{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:48:58Z","timestamp":1782204538959,"version":"3.54.5"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91938301"],"award-info":[{"award-number":["91938301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1007\/s10664-026-10838-y","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T09:33:31Z","timestamp":1779096811000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Meta-enhanced code: leveraging structural and functional features for precise cross-modal code search"],"prefix":"10.1007","volume":"31","author":[{"given":"Le","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9374-0192","authenticated-orcid":false,"given":"Shaohua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangwei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianlu","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songbo","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"10838_CR1","doi-asserted-by":"publisher","unstructured":"Song Y, Lothritz C, Tang D, et al (2024) Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance. https:\/\/doi.org\/10.48550\/arXiv.2404.08817","DOI":"10.48550\/arXiv.2404.08817"},{"key":"10838_CR2","unstructured":"Athiwaratkun B, Gouda SK, Wang Z, et al (2023) MULTI-LINGUAL EVALUATION OF CODE GENERATION MODELS. In: The Eleventh International Conference on Learning Representations"},{"key":"10838_CR3","doi-asserted-by":"publisher","unstructured":"Austin J, Odena A, Nye M, et al (2021) Program Synthesis with Large Language Models. https:\/\/doi.org\/10.48550\/arXiv.2108.07732","DOI":"10.48550\/arXiv.2108.07732"},{"key":"10838_CR4","doi-asserted-by":"publisher","unstructured":"Bajracharya S, Ngo T, Linstead E, et al (2006) Sourcerer: a search engine for open source code supporting structure-based search. In: Companion to the 21st ACM SIGPLAN symposium on Object-oriented programming systems, languages, and applications. ACM, Portland Oregon USA, pp 681\u2013682. https:\/\/doi.org\/10.1145\/1176617.1176671","DOI":"10.1145\/1176617.1176671"},{"key":"10838_CR5","first-page":"1877","volume-title":"Larochelle H","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N et al (2020) Language Models are Few-Shot Learners. In: Ranzato M, Hadsell R et al (eds) Larochelle H. Systems. Curran Associates Inc, Advances in Neural Information Processing, pp 1877\u20131901"},{"key":"10838_CR6","doi-asserted-by":"publisher","unstructured":"Chen B, Abedjan Z (2023) Duetcs: Code Style Transfer through Generation and Retrieval. In: 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). IEEE, Melbourne, Australia, pp 2362\u20132373. https:\/\/doi.org\/10.1109\/ICSE48619.2023.00198","DOI":"10.1109\/ICSE48619.2023.00198"},{"key":"10838_CR7","doi-asserted-by":"publisher","unstructured":"Chen M, Tworek J, Jun H, et al (2021) Evaluating Large Language Models Trained on Code. https:\/\/doi.org\/10.48550\/arXiv.2107.03374","DOI":"10.48550\/arXiv.2107.03374"},{"key":"10838_CR8","doi-asserted-by":"publisher","unstructured":"Dai Z, Yao C, Han W, et al (2024) MPCoder: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Bangkok, Thailand, pp 3765\u20133780. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.207","DOI":"10.18653\/v1\/2024.acl-long.207"},{"key":"10838_CR9","doi-asserted-by":"publisher","unstructured":"Feng Z, Guo D, Tang D, et al (2020) CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, pp 1536\u20131547. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.139","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"10838_CR10","doi-asserted-by":"publisher","unstructured":"Feng J, Li W, Wei Z, et al (2024) Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning. https:\/\/doi.org\/10.48550\/arXiv.2408.09345","DOI":"10.48550\/arXiv.2408.09345"},{"key":"10838_CR11","doi-asserted-by":"publisher","unstructured":"Galliamov K, Khaertdinova L, Denisova K (2024) Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning. https:\/\/doi.org\/10.48550\/arXiv.2405.04126","DOI":"10.48550\/arXiv.2405.04126"},{"key":"10838_CR12","doi-asserted-by":"publisher","unstructured":"Gao L, Ma X, Lin J, Callan J (2023) Precise Zero-Shot Dense Retrieval without Relevance Labels. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Toronto, Canada, pp 1762\u20131777. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.99","DOI":"10.18653\/v1\/2023.acl-long.99"},{"key":"10838_CR13","doi-asserted-by":"publisher","unstructured":"Grattafiori A, Dubey A, Jauhri A, et al (2024) The Llama 3 Herd of Models. https:\/\/doi.org\/10.48550\/arXiv.2407.21783","DOI":"10.48550\/arXiv.2407.21783"},{"key":"10838_CR14","doi-asserted-by":"publisher","unstructured":"Gu X, Zhang H, Kim S (2018) Deep code search. In: Proceedings of the 40th International Conference on Software Engineering. ACM, Gothenburg Sweden, pp 933\u2013944. https:\/\/doi.org\/10.1145\/3180155.3180167","DOI":"10.1145\/3180155.3180167"},{"key":"10838_CR15","unstructured":"Guo D, Ren S, Lu S, et al (2021) GraphCodeBERT: Pre-training Code Representations with Data Flow. In: International Conference on Learning Representations"},{"key":"10838_CR16","doi-asserted-by":"publisher","unstructured":"Guo D, Lu S, Duan N, et al (2022) UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, pp 7212\u20137225. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.499","DOI":"10.18653\/v1\/2022.acl-long.499"},{"key":"10838_CR17","unstructured":"Hendrycks D, Basart S, Kadavath S, et al (2021) Measuring Coding Challenge Competence With APPS. In: Vanschoren J, Yeung S (eds) Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks"},{"key":"10838_CR18","doi-asserted-by":"publisher","unstructured":"Heyman G, Cutsem TV (2020) Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent. https:\/\/doi.org\/10.48550\/arXiv.2008.12193","DOI":"10.48550\/arXiv.2008.12193"},{"key":"10838_CR19","unstructured":"Houlsby N, Giurgiu A, Jastrzebski S, Morrone B (2019) Parameter-Efficient Transfer Learning for NLP. In: Proceedings of the 36th International Conference on Machine Learning. PMLR, pp 2790\u20132799. https:\/\/proceedings.mlr.press\/v97\/houlsby19a.html"},{"key":"10838_CR20","doi-asserted-by":"crossref","unstructured":"Huang J, Tang D, Shou L, et al. CoSQA: 20,000+ Web Queries for Code Search and Question Answering[C]\/\/Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021: 5690\u20135700.","DOI":"10.18653\/v1\/2021.acl-long.442"},{"key":"10838_CR21","doi-asserted-by":"publisher","unstructured":"Husain H, Wu H-H, Gazit T, et al (2020) CodeSearchNet Challenge: Evaluating the State of Semantic Code Search. https:\/\/doi.org\/10.48550\/arXiv.1909.09436","DOI":"10.48550\/arXiv.1909.09436"},{"key":"10838_CR22","doi-asserted-by":"publisher","unstructured":"Izacard G, Caron M, Hosseini L, et al (2022) Unsupervised Dense Information Retrieval with Contrastive Learning. https:\/\/doi.org\/10.48550\/arXiv.2112.09118","DOI":"10.48550\/arXiv.2112.09118"},{"key":"10838_CR23","doi-asserted-by":"publisher","unstructured":"Li H, Miao C, Leung C, et al (2022) Exploring Representation-level Augmentation for Code Search. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, pp 4924\u20134936. https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.327","DOI":"10.18653\/v1\/2022.emnlp-main.327"},{"key":"10838_CR24","doi-asserted-by":"publisher","unstructured":"Li H, Zhou X, Luu A, Miao C (2023) Rethinking Negative Pairs in Code Search. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Singapore, pp 12760\u201312774. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.786","DOI":"10.18653\/v1\/2023.emnlp-main.786"},{"key":"10838_CR25","doi-asserted-by":"publisher","unstructured":"Li H, Zhou X, Shen Z (2024) Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Bangkok, Thailand, pp 1371\u20131389. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.75","DOI":"10.18653\/v1\/2024.acl-long.75"},{"key":"10838_CR26","doi-asserted-by":"publisher","unstructured":"Lin J, Xie Y, Yu Y, et al (2024) Toward Exploring the Code Understanding Capabilities of Pre-trained Code Generation Models. https:\/\/doi.org\/10.48550\/arXiv.2406.12326","DOI":"10.48550\/arXiv.2406.12326"},{"key":"10838_CR27","unstructured":"Luo Z, Xu C, Zhao P, et al (2024) WizardCoder: Empowering Code Large Language Models with Evol-Instruct. In: The Twelfth International Conference on Learning Representations"},{"key":"10838_CR28","doi-asserted-by":"publisher","unstructured":"Mao Y, He P, Liu X, et al (2021) Generation-Augmented Retrieval for Open-Domain Question Answering. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, pp 4089\u20134100. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.316","DOI":"10.18653\/v1\/2021.acl-long.316"},{"key":"10838_CR29","doi-asserted-by":"publisher","unstructured":"Mao Y, Wan C, Jiang Y, Gu X (2023) Self-Supervised Query Reformulation for Code Search. In: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, San Francisco CA USA, pp 363\u2013374. https:\/\/doi.org\/10.1145\/3611643.3616306","DOI":"10.1145\/3611643.3616306"},{"key":"10838_CR30","doi-asserted-by":"publisher","unstructured":"McMillan C, Grechanik M, Poshyvanyk D, et al (2011) Portfolio: finding relevant functions and their usage. In: Proceedings of the 33rd International Conference on Software Engineering. ACM, Waikiki, Honolulu HI USA, pp 111\u2013120. https:\/\/doi.org\/10.1145\/1985793.1985809","DOI":"10.1145\/1985793.1985809"},{"key":"10838_CR31","unstructured":"Nijkamp E, Pang B, Hayashi H, et al (2023) CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. In: The Eleventh International Conference on Learning Representations"},{"key":"10838_CR32","doi-asserted-by":"publisher","unstructured":"Oord A van den, Li Y, Vinyals O (2019) Representation Learning with Contrastive Predictive Coding. https:\/\/doi.org\/10.48550\/arXiv.1807.03748","DOI":"10.48550\/arXiv.1807.03748"},{"key":"10838_CR33","unstructured":"OpenAI (2022) Introducing ChatGPT. https:\/\/openai.com\/index\/chatgpt\/"},{"key":"10838_CR34","unstructured":"OpenAI (2024) New embedding models and API updates. https:\/\/openai.com\/index\/new-embedding-models-and-api-updates\/"},{"key":"10838_CR35","unstructured":"OpenAI, Ahmed El-Kishky, Daniel Selsam, Ilya Sutskever (2024) OpenAI o1 System Card. https:\/\/openai.com\/index\/openai-o1-system-card\/"},{"key":"10838_CR36","doi-asserted-by":"publisher","unstructured":"OpenAI, Hurst A, Lerer A, et al (2024) GPT-4o System Card. https:\/\/doi.org\/10.48550\/arXiv.2410.21276","DOI":"10.48550\/arXiv.2410.21276"},{"key":"10838_CR37","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, et al. Bleu: a method for automatic evaluation of machine translation[C]\/\/Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 2002: 311\u2013318.","DOI":"10.3115\/1073083.1073135"},{"key":"10838_CR38","doi-asserted-by":"publisher","unstructured":"Qin Z, Jagerman R, Hui K, et al (2024) Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. In: Findings of the Association for Computational Linguistics: NAACL 2024. Association for Computational Linguistics, Mexico City, Mexico, pp 1504\u20131518. https:\/\/doi.org\/10.18653\/v1\/2024.findings-naacl.97","DOI":"10.18653\/v1\/2024.findings-naacl.97"},{"key":"10838_CR39","doi-asserted-by":"publisher","unstructured":"Rao N, Bansal C, Guan J (2021) Search4Code: Code Search Intent Classification Using Weak Supervision. In: 2021 IEEE\/ACM 18th International Conference on Mining Software Repositories (MSR). IEEE, Madrid, Spain, pp 575\u2013579. https:\/\/doi.org\/10.1109\/MSR52588.2021.00077","DOI":"10.1109\/MSR52588.2021.00077"},{"key":"10838_CR40","unstructured":"Ren S, Guo D, Lu S, et al. Codebleu: a method for automatic evaluation of code synthesis[J]. arXiv preprint arXiv:2009.10297, 2020."},{"key":"10838_CR41","doi-asserted-by":"publisher","unstructured":"Rozi\u00e8re B, Gehring J, Gloeckle F, et al (2024) Code Llama: Open Foundation Models for Code. https:\/\/doi.org\/10.48550\/arXiv.2308.12950","DOI":"10.48550\/arXiv.2308.12950"},{"key":"10838_CR42","doi-asserted-by":"publisher","unstructured":"Saieva A, Chakraborty S, Kaiser G (2024) REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models. https:\/\/doi.org\/10.48550\/arXiv.2305.03843","DOI":"10.48550\/arXiv.2305.03843"},{"key":"10838_CR43","doi-asserted-by":"publisher","unstructured":"Sun Z, Liu Y, Yang C, Qian Y (2020) PSCS: A Path-based Neural Model for Semantic Code Search. https:\/\/doi.org\/10.48550\/arXiv.2008.03042","DOI":"10.48550\/arXiv.2008.03042"},{"key":"10838_CR44","doi-asserted-by":"publisher","first-page":"16485","DOI":"10.1609\/aaai.v37i13.27087","volume":"37","author":"C-K Ting","year":"2023","unstructured":"Ting C-K, Munson K, Wade S et al (2023) Codestylist: a system for performing code style transfer using neural networks. AAAI 37:16485\u201316487. https:\/\/doi.org\/10.1609\/aaai.v37i13.27087","journal-title":"AAAI"},{"key":"10838_CR45","doi-asserted-by":"publisher","unstructured":"Wang Y, Wang W, Joty S, Hoi SCH (2021) CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, pp 8696\u20138708. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.685","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"10838_CR46","doi-asserted-by":"publisher","unstructured":"Wang Y, Le H, Gotmare A, et al (2023) CodeT5+: Open Code Large Language Models for Code Understanding and Generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Singapore, pp 1069\u20131088. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.68","DOI":"10.18653\/v1\/2023.emnlp-main.68"},{"key":"10838_CR47","doi-asserted-by":"publisher","unstructured":"Wang Y, Guo L, Shi E, et al (2023) You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search. In: 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, Bogot\u00e1, Colombia, pp 14\u201325. https:\/\/doi.org\/10.1109\/ICSME58846.2023.00014","DOI":"10.1109\/ICSME58846.2023.00014"},{"key":"10838_CR48","doi-asserted-by":"publisher","unstructured":"Wang L, Yang N, Wei F (2023) Query2doc: Query Expansion with Large Language Models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Singapore, pp 9414\u20139423. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.585","DOI":"10.18653\/v1\/2023.emnlp-main.585"},{"key":"10838_CR49","doi-asserted-by":"publisher","unstructured":"Wang C, Yao P, Tang W, et al (2023) Synthesizing Conjunctive Queries for Code Search. LIPIcs, Volume 263, ECOOP 2023 263:36:1\u201336:30. https:\/\/doi.org\/10.4230\/LIPICS.ECOOP.2023.36","DOI":"10.4230\/LIPICS.ECOOP.2023.36"},{"key":"10838_CR50","doi-asserted-by":"publisher","unstructured":"Xian Z, Cui C, Huang R, et al (2024) zsLLMCode: An Effective Approach for Functional Code Embedding via LLM with Zero-Shot Learning. https:\/\/doi.org\/10.48550\/arXiv.2409.14644","DOI":"10.48550\/arXiv.2409.14644"},{"key":"10838_CR51","doi-asserted-by":"publisher","unstructured":"Xiangtan, Haize Hu, Jianxun Liu, Lin Xiao (2024) Multi-intent Description of Keyword Expansion for Code Search. In: Biao Luo, Long Cheng, Zheng-Guang Wu, et al (eds) Neural Information Processing. Springer Nature Singapore, Singapore, pp 3\u201314. https:\/\/doi.org\/10.1007\/978-981-99-8145-8_1","DOI":"10.1007\/978-981-99-8145-8_1"},{"key":"10838_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3546066","volume":"32","author":"C Zeng","year":"2023","unstructured":"Zeng C, Yu Y, Li S et al (2023) DeGraphCS\u202f: embedding variable-based flow graph for neural code search. ACM Trans Softw Eng Methodol 32:1\u201327. https:\/\/doi.org\/10.1145\/3546066","journal-title":"ACM Trans Softw Eng Methodol"},{"key":"10838_CR53","unstructured":"Zhang Z, Chen C, Liu B, et al (2024) Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code. Transactions on Machine Learning Research"},{"key":"10838_CR54","doi-asserted-by":"publisher","unstructured":"Team C, Zhao H, Hui J, et al (2024) CodeGemma: Open Code Models Based on Gemma. https:\/\/doi.org\/10.48550\/arXiv.2406.11409","DOI":"10.48550\/arXiv.2406.11409"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-026-10838-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-026-10838-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-026-10838-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:08:28Z","timestamp":1782202108000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-026-10838-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":54,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10838"],"URL":"https:\/\/doi.org\/10.1007\/s10664-026-10838-y","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,18]]},"assertion":[{"value":"23 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Shaohua Liu reports financial support was provided by the National Natural Science Foundation of China. Other authors declare no competing interests.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"144"}}