{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:29:22Z","timestamp":1772555362317,"version":"3.50.1"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004884","name":"Education Department of Sichuan Province","doi-asserted-by":"publisher","award":["15TD0050"],"award-info":[{"award-number":["15TD0050"]}],"id":[{"id":"10.13039\/501100004884","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022NYXXS095"],"award-info":[{"award-number":["2022NYXXS095"]}],"id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018927","name":"Southwest Minzu University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018927","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019075","name":"National Ethnic Affairs Commission of the People's Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019075","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71774134"],"award-info":[{"award-number":["71774134"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72174172"],"award-info":[{"award-number":["72174172"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1016\/j.cmpb.2023.107560","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T05:30:57Z","timestamp":1681968657000},"page":"107560","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"special_numbering":"C","title":["Automated inter-patient arrhythmia classification with dual attention neural network"],"prefix":"10.1016","volume":"236","author":[{"given":"He","family":"Lyu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9838-4588","authenticated-orcid":false,"given":"Xiangkui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5737-4159","authenticated-orcid":false,"given":"Chenchen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuezhi","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8992-6667","authenticated-orcid":false,"given":"Fanxin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6668-9790","authenticated-orcid":false,"given":"Ye","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinzhen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4347-7015","authenticated-orcid":false,"given":"Dong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2023.107560_bib0001","unstructured":"\u201cCardiovascular diseases.\u201d https:\/\/www.who.int\/health-topics\/cardiovascular-diseases"},{"issue":"10159","key":"10.1016\/j.cmpb.2023.107560_bib0002","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1016\/S0140-6736(18)32203-7","article-title":"Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980\u20132017: a systematic analysis for the Global Burden of Disease Study 2017","volume":"392","author":"Roth","year":"2018","journal-title":"Lancet"},{"key":"10.1016\/j.cmpb.2023.107560_bib0003","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci. (Ny)."},{"key":"10.1016\/j.cmpb.2023.107560_bib0004","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.bspc.2018.03.003","article-title":"A survey on ECG analysis","volume":"43","author":"Kaplan Berkaya","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"issue":"7","key":"10.1016\/j.cmpb.2023.107560_bib0005","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","article-title":"Automatic classification of heartbeats using ECG morphology and heartbeat interval features","volume":"51","author":"De Chazal","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"10","key":"10.1016\/j.cmpb.2023.107560_bib0006","doi-asserted-by":"crossref","first-page":"2930","DOI":"10.1109\/TBME.2012.2213253","article-title":"Heartbeat classification using morphological and dynamic features of ECG signals","volume":"59","author":"Ye","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2023.107560_bib0007","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.bspc.2018.08.007","article-title":"Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers","volume":"47","author":"Mond\u00e9jar-Guerra","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"10.1016\/j.cmpb.2023.107560_bib0008","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.bspc.2013.01.005","article-title":"ECG beat classification using PCA, LDA, ICA and discrete wavelet transform","volume":"8","author":"Martis","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107560_bib0009","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/TBME.2010.2071872","article-title":"A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads","volume":"58","author":"Fayn","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"8","key":"10.1016\/j.cmpb.2023.107560_bib0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/e18080285","article-title":"ECG classification using wavelet packet entropy and random forests","volume":"18","author":"Li","year":"2016","journal-title":"Entropy"},{"issue":"PA","key":"10.1016\/j.cmpb.2023.107560_bib0011","article-title":"Imbalanced heartbeat classification using EasyEnsemble technique and global heartbeat information","volume":"71","author":"Wang","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.cmpb.2023.107560_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107187","article-title":"Deep learning in ECG diagnosis: a review","volume":"227","author":"Liu","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.cmpb.2023.107560_bib0013","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"89","author":"Acharya","year":"2017","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"10.1016\/j.cmpb.2023.107560_bib0014","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/JBHI.2019.2942938","article-title":"Inter-patient ECG classification with symbolic representations and multi-perspective convolutional neural networks","volume":"24","author":"Niu","year":"2020","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"10.1016\/j.cmpb.2023.107560_bib0015","series-title":"Int. Conf. ICT Converg.","first-page":"1101","article-title":"Arrhythmia detection using convolutional neural networks with temporal attention mechanism","volume":"vol. 2021-Octob","author":"Zubair","year":"2021"},{"issue":"6","key":"10.1016\/j.cmpb.2023.107560_bib0016","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ac51a3","article-title":"A novel arrhythmia classification of electrocardiogram signal based on modified HRNet and ECA","volume":"33","author":"Hua","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.cmpb.2023.107560_bib0017","article-title":"Interpatient ECG arrhythmia detection by residual attention CNN","volume":"2022","author":"Xu","year":"2022","journal-title":"Comput. Math. Methods Med."},{"issue":"1","key":"10.1016\/j.cmpb.2023.107560_bib0018","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.cmpb.2023.107560_bib0019","first-page":"1","article-title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","volume":"11","author":"Ribeiro","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.cmpb.2023.107560_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107375","article-title":"WavelNet: a novel convolutional neural network architecture for arrhythmia classification from electrocardiograms","volume":"231","author":"Kim","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2023.107560_bib0021","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/9370517","article-title":"A deep neural network ensemble classifier with focal loss for automatic arrhythmia classification","volume":"2022","author":"Wu","year":"2022","journal-title":"J. Healthc. Eng."},{"issue":"January","key":"10.1016\/j.cmpb.2023.107560_bib0022","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.compbiomed.2018.03.016","article-title":"A novel wavelet sequences based on deep bidirectional LSTM network model for ECG signal classification","volume":"96","author":"Yildirim","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2023.107560_bib0023","doi-asserted-by":"crossref","first-page":"109870","DOI":"10.1109\/ACCESS.2019.2933473","article-title":"Interpretability analysis of heartbeat classification based on heartbeat activity's global sequence features and bilstm-attention neural network","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2023.107560_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105479","article-title":"Multi-information fusion neural networks for arrhythmia automatic detection","volume":"193","author":"Chen","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2023.107560_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105036","article-title":"Automated heartbeat classification based on deep neural network with multiple input layers","volume":"188","author":"Shi","year":"2020","journal-title":"Knowl.-Based Syst."},{"issue":"1657260","key":"10.1016\/j.cmpb.2023.107560_bib0026","first-page":"1308","article-title":"Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach","volume":"2019-May","author":"Mousavi","year":"2019","journal-title":"ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc."},{"key":"10.1016\/j.cmpb.2023.107560_bib0027","volume":"12085 LNAI","author":"He","year":"2020"},{"key":"10.1016\/j.cmpb.2023.107560_bib0028","series-title":"Proc. - 2020 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2020","first-page":"1029","article-title":"A two-level attention-based sequence-to-sequence model for accurate inter-patient arrhythmia detection","author":"Jiang","year":"2020"},{"issue":"July","key":"10.1016\/j.cmpb.2023.107560_bib0029","first-page":"1","article-title":"An attention-based hybrid LSTM-CNN model for arrhythmias classification","volume":"2019-July","author":"Liu","year":"2019","journal-title":"Proc. Int. Jt. Conf. Neural Networks"},{"key":"10.1016\/j.cmpb.2023.107560_bib0030","first-page":"1","article-title":"Automatic classification of arrhythmias by residual network and BiGRU with attention mechanism","volume":"2020-Septe","author":"He","year":"2020","journal-title":"Comput. Cardiol."},{"key":"10.1016\/j.cmpb.2023.107560_bib0031","doi-asserted-by":"crossref","first-page":"103452","DOI":"10.1109\/ACCESS.2021.3098986","article-title":"An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification","volume":"9","author":"Essa","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2023.107560_bib0032","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"issue":"July","key":"10.1016\/j.cmpb.2023.107560_bib0033","article-title":"Cov-Net: a computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision","volume":"207","author":"Li","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"November 2022","key":"10.1016\/j.cmpb.2023.107560_bib0034","article-title":"AGGN: attention-based glioma grading network with multi-scale feature extraction and multi-modal information fusion","volume":"152","author":"Wu","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"PA","key":"10.1016\/j.cmpb.2023.107560_bib0035","article-title":"A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis","volume":"151","author":"Li","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2023.107560_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106533","article-title":"Inter-patient automated arrhythmia classification: a new approach of weight capsule and sequence to sequence combination","volume":"214","author":"Li","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2023.107560_bib0037","doi-asserted-by":"crossref","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.cmpb.2023.107560_bib0038","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary","volume":"61","author":"Fern\u00e1ndez","year":"2018","journal-title":"J. Artif. Intell. Res."},{"issue":"23","key":"10.1016\/j.cmpb.2023.107560_bib0039","doi-asserted-by":"crossref","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"10.1016\/j.cmpb.2023.107560_bib0040","unstructured":"Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. Association for the Advancement of Medical Instrumentation [AAMI]."},{"key":"10.1016\/j.cmpb.2023.107560_bib0041","doi-asserted-by":"crossref","unstructured":"D. Misra, \u201cMish: a self regularized non-monotonic activation function,\u201d 2019, [Online]. Available: http:\/\/arxiv.org\/abs\/1908.08681","DOI":"10.5244\/C.34.191"},{"key":"10.1016\/j.cmpb.2023.107560_bib0042","first-page":"3","article-title":"CBAM: convolutional block attention module","volume":"11211 LNCS","author":"Woo","year":"2018","journal-title":"Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)"},{"key":"10.1016\/j.cmpb.2023.107560_bib0043","unstructured":"J. L. Ba, J. R. Kiros, and G. E. Hinton, \u201cLayer normalization,\u201d 2016, [Online]. Available: http:\/\/arxiv.org\/abs\/1607.06450"},{"key":"10.1016\/j.cmpb.2023.107560_bib0044","series-title":"EMNLP 2014 - 2014 Conf. Empir. Methods Nat. Lang. Process. Proc. Conf","first-page":"1724","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014"},{"issue":"January","key":"10.1016\/j.cmpb.2023.107560_bib0045","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume":"4","author":"Sutskever","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"8","key":"10.1016\/j.cmpb.2023.107560_bib0046","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.cmpb.2023.107560_bib0047","first-page":"2047","article-title":"Framewise phoneme classification with bidirectional LSTM networks","volume":"4","author":"Graves","year":"2005","journal-title":"Proc. Int. Jt. Conf. Neural Networks"},{"key":"10.1016\/j.cmpb.2023.107560_bib0048","series-title":"Conf. Proc. - EMNLP 2015 Conf. Empir. Methods Nat. Lang. Process","first-page":"1412","article-title":"Effective approaches to attention-based neural machine translation","author":"Luong","year":"2015"},{"key":"10.1016\/j.cmpb.2023.107560_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102289","article-title":"Machine learning-based heart disease diagnosis: a systematic literature review","volume":"128","author":"Ahsan","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.cmpb.2023.107560_bib0050","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.neucom.2022.07.054","article-title":"Polarized self-attention: towards high-quality pixel-wise mapping","volume":"506","author":"Liu","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cmpb.2023.107560_bib0051","series-title":"Proceedings of the Asian Conference on Computer Vision (ACCV)","first-page":"541","article-title":"EPSANet: an efficient pyramid squeeze attention block on convolutional neural network","author":"Zhang","year":"2023"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723002250?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723002250?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:18:22Z","timestamp":1759205902000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260723002250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6]]},"references-count":51,"alternative-id":["S0169260723002250"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107560","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2023,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automated inter-patient arrhythmia classification with dual attention neural network","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107560","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107560"}}