{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T05:16:49Z","timestamp":1771737409245,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00530-024-01636-x","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T13:47:18Z","timestamp":1737121638000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Topic-guided multi-domain fake news detection"],"prefix":"10.1007","volume":"31","author":[{"given":"Lingtao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yong","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"1636_CR1","unstructured":"Zhang, Y., Liu, S., Wang, Y., Fan, X.: Detecting Chinese fake news on Twitter during the COVID-19 pandemic (2023). http:\/\/arxiv.org\/abs\/2304.03454"},{"key":"1636_CR2","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345\u20131359 (2010). https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1636_CR3","doi-asserted-by":"publisher","unstructured":"Zhu, Y., Zhuang, F., Wang, D.: Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 5989\u20135996. AAAI Press (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33015989","DOI":"10.1609\/aaai.v33i01.33015989"},{"key":"1636_CR4","doi-asserted-by":"publisher","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 1406\u20131415. IEEE, Seoul, Korea (South) (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00149","DOI":"10.1109\/ICCV.2019.00149"},{"key":"1636_CR5","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE 109, 43\u201376 (2020)","journal-title":"Proc. IEEE"},{"key":"1636_CR6","doi-asserted-by":"publisher","unstructured":"Karimi, H., Tang, J.: Learning hierarchical discourse-level structure for fake news detection. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 3432\u20133442. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1347.","DOI":"10.18653\/v1\/N19-1347"},{"key":"1636_CR7","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.patrec.2021.07.020","volume":"151","author":"G Kim","year":"2021","unstructured":"Kim, G., Ko, Y.: Effective fake news detection using graph and summarization techniques. Pattern Recognit. Lett. 151, 135\u2013139 (2021). https:\/\/doi.org\/10.1016\/j.patrec.2021.07.020","journal-title":"Pattern Recognit. Lett."},{"key":"1636_CR8","unstructured":"Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K.-F., Cha, M.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. pp. 3818\u20133824. AAAI Press, New York, USA (2016)"},{"key":"1636_CR9","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., Wong, K.-F.: Detect rumors on twitter by promoting information campaigns with generative adversarial learning. In: The World Wide Web Conference. pp. 3049\u20133055. ACM, San Francisco CA USA (2019). https:\/\/doi.org\/10.1145\/3308558.3313741","DOI":"10.1145\/3308558.3313741"},{"key":"1636_CR10","doi-asserted-by":"publisher","unstructured":"Sheng, Q., Cao, J., Zhang, X., Li, R., Wang, D., Zhu, Y.: Zoom out and observe: news environment perception for fake news detection. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. pp. 4543\u20134556. Association for Computational Linguistics, Dublin, Ireland (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.311","DOI":"10.18653\/v1\/2022.acl-long.311"},{"key":"1636_CR11","doi-asserted-by":"publisher","unstructured":"Vo, N., Lee, K.: Learning from fact-checkers: analysis and generation of fact-checking language. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 335\u2013344. ACM, Paris France (2019). https:\/\/doi.org\/10.1145\/3331184.3331248","DOI":"10.1145\/3331184.3331248"},{"key":"1636_CR12","doi-asserted-by":"publisher","unstructured":"Li, J., Ni, S., Kao, H.-Y.: Meet the truth: leverage objective facts and subjective views for interpretable rumor detection. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. pp. 705\u2013715. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.63","DOI":"10.18653\/v1\/2021.findings-acl.63"},{"key":"1636_CR13","doi-asserted-by":"publisher","first-page":"5079","DOI":"10.1109\/TCSS.2023.3298480","volume":"11","author":"Z Guo","year":"2024","unstructured":"Guo, Z., Zhang, Q., Ding, F., Zhu, X., Yu, K.: A novel fake news detection model for context of mixed languages through multiscale transformer. IEEE Trans. Comput. Soc. Syst. 11, 5079\u20135089 (2024). https:\/\/doi.org\/10.1109\/TCSS.2023.3298480","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"1636_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114090","volume":"166","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Wang, L., Yang, Y., Lian, T.: SemSeq4FD: integrating global semantic relationship and local sequential order to enhance text representation for fake news detection. Expert Syst. Appl. 166, 114090 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114090","journal-title":"Expert Syst. Appl."},{"key":"1636_CR15","doi-asserted-by":"publisher","unstructured":"Xing, F., Guo, C.: Mining semantic information in rumor detection via a deep visual perception based recurrent neural networks. In: 2019 IEEE International Congress on Big Data (BigData Congress). pp. 17\u201323. IEEE, Milan, Italy (2019). https:\/\/doi.org\/10.1109\/BigDataCongress.2019.00016","DOI":"10.1109\/BigDataCongress.2019.00016"},{"key":"1636_CR16","doi-asserted-by":"publisher","unstructured":"Zhou, K., Shu, C., Li, B., Lau, J.H.: Early rumour detection. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 1614\u20131623. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1163","DOI":"10.18653\/v1\/N19-1163"},{"key":"1636_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3395046","volume":"53","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. 53, 1\u201340 (2021). https:\/\/doi.org\/10.1145\/3395046","journal-title":"ACM Comput. Surv."},{"key":"1636_CR18","doi-asserted-by":"publisher","unstructured":"Zhu, Y., Sheng, Q., Cao, J., Li, S., Wang, D., Zhuang, F.: Generalizing to the future: mitigating entity bias in fake news detection. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2120\u20132125. ACM, Madrid Spain (2022). https:\/\/doi.org\/10.1145\/3477495.3531816","DOI":"10.1145\/3477495.3531816"},{"key":"1636_CR19","doi-asserted-by":"publisher","unstructured":"Dou, Y., Shu, K., Xia, C., Yu, P.S., Sun, L.: User preference-aware fake news detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2051\u20132055. ACM, Virtual Event Canada (2021). https:\/\/doi.org\/10.1145\/3404835.3462990","DOI":"10.1145\/3404835.3462990"},{"key":"1636_CR20","doi-asserted-by":"publisher","unstructured":"Li, Q., Zhang, Q., Si, L.: Rumor detection by exploiting user credibility information, attention and multi-task learning. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1173\u20131179. Association for Computational Linguistics, Florence, Italy (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1113","DOI":"10.18653\/v1\/P19-1113"},{"key":"1636_CR21","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. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 549\u2013556. AAAI Press (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5393","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"1636_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102678","volume":"58","author":"X Chen","year":"2021","unstructured":"Chen, X., Zhou, F., Zhang, F., Bonsangue, M.: Catch me if you can: a participant-level rumor detection framework via fine-grained user representation learning. Inf. Process. Manag. 58, 102678 (2021). https:\/\/doi.org\/10.1016\/j.ipm.2021.102678","journal-title":"Inf. Process. Manag."},{"key":"1636_CR23","doi-asserted-by":"publisher","unstructured":"Liu, Y., Wu, Y.-F.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 354\u2013361. AAAI Press (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11268","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"1636_CR24","doi-asserted-by":"publisher","first-page":"15486","DOI":"10.1109\/TITS.2022.3185013","volume":"24","author":"Z Guo","year":"2022","unstructured":"Guo, Z., Yu, K., Jolfaei, A., Li, G.: Mixed graph neural network-based fake news detection for sustainable vehicular social networks. IEEE Trans. Intell. Transp. Syst. 24, 15486\u201315498 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1636_CR25","doi-asserted-by":"publisher","first-page":"106907","DOI":"10.1109\/ACCESS.2021.3100245","volume":"9","author":"S Ni","year":"2021","unstructured":"Ni, S., Li, J., Kao, H.-Y.: MVAN: multi-view attention networks for fake news detection on social media. IEEE Access. 9, 106907\u2013106917 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3100245","journal-title":"IEEE Access."},{"key":"1636_CR26","doi-asserted-by":"publisher","unstructured":"Yuan, C., Ma, Q., Zhou, W., Han, J., Hu, S.: Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In: 2019 IEEE International Conference on Data Mining (ICDM). pp. 796\u2013805. IEEE, Beijing, China (2019). https:\/\/doi.org\/10.1109\/ICDM.2019.00090","DOI":"10.1109\/ICDM.2019.00090"},{"key":"1636_CR27","unstructured":"P\u00e9rez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 3391\u20133401. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2017)"},{"key":"1636_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-58347-1_10","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. Domain Adapt. Comput. Vis. Appl. 17, 1\u201335 (2016). https:\/\/doi.org\/10.1007\/978-3-319-58347-1_10","journal-title":"Domain Adapt. Comput. Vis. Appl."},{"key":"1636_CR29","doi-asserted-by":"publisher","unstructured":"Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., Chi, E.H.: Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1930\u20131939. ACM, London United Kingdom (2018). https:\/\/doi.org\/10.1145\/3219819.3220007","DOI":"10.1145\/3219819.3220007"},{"key":"1636_CR30","doi-asserted-by":"publisher","unstructured":"Qin, Z., Cheng, Y., Zhao, Z., Chen, Z., Metzler, D., Qin, J.: Multitask mixture of sequential experts for user activity streams. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 3083\u20133091. ACM, Virtual Event CA USA (2020). https:\/\/doi.org\/10.1145\/3394486.3403359","DOI":"10.1145\/3394486.3403359"},{"key":"1636_CR31","doi-asserted-by":"publisher","unstructured":"Tang, H., Liu, J., Zhao, M., Gong, X.: Progressive layered extraction (PLE): a novel multi-task learning (MTL) model for personalized recommendations. In: Fourteenth ACM Conference on Recommender Systems. pp. 269\u2013278. ACM, Virtual Event Brazil (2020). https:\/\/doi.org\/10.1145\/3383313.3412236","DOI":"10.1145\/3383313.3412236"},{"key":"1636_CR32","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-031-17189-5_4","volume":"13552","author":"C Liang","year":"2022","unstructured":"Liang, C., Zhang, Y., Li, X., Zhang, J., Yu, Y.: FuDFEND: fuzzy-domain for multi-domain fake news detection. Nat. Lang. Process. Chin. Comput. 13552, 45\u201357 (2022). https:\/\/doi.org\/10.1007\/978-3-031-17189-5_4","journal-title":"Nat. Lang. Process. Chin. Comput."},{"key":"1636_CR33","doi-asserted-by":"publisher","unstructured":"Nan, Q., Cao, J., Zhu, Y., Wang, Y., Li, J.: MDFEND: multi-domain fake news detection. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. pp. 3343\u20133347. ACM, Virtual Event Queensland Australia (2021). https:\/\/doi.org\/10.1145\/3459637.3482139","DOI":"10.1145\/3459637.3482139"},{"key":"1636_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2022.3185151","volume":"35","author":"Y Zhu","year":"2022","unstructured":"Zhu, Y., Sheng, Q., Cao, J., Nan, Q., Shu, K., Wu, M., Wang, J., Zhuang, F.: Memory-guided multi-view multi-domain fake news detection. IEEE Trans. Knowl. Data Eng. 35, 1\u201314 (2022). https:\/\/doi.org\/10.1109\/TKDE.2022.3185151","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1636_CR35","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: a robustly optimized BERT pretraining approach (2019). http:\/\/arxiv.org\/abs\/1907.11692."},{"key":"1636_CR36","doi-asserted-by":"publisher","first-page":"3504","DOI":"10.1109\/TASLP.2021.3124365","volume":"29","author":"Y Cui","year":"2021","unstructured":"Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z.: Pre-training with whole word masking for Chinese BERT. IEEEACM Trans. Audio Speech Lang. Process. 29, 3504\u20133514 (2021). https:\/\/doi.org\/10.1109\/TASLP.2021.3124365","journal-title":"IEEEACM Trans. Audio Speech Lang. Process."},{"key":"1636_CR37","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"1636_CR38","unstructured":"Grootendorst, M.: BERTopic: neural topic modeling with a class-based TF-IDF procedure (2022). http:\/\/arxiv.org\/abs\/2203.05794."},{"key":"1636_CR39","unstructured":"Yang, Y., Cao, J., Lu, M., Li, J., Lin, C.-W.: How to write high-quality news on social network? Predicting news quality by mining writing style (2021). http:\/\/arxiv.org\/abs\/1902.00750."},{"key":"1636_CR40","doi-asserted-by":"publisher","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 1746\u20131751. Association for Computational Linguistics, Doha, Qatar (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"1636_CR41","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770\u2013778. IEEE, Las Vegas, NV, USA (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1636_CR42","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2261\u20132269. IEEE, Honolulu, HI (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"1636_CR43","doi-asserted-by":"publisher","unstructured":"Tao, C., Wu, W., Xu, C., Hu, W., Zhao, D., Yan, R.: Multi-representation fusion network for multi-turn response selection in retrieval-based Chatbots. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. pp. 267\u2013275. ACM, Melbourne VIC Australia (2019). https:\/\/doi.org\/10.1145\/3289600.3290985","DOI":"10.1145\/3289600.3290985"},{"key":"1636_CR44","doi-asserted-by":"publisher","unstructured":"Przybyla, P.: Capturing the style of fake news. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 490\u2013497. AAAI Press (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5386","DOI":"10.1609\/aaai.v34i01.5386"},{"key":"1636_CR45","doi-asserted-by":"publisher","unstructured":"Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., Gao, J.: EANN: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 849\u2013857. ACM, London United Kingdom (2018). https:\/\/doi.org\/10.1145\/3219819.3219903","DOI":"10.1145\/3219819.3219903"},{"key":"1636_CR46","doi-asserted-by":"publisher","unstructured":"Silva, A., Luo, L., Karunasekera, S., Leckie, C.: Embracing domain differences in fake news: cross-domain fake news detection using multi-modal data. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 557\u2013565. AAAI Press (2021). https:\/\/doi.org\/10.1609\/aaai.v35i1.16134","DOI":"10.1609\/aaai.v35i1.16134"},{"key":"1636_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103807","volume":"6","author":"P Bazmi","year":"2024","unstructured":"Bazmi, P.: Entity-centric multi-domain transformer for improving generalization in fake news detection. Inf. Process. Manag. 6, 103807 (2024). https:\/\/doi.org\/10.1016\/j.ipm.2024.103807","journal-title":"Inf. Process. Manag."},{"key":"1636_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116071","volume":"189","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Wang, Y., Wang, L., Meng, J.: PostCom2DR: utilizing information from post and comments to detect rumors. Expert Syst. Appl. 189, 116071 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2021.116071","journal-title":"Expert Syst. Appl."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01636-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01636-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01636-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T19:33:00Z","timestamp":1745263980000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01636-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,17]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1636"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01636-x","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,17]]},"assertion":[{"value":"16 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"75"}}