{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T10:32:18Z","timestamp":1761388338843,"version":"build-2065373602"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Natural Science Foundation of Shandong Province","award":["No.ZR2022MF268"],"award-info":[{"award-number":["No.ZR2022MF268"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["No.2022ZD0119501"],"award-info":[{"award-number":["No.2022ZD0119501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No.52374221"],"award-info":[{"award-number":["No.52374221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00530-025-01944-w","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T12:24:34Z","timestamp":1755779074000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneous graph structure learning based on feature and topology information extraction"],"prefix":"10.1007","volume":"31","author":[{"given":"Chao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangkai","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingtian","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nengfu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"1944_CR1","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Gao, H., Pei, J., Huang, H.: Robust self-supervised structural graph neural network for social network prediction. Paper presented at the ACM Web Conference 2022, Virtual Event, Lyon France, https:\/\/doi.org\/10.1145\/3485447.3512182","DOI":"10.1145\/3485447.3512182"},{"key":"1944_CR2","doi-asserted-by":"publisher","unstructured":"Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. Paper presented at the world wide web conference, San Francisco CA USA, May 2019 (2019). https:\/\/doi.org\/10.1145\/3308558.3313488","DOI":"10.1145\/3308558.3313488"},{"key":"1944_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ddtec.2020.11.009","volume":"37","author":"O Wieder","year":"2020","unstructured":"Wieder, O., Kohlbacher, S., Kuenemann, M., Garon, A., Ducrot, P., Seidel, T., Langer, T.: A compact review of molecular property prediction with graph neural networks. Drug Discov. Today Technol. 37, 1\u201312 (2020). https:\/\/doi.org\/10.1016\/j.ddtec.2020.11.009","journal-title":"Drug Discov. Today Technol."},{"key":"1944_CR4","doi-asserted-by":"publisher","unstructured":"Hao, Z., Lu, C., Huang, Z., Wang, H., Hu, Z., Liu, Q., Chen, E., Lee, C.: ASGN: An active semi-supervised graph neural network for molecular property prediction. Paper presented at the 26th ACM SIGKDD international conference on knowledge discovery & data mining, Virtual Event CA USA (2020). https:\/\/doi.org\/10.1145\/3394486.3403117","DOI":"10.1145\/3394486.3403117"},{"key":"1944_CR5","doi-asserted-by":"publisher","unstructured":"Xu, W., Wu, J., Liu, Q., Wu, S., Wang, L.: Evidence-aware fake news detection with graph neural networks. Paper presented at the ACM web conference 2022, Virtual Event, Lyon France, (2022). https:\/\/doi.org\/10.1145\/3485447.3512122","DOI":"10.1145\/3485447.3512122"},{"key":"1944_CR6","doi-asserted-by":"publisher","unstructured":"Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., Huang, J.: Rumor detection on social media with bi-directional graph convolutional networks. Paper presented at the AAAI conference on artificial intelligence, Vancouver, Canada, 34\u201301 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5393","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"1944_CR7","doi-asserted-by":"publisher","unstructured":"Li, Z., Chen, D., Liu, Q., Wu, S.: The devil is in the conflict: Disentangled information graph neural networks for fraud detection. Paper presented at 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, (2022). https:\/\/doi.org\/10.1109\/ICDM54844.2022.00131","DOI":"10.1109\/ICDM54844.2022.00131"},{"key":"1944_CR8","doi-asserted-by":"publisher","unstructured":"Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., He, Q.: Pick and choose: a GNN-based imbalanced learning approach for fraud detection. Paper presented at the web conference 2021, Ljubljana Slovenia, April 2021 (2021). https:\/\/doi.org\/10.1145\/3442381.3449989","DOI":"10.1145\/3442381.3449989"},{"key":"1944_CR9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26283","author":"X Yang","year":"2023","unstructured":"Yang, X., Yan, M., Pan, S., Ye, X., Fan, D.: Simple and efficient heterogeneous graph neural network. Paper presented at the AAAI conference on artificial intelligence, Washington DC, USA (2023). https:\/\/doi.org\/10.1609\/aaai.v37i9.26283","journal-title":"Paper presented at the AAAI conference on artificial intelligence, Washington DC, USA"},{"key":"1944_CR10","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2310.05174","author":"Z Li","year":"2024","unstructured":"Li, Z., Sun, X., Luo, Y., Zhu, Y., Chen, D., Luo, Y., Zhou, X., Liu, Q., Wu, S., Wang, L.: Gslb: the graph structure learning benchmark. Adv. Neural. Inf. Process. Syst. (2024). https:\/\/doi.org\/10.48550\/arXiv.2310.05174","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1944_CR11","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2306.10280","author":"Z Zhou","year":"2024","unstructured":"Zhou, Z., Zhou, S., Mao, B., Zhou, X., Chen, J., Tan, Q., Zha, D., Feng, Y., Chen, C., Wang, C.: Opengsl: a comprehensive benchmark for graph structure learning. Adv. Neural. Inf. Process. Syst. (2024). https:\/\/doi.org\/10.48550\/arXiv.2306.10280","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1944_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67664-3_23","author":"D Yu","year":"2021","unstructured":"Yu, D., Zhang, R., Jiang, Z., Wu, Y., Yang, Y.: Graph-revised convolutional network. Paper presented at the Machine Learning and Knowledge Discovery in Databases, Ghent, Belgium (2021). https:\/\/doi.org\/10.1007\/978-3-030-67664-3_23","journal-title":"Paper presented at the Machine Learning and Knowledge Discovery in Databases, Ghent, Belgium"},{"key":"1944_CR13","doi-asserted-by":"publisher","unstructured":"Chen, Y., Wu, L., Zaki, M.: Iterative deep graph learning for graph neural networks: Better and robust node embeddings. Paper presented at the 34th International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 33 2020 (2020). https:\/\/doi.org\/10.5555\/3495724.3497344","DOI":"10.5555\/3495724.3497344"},{"key":"1944_CR14","doi-asserted-by":"publisher","unstructured":"Wang, R., Mou, S., Wang, X., Xiao, W., Ju, Q., Shi, C., Xie, X.: Graph structure estimation neural networks. Paper presented at the web conference 2021, Ljubljana Slovenia, (2021). https:\/\/doi.org\/10.1145\/3442381.3449952","DOI":"10.1145\/3442381.3449952"},{"key":"1944_CR15","doi-asserted-by":"publisher","unstructured":"Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.: Heterogeneous graph neural network. Paper presented at the 25th ACM SIGKDD international conference on knowledge discovery & data mining, Anchorage AK USA, (2019). https:\/\/doi.org\/10.1145\/3292500.3330961","DOI":"10.1145\/3292500.3330961"},{"key":"1944_CR16","doi-asserted-by":"publisher","unstructured":"Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., Yu, P.: Heterogeneous graph attention network. Paper presented at the world wide web conference, San Francisco CA USA, (2019). https:\/\/doi.org\/10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"1944_CR17","doi-asserted-by":"publisher","unstructured":"Fu, X., Zhang, J., Meng, Z., King, I.: Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. Paper presented at the web conference 2020, Taipei Taiwan, (2020). https:\/\/doi.org\/10.1145\/3366423.3380297","DOI":"10.1145\/3366423.3380297"},{"key":"1944_CR18","doi-asserted-by":"publisher","unstructured":"Zhao, J., Wang, X., Shi, C., Hu, B., Song, G., Ye, Y.: Heterogeneous graph structure learning for graph neural networks. Paper presented at the AAAI Conference on Artificial Intelligence, 35\u20135, (2021). https:\/\/doi.org\/10.1609\/aaai.v35i5.16600","DOI":"10.1609\/aaai.v35i5.16600"},{"key":"1944_CR19","unstructured":"Gilmer, J., Schoenholz, S., Riley, P., Vinyals, O., Dahl, G.: Neural message passing for quantum chemistry. Paper presented at the the 34th International Conference on Machine Learning, 70, (2017)"},{"key":"1944_CR20","doi-asserted-by":"publisher","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) https:\/\/doi.org\/10.48550\/arXiv.1810.00826","DOI":"10.48550\/arXiv.1810.00826"},{"key":"1944_CR21","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. Paper presented at the 35th International Conference on Machine Learning, 80, (2018)"},{"key":"1944_CR22","first-page":"2019","volume":"97","author":"J You","year":"2019","unstructured":"You, J., Ying, R., Leskovec, J.: Position-aware graph neural networks. Paper presented at the International conference on machine learning 97, 2019 (2019)","journal-title":"Paper presented at the International conference on machine learning"},{"key":"1944_CR23","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. Advances in neural information processing systems 31 (2018)"},{"key":"1944_CR24","doi-asserted-by":"publisher","unstructured":"Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. Paper presented at the web conference 2020,Taipei Taiwan, (2020). https:\/\/doi.org\/10.1145\/3366423.3380027","DOI":"10.1145\/3366423.3380027"},{"key":"1944_CR25","doi-asserted-by":"publisher","unstructured":"Zhang, M., Wang, X., Zhu, M., Shi, C., Zhang, Z., Zhou, J.: Robust heterogeneous graph neural networks against adversarial attacks. Paper presented at the AAI Conference on Artificial Intelligence, 36\u20134 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i4.20357","DOI":"10.1609\/aaai.v36i4.20357"},{"key":"1944_CR26","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TKDE.2021.3079239","volume":"35","author":"H Ji","year":"2023","unstructured":"Ji, H., Wang, X., Shi, C., Wang, B., Philip, S.: Heterogeneous graph propagation network. IEEE Transactions on Knowledge & Data Engineering 35, 74\u201380 (2023). https:\/\/doi.org\/10.1109\/TKDE.2021.3079239","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"key":"1944_CR27","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1109\/TKDE.2021.3101356","volume":"35","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Guan, Z., Li, J., Zhao, W., Cui, J., Wang, Q.: Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge & Data Engineering 35, 1637\u20131650 (2023). https:\/\/doi.org\/10.1109\/TKDE.2021.3101356","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"key":"1944_CR28","doi-asserted-by":"publisher","unstructured":"in, D., Huo, C., Liang, C., Yang, L.: Heterogeneous graph neural network via attribute completion. Paper presented at the web conference 2021, Ljubljana Slovenia, (2021). https:\/\/doi.org\/10.1145\/3442381.3449914","DOI":"10.1145\/3442381.3449914"},{"key":"1944_CR29","doi-asserted-by":"publisher","unstructured":"Zhu, Y., Xu, W., Zhang, J., Du, Y., Zhang, J., Liu, Q., Yang, C., Wu, S.: A survey on graph structure learning: Progress and opportunities https:\/\/doi.org\/10.48550\/arXiv.2103.03036","DOI":"10.48550\/arXiv.2103.03036"},{"key":"1944_CR30","first-page":"22667","volume":"34","author":"B Fatemi","year":"2021","unstructured":"Fatemi, B., El Asri, L., Kazemi, S.: Slaps: self-supervision improves structure learning for graph neural networks. Adv. Neural. Inf. Process. Syst. 34, 22667\u201322681 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1944_CR31","doi-asserted-by":"publisher","unstructured":"Li, K., Liu, Y., Ao, X., Chi, J., Feng, J., Yang, H., He, Q.: Reliable representations make a stronger defender: Unsupervised structure refinement for robust gnn. Paper presented at the 28th ACM SIGKDD conference on knowledge discovery and data mining, Washington DC USA, (2022). https:\/\/doi.org\/10.1145\/3534678.3539484","DOI":"10.1145\/3534678.3539484"},{"key":"1944_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3711118","volume":"57","author":"D Zha","year":"2025","unstructured":"Zha, D., Bhat, Z., Lai, K., Jiang, Z., Shaochen, Z., Xia, H.: Data-centric artificial intelligence: a survey. ACM Comput. Surv. 57, 1\u201342 (2025). https:\/\/doi.org\/10.1145\/3711118","journal-title":"ACM Comput. Surv."},{"key":"1944_CR33","unstructured":"Mazumder, M., Banbury, C., Yao, X., Karla\u0161, B., R., G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H., al.: Dataperf: Benchmarks for data-centric ai development. Advances in Neural Information Processing Systems 36 (2024)"},{"key":"1944_CR34","unstructured":"Cosmo, L., Kazi, A., Ahmadi, S., Navab, N., Bronstein, M.: Latent Patient Network Learning for Automatic Diagnosis. (2020)"},{"key":"1944_CR35","doi-asserted-by":"crossref","unstructured":"Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., \u017d\u00eddek, A., Potapenko, A., al.: Highly accurate protein structure prediction with alphafold. nature 596, 583\u2013589 (2021)","DOI":"10.1038\/s41586-021-03819-2"},{"key":"1944_CR36","unstructured":"Franceschi, L., Niepert, M., Pontil, M., He, X.: Learning discrete structures for graph neural networks. Paper presented at the 36th International Conference on Machine Learning, 97 (2019)"},{"key":"1944_CR37","doi-asserted-by":"publisher","unstructured":"Jin, W., Ma, Y., Liu, X., Tang, X., Wang, S., Tang, J.: Graph structure learning for robust graph neural networks. Paper presented at the 6th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, CA USA, (2020). https:\/\/doi.org\/10.1145\/3394486.3403049","DOI":"10.1145\/3394486.3403049"},{"key":"1944_CR38","doi-asserted-by":"publisher","unstructured":"Liu, Y., Zheng, Y., Zhang, D., Chen, H., Peng, H., Pan, S.: Towards unsupervised deep graph structure learning. Paper presented at the ACM Web Conference 2022, Lyon France (2022). https:\/\/doi.org\/10.1145\/3485447.3512186","DOI":"10.1145\/3485447.3512186"},{"key":"1944_CR39","doi-asserted-by":"publisher","unstructured":"Liu, N., Wang, X., Wu, L., Chen, Y., Guo, X., Shi, C.: Compact graph structure learning via mutual information compression. Paper presented at the ACM Web Conference 2022, Lyon France, (2022). https:\/\/doi.org\/10.1145\/3485447.3512206","DOI":"10.1145\/3485447.3512206"},{"key":"1944_CR40","doi-asserted-by":"publisher","first-page":"12358","DOI":"10.1109\/TNNLS.2023.3257325","volume":"35","author":"L Wu","year":"2023","unstructured":"Wu, L., Lin, H., Liu, Z., Liu, Z., Huang, Y., Li, S.: Homophily-enhanced self-supervision for graph structure learning: insights and directions. IEEE Transactions on Neural Networks and Learning Systems 35, 12358\u201312372 (2023). https:\/\/doi.org\/10.1109\/TNNLS.2023.3257325","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1944_CR41","unstructured":"Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.: Graph transformer networks. Advances in neural information processing systems 32 (2019)"},{"key":"1944_CR42","unstructured":"Zhang, Z., Bu, J., Ester, M., Zhang, J., Yao, C., Yu, Z., Wang, C.: Hierarchical graph pooling with structure learning (2019) https:\/\/doi.org\/10.48550\/arXiv.1911.05954"},{"key":"1944_CR43","doi-asserted-by":"publisher","unstructured":"Sun, Q., Li, J., Peng, H., Wu, J., Fu, X., Ji, C., Philip, S.: Graph structure learning with variational information bottleneck. Paper presented at the the AAAI Conference on Artificial Intelligence, 36\u20134 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i4.20335","DOI":"10.1609\/aaai.v36i4.20335"},{"key":"1944_CR44","doi-asserted-by":"publisher","unstructured":"Wang, X., Liu, N., Han, H., Shi, C.: Self-supervised heterogeneous graph neural network with co-contrastive learning. Paper presented at the 27th ACM SIGKDD conference on knowledge discovery & data mining, Singapore, (2021). https:\/\/doi.org\/10.1145\/3447548.3467415","DOI":"10.1145\/3447548.3467415"},{"key":"1944_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120115","volume":"225","author":"Y Yan","year":"2023","unstructured":"Yan, Y., Li, C., Yu, Y., Li, X., Zhao, Z.: Osgnn: original graph and subgraph aggregated graph neural network. Expert Syst. Appl. 225, 120115 (2023)","journal-title":"Expert Syst. Appl."},{"key":"1944_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.120004","volume":"658","author":"J Fu","year":"2024","unstructured":"Fu, J., Li, C., Zhao, Z., Zeng, Q.: Heterogeneous graph knowledge distillation neural network incorporating multiple relations and cross-semantic interactions. Inf. Sci. 658, 120004 (2024)","journal-title":"Inf. Sci."},{"key":"1944_CR47","doi-asserted-by":"publisher","first-page":"41","DOI":"10.5815\/ijisa.2013.02.05","volume":"5","author":"R Aldahdooh","year":"2013","unstructured":"Aldahdooh, R., Ashour, W.: Dimk-means distance-based initialization method for k-means clustering algorithm. International Journal of Intelligent Systems and Applications 5, 41\u201345 (2013). https:\/\/doi.org\/10.5815\/ijisa.2013.02.05","journal-title":"International Journal of Intelligent Systems and Applications"},{"key":"1944_CR48","first-page":"2579","volume":"9","author":"G Hinton","year":"2008","unstructured":"Hinton, G., Van Der Maaten, L.: Visualizing data using t-sne journal of machine learning research. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01944-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01944-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01944-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T10:25:26Z","timestamp":1761387926000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01944-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["1944"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01944-w","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2025,8,21]]},"assertion":[{"value":"24 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2025","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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"There is the consent of all authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"There is the consent of all authors.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"369"}}