{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:34:57Z","timestamp":1792082097665,"version":"4.4.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-16154-7","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T08:58:38Z","timestamp":1756371518000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Integrating physiological signals for enhanced sleep apnea diagnosis with SleepNet"],"prefix":"10.1038","volume":"15","author":[{"given":"Prashant","family":"Hemrajani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijaypal Singh","family":"Dhaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geeta","family":"Rani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahil","family":"Verma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Kavita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Wo\u017aniak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jana","family":"Shafi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Fazal","family":"Ijaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"16154_CR1","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1109\/JBHI.2017.2740120","volume":"22","author":"S Gutta","year":"2017","unstructured":"Gutta, S., Cheng, Q., Nguyen, H. D. & Benjamin, B. A. Cardiorespiratory model-based data-driven approach for sleep apnea detection. IEEE J. Biomed. Health Inform. 22, 1036\u20131045 (2017).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"16154_CR2","first-page":"1","volume":"70","author":"Q Shen","year":"2021","unstructured":"Shen, Q., Qin, H., Wei, K. & Liu, G. Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal. IEEE Trans. Instrum. Meas. 70, 1\u201313 (2021).","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"16154_CR3","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1109\/TBCAS.2018.2824659","volume":"12","author":"G Surrel","year":"2018","unstructured":"Surrel, G., Aminifar, A., Rinc\u00f3n, F., Murali, S. & Atienza, D. Online obstructive sleep apnea detection on medical wearable sensors. IEEE Trans. Biomed. Circuits Syst. 12, 762\u2013773 (2018).","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"16154_CR4","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.5664\/jcsm.8482","volume":"16","author":"J Acquavella","year":"2020","unstructured":"Acquavella, J., Mehra, R., Bron, M., Suomi, J.M.-H. & Hess, G. P. Prevalence of narcolepsy and other sleep disorders and frequency of diagnostic tests from 2013\u20132016 in insured patients actively seeking care. J. Clin. Sleep Med. 16, 1255\u20131263 (2020).","journal-title":"J. Clin. Sleep Med."},{"key":"16154_CR5","unstructured":"Altevogt, B.\u00a0M. & Colten, H.\u00a0R. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem (National Academies Press, Washington, DC, 2006). Copyright\u00a9 2006, National Academy of Sciences."},{"key":"16154_CR6","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1093\/aje\/kws342","volume":"177","author":"PE Peppard","year":"2013","unstructured":"Peppard, P. E. et al. Increased prevalence of sleep-disordered breathing in adults. Am. J. Epidemiol. 177, 1006\u20131014 (2013).","journal-title":"Am. J. Epidemiol."},{"key":"16154_CR7","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1111\/resp.13838","volume":"25","author":"MM Lyons","year":"2020","unstructured":"Lyons, M. M., Bhatt, N. Y., Pack, A. I. & Magalang, U. J. Global burden of sleep-disordered breathing and its implications. Respirology 25, 690\u2013702 (2020).","journal-title":"Respirology"},{"key":"16154_CR8","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1159\/000236900","volume":"56","author":"AB Neikrug","year":"2010","unstructured":"Neikrug, A. B. & Ancoli-Israel, S. Sleep disorders in the older adult-a mini-review. Gerontology 56, 181\u2013189 (2010).","journal-title":"Gerontology"},{"key":"16154_CR9","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","volume":"294","author":"K Li","year":"2018","unstructured":"Li, K., Pan, W., Li, Y., Jiang, Q. & Liu, G. A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ecg signal. Neurocomputing 294, 94\u2013101 (2018).","journal-title":"Neurocomputing"},{"key":"16154_CR10","doi-asserted-by":"crossref","unstructured":"Papini, G.\u00a0B. et al. On the generalizability of ecg-based obstructive sleep apnea monitoring: merits and limitations of the apnea-ecg database. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 6022\u20136025 (IEEE, 2018).","DOI":"10.1109\/EMBC.2018.8513660"},{"key":"16154_CR11","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.7731","volume":"7","author":"T Wang","year":"2019","unstructured":"Wang, T., Lu, C., Shen, G. & Hong, F. Sleep apnea detection from a single-lead ecg signal with automatic feature-extraction through a modified lenet-5 convolutional neural network. PeerJ 7, e7731 (2019).","journal-title":"PeerJ"},{"key":"16154_CR12","first-page":"1","volume":"70","author":"K Feng","year":"2020","unstructured":"Feng, K., Qin, H., Wu, S., Pan, W. & Liu, G. A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram. IEEE Trans. Instrum. Meas. 70, 1\u201312 (2020).","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"16154_CR13","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s12530-022-09445-1","volume":"14","author":"A Ayatollahi","year":"2023","unstructured":"Ayatollahi, A., Afrakhteh, S., Soltani, F. & Saleh, E. Sleep apnea detection from ECG signal using deep cnn-based structures. Evol. Syst. 14, 191\u2013206 (2023).","journal-title":"Evol. Syst."},{"key":"16154_CR14","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TBME.2015.2498199","volume":"63","author":"C Song","year":"2015","unstructured":"Song, C., Liu, K., Zhang, X., Chen, L. & Xian, X. An obstructive sleep apnea detection approach using a discriminative hidden markov model from ECG signals. IEEE Trans. Biomed. Eng. 63, 1532\u20131542 (2015).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"16154_CR15","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.cmpb.2019.05.002","volume":"176","author":"L Wang","year":"2019","unstructured":"Wang, L., Lin, Y. & Wang, J. A RR interval based automated apnea detection approach using residual network. Comput. Methods Programs Biomed. 176, 93\u2013104 (2019).","journal-title":"Comput. Methods Programs Biomed."},{"key":"16154_CR16","doi-asserted-by":"publisher","first-page":"1950026","DOI":"10.1142\/S021951941950026X","volume":"19","author":"SA Singh","year":"2019","unstructured":"Singh, S. A. & Majumder, S. A novel approach osa detection using single-lead ECG scalogram based on deep neural network. J. Mech. Med. Biol. 19, 1950026 (2019).","journal-title":"J. Mech. Med. Biol."},{"key":"16154_CR17","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1109\/TITB.2009.2034975","volume":"14","author":"A Burgos","year":"2009","unstructured":"Burgos, A., Goni, A., Illarramendi, A. & Bermudez, J. Real-time detection of apneas on a PDA. IEEE Trans. Inf. Technol. Biomed. 14, 995\u20131002 (2009).","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"16154_CR18","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aad5a9","volume":"39","author":"J Malik","year":"2018","unstructured":"Malik, J., Lo, Y.-L. & Wu, H.-T. Sleep-wake classification via quantifying heart rate variability by convolutional neural network. Physiol. Meas. 39, 085004 (2018).","journal-title":"Physiol. Meas."},{"key":"16154_CR19","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/JBHI.2017.2712861","volume":"22","author":"H Yoon","year":"2017","unstructured":"Yoon, H. et al. Slow-wave sleep estimation for healthy subjects and osa patients using R\u2013R intervals. IEEE J. Biomed. Health Inform. 22, 119\u2013128 (2017).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"16154_CR20","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11517-017-1706-y","volume":"56","author":"C Kalkbrenner","year":"2018","unstructured":"Kalkbrenner, C. et al. Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders. Med. Biol. Eng. Comput. 56, 671\u2013681 (2018).","journal-title":"Med. Biol. Eng. Comput."},{"key":"16154_CR21","doi-asserted-by":"publisher","first-page":"138","DOI":"10.4015\/S1016237206000233","volume":"18","author":"R Lin","year":"2006","unstructured":"Lin, R. et al. A new approach for identifying sleep apnea syndrome using wavelet transform and neural networks. Biomed. Eng. Appl. Basis Commun. 18, 138\u2013143 (2006).","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"16154_CR22","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/TITB.2012.2188299","volume":"16","author":"B Xie","year":"2012","unstructured":"Xie, B. & Minn, H. Real-time sleep apnea detection by classifier combination. IEEE Trans. Inf. Technol. Biomed. 16, 469\u2013477 (2012).","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"16154_CR23","doi-asserted-by":"publisher","first-page":"295","DOI":"10.3390\/electronics8030295","volume":"8","author":"M Zhang","year":"2019","unstructured":"Zhang, M. et al. Optimized compression for implementing convolutional neural networks on FPGA. Electronics 8, 295 (2019).","journal-title":"Electronics"},{"key":"16154_CR24","doi-asserted-by":"publisher","first-page":"282","DOI":"10.3390\/electronics10030282","volume":"10","author":"N Zhang","year":"2021","unstructured":"Zhang, N., Wei, X., Chen, H. & Liu, W. FPGA implementation for CNN-based optical remote sensing object detection. Electronics 10, 282 (2021).","journal-title":"Electronics"},{"key":"16154_CR25","doi-asserted-by":"publisher","first-page":"184","DOI":"10.5001\/omj.2018.36","volume":"33","author":"B Amra","year":"2018","unstructured":"Amra, B., Rahmati, B., Soltaninejad, F. & Feizi, A. Screening questionnaires for obstructive sleep apnea: An updated systematic review. Oman Med. J. 33, 184 (2018).","journal-title":"Oman Med. J."},{"key":"16154_CR26","doi-asserted-by":"crossref","unstructured":"Tsuiki, S. et al. Machine learning for image-based detection of patients with obstructive sleep apnea: An exploratory study. Sleep Breath. 25, 2297\u20132305 (2021).","DOI":"10.1007\/s11325-021-02301-7"},{"key":"16154_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/9891852","volume":"2016","author":"M Mabrouk","year":"2016","unstructured":"Mabrouk, M. et al. Human breathing rate estimation from radar returns using harmonically related filters. J. Sens. 2016, 1\u20137 (2016).","journal-title":"J. Sens."},{"key":"16154_CR28","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1249\/MSS.0000000000001947","volume":"51","author":"M De Zambotti","year":"2019","unstructured":"De Zambotti, M., Cellini, N., Goldstone, A., Colrain, I. M. & Baker, F. C. Wearable sleep technology in clinical and research settings. Med. Sci. Sports Exerc. 51, 1538 (2019).","journal-title":"Med. Sci. Sports Exerc."},{"key":"16154_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.972581","volume":"16","author":"J Chen","year":"2022","unstructured":"Chen, J., Shen, M., Ma, W. & Zheng, W. A spatio-temporal learning-based model for sleep apnea detection using single-lead ecg signals. Front. Neurosci. 16, 972581 (2022).","journal-title":"Front. Neurosci."},{"key":"16154_CR30","first-page":"S1703","volume":"26","author":"C Avc\u0131","year":"2015","unstructured":"Avc\u0131, C. & Akba\u015f, A. Sleep apnea classification based on respiration signals by using ensemble methods. Bio-Med. Mater. Eng. 26, S1703\u2013S1710 (2015).","journal-title":"Bio-Med. Mater. Eng."},{"key":"16154_CR31","doi-asserted-by":"crossref","unstructured":"Ozdemir, G., Nasifoglu, H. & Erogul, O. A time-series approach to predict obstructive sleep apnea (OSA) episodes. In Proceedings of the 2nd World Congress on Electrical Engineering and Computer Systems and Science 1\u20138 (2016).","DOI":"10.11159\/icbes16.117"},{"key":"16154_CR32","doi-asserted-by":"crossref","unstructured":"Thommandram, A., Eklund, J.\u00a0M. & McGregor, C. Detection of apnoea from respiratory time series data using clinically recognizable features and knn classification. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 5013\u20135016 (IEEE, 2013).","DOI":"10.1109\/EMBC.2013.6610674"},{"key":"16154_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120484","volume":"229","author":"AK Abasi","year":"2023","unstructured":"Abasi, A. K., Aloqaily, M. & Guizani, M. Optimization of CNN using modified honey badger algorithm for sleep apnea detection. Expert Syst. Appl. 229, 120484 (2023).","journal-title":"Expert Syst. Appl."},{"key":"16154_CR34","doi-asserted-by":"publisher","first-page":"4692","DOI":"10.3390\/s23104692","volume":"23","author":"P Hemrajani","year":"2023","unstructured":"Hemrajani, P., Dhaka, V. S., Rani, G., Shukla, P. & Bavirisetti, D. P. Efficient deep learning based hybrid model to detect obstructive sleep apnea. Sensors 23, 4692 (2023).","journal-title":"Sensors"},{"key":"16154_CR35","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.3390\/s20226481","volume":"20","author":"K McClure","year":"2020","unstructured":"McClure, K. et al. Classification and detection of breathing patterns with wearable sensors and deep learning. Sensors 20, 6481 (2020).","journal-title":"Sensors"},{"key":"16154_CR36","doi-asserted-by":"crossref","unstructured":"Penzel, T., Moody, G.\u00a0B., Mark, R.\u00a0G., Goldberger, A.\u00a0L. & Peter, J.\u00a0H. The apnea-ecg database. In Computers in Cardiology 2000. Vol. 27 (Cat. 00CH37163) 255\u2013258 (IEEE, 2000).","DOI":"10.1109\/CIC.2000.898505"},{"key":"16154_CR37","doi-asserted-by":"crossref","unstructured":"Goldberger, A.\u00a0L. et al. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation101, e215\u2013e220 (2000).","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"16154_CR38","first-page":"379","volume":"2022","author":"T Paul","year":"2022","unstructured":"Paul, T. et al. ECG and SpO2 signal-based real-time sleep apnea detection using feed-forward artificial neural network. AMIA Summits Transl. Sci. Proc. 2022, 379 (2022).","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"16154_CR39","doi-asserted-by":"publisher","first-page":"510","DOI":"10.3390\/s22020510","volume":"22","author":"C-Y Yeh","year":"2022","unstructured":"Yeh, C.-Y., Chang, H.-Y., Hu, J.-Y. & Lin, C.-C. Contribution of different subbands of ecg in sleep apnea detection evaluated using filter bank decomposition and a convolutional neural network. Sensors 22, 510 (2022).","journal-title":"Sensors"},{"key":"16154_CR40","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/TASE.2014.2345667","volume":"12","author":"L Chen","year":"2014","unstructured":"Chen, L., Zhang, X. & Song, C. An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram. IEEE Trans. Autom. Sci. Eng. 12, 106\u2013115 (2014).","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"16154_CR41","doi-asserted-by":"crossref","unstructured":"Cheng, M., Sori, W.\u00a0J., Jiang, F., Khan, A. & Liu, S. Recurrent neural network based classification of ecg signal features for obstruction of sleep apnea detection. In 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), vol.\u00a02, 199\u2013202 (IEEE, 2017).","DOI":"10.1109\/CSE-EUC.2017.220"},{"key":"16154_CR42","doi-asserted-by":"crossref","unstructured":"Liang, X., Qiao, X. & Li, Y. Obstructive sleep apnea detection using combination of CNN and LSTM techniques. In 2019 IEEE 8th Joint International Information Technology and Artificial iIntelligence Conference (ITAIC) 1733\u20131736 (IEEE, 2019).","DOI":"10.1109\/ITAIC.2019.8785833"},{"key":"16154_CR43","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.irbm.2020.05.006","volume":"41","author":"F Bozkurt","year":"2020","unstructured":"Bozkurt, F., U\u00e7ar, M. K., Bozkurt, M. R. & Bilgin, C. Detection of abnormal respiratory events with single channel ECG and hybrid machine learning model in patients with obstructive sleep apnea. Irbm 41, 241\u2013251 (2020).","journal-title":"Irbm"},{"key":"16154_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01736-y","volume":"21","author":"J Cheng","year":"2021","unstructured":"Cheng, J., Zou, Q. & Zhao, Y. ECG signal classification based on deep CNN and bilstm. BMC Med. Inform. Decis. Mak. 21, 1\u201312 (2021).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"16154_CR45","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.5664\/jcsm.8020","volume":"15","author":"R Stretch","year":"2019","unstructured":"Stretch, R. et al. Predicting nondiagnostic home sleep apnea tests using machine learning. J. Clin. Sleep Med. 15, 1599\u20131608 (2019).","journal-title":"J. Clin. Sleep Med."},{"key":"16154_CR46","doi-asserted-by":"crossref","unstructured":"Cho, K. et al. Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. arXiv:1406.1078 (2014).","DOI":"10.3115\/v1\/D14-1179"},{"key":"16154_CR47","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.compbiomed.2017.10.004","volume":"91","author":"S Martin-Gonzalez","year":"2017","unstructured":"Martin-Gonzalez, S. et al. Heart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorder. Comput. Biol. Med. 91, 47\u201358 (2017).","journal-title":"Comput. Biol. Med."},{"key":"16154_CR48","doi-asserted-by":"crossref","unstructured":"Qatmh, M. et al. Sleep apnea detection based on ECG signals using discrete wavelet transform and artificial neural network. In 2022 Advances in Science and Engineering Technology International Conferences (ASET) 1\u20135 (IEEE, 2022).","DOI":"10.1109\/ASET53988.2022.9735064"},{"key":"16154_CR49","doi-asserted-by":"publisher","first-page":"4733","DOI":"10.1007\/s00521-018-3833-2","volume":"32","author":"E Urtnasan","year":"2020","unstructured":"Urtnasan, E., Park, J.-U. & Lee, K.-J. Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal. Neural Comput. Appl. 32, 4733\u20134742 (2020).","journal-title":"Neural Comput. Appl."},{"key":"16154_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105001","volume":"180","author":"U Erdenebayar","year":"2019","unstructured":"Erdenebayar, U., Kim, Y. J., Park, J.-U., Joo, E. Y. & Lee, K.-J. Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram. Comput. Methods Programs Biomed. 180, 105001 (2019).","journal-title":"Comput. Methods Programs Biomed."},{"key":"16154_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104008","volume":"78","author":"Y Fei","year":"2022","unstructured":"Fei, Y. et al. Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units. Biomed. Signal Process. Control 78, 104008 (2022).","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16154-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16154-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16154-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T19:56:48Z","timestamp":1757447808000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16154-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,28]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["16154"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-16154-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,28]]},"assertion":[{"value":"28 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 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":"Competing interests"}}],"article-number":"31715"}}