{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T03:35:48Z","timestamp":1793763348814,"version":"4.6.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"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":["Microsyst Nanoeng"],"DOI":"10.1038\/s41378-025-00924-4","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T03:15:03Z","timestamp":1747365303000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Direct extraction of respiratory information from pulse waves using a finger-inspired flexible pressure sensor system"],"prefix":"10.1038","volume":"11","author":[{"given":"Xikuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingxiao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixuan","family":"Mei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5553-9009","authenticated-orcid":false,"given":"Yunlong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danfeng","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengxing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5964-2337","authenticated-orcid":false,"given":"Libo","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"924_CR1","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1136\/thorax-2024-221939","volume":"79","author":"C Routley","year":"2024","unstructured":"Routley, C., Walker, S., Alton, E. W. & Hall, I. P. Fixing lung health in the UK: accelerating respiratory research and innovation. Thorax 79, 809\u2013810 (2024).","journal-title":"Thorax"},{"key":"924_CR2","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1038\/s41576-024-00695-0","volume":"25","author":"I Sayers","year":"2024","unstructured":"Sayers, I., John, C., Chen, J. & Hall, I. P. Genetics of chronic respiratory disease. Nat. Rev. Genet. 25, 534\u2013547 (2024).","journal-title":"Nat. Rev. Genet."},{"key":"924_CR3","doi-asserted-by":"publisher","first-page":"2400335","DOI":"10.1183\/13993003.00335-2024","volume":"63","author":"AE Holland","year":"2024","unstructured":"Holland, A. E. et al. European Respiratory Society clinical practice guideline on symptom management for adults with serious respiratory illness. Eur. Respir. J. 63, 2400335 (2024).","journal-title":"Eur. Respir. J."},{"key":"924_CR4","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.mattod.2023.12.003","volume":"72","author":"S Shen","year":"2024","unstructured":"Shen, S. et al. Advances in wearable respiration sensors. Mater. Today 72, 140\u2013162 (2024).","journal-title":"Mater. Today"},{"key":"924_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41427-023-00513-9","volume":"16","author":"Z Yin","year":"2024","unstructured":"Yin, Z. et al. Wearable respiratory sensors for health monitoring. NPG Asia Mater. 16, 8 (2024).","journal-title":"NPG Asia Mater."},{"key":"924_CR6","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1007\/s42765-024-00420-w","volume":"6","author":"B Lan","year":"2024","unstructured":"Lan, B. et al. A highly sensitive coaxial nanofiber mask for respiratory monitoring assisted with machine learning. Adv. Fiber Mater. 6, 1402\u20131412 (2024).","journal-title":"Adv. Fiber Mater."},{"key":"924_CR7","doi-asserted-by":"publisher","first-page":"47337","DOI":"10.1021\/acsami.4c11493","volume":"16","author":"W Hong","year":"2024","unstructured":"Hong, W. Twistable and stretchable nasal patch for monitoring sleep-related breathing disorders based on a stacking ensemble learning model. ACS Appl. Mater. Interfaces 16, 47337\u201347347 (2024).","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"924_CR8","doi-asserted-by":"publisher","first-page":"2404348","DOI":"10.1002\/adfm.202404348","volume":"34","author":"BA Vicente","year":"2024","unstructured":"Vicente, B. A., Sebasti\u00e3o, R. & Sencadas, V. Wearable devices for respiratory monitoring. Adv. Funct. Mater. 34, 2404348 (2024).","journal-title":"Adv. Funct. Mater."},{"key":"924_CR9","doi-asserted-by":"publisher","first-page":"2970","DOI":"10.1109\/TBME.2022.3158544","volume":"69","author":"C Qiu","year":"2022","unstructured":"Qiu, C., Wu, F., Han, W. & Yuce, M. R. A wearable bioimpedance chest patch for real-time ambulatory respiratory monitoring. IEEE Trans. Biomed. Eng. 69, 2970\u20132981 (2022).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"924_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103746","volume":"77","author":"K Selvakumar","year":"2022","unstructured":"Selvakumar, K. et al. Realtime PPG based respiration rate estimation for remote health monitoring applications. Biomed. Signal Process. Control 77, 103746 (2022).","journal-title":"Biomed. Signal Process. Control"},{"key":"924_CR11","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1109\/JBHI.2017.2679108","volume":"22","author":"MA Motin","year":"2018","unstructured":"Motin, M. A., Karmakar, C. K. & Palaniswami, M. Ensemble empirical mode decomposition with principal component analysis: a novel approach for extracting respiratory rate and heart rate from photoplethysmographic signal. IEEE J. Biomed. Health Inform. 22, 766\u2013774 (2018).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"924_CR12","doi-asserted-by":"publisher","first-page":"108911","DOI":"10.1016\/j.compbiomed.2024.108911","volume":"180","author":"VR Bondala","year":"2024","unstructured":"Bondala, V. R. & Komalla, A. R. An efficient model for extracting respiratory and blood oxygen saturation data from photoplethysmogram signals by removing motion artifacts using heuristic-aided ensemble learning model. Comput. Biol. Med. 180, 108911 (2024).","journal-title":"Comput. Biol. Med."},{"key":"924_CR13","doi-asserted-by":"publisher","first-page":"eadf1783","DOI":"10.1126\/sciadv.adf1783","volume":"9","author":"G Laloy-Borgna","year":"2023","unstructured":"Laloy-Borgna, G., Puyo, L., Nishino, H., Atlan, M. & Catheline, S. Observation of natural flexural pulse waves in retinal and carotid arteries for wall elasticity estimation. Sci. Adv. 9, eadf1783 (2023).","journal-title":"Sci. Adv."},{"key":"924_CR14","doi-asserted-by":"publisher","first-page":"ehad655.2305","DOI":"10.1093\/eurheartj\/ehad655.2305","volume":"44","author":"A Akhmimi","year":"2023","unstructured":"Akhmimi, A., Sprung, V. S., Lip, G. Y. H., Oxborough, D. & Shantsila, A. Arterial stiffness assessment in patients with resistant hypertension using pulse wave analysis. Eur. Heart J. 44, ehad655.2305 (2023).","journal-title":"Eur. Heart J."},{"key":"924_CR15","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1007\/s13239-022-00628-0","volume":"13","author":"C H\u00f6randtner","year":"2022","unstructured":"H\u00f6randtner, C. et al. Attractor reconstruction for quantifying the arterial pulse wave morphology during device-guided slow breathing. Cardiovasc. Eng. Technol. 13, 939\u2013949 (2022).","journal-title":"Cardiovasc. Eng. Technol."},{"key":"924_CR16","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1007\/s11325-012-0771-6","volume":"17","author":"A Ramirez","year":"2013","unstructured":"Ramirez, A. et al. Assessment of sleep quality by pulse wave amplitude and actigraphy in children with sleep-disordered breathing: evaluation at diagnosis and under non-invasive ventilation. Sleep. Breath. 17, 827\u2013835 (2013).","journal-title":"Sleep. Breath."},{"key":"924_CR17","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1097\/MBP.0000000000000628","volume":"28","author":"T Mengden","year":"2023","unstructured":"Mengden, T., Bachler, M., Sehnert, W., Marschall, P. & Wassertheurer, S. Device-guided slow breathing with direct biofeedback of pulse wave velocity\u2014acute effects on pulse arrival time and self-measured blood pressure. Blood Press. Monit. 28, 52\u201358 (2023).","journal-title":"Blood Press. Monit."},{"key":"924_CR18","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1109\/TBME.2016.2613124","volume":"64","author":"MAF Pimentel","year":"2017","unstructured":"Pimentel, M. A. F. et al. Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Trans. Biomed. Eng. 64, 1914\u20131923 (2017).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"924_CR19","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1088\/0967-3334\/37\/4\/610","volume":"37","author":"PH Charlton","year":"2016","unstructured":"Charlton, P. H. et al. An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiol. Meas. 37, 610\u2013626 (2016).","journal-title":"Physiol. Meas."},{"key":"924_CR20","doi-asserted-by":"publisher","first-page":"e26036","DOI":"10.1016\/j.heliyon.2024.e26036","volume":"10","author":"EJ Arg\u00fcello-Prada","year":"2024","unstructured":"Arg\u00fcello-Prada, E. J., Marcillo Ibarra, K. D. & D\u00edaz Jim\u00e9nez, K. L. The use of successive systolic differences in photoplethysmographic (PPG) signals for respiratory rate estimation. Heliyon 10, e26036 (2024).","journal-title":"Heliyon"},{"key":"924_CR21","doi-asserted-by":"crossref","unstructured":"Leier, M., Jervan, G. & Stork, W. Respiration signal extraction from photoplethysmogram using pulse wave amplitude variation. in 2014 IEEE International Conference on Communications (ICC) 3535\u20133540 (IEEE, 2014).","DOI":"10.1109\/ICC.2014.6883869"},{"key":"924_CR22","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.bspc.2018.05.020","volume":"47","author":"J Song","year":"2019","unstructured":"Song, J., Li, D., Ma, X., Teng, G. & Wei, J. PQR signal quality indexes: a method for real-time photoplethysmogram signal quality estimation based on noise interferences. Biomed. Signal Process. Control 47, 88\u201395 (2019).","journal-title":"Biomed. Signal Process. Control"},{"key":"924_CR23","doi-asserted-by":"crossref","unstructured":"Gao, H., Wu, X., Shi, C., Gao, Q. & Geng, J. A LSTM-based realtime signal quality assessment for photoplethysmogram and remote photoplethysmogram. in 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 3826\u20133835 (IEEE, 2021).","DOI":"10.1109\/CVPRW53098.2021.00424"},{"key":"924_CR24","doi-asserted-by":"publisher","first-page":"103035","DOI":"10.1016\/j.bspc.2021.103035","volume":"70","author":"A Ign\u00e1cz","year":"2021","unstructured":"Ign\u00e1cz, A., F\u00f6ldi, S., S\u00f3tonyi, P. & Cserey, G. NB-SQI: a novel non-binary signal quality index for continuous blood pressure waveforms. Biomed. Signal Process. Control 70, 103035 (2021).","journal-title":"Biomed. Signal Process. Control"},{"key":"924_CR25","doi-asserted-by":"publisher","first-page":"14126","DOI":"10.1109\/JIOT.2022.3146926","volume":"9","author":"C He","year":"2022","unstructured":"He, C. et al. A smart flexible vital signs and sleep monitoring belt based on MEMS triaxial accelerometer and pressure sensor. IEEE Internet Things J. 9, 14126\u201314136 (2022).","journal-title":"IEEE Internet Things J."},{"key":"924_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e14728","volume":"9","author":"R Deng","year":"2023","unstructured":"Deng, R., Gao, Y., Huang, X., Zhang, L. & Wu, P. A research of the respiratory status detection method measured by abdominal rise and fall. Heliyon 9, e14728 (2023).","journal-title":"Heliyon"},{"key":"924_CR27","doi-asserted-by":"publisher","first-page":"799621","DOI":"10.3389\/fphys.2022.799621","volume":"13","author":"S He","year":"2022","unstructured":"He, S., Han, Z., Iglesias, C., Mehta, V. & Bolic, M. A real-time respiration monitoring and classification system using a depth camera and radars. Front. Physiol. 13, 799621 (2022).","journal-title":"Front. Physiol."},{"key":"924_CR28","doi-asserted-by":"publisher","first-page":"6870","DOI":"10.1039\/D1TB01237A","volume":"9","author":"N Bokka","year":"2021","unstructured":"Bokka, N., Karhade, J. & Sahatiya, P. Deep learning enabled classification of real-time respiration signals acquired by MoSSe quantum dot-based flexible sensors. J. Mater. Chem. B 9, 6870\u20136880 (2021).","journal-title":"J. Mater. Chem. B"},{"key":"924_CR29","doi-asserted-by":"publisher","first-page":"19136","DOI":"10.1109\/JSEN.2024.3395285","volume":"24","author":"J Zhang","year":"2024","unstructured":"Zhang, J. et al. Intelligent respiratory status monitoring via a 1DCNN-assisted microfiber sensor. IEEE Sens. J. 24, 19136\u201319142 (2024).","journal-title":"IEEE Sens. J."},{"key":"924_CR30","doi-asserted-by":"publisher","first-page":"107367","DOI":"10.1016\/j.compeleceng.2021.107367","volume":"94","author":"N Asatani","year":"2021","unstructured":"Asatani, N., Kamiya, T., Mabu, S. & Kido, S. Classification of respiratory sounds using improved convolutional recurrent neural network. Comput. Electr. Eng. 94, 107367 (2021).","journal-title":"Comput. Electr. Eng."},{"key":"924_CR31","doi-asserted-by":"crossref","unstructured":"Wall, C., Zhang, L., Yu, Y. & Mistry, K. Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification. in 2021 International Joint Conference on Neural Networks (IJCNN) 1\u20138 (IEEE, 2021).","DOI":"10.1109\/IJCNN52387.2021.9533966"},{"key":"924_CR32","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TNNLS.2020.2979121","volume":"32","author":"I Karabayir","year":"2021","unstructured":"Karabayir, I., Akbilgic, O. & Tas, N. A novel learning algorithm to optimize deep neural networks: evolved gradient direction optimizer (EVGO). IEEE Trans. Neural Netw. Learn. Syst. 32, 685\u2013694 (2021).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"924_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.nanoen.2021.105837","volume":"83","author":"H Park","year":"2021","unstructured":"Park, H. et al. High performance flexible micro-supercapacitor for powering a vertically integrated skin-attachable strain sensor on a bio-inspired adhesive. Nano Energy 83, 105837 (2021).","journal-title":"Nano Energy"},{"key":"924_CR34","doi-asserted-by":"publisher","first-page":"21947","DOI":"10.1021\/acsnano.3c08287","volume":"17","author":"W Park","year":"2023","unstructured":"Park, W. et al. Skin-integrated wireless odor message delivery electronics for the deaf-blind. ACS Nano 17, 21947\u201321961 (2023).","journal-title":"ACS Nano"},{"key":"924_CR35","doi-asserted-by":"publisher","first-page":"2428","DOI":"10.1039\/D3MH01999C","volume":"11","author":"S Tian","year":"2024","unstructured":"Tian, S., Wang, L. & Zhu, R. A flexible multimodal pulse sensor for wearable continuous blood pressure monitoring. Mater. Horiz. 11, 2428\u20132437 (2024).","journal-title":"Mater. Horiz."},{"key":"924_CR36","doi-asserted-by":"publisher","first-page":"2203460","DOI":"10.1002\/advs.202203460","volume":"9","author":"S Chen","year":"2022","unstructured":"Chen, S. et al. Quantitative and real\u2010time evaluation of human respiration signals with a shape\u2010conformal wireless sensing system. Adv. Sci. 9, 2203460 (2022).","journal-title":"Adv. Sci."},{"key":"924_CR37","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1109\/JBHI.2015.2458965","volume":"20","author":"AR Fekr","year":"2016","unstructured":"Fekr, A. R., Janidarmian, M., Radecka, K. & Zilic, Z. Respiration disorders classification with informative features for m-Health applications. IEEE J. Biomed. Health Inform. 20, 733\u2013747 (2016).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"924_CR38","doi-asserted-by":"publisher","first-page":"5275","DOI":"10.3390\/s23115275","volume":"23","author":"J-W Hong","year":"2023","unstructured":"Hong, J.-W., Kim, S.-H. & Han, G.-T. Detection of multiple respiration patterns based on 1D SNN from continuous human breathing signals and the range classification method for each respiration pattern. Sensors 23, 5275 (2023).","journal-title":"Sensors"},{"key":"924_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.nanoen.2019.06.025","volume":"63","author":"S Wang","year":"2019","unstructured":"Wang, S. et al. An integrated flexible self-powered wearable respiration sensor. Nano Energy 63, 103829 (2019).","journal-title":"Nano Energy"},{"key":"924_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.nantod.2021.101214","volume":"39","author":"Y Li","year":"2021","unstructured":"Li, Y. et al. Graphdiyne-based flexible respiration sensors for monitoring human health. Nano Today 39, 101214 (2021).","journal-title":"Nano Today"},{"key":"924_CR41","doi-asserted-by":"publisher","unstructured":"Park, S. W., Das, P. S., Chhetry, A. & Park, J. Y. A flexible capacitive pressure sensor for wearable respiration monitoring system. IEEE Sens. J. 1 https:\/\/doi.org\/10.1109\/JSEN.2017.2749233 (2017).","DOI":"10.1109\/JSEN.2017.2749233"},{"key":"924_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.nanoen.2023.108460","volume":"112","author":"L Ye","year":"2023","unstructured":"Ye, L. et al. Face mask integrated with flexible and wearable manganite oxide respiration sensor. Nano Energy 112, 108460 (2023).","journal-title":"Nano Energy"},{"key":"924_CR43","doi-asserted-by":"publisher","first-page":"4549","DOI":"10.1039\/C8TC00238J","volume":"6","author":"B Li","year":"2018","unstructured":"Li, B. et al. A flexible humidity sensor based on silk fabrics for human respiration monitoring. J. Mater. Chem. C 6, 4549\u20134554 (2018).","journal-title":"J. Mater. Chem. C"},{"key":"924_CR44","doi-asserted-by":"publisher","first-page":"064004","DOI":"10.1088\/1361-6641\/aa68d1","volume":"32","author":"Z Liu","year":"2017","unstructured":"Liu, Z. et al. Flexible piezoelectric nanogenerator in wearable self-powered active sensor for respiration and healthcare monitoring. Semicond. Sci. Technol. 32, 064004 (2017).","journal-title":"Semicond. Sci. Technol."},{"key":"924_CR45","doi-asserted-by":"publisher","first-page":"125302","DOI":"10.1088\/1361-6528\/ab5cda","volume":"31","author":"C Zhou","year":"2020","unstructured":"Zhou, C. et al. Rapid response flexible humidity sensor for respiration monitoring using nano-confined strategy. Nanotechnology 31, 125302 (2020).","journal-title":"Nanotechnology"},{"key":"924_CR46","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/s41378-021-00324-4","volume":"7","author":"X Wang","year":"2021","unstructured":"Wang, X. et al. An ultrafast-response and flexible humidity sensor for human respiration monitoring and noncontact safety warning. Microsyst. Nanoeng. 7, 99 (2021).","journal-title":"Microsyst. Nanoeng."},{"key":"924_CR47","doi-asserted-by":"publisher","first-page":"7427","DOI":"10.1039\/D3NR06664A","volume":"16","author":"Y Teng","year":"2024","unstructured":"Teng, Y. et al. Fully printed minimum port flexible interdigital electrode sensor arrays. Nanoscale 16, 7427\u20137436 (2024).","journal-title":"Nanoscale"},{"key":"924_CR48","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40763-3","volume":"14","author":"J Li","year":"2023","unstructured":"Li, J. et al. Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. Nat. Commun. 14, 5009 (2023).","journal-title":"Nat. Commun."},{"key":"924_CR49","doi-asserted-by":"publisher","first-page":"24242","DOI":"10.1021\/acsnano.3c09766","volume":"17","author":"C Yao","year":"2023","unstructured":"Yao, C. et al. Personalized machine learning-coupled nanopillar triboelectric pulse sensor for cuffless blood pressure continuous monitoring. ACS Nano 17, 24242\u201324258 (2023).","journal-title":"ACS Nano"}],"container-title":["Microsystems &amp; Nanoengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41378-025-00924-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41378-025-00924-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41378-025-00924-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T04:03:48Z","timestamp":1747368228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41378-025-00924-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,16]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["924"],"URL":"https:\/\/doi.org\/10.1038\/s41378-025-00924-4","relation":{},"ISSN":["2055-7434"],"issn-type":[{"value":"2055-7434","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,16]]},"assertion":[{"value":"15 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2025","order":4,"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":"Conflict of interest"}},{"value":"All human subject studies were approved by the Medical Ethics Committee of Xia Men University (Protocol: XDYX202311K70), and the volunteers gave informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"90"}}