{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:02:20Z","timestamp":1789646540115,"version":"4.0.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Sports Sci Med Rehabil"],"DOI":"10.1186\/s13102-026-02080-2","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:45:13Z","timestamp":1789541113000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-time continuous assessment of fatigue from surface electromyography with deep learning for training load regulation in elite cyclists"],"prefix":"10.1186","volume":"18","author":[{"given":"Rongxuan","family":"Zhai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxin","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxin","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lejun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"issue":"3","key":"2080_CR1","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1152\/japplphysiol.00942.2016","volume":"122","author":"MI Black","year":"2017","unstructured":"Black MI, Jones AM, Blackwell JR, Bailey SJ, Wylie LJ, McDonagh ST, Thompson C, Kelly J, Sumners P, Mileva KN, et al. Muscle metabolic and neuromuscular determinants of fatigue during cycling in different exercise intensity domains. J Appl Physiol (1985). 2017;122(3):446\u201359. https:\/\/doi.org\/10.1152\/japplphysiol.00942.2016.","journal-title":"J Appl Physiol (1985)"},{"issue":"4","key":"2080_CR2","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1123\/ijspp.2019-0367","volume":"15","author":"NW Almquist","year":"2020","unstructured":"Almquist NW, Ettema G, Hopker J, Sandbakk \u00d8, R\u00f8nnestad BR. The Effect of 30-Second Sprints During Prolonged Exercise on Gross Efficiency, Electromyography, and Pedaling Technique in Elite Cyclists. Int J Sports Physiol Perform. 2020;15(4):562\u201370. https:\/\/doi.org\/10.1123\/ijspp.2019-0367.","journal-title":"Int J Sports Physiol Perform"},{"key":"2080_CR3","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neulet.2014.05.023","volume":"576","author":"SJ O\u2019Bryan","year":"2014","unstructured":"O\u2019Bryan SJ, Brown NA, Billaut F, Rouffet DM. Changes in muscle coordination and power output during sprint cycling. Neurosci Lett. 2014;576:11\u20136. https:\/\/doi.org\/10.1016\/j.neulet.2014.05.023.","journal-title":"Neurosci Lett"},{"issue":"6","key":"2080_CR4","doi-asserted-by":"publisher","first-page":"407","DOI":"10.2165\/00007256-200333060-00003","volume":"33","author":"VL Billat","year":"2003","unstructured":"Billat VL, Sirvent P, Py G, Koralsztein JP, Mercier J. The concept of maximal lactate steady state: a bridge between biochemistry, physiology and sport science. Sports Med. 2003;33(6):407\u201326. https:\/\/doi.org\/10.2165\/00007256-200333060-00003.","journal-title":"Sports Med"},{"issue":"11","key":"2080_CR5","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.1249\/mss.0000000000000929","volume":"48","author":"RM Enoka","year":"2016","unstructured":"Enoka RM, Duchateau J. Translating Fatigue to Human Performance. Med Sci Sports Exerc. 2016;48(11):2228\u201338. https:\/\/doi.org\/10.1249\/mss.0000000000000929.","journal-title":"Med Sci Sports Exerc"},{"issue":"5","key":"2080_CR6","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1007\/s40279-016-0619-5","volume":"47","author":"CM Jones","year":"2017","unstructured":"Jones CM, Griffiths PC, Mellalieu SD. Training Load and Fatigue Marker Associations with Injury and Illness: A Systematic Review of Longitudinal Studies. Sports Med. 2017;47(5):943\u201374. https:\/\/doi.org\/10.1007\/s40279-016-0619-5.","journal-title":"Sports Med"},{"issue":"10","key":"2080_CR7","doi-asserted-by":"publisher","first-page":"865","DOI":"10.2165\/00007256-200535100-00004","volume":"35","author":"CR Abbiss","year":"2005","unstructured":"Abbiss CR, Laursen PB. Models to explain fatigue during prolonged endurance cycling. Sports Med. 2005;35(10):865\u201398. https:\/\/doi.org\/10.2165\/00007256-200535100-00004.","journal-title":"Sports Med"},{"issue":"4","key":"2080_CR8","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.clinbiomech.2009.01.010","volume":"24","author":"M Cifrek","year":"2009","unstructured":"Cifrek M, Medved V, Tonkovi\u0107 S, Ostoji\u0107 S. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech (Bristol). 2009;24(4):327\u201340. https:\/\/doi.org\/10.1016\/j.clinbiomech.2009.01.010.","journal-title":"Clin Biomech (Bristol)"},{"key":"2080_CR9","doi-asserted-by":"publisher","first-page":"3611","DOI":"10.1109\/embc48229.2022.9871981","volume":"2022","author":"D Sasidharan","year":"2022","unstructured":"Sasidharan D, Ramakrishnan GV. Muscle Fatigue Analysis by Visualization of Dynamic Surface EMG Signals Using Markov Transition Field. Annu Int Conf IEEE Eng Med Biol Soc. 2022;2022:3611\u20134. https:\/\/doi.org\/10.1109\/embc48229.2022.9871981.","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"5","key":"2080_CR10","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1016\/j.jelekin.2008.08.003","volume":"19","author":"L Mesin","year":"2009","unstructured":"Mesin L, Cescon C, Gazzoni M, Merletti R, Rainoldi A. A bi-dimensional index for the selective assessment of myoelectric manifestations of peripheral and central muscle fatigue. J Electromyogr Kinesiol. 2009;19(5):851\u201363. https:\/\/doi.org\/10.1016\/j.jelekin.2008.08.003.","journal-title":"J Electromyogr Kinesiol"},{"key":"2080_CR11","doi-asserted-by":"publisher","unstructured":"Wang L, Wang Y, Ma A, Ma G, Ye Y, Li R, Lu T. A Comparative Study of EMG Indices in Muscle Fatigue Evaluation Based on Grey Relational Analysis during All-Out Cycling Exercise. Biomed Res Int. 2018;20189341215. https:\/\/doi.org\/10.1155\/2018\/9341215.","DOI":"10.1155\/2018\/9341215"},{"issue":"14","key":"2080_CR12","doi-asserted-by":"publisher","first-page":"8839","DOI":"10.3390\/ijerph19148839","volume":"19","author":"M Jurasz","year":"2022","unstructured":"Jurasz M, Boraczy\u0144ski M, W\u00f3jcik Z, Gronek P. Neuromuscular Fatigue Responses of Endurance- and Strength-Trained Athletes during Incremental Cycling Exercise. Int J Environ Res Public Health. 2022;19(14):8839. https:\/\/doi.org\/10.3390\/ijerph19148839.","journal-title":"Int J Environ Res Public Health"},{"issue":"5","key":"2080_CR13","doi-asserted-by":"publisher","first-page":"529","DOI":"10.3390\/e22050529","volume":"22","author":"S Rampichini","year":"2020","unstructured":"Rampichini S, Vieira TM, Castiglioni P, Merati G. Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review. Entropy. 2020;22(5):529. https:\/\/doi.org\/10.3390\/e22050529.","journal-title":"Entropy"},{"issue":"1","key":"2080_CR14","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.jelekin.2006.08.003","volume":"18","author":"MA Yassierli, Nussbaum","year":"2008","unstructured":"Yassierli, Nussbaum MA. Utility of traditional and alternative EMG-based measures of fatigue during low-moderate level isometric efforts. J Electromyogr Kinesiol. 2008;18(1):44\u201353. https:\/\/doi.org\/10.1016\/j.jelekin.2006.08.003.","journal-title":"J Electromyogr Kinesiol"},{"key":"2080_CR15","doi-asserted-by":"publisher","unstructured":"De Luca CJ. Physiology and mathematics of myoelectric signals. IEEE Trans Biomed Eng. 2007;(6):313\u201325 https:\/\/doi.org\/10.1109\/TBME.1979.326534.","DOI":"10.1109\/TBME.1979.326534"},{"key":"2080_CR16","doi-asserted-by":"publisher","unstructured":"Wang Y, Lu C, Zhang M, Wu J, Tang Z. Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training. Healthc (Basel). 2022;10(11). https:\/\/doi.org\/10.3390\/healthcare10112292.","DOI":"10.3390\/healthcare10112292"},{"issue":"12","key":"2080_CR17","doi-asserted-by":"publisher","first-page":"e0276921","DOI":"10.1371\/journal.pone.0276921","volume":"17","author":"D Mu","year":"2022","unstructured":"Mu D, Li F, Yu L, Du C, Ge L, Sun T. Study on exercise muscle fatigue based on sEMG and ECG data fusion and temporal convolutional network. PLoS ONE. 2022;17(12):e0276921. https:\/\/doi.org\/10.1371\/journal.pone.0276921.","journal-title":"PLoS ONE"},{"issue":"2","key":"2080_CR18","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/tbme.2020.3012783","volume":"68","author":"A Moniri","year":"2021","unstructured":"Moniri A, Terracina D, Rodriguez-Manzano J, Strutton PH, Georgiou P. Real-Time Forecasting of sEMG Features for Trunk Muscle Fatigue Using Machine Learning. IEEE Trans Biomed Eng. 2021;68(2):718\u201327. https:\/\/doi.org\/10.1109\/tbme.2020.3012783.","journal-title":"IEEE Trans Biomed Eng"},{"key":"2080_CR19","doi-asserted-by":"publisher","unstructured":"Hwang S, Kwon N, Lee D, Kim J, Yang S, Youn I, Moon HJ, Sung JK, Han S. A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model. Sens (Basel). 2025;25(11). https:\/\/doi.org\/10.3390\/s25113309.","DOI":"10.3390\/s25113309"},{"key":"2080_CR20","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G, Ieee. Squeeze-and-Excitation Networks. In: 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Jun 18\u201323 2018; Salt Lake City, UT: Ieee; 2018:7132\u201341. https:\/\/doi.org\/10.1109\/CVPR.2018.00745.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2080_CR21","doi-asserted-by":"publisher","unstructured":"Tanaka T, Nambu I, Maruyama Y, Wada Y. Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography. Sens (Basel). 2022;22(13). https:\/\/doi.org\/10.3390\/s22135005.","DOI":"10.3390\/s22135005"},{"issue":"2","key":"2080_CR22","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1103\/physreva.36.842","volume":"36","author":"F Kaspar","year":"1987","unstructured":"Kaspar F, Schuster HG. Easily calculable measure for the complexity of spatiotemporal patterns. Phys Rev Gen Phys. 1987;36(2):842\u20138. https:\/\/doi.org\/10.1103\/physreva.36.842.","journal-title":"Phys Rev Gen Phys"},{"issue":"1","key":"2080_CR23","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1088\/0967-3334\/37\/1\/162","volume":"37","author":"G Boccia","year":"2016","unstructured":"Boccia G, Dardanello D, Beretta-Piccoli M, Cescon C, Coratella G, Rinaldo N, Barbero M, Lanza M, Schena F, Rainoldi A. Muscle fiber conduction velocity and fractal dimension of EMG during fatiguing contraction of young and elderly active men. Physiol Meas. 2016;37(1):162\u201374. https:\/\/doi.org\/10.1088\/0967-3334\/37\/1\/162.","journal-title":"Physiol Meas"},{"issue":"6","key":"2080_CR24","doi-asserted-by":"publisher","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","volume":"278","author":"JS Richman","year":"2000","unstructured":"Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278(6):H2039\u20132049. https:\/\/doi.org\/10.1152\/ajpheart.2000.278.6.H2039.","journal-title":"Am J Physiol Heart Circ Physiol"},{"issue":"1","key":"2080_CR25","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/1271436","volume":"42","author":"AE Hoerl","year":"2000","unstructured":"Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics. 2000;42(1):80\u20136. https:\/\/doi.org\/10.2307\/1271436.","journal-title":"Technometrics"},{"issue":"1","key":"2080_CR26","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the Lasso. J Royal Stat Soc Ser B-Statistical Methodol. 1996;58(1):267\u201388. https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x.","journal-title":"J Royal Stat Soc Ser B-Statistical Methodol"},{"issue":"3","key":"2080_CR27","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1023\/b:Stco.0000035301.49549.88","volume":"14","author":"AJ Smola","year":"2004","unstructured":"Smola AJ, Sch\u00f6lkopf B. A tutorial on support vector regression. Stat Comput. 2004;14(3):199\u2013222. https:\/\/doi.org\/10.1023\/b:Stco.0000035301.49549.88.","journal-title":"Stat Comput"},{"issue":"1","key":"2080_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332. https:\/\/doi.org\/10.1023\/a:1010933404324.","journal-title":"Mach Learn"},{"issue":"5","key":"2080_CR29","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001;29(5):1189\u2013232. https:\/\/doi.org\/10.1214\/aos\/1013203451.","journal-title":"Ann Stat"},{"key":"2080_CR30","doi-asserted-by":"publisher","unstructured":"Chen TQ, Guestrin C, Assoc Comp M. XGBoost: A Scalable Tree Boosting System. In: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD): Aug 13\u201317 2016; San Francisco, CA; 2016:785\u201394. https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"issue":"5","key":"2080_CR31","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.jelekin.2005.02.002","volume":"15","author":"PL Coorevits","year":"2005","unstructured":"Coorevits PL, Danneels LA, Ramon H, Van Audekercke R, Cambier DC, Vanderstraeten GG. Statistical modelling of fatigue-related electromyographic median frequency characteristics of back and hip muscles during a standardized isometric back extension test. J Electromyogr Kinesiol. 2005;15(5):444\u201351. https:\/\/doi.org\/10.1016\/j.jelekin.2005.02.002.","journal-title":"J Electromyogr Kinesiol"},{"issue":"1","key":"2080_CR32","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s12891-018-2350-x","volume":"20","author":"FN Golabchi","year":"2019","unstructured":"Golabchi FN, Sapienza S, Severini G, Reaston P, Tomecek F, Demarchi D, Reaston M, Bonato P. Assessing aberrant muscle activity patterns via the analysis of surface EMG data collected during a functional evaluation. BMC Musculoskelet Disord. 2019;20(1):13. https:\/\/doi.org\/10.1186\/s12891-018-2350-x.","journal-title":"BMC Musculoskelet Disord"},{"key":"2080_CR33","doi-asserted-by":"publisher","first-page":"95079","DOI":"10.1109\/ACCESS.2023.3309741","volume":"11","author":"J Liu","year":"2023","unstructured":"Liu J, Tao Q, Wu B. Dynamic Muscle Fatigue State Recognition Based on Deep Learning Fusion Model. IEEE Access. 2023;11:95079\u201391. https:\/\/doi.org\/10.1109\/ACCESS.2023.3309741.","journal-title":"IEEE Access"},{"key":"2080_CR34","doi-asserted-by":"publisher","first-page":"103111","DOI":"10.1016\/j.jelekin.2026.103111","volume":"87","author":"L Garofalo","year":"2026","unstructured":"Garofalo L, Defauw S, Calcagno G, Fiorilli G, Di Cagno A. Estimating proximity to muscular failure using surface EMG and deep learning. J Electromyogr Kinesiol. 2026;87:103111. https:\/\/doi.org\/10.1016\/j.jelekin.2026.103111.","journal-title":"J Electromyogr Kinesiol"},{"issue":"11","key":"2080_CR35","doi-asserted-by":"publisher","first-page":"e0310035","DOI":"10.1371\/journal.pone.0310035","volume":"19","author":"D Mu","year":"2024","unstructured":"Mu D, Wang J, Li F, Hu W, Chen R. Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion. PLoS ONE. 2024;19(11):e0310035. https:\/\/doi.org\/10.1371\/journal.pone.0310035.","journal-title":"PLoS ONE"},{"issue":"11","key":"2080_CR36","doi-asserted-by":"publisher","first-page":"3309","DOI":"10.3390\/s25113309","volume":"25","author":"S Hwang","year":"2025","unstructured":"Hwang S, Kwon N, Lee D, Kim J, Yang S, Youn I, Moon H-J, Sung J-K, Han S. A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model. Sensors. 2025;25(11):3309.","journal-title":"Sensors"},{"issue":"11","key":"2080_CR37","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1139\/apnm-2018-0080","volume":"43","author":"BW Collins","year":"2018","unstructured":"Collins BW, Pearcey GEP, Buckle NCM, Power KE, Button DC. Neuromuscular fatigue during repeated sprint exercise: underlying physiology and methodological considerations. Appl Physiol Nutr Metab. 2018;43(11):1166\u201375. https:\/\/doi.org\/10.1139\/apnm-2018-0080.","journal-title":"Appl Physiol Nutr Metab"},{"issue":"1","key":"2080_CR38","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/s004210100508","volume":"86","author":"JM Wakeling","year":"2001","unstructured":"Wakeling JM, Pascual SA, Nigg BM, von Tscharner V. Surface EMG shows distinct populations of muscle activity when measured during sustained sub-maximal exercise. Eur J Appl Physiol. 2001;86(1):40\u20137. https:\/\/doi.org\/10.1007\/s004210100508.","journal-title":"Eur J Appl Physiol"},{"key":"2080_CR39","doi-asserted-by":"publisher","unstructured":"Lin R, Wang F, Kawata S, Zhao J, She J, Chugo D, Ieee Computer SOC. Reducing the Influence of Individual Variability in sEMG-Based Fatigue Detection Using Transfer Learning. In: 22nd IEEE Conference on Pervasive and Intelligent Computing: Nov 05\u201308 2024; null, PHILIPPINES: Ieee Computer Soc. 2024:142\u2013147. https:\/\/doi.org\/10.1109\/PICom64201.2024.00027.","DOI":"10.1109\/PICom64201.2024.00027"},{"issue":"4","key":"2080_CR40","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1145\/2382577.2382582","volume":"6","author":"R Chattopadhyay","year":"2012","unstructured":"Chattopadhyay R, Sun Q, Fan W, Davidson I, Panchanathan S, Ye JP. Multisource Domain Adaptation and Its Application to Early Detection of Fatigue. ACM Trans Knowl Discov Data. 2012;6(4):26. https:\/\/doi.org\/10.1145\/2382577.2382582.","journal-title":"ACM Trans Knowl Discov Data"}],"container-title":["BMC Sports Science, Medicine and Rehabilitation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13102-026-02080-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13102-026-02080-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13102-026-02080-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:10:09Z","timestamp":1789643409000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13102-026-02080-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2080"],"URL":"https:\/\/doi.org\/10.1186\/s13102-026-02080-2","relation":{},"ISSN":["2052-1847"],"issn-type":[{"value":"2052-1847","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]},"assertion":[{"value":"15 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was approved by the Ethics Committee of Tongji University, Shanghai, China (Approval No. 2020tjdx006) and was performed in accordance with ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent to participate in this study was provided by the participants.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"425"}}