{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T12:34:20Z","timestamp":1775219660842,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"8079","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"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":["Nature"],"published-print":{"date-parts":[[2025,9,4]]},"DOI":"10.1038\/s41586-025-09426-9","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T15:03:29Z","timestamp":1755702209000},"page":"115-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Electron flow matching for generative reaction mechanism prediction"],"prefix":"10.1038","volume":"645","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5976-2048","authenticated-orcid":false,"given":"Joonyoung F.","family":"Joung","sequence":"first","affiliation":[]},{"given":"Mun Hong","family":"Fong","sequence":"additional","affiliation":[]},{"given":"Nicholas","family":"Casetti","sequence":"additional","affiliation":[]},{"given":"Jordan P.","family":"Liles","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6585-6551","authenticated-orcid":false,"given":"Ne S.","family":"Dassanayake","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-8723","authenticated-orcid":false,"given":"Connor W.","family":"Coley","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"9426_CR1","unstructured":"Lavoisier, A. Trait\u00e9 \u00c9l\u00e9mentaire de Chimie (Elementary Treatise on Chemistry) (Cuchet, 1789)."},{"key":"9426_CR2","doi-asserted-by":"crossref","unstructured":"Do, K., Tran, T. & Venkatesh, S. Graph transformation policy network for chemical reaction prediction. In Proc. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD\u201919) 750\u2013760 (ACM, 2019).","DOI":"10.1145\/3292500.3330958"},{"key":"9426_CR3","unstructured":"Jin, W., Coley, C., Barzilay, R. & Jaakkola, T. Predicting organic reaction outcomes with Weisfeiler-Lehman network. In Proc. 31st International Conference on Neural Information Processing Systems (NIPS\u201917) 2604\u20132613 (ACM, 2017)."},{"key":"9426_CR4","unstructured":"Bradshaw, J., Kusner, M. J., Paige, B., Segler, M. H. & Hern\u00e1ndez-Lobato, J. M. A generative model for electron paths. In International Conference on Learning Representations (2019)."},{"key":"9426_CR5","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1039\/C8SC04228D","volume":"10","author":"CW Coley","year":"2019","unstructured":"Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10, 370\u2013377 (2019).","journal-title":"Chem. Sci."},{"key":"9426_CR6","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1021\/acscentsci.9b00576","volume":"5","author":"P Schwaller","year":"2019","unstructured":"Schwaller, P. et al. Molecular Transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 5, 1572\u20131583 (2019).","journal-title":"ACS Cent. Sci."},{"key":"9426_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19266-y","volume":"11","author":"IV Tetko","year":"2020","unstructured":"Tetko, I. V., Karpov, P., Van Deursen, R. & Godin, G. State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat. Commun. 11, 5575 (2020).","journal-title":"Nat. Commun."},{"key":"9426_CR8","unstructured":"Bi, H. et al. Non-autoregressive electron redistribution modeling for reaction prediction. In Proc. 38th International Conference on Machine Learning 904\u2013913 (PMLR, 2021)."},{"key":"9426_CR9","doi-asserted-by":"publisher","first-page":"3503","DOI":"10.1021\/acs.jcim.2c00321","volume":"62","author":"Z Tu","year":"2022","unstructured":"Tu, Z. & Coley, C. W. Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. J. Chem. Inf. Model. 62, 3503\u20133513 (2022).","journal-title":"J. Chem. Inf. Model."},{"key":"9426_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-022-00638-z","volume":"14","author":"X Wang","year":"2022","unstructured":"Wang, X. et al. From theory to experiment: transformer-based generation enables rapid discovery of novel reactions. J. Cheminform. 14, 60 (2022).","journal-title":"J. Cheminform."},{"key":"9426_CR11","doi-asserted-by":"publisher","first-page":"e1604","DOI":"10.1002\/wcms.1604","volume":"12","author":"P Schwaller","year":"2022","unstructured":"Schwaller, P. et al. Machine intelligence for chemical reaction space. Wiley Interdiscip. Rev. Comput. Mol. Sci. 12, e1604 (2022).","journal-title":"Wiley Interdiscip. Rev. Comput. Mol. Sci."},{"key":"9426_CR12","first-page":"e202411296","volume":"63","author":"JF Joung","year":"2024","unstructured":"Joung, J. F. et al. Reproducing reaction mechanisms with machine-learning models trained on a large-scale mechanistic dataset. Angew. Chem. Int. Ed. 63, e202411296 (2024).","journal-title":"Angew. Chem. Int. Ed."},{"key":"9426_CR13","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1021\/acscentsci.5c00055","volume":"11","author":"J Bradshaw","year":"2025","unstructured":"Bradshaw, J. et al. Challenging reaction prediction models to generalize to novel chemistry. ACS Cent. Sci. 11, 539\u2013549 (2025).","journal-title":"ACS Cent. Sci."},{"key":"9426_CR14","unstructured":"Tong, A. et al. Conditional flow matching: simulation-free dynamic optimal transport. Preprint at https:\/\/arxiv.org\/abs\/2302.00482v1 (2023)."},{"key":"9426_CR15","unstructured":"Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M. & Le, M. Flow matching for generative modeling. In International Conference on Learning Representations (2023)."},{"key":"9426_CR16","unstructured":"Liu, X., Gong, C. & Liu, Q. Flow straight and fast: learning to generate and transfer data with rectified flow. In International Conference on Learning Representations (2023)."},{"key":"9426_CR17","unstructured":"Dugundji, J. & Ugi, I. in Computers in Chemistry, 19\u201364 (Springer, 1973)."},{"key":"9426_CR18","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1002\/anie.199302011","volume":"32","author":"I Ugi","year":"1993","unstructured":"Ugi, I. et al. Computer-assisted solution of chemical problems-the historical development and the present state of the art of a new discipline of chemistry. Angew. Chem. Int. Ed. Engl. 32, 201\u2013227 (1993).","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"9426_CR19","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1039\/D2SC05089G","volume":"14","author":"Z Tu","year":"2023","unstructured":"Tu, Z., Stuyver, T. & Coley, C. W. Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery. Chem. Sci. 14, 226\u2013244 (2023).","journal-title":"Chem. Sci."},{"key":"9426_CR20","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.1021\/ci900157k","volume":"49","author":"JH Chen","year":"2009","unstructured":"Chen, J. H. & Baldi, P. No electron left behind: a rule-based expert system to predict chemical reactions and reaction mechanisms. J. Chem. Inf. Model. 49, 2034\u20132043 (2009).","journal-title":"J. Chem. Inf. Model."},{"key":"9426_CR21","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1021\/ci200207y","volume":"51","author":"MA Kayala","year":"2011","unstructured":"Kayala, M. A., Azencott, C.-A., Chen, J. H. & Baldi, P. Learning to predict chemical reactions. J. Chem. Inf. Model. 51, 2209\u20132222 (2011).","journal-title":"J. Chem. Inf. Model."},{"key":"9426_CR22","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.1021\/ci3003039","volume":"52","author":"MA Kayala","year":"2012","unstructured":"Kayala, M. A. & Baldi, P. ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning. J. Chem. Inf. Model. 52, 2526\u20132540 (2012).","journal-title":"J. Chem. Inf. Model."},{"key":"9426_CR23","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1039\/C7ME00107J","volume":"3","author":"D Fooshee","year":"2018","unstructured":"Fooshee, D. et al. Deep learning for chemical reaction prediction. Mol. Syst. Des. Eng. 3, 442\u2013452 (2018).","journal-title":"Mol. Syst. Des. Eng."},{"key":"9426_CR24","unstructured":"Tavakoli, M. et al. AI for interpretable chemistry: predicting radical mechanistic pathways via contrastive learning. In Proc. 37th International Conference on Neural Information Processing Systems (NIPS\u201923) (ACM, 2024)."},{"key":"9426_CR25","unstructured":"Song, Y. et al. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations (2021)."},{"key":"9426_CR26","unstructured":"Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. In Proc. 34th International Conference on Neural Information Processing Systems (NIPS\u201920) 6840\u20136851 (ACM, 2020)."},{"key":"9426_CR27","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1038\/s41586-023-06415-8","volume":"620","author":"JL Watson","year":"2023","unstructured":"Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089\u20131100 (2023).","journal-title":"Nature"},{"key":"9426_CR28","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1038\/s41586-023-06728-8","volume":"623","author":"JB Ingraham","year":"2023","unstructured":"Ingraham, J. B. et al. Illuminating protein space with a programmable generative model. Nature 623, 1070\u20131078 (2023).","journal-title":"Nature"},{"key":"9426_CR29","doi-asserted-by":"publisher","first-page":"eadl2528","DOI":"10.1126\/science.adl2528","volume":"384","author":"R Krishna","year":"2024","unstructured":"Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold all-atom. Science 384, eadl2528 (2024).","journal-title":"Science"},{"key":"9426_CR30","unstructured":"Hoogeboom, E., Satorras, V. G., Vignac, C. & Welling, M. Equivariant diffusion for molecule generation in 3D. In Proc. 39th International Conference on Machine Learning 8867\u20138887 (PMLR, 2022)."},{"key":"9426_CR31","unstructured":"Xu, M., Powers, A. S., Dror, R. O., Ermon, S. & Leskovec, J. Geometric latent diffusion models for 3D molecule generation. In Proc. 40th International Conference on Machine Learning 38592\u201338610 (PMLR, 2023)."},{"key":"9426_CR32","unstructured":"Igashov, I., Schneuing, A., Segler, M., Bronstein, M. & Correia, B. RetroBridge: modeling retrosynthesis with Markov bridges. In International Conference on Learning Representations (2024)."},{"key":"9426_CR33","unstructured":"Wang, Y. et al. RetroDiff: retrosynthesis as multi-stage distribution interpolation. Preprint at https:\/\/arxiv.org\/html\/2311.14077v1 (2023)."},{"key":"9426_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-44629-6","volume":"15","author":"S Kim","year":"2024","unstructured":"Kim, S., Woo, J. & Kim, W. Y. Diffusion-based generative AI for exploring transition states from 2D molecular graphs. Nat. Commun. 15, 341 (2024).","journal-title":"Nat. Commun."},{"key":"9426_CR35","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1038\/s43588-023-00563-7","volume":"3","author":"C Duan","year":"2023","unstructured":"Duan, C., Du, Y., Jia, H. & Kulik, H. J. Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. Nat. Comput. Sci. 3, 1045\u20131055 (2023).","journal-title":"Nat. Comput. Sci."},{"key":"9426_CR36","unstructured":"Dai, H., Li, C., Coley, C., Dai, B. & Song, L. Retrosynthesis prediction with conditional graph logic network. In Proc. 33rd International Conference on Neural Information Processing Systems 8872\u20138882 (ACM, 2019)."},{"key":"9426_CR37","unstructured":"O\u2019Boyle, N. & Dalke, A. DeepSMILES: an adaptation of SMILES for use in machine-learning of chemical structures. Preprint at https:\/\/chemrxiv.org\/engage\/chemrxiv\/article-details\/60c73ed6567dfe7e5fec388d (2018)."},{"key":"9426_CR38","doi-asserted-by":"publisher","first-page":"045024","DOI":"10.1088\/2632-2153\/aba947","volume":"1","author":"M Krenn","year":"2020","unstructured":"Krenn, M., H\u00e4se, F., Nigam, A., Friederich, P. & Aspuru-Guzik, A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1, 045024 (2020).","journal-title":"Mach. Learn. Sci. Technol."},{"key":"9426_CR39","unstructured":"NextMove Software. Pistachio. https:\/\/www.nextmovesoftware.com\/pistachio.html."},{"key":"9426_CR40","unstructured":"Kotian, P. L. et al. Human plasma kallikrein inhibitors https:\/\/patents.google.com\/patent\/US20240150295A1\/en (2024)."},{"key":"9426_CR41","unstructured":"Kotian, P. L. et al. Human plasma kallikrein inhibitors https:\/\/patents.google.com\/patent\/US20240150296A1\/en (2024)."},{"key":"9426_CR42","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1038\/s43588-021-00101-3","volume":"1","author":"Q Zhao","year":"2021","unstructured":"Zhao, Q. & Savoie, B. M. Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks. Nat. Comput. Sci. 1, 479\u2013490 (2021).","journal-title":"Nat. Comput. Sci."},{"key":"9426_CR43","doi-asserted-by":"publisher","first-page":"4248","DOI":"10.1021\/acs.jctc.5b00407","volume":"11","author":"YV Suleimanov","year":"2015","unstructured":"Suleimanov, Y. V. & Green, W. H. Automated discovery of elementary chemical reaction steps using freezing string and Berny optimization methods. J. Chem. Theory Comput. 11, 4248\u20134259 (2015).","journal-title":"J. Chem. Theory Comput."},{"key":"9426_CR44","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1002\/jcc.23271","volume":"34","author":"PM Zimmerman","year":"2013","unstructured":"Zimmerman, P. M. Automated discovery of chemically reasonable elementary reaction steps. J. Comput. Chem. 34, 1385\u20131392 (2013).","journal-title":"J. Comput. Chem."},{"key":"9426_CR45","doi-asserted-by":"publisher","first-page":"3407","DOI":"10.1021\/acs.jpca.9b01014","volume":"123","author":"I Ismail","year":"2019","unstructured":"Ismail, I., Stuttaford-Fowler, H. B. V. A., Ochan Ashok, C., Robertson, C. & Habershon, S. Automatic proposal of multistep reaction mechanisms using a graph-driven search. J. Phys. Chem. A 123, 3407\u20133417 (2019).","journal-title":"J. Phys. Chem. A"},{"key":"9426_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18671-7","volume":"11","author":"G Pesciullesi","year":"2020","unstructured":"Pesciullesi, G., Schwaller, P., Laino, T. & Reymond, J.-L. Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates. Nat. Commun. 11, 4874 (2020).","journal-title":"Nat. Commun."},{"key":"9426_CR47","doi-asserted-by":"publisher","first-page":"9368","DOI":"10.1039\/D0CC02657C","volume":"56","author":"L Wang","year":"2020","unstructured":"Wang, L., Zhang, C., Bai, R., Li, J. & Duan, H. Heck reaction prediction using a transformer model based on a transfer learning strategy. Chem. Commun. 56, 9368\u20139371 (2020).","journal-title":"Chem. Commun."},{"key":"9426_CR48","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1039\/D0QO01636E","volume":"8","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y. et al. Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes. Org. Chem. Front. 8, 1415\u20131423 (2021).","journal-title":"Org. Chem. Front."},{"key":"9426_CR49","doi-asserted-by":"crossref","unstructured":"Luo, Y. et al. An empirical study of catastrophic forgetting in large language models during continual fine-tuning. Preprint at https:\/\/arxiv.org\/abs\/2308.08747 (2025).","DOI":"10.1109\/TASLPRO.2025.3606231"},{"key":"9426_CR50","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1021\/acs.jcim.2c01359","volume":"63","author":"M Tavakoli","year":"2023","unstructured":"Tavakoli, M., Chiu, Y. T. T., Baldi, P., Carlton, A. M. & Van Vranken, D. RMechDB: a public database of elementary radical reaction steps. J. Chem. Inf. Model. 63, 1114\u20131123 (2023).","journal-title":"J. Chem. Inf. Model."},{"key":"9426_CR51","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1021\/acs.jcim.3c01810","volume":"64","author":"M Tavakoli","year":"2024","unstructured":"Tavakoli, M. et al. PMechDB: a public database of elementary polar reaction steps. J. Chem. Inf. Model. 64, 1975\u20131983 (2024).","journal-title":"J. Chem. Inf. Model."},{"key":"9426_CR52","doi-asserted-by":"publisher","first-page":"5648","DOI":"10.1063\/1.464913","volume":"98","author":"AD Becke","year":"1993","unstructured":"Becke, A. D. Density functional thermochemistry. III. The role of exact exchange. J. Chem. Phys. 98, 5648\u20135652 (1993).","journal-title":"J. Chem. Phys."},{"key":"9426_CR53","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1063\/1.438955","volume":"72","author":"R Krishnan","year":"1980","unstructured":"Krishnan, R., Binkley, J. S., Seeger, R. & Pople, J. A. Self consistent molecular orbital methods. XX. A basis set for correlated wave functions. J. Chem. Phys. 72, 650\u2013654 (1980).","journal-title":"J. Chem. Phys."},{"key":"9426_CR54","doi-asserted-by":"publisher","first-page":"6378","DOI":"10.1021\/jp810292n","volume":"113","author":"AV Marenich","year":"2009","unstructured":"Marenich, A. V., Cramer, C. J. & Truhlar, D. G. Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J. Phys. Chem. B 113, 6378\u20136396 (2009).","journal-title":"J. Phys. Chem. B"},{"key":"9426_CR55","doi-asserted-by":"publisher","first-page":"224108","DOI":"10.1063\/5.0004608","volume":"152","author":"F Neese","year":"2020","unstructured":"Neese, F., Wennmohs, F., Becker, U. & Riplinger, C. The ORCA quantum chemistry program package. J. Chem. Phys. 152, 224108 (2020).","journal-title":"J. Chem. Phys."},{"key":"9426_CR56","unstructured":"Sch\u00fctt, K. et al. SchNet: a continuous-filter convolutional neural network for modeling quantum interactions. In Proc. 31st International Conference on Neural Information Processing Systems (NIPS\u201917) 992\u20131002 (ACM, 2017)."},{"key":"9426_CR57","unstructured":"DeBoer, C. Iteround. GitHub https:\/\/github.com\/cgdeboer\/iteround\/ (2018)."},{"key":"9426_CR58","unstructured":"NextMove Software. NameRxn. https:\/\/www.nextmovesoftware.com\/namerxn.html."},{"key":"9426_CR59","doi-asserted-by":"publisher","first-page":"eabe4166","DOI":"10.1126\/sciadv.abe4166","volume":"7","author":"P Schwaller","year":"2021","unstructured":"Schwaller, P., Hoover, B., Reymond, J.-L., Strobelt, H. & Laino, T. Extraction of organic chemistry grammar from unsupervised learning of chemical reactions. Sci. Adv. 7, eabe4166 (2021).","journal-title":"Sci. Adv."},{"key":"9426_CR60","unstructured":"Vaswani, A. Attention is all you need. In Proc. 31st International Conference on Neural Information Processing Systems (NIPS\u201917) 6000\u20136010 (ACM, 2017)."},{"key":"9426_CR61","unstructured":"Ying, C. et al. Do transformers really perform bad for graph representation? In Proc. 35th International Conference on Neural Information Processing Systems (NIPS\u201921) 28877\u201328888 (2021)."},{"key":"9426_CR62","unstructured":"Joung, J. F. et al. FlowER - mechanistic datasets and model checkpoint. Figshare https:\/\/figshare.com\/articles\/dataset\/FlowER_-_Mechanistic_datasets_and_model_checkpoint\/28359407 (2025)."},{"key":"9426_CR63","unstructured":"FongMunHong. FongMunHong\/FlowER: release v1.0.0. Zenodo https:\/\/zenodo.org\/records\/15776086 (2025)."},{"key":"9426_CR64","unstructured":"FongMunHong. FongMunHong\/FlowER: release v2.0.0. Zenodo https:\/\/zenodo.org\/records\/15786107 (2025)."}],"container-title":["Nature"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-09426-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-09426-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-09426-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T20:46:08Z","timestamp":1757450768000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-09426-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":64,"journal-issue":{"issue":"8079","published-print":{"date-parts":[[2025,9,4]]}},"alternative-id":["9426"],"URL":"https:\/\/doi.org\/10.1038\/s41586-025-09426-9","relation":{},"ISSN":["0028-0836","1476-4687"],"issn-type":[{"value":"0028-0836","type":"print"},{"value":"1476-4687","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"18 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}