{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T01:12:34Z","timestamp":1778375554680,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-08151-4","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:13:09Z","timestamp":1767769989000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Few-shot with prototype augmentation for low-resource domains rumor detection"],"prefix":"10.1007","volume":"82","author":[{"given":"Yang","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Ran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaogong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"8151_CR1","unstructured":"Qazvinian V, Rosengren E, Radev DR, Mei Q (2011) Rumor has it: identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP \u201911), pp 1589\u20131599"},{"key":"8151_CR2","doi-asserted-by":"crossref","unstructured":"Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp 675\u2013684","DOI":"10.1145\/1963405.1963500"},{"key":"8151_CR3","first-page":"387","volume":"1","author":"AY Chua","year":"2016","unstructured":"Chua AY, Banerjee S (2016) Linguistic predictors of rumor veracity on the internet. Proc Int Multiconf Eng Comput Sci 1:387\u2013391","journal-title":"Proc Int Multiconf Eng Comput Sci"},{"key":"8151_CR4","doi-asserted-by":"crossref","unstructured":"Popat K (2017) Assessing the credibility of claims on the web. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp 735\u2013739","DOI":"10.1145\/3041021.3053379"},{"key":"8151_CR5","doi-asserted-by":"publisher","first-page":"152788","DOI":"10.1109\/ACCESS.2019.2947855","volume":"7","author":"M Al-Sarem","year":"2019","unstructured":"Al-Sarem M, Boulila W, Al-Harby M, Qadir J, Alsaeedi A (2019) Deep learning-based rumor detection on microblogging platforms: a systematic review. IEEE Access 7:152788\u2013152812","journal-title":"IEEE Access"},{"issue":"5","key":"8151_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103029","volume":"59","author":"G Jiang","year":"2022","unstructured":"Jiang G, Liu S, Zhao Y, Sun Y, Zhang M (2022) Fake news detection via knowledgeable prompt learning. Inf Process Manag 59(5):103029","journal-title":"Inf Process Manag"},{"key":"8151_CR7","unstructured":"Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong K-F, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp 3818\u20133824"},{"key":"8151_CR8","doi-asserted-by":"crossref","unstructured":"Yu F, Liu Q, Wu S, Wang L, Tan T (2017) A convolutional approach for misinformation identification. In: Twenty-Sixth International Joint Conference on Artificial Intelligence","DOI":"10.24963\/ijcai.2017\/545"},{"issue":"16","key":"8151_CR9","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.3390\/math11163461","volume":"11","author":"J Chen","year":"2023","unstructured":"Chen J, Zhang W, Ma H, Yang S (2023) Rumor detection in social media based on multi-hop graphs and differential time series. Mathematics 11(16):3461","journal-title":"Mathematics"},{"key":"8151_CR10","doi-asserted-by":"crossref","unstructured":"Ma J, Gao W, Wong K-F (2018) Rumor detection on twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","DOI":"10.18653\/v1\/P18-1184"},{"key":"8151_CR11","first-page":"8783","volume":"34","author":"LMS Khoo","year":"2020","unstructured":"Khoo LMS, Chieu HL, Qian Z, Jiang J (2020) Interpretable rumor detection in microblogs by attending to user interactions. Proc AAAI Conf Artif Intell 34:8783\u20138790","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR12","doi-asserted-by":"crossref","unstructured":"Li J, Ni S, Kao H-Y (2021) Meet the truth: leverage objective facts and subjective views for interpretable rumor detection. arXiv preprint arXiv:2107.10747","DOI":"10.18653\/v1\/2021.findings-acl.63"},{"issue":"1","key":"8151_CR13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s40537-023-00725-4","volume":"10","author":"W Cui","year":"2023","unstructured":"Cui W, Shang M (2023) Kagn: knowledge-powered attention and graph convolutional networks for social media rumor detection. J Big Data 10(1):45","journal-title":"J Big Data"},{"key":"8151_CR14","first-page":"81","volume":"35","author":"Y Dun","year":"2021","unstructured":"Dun Y, Tu K, Chen C, Hou C, Yuan X (2021) Kan: knowledge-aware attention network for fake news detection. Proc AAAI Conf Artif Intell 35:81\u201389","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR15","doi-asserted-by":"crossref","unstructured":"Wang X, Luo C, Guo T, Liu Z, Zhang J, Wang H (2023) Bgek: external knowledge-enhanced graph convolutional networks for rumor detection in online social networks. In: International Conference on Artificial Neural Networks, Springer, pp 291\u2013303","DOI":"10.1007\/978-3-031-44216-2_24"},{"key":"8151_CR16","doi-asserted-by":"crossref","unstructured":"Hu L, Yang T, Zhang L, Zhong W, Tang D, Shi C, Duan N, Zhou M (2021) Compare to the knowledge: graph neural fake news detection with external knowledge. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol 1, long papers, pp 754\u2013763","DOI":"10.18653\/v1\/2021.acl-long.62"},{"key":"8151_CR17","doi-asserted-by":"crossref","unstructured":"Du J, Dou Y, Xia C, Cui L, Ma J, Philip SY (2021) Cross-lingual covid-19 fake news detection. In: 2021 International Conference on Data Mining Workshops (ICDMW), IEEE, pp 859\u2013862","DOI":"10.1109\/ICDMW53433.2021.00110"},{"key":"8151_CR18","first-page":"5213","volume":"37","author":"H Lin","year":"2023","unstructured":"Lin H, Yi P, Ma J et al (2023) Zero-shot rumor detection with propagation structure via prompt learning. Proc AAAI Conf Artif Intell 37:5213\u20135221","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR19","doi-asserted-by":"crossref","unstructured":"Wen Z, Fang Y (2023) Augmenting low-resource text classification with graph-grounded pre-training and prompting. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 506\u2013516","DOI":"10.1145\/3539618.3591641"},{"key":"8151_CR20","first-page":"1","volume":"30","author":"J Snell","year":"2017","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst 30:1","journal-title":"Adv Neural Inf Process Syst"},{"issue":"12","key":"8151_CR21","doi-asserted-by":"publisher","first-page":"17347","DOI":"10.1007\/s11042-022-12761-y","volume":"81","author":"S Shelke","year":"2022","unstructured":"Shelke S, Attar V (2022) Rumor detection in social network based on user, content and lexical features. Multimed Tools Appl 81(12):17347\u201317368","journal-title":"Multimed Tools Appl"},{"key":"8151_CR22","doi-asserted-by":"crossref","unstructured":"Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J (2020) Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"8151_CR23","first-page":"3178","volume":"2021","author":"A Lao","year":"2021","unstructured":"Lao A, Shi C, Yang Y (2021) Rumor detection with field of linear and non-linear propagation. Proc Web Conf 2021:3178\u20133187","journal-title":"Proc Web Conf"},{"issue":"3","key":"8151_CR24","doi-asserted-by":"publisher","first-page":"2851","DOI":"10.1007\/s10489-024-05312-5","volume":"54","author":"Q Zhao","year":"2024","unstructured":"Zhao Q, Zhang Y, Feng X (2024) Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection. Appl Intell 54(3):2851\u20132866","journal-title":"Appl Intell"},{"key":"8151_CR25","unstructured":"Karimi H, Roy P, Saba-Sadiya S, Tang J (2018) Multi-source multi-class fake news detection. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 1546\u20131557"},{"key":"8151_CR26","doi-asserted-by":"crossref","unstructured":"Birunda SS, Devi RK (2021) A novel score-based multi-source fake news detection using gradient boosting algorithm. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), IEEE, pp 406\u2013414","DOI":"10.1109\/ICAIS50930.2021.9395896"},{"key":"8151_CR27","doi-asserted-by":"crossref","unstructured":"Kang Z, Cao Y, Shang Y, Liang T, Tang H, Tong L (2021) Fake news detection with heterogenous deep graph convolutional network. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, pp 408\u2013420","DOI":"10.1007\/978-3-030-75762-5_33"},{"key":"8151_CR28","unstructured":"Qin T, Li W, Shi Y, Gao Y (2020) Diversity helps: unsupervised few-shot learning via distribution shift-based data augmentation. arXiv preprint arXiv:2004.05805"},{"issue":"9","key":"8151_CR29","doi-asserted-by":"publisher","first-page":"4594","DOI":"10.1109\/TIP.2019.2910052","volume":"28","author":"Z Chen","year":"2019","unstructured":"Chen Z, Fu Y, Zhang Y, Jiang Y-G, Xue X, Sigal L (2019) Multi-level semantic feature augmentation for one-shot learning. IEEE Trans Image Process 28(9):4594\u20134605","journal-title":"IEEE Trans Image Process"},{"key":"8151_CR30","doi-asserted-by":"crossref","unstructured":"Kang S, Hwang D, Eo M, Kim T, Rhee W (2023) Meta-learning with a geometry-adaptive preconditioner. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 16080\u201316090","DOI":"10.1109\/CVPR52729.2023.01543"},{"key":"8151_CR31","first-page":"16687","volume":"38","author":"B Zhang","year":"2024","unstructured":"Zhang B, Luo C, Yu D, Li X, Lin H, Ye Y, Zhang B (2024) Metadiff: meta-learning with conditional diffusion for few-shot learning. Proc AAAI Conf Artif Intell 38:16687\u201316695","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR32","first-page":"1","volume":"29","author":"O Vinyals","year":"2016","unstructured":"Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29:1","journal-title":"Adv Neural Inf Process Syst"},{"key":"8151_CR33","first-page":"24581","volume":"34","author":"S Laenen","year":"2021","unstructured":"Laenen S, Bertinetto L (2021) On episodes, prototypical networks, and few-shot learning. Adv Neural Inf Process Syst 34:24581\u201324592","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"8151_CR34","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1109\/TCSVT.2020.2995754","volume":"31","author":"W Jiang","year":"2020","unstructured":"Jiang W, Huang K, Geng J, Deng X (2020) Multi-scale metric learning for few-shot learning. IEEE Trans Circuits Syst Video Technol 31(3):1091\u20131102","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"4","key":"8151_CR35","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1049\/cit2.12181","volume":"8","author":"Q Ran","year":"2023","unstructured":"Ran Q, Zhou Y, Hong D, Bi M, Ni L, Li X, Ahmad M (2023) Deep transformer and few-shot learning for hyperspectral image classification. CAAI Trans Intell Technol 8(4):1323\u20131336","journal-title":"CAAI Trans Intell Technol"},{"key":"8151_CR36","doi-asserted-by":"crossref","unstructured":"Yang X, Feng S, Wang D, Zhang Y, Poria S (2023) Few-shot multimodal sentiment analysis based on multimodal probabilistic fusion prompts. In: Proceedings of the 31st ACM International Conference on Multimedia, pp 6045\u20136053","DOI":"10.1145\/3581783.3612181"},{"key":"8151_CR37","unstructured":"Wang H (2024) Adlda: a method to reduce the harm of data distribution shift in data augmentation. arXiv preprint arXiv:2405.06893"},{"issue":"145","key":"8151_CR38","first-page":"1","volume":"24","author":"B Wang","year":"2023","unstructured":"Wang B, Yuan Z, Ying Y, Yang T (2023) Memory-based optimization methods for model-agnostic meta-learning and personalized federated learning. J Mach Learn Res 24(145):1\u201346","journal-title":"J Mach Learn Res"},{"key":"8151_CR39","first-page":"7091","volume":"2025","author":"J Cui","year":"2025","unstructured":"Cui J, Wang X, Suzuki Y, Fukumoto F (2025) Causal denoising prototypical network for few-shot multi-label aspect category detection. Find Assoc Comput Linguis ACL 2025:7091\u20137104","journal-title":"Find Assoc Comput Linguis ACL"},{"key":"8151_CR40","first-page":"9011","volume":"37","author":"Q Lyu","year":"2023","unstructured":"Lyu Q, Wang W (2023) Compositional prototypical networks for few-shot classification. Proc AAAI Conf Artif Intell 37:9011\u20139019","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR41","first-page":"1019","volume":"38","author":"D Chen","year":"2024","unstructured":"Chen D, Zhang J, Zheng WSEA (2024) Featwalk: enhancing few-shot classification through local view leveraging. Proc AAAI Conf Artif Intell 38:1019\u20131027","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR42","first-page":"18644","volume":"38","author":"H Liu","year":"2024","unstructured":"Liu H, Zhao S, Xea Z (2024) Liberating seen classes: boosting few-shot and zero-shot text classification via anchor generation and classification reframing. Proc AAAI Conf Artif Intell 38:18644\u201318652","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123586","volume":"249","author":"P Zhao","year":"2024","unstructured":"Zhao P, Wang L, Zhao X, Liu H, Ji X (2024) Few-shot learning based on prototype rectification with a self-attention mechanism. Expert Syst Appl 249:123586","journal-title":"Expert Syst Appl"},{"issue":"6","key":"8151_CR44","doi-asserted-by":"publisher","first-page":"8249","DOI":"10.1007\/s40747-024-01571-4","volume":"10","author":"Z Ruan","year":"2024","unstructured":"Ruan Z, Wei Y, Guo Y, Xie Y (2024) Hybrid attentive prototypical network for few-shot action recognition. Complex Intell Syst 10(6):8249\u20138272","journal-title":"Complex Intell Syst"},{"key":"8151_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102538","volume":"61","author":"X Zhang","year":"2024","unstructured":"Zhang X, Huang W, Ding C, Wang J, Shen C, Shi J (2024) Cross-supervised multisource prototypical network: a novel domain adaptation method for multi-source few-shot fault diagnosis. Adv Eng Inform 61:102538","journal-title":"Adv Eng Inform"},{"key":"8151_CR46","doi-asserted-by":"crossref","unstructured":"Kumar S, Carley KM (2019) Tree LSTMS with convolution units to predict stance and rumor veracity in social media conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 5047\u20135058","DOI":"10.18653\/v1\/P19-1498"},{"key":"8151_CR47","doi-asserted-by":"crossref","unstructured":"Rao D, Miao X, Jiang Z, Li R (2021) Stanker: stacking network based on level-grained attention-masked BERT for rumor detection on social media. In: EMNLP","DOI":"10.18653\/v1\/2021.emnlp-main.269"},{"key":"8151_CR48","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) 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, vol 1 (Long and Short Papers), pp 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"8151_CR49","first-page":"6407","volume":"33","author":"T Gao","year":"2019","unstructured":"Gao T, Han X, Zea L (2019) Hybrid attention-based prototypical networks for noisy few-shot relation classification. Proc AAAI Conf Artif Intell 33:6407\u20136414","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR50","first-page":"22105","volume":"38","author":"B Hu","year":"2024","unstructured":"Hu B, Sheng Q, Cao J, Shi Y, Li Y, Wang D, Qi P (2024) Bad actor, good advisor: exploring the role of large language models in fake news detection. Proc AAAI Conf Artif Intell 38:22105\u201322113","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8151_CR51","doi-asserted-by":"crossref","unstructured":"Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1199\u20131208","DOI":"10.1109\/CVPR.2018.00131"},{"key":"8151_CR52","doi-asserted-by":"publisher","first-page":"2467539","DOI":"10.1155\/2023\/2467539","volume":"1","author":"D Chen","year":"2023","unstructured":"Chen D, Chen X, Lu P, Wang X (2023) Cnfrd: a few-shot rumor detection framework via capsule network for covid-19. Int J Intell Syst 1:2467539","journal-title":"Int J Intell Syst"},{"key":"8151_CR53","doi-asserted-by":"crossref","unstructured":"Lin H, Ma J, Chen L, Yang Z, Cheng M, Chen G (2022) Detect rumors in microblog posts for low-resource domains via adversarial contrastive learning. arXiv preprint arXiv:2204.08143","DOI":"10.18653\/v1\/2022.findings-naacl.194"},{"key":"8151_CR54","unstructured":"Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, De\u00a0Laroussilhe Q, Gesmundo A, Attariyan M, Gelly S (2019) Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning, PMLR, pp 2790\u20132799"},{"key":"8151_CR55","unstructured":"He J, Zhou C, Ma X, Berg-Kirkpatrick T, Neubig G (2021) Towards a unified view of parameter-efficient transfer learning. In: International Conference on Learning Representations"},{"key":"8151_CR56","doi-asserted-by":"crossref","unstructured":"Lin XV, Mihaylov T, Artetxe M, Wang T, Chen S, Simig D, Ott M, Goyal N, Bhosale S, Du Jea (2021) Few-shot learning with multilingual language models. arXiv preprint arXiv:2112.10668","DOI":"10.18653\/v1\/2022.emnlp-main.616"},{"key":"8151_CR57","doi-asserted-by":"crossref","unstructured":"Zhao M, Schutze H (2021) Discrete and soft prompting for multilingual models. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 8547\u20138555","DOI":"10.18653\/v1\/2021.emnlp-main.672"},{"key":"8151_CR58","doi-asserted-by":"crossref","unstructured":"Lester B, Al-Rfou R, Constant N (2021) The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 3045\u20133059","DOI":"10.18653\/v1\/2021.emnlp-main.243"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08151-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08151-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08151-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:13:25Z","timestamp":1767770005000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08151-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":58,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["8151"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08151-4","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]},"assertion":[{"value":"23 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"52"}}