging (ISBI), pp 1\u20135 IEEE. https:\/\/doi.org\/10.1109\/ISBI52829.2022.9761402","DOI":"10.1109\/ISBI52829.2022.9761402"},{"key":"16805_CR27","doi-asserted-by":"publisher","unstructured":"Patel K, Bur AM, Wang G (2021) Enhanced u-net: A feature enhancement network for polyp segmentation. In: 2021 18th conference on robots and vision (CRV), pp 181\u2013188 IEEE. https:\/\/doi.org\/10.1109\/CRV52889.2021.00032","DOI":"10.1109\/CRV52889.2021.00032"},{"key":"16805_CR28","unstructured":"Mnih V, Heess N, Graves A et al (2014) Recurrent models of visual attention. Adv Neural Inf Process Syst 27"},{"key":"16805_CR29","doi-asserted-by":"publisher","unstructured":"Xiao T, Xu Y, Yang K, Zhang J, Peng Y, Zhang Z (2015) The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 842\u2013850. https:\/\/doi.org\/10.1109\/cvpr.2015.7298685","DOI":"10.1109\/cvpr.2015.7298685"},{"key":"16805_CR30","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR), pp 7132\u20137141. https:\/\/doi.org\/10.1109\/cvpr.2018.00745","DOI":"10.1109\/cvpr.2018.00745"},{"key":"16805_CR31","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"16805_CR32","unstructured":"Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: Proceedings of the 36th international conference on machine learning, vol 97, pp 7354\u20137363"},{"key":"16805_CR33","doi-asserted-by":"publisher","unstructured":"Sun J, Darbehani F, Zaidi M, Wang B (2020) Saunet: Shape attentive u-net for interpretable medical image segmentation. In: Medical image computing and computer assisted intervention \u2013 MICCAI 2020, pp 797\u2013806 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-59719-1_77","DOI":"10.1007\/978-3-030-59719-1_77"},{"key":"16805_CR34","unstructured":"Ho J, Kalchbrenner N, Weissenborn D, Salimans T (2019) Axial attention in multidimensional transformers. Preprint at arXiv:1912.12180"},{"key":"16805_CR35","first-page":"9355","volume":"34","author":"X Chu","year":"2021","unstructured":"Chu X, Tian Z, Wang Y, Zhang B, Ren H, Wei X, Xia H, Shen C (2021) Twins: Revisiting the design of spatial attention in vision transformers. Adv Neural Inf Process Syst 34:9355\u20139366","journal-title":"Adv Neural Inf Process Syst"},{"key":"16805_CR36","doi-asserted-by":"publisher","unstructured":"Wang H, Zhu Y, Green B, Adam H, Yuille A, Chen L-C (2020) Axial-deeplab: Stand-alone axial-attention for panoptic segmentation. In: European conference on computer vision \u2013 ECCV 2020, pp 108\u2013126 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-58548-8_7","DOI":"10.1007\/978-3-030-58548-8_7"},{"key":"16805_CR37","doi-asserted-by":"publisher","unstructured":"Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G (2018) Understanding convolution for semantic segmentation. In: 2018 IEEE winter conference on applications of computer vision (WACV), pp 1451\u20131460 IEEE. https:\/\/doi.org\/10.1109\/WACV.2018.00163","DOI":"10.1109\/WACV.2018.00163"},{"key":"16805_CR38","doi-asserted-by":"publisher","unstructured":"Zhao X, Zhang L, Lu H (2021) Automatic polyp segmentation via multi-scale subtraction network. In: Medical image computing and computer assisted intervention \u2013 MICCAI 2021, pp 120\u2013130 Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-87193-2_12","DOI":"10.1007\/978-3-030-87193-2_12"},{"key":"16805_CR39","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448\u2013456 PMLR"},{"key":"16805_CR40","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, vol 15, pp 315\u2013323 PMLR"},{"key":"16805_CR41","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Preprint at arXiv:1412.6980"},{"key":"16805_CR42","doi-asserted-by":"publisher","unstructured":"Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: 2009 IEEE conference on computer vision and pattern recognition(CVPR), pp 1597\u20131604. https:\/\/doi.org\/10.1109\/CVPR.2009.5206596","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"16805_CR43","doi-asserted-by":"publisher","unstructured":"Fan D-P, Cheng M-M, Liu Y, Li T, Borji A (2017) Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE international conference on computer vision, pp 4548\u20134557. https:\/\/doi.org\/10.1109\/iccv.2017.487","DOI":"10.1109\/iccv.2017.487"},{"key":"16805_CR44","doi-asserted-by":"crossref","unstructured":"Fan D-P, Gong C, Cao Y, Ren B, Cheng M-M, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. Preprint at arXiv:1805.10421","DOI":"10.24963\/ijcai.2018\/97"},{"key":"16805_CR45","doi-asserted-by":"publisher","unstructured":"Perazzi F, Kr\u00e4henb\u00fchl P, Pritch Y, Hornung A (2012) Saliency filters: Contrast based filtering for salient region detection. In: 2012 IEEE conference on computer vision and pattern recognition(CVPR), pp 733\u2013740. https:\/\/doi.org\/10.1109\/CVPR.2012.6247743","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"16805_CR46","unstructured":"Dong B, Wang W, Fan D-P, Li J, Fu H, Shao L (2021) Polyp-pvt: Polyp segmentation with pyramid vision transformers. Preprint at arXiv:2108.06932"},{"key":"16805_CR47","doi-asserted-by":"publisher","unstructured":"Chao P, Kao C-Y, Ruan Y, Huang C-H, Lin Y-L (2019) Hardnet: A low memory traffic network. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 3551\u20133560. https:\/\/doi.org\/10.1109\/ICCV.2019.00365","DOI":"10.1109\/ICCV.2019.00365"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16805-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16805-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16805-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T07:04:49Z","timestamp":1709881489000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16805-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16805"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16805-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,25]]},"assertion":[{"value":"25 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}