{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:17:50Z","timestamp":1791245870874,"version":"4.1.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Science & Technology Development Fund of Tianjin Education Commission for Higher Education.","award":["[2018KJ082 to S.Z.]"],"award-info":[{"award-number":["[2018KJ082 to S.Z.]"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The accurate detection of leukocytes is the basis for the diagnosis of blood system diseases. However, diagnosing leukocyte disorders by doctors is time-consuming and requires extensive experience. Automated detection methods with high accuracy can improve detection efficiency and provide recommendations to inexperienced doctors. Current methods and instruments either fail to automate the identification process fully or have low performance and need suitable leukocyte data sets for further study. To improve the current status, we need to develop more intelligent strategies. This paper investigates fulfilling high-performance automatic detection for leukocytes using a deep learning-based method. We established a new dataset more suitable for leukocyte detection, containing 6273 images (8595 leukocytes) and considering nine common clinical interference factors. Based on the dataset, the performance evaluation of six mainstream detection models is carried out, and a more robust ensemble model is proposed. The mean of average precision (mAP) @IoU\u2009=\u20090.50:0.95 and mean of average recall (mAR)@IoU\u2009=\u20090.50:0.95 of the ensemble model on the test set are 0.853 and 0.922, respectively. The detection performance of poor-quality images is robust. For the first time, it is found that the ensemble model yields an accuracy of 98.84% for detecting incomplete leukocytes. In addition, we also compared the test results of different models and found multiple identical false detections of the models, then provided correct suggestions for the clinic.<\/jats:p>","DOI":"10.1038\/s41598-023-29331-3","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T06:03:58Z","timestamp":1675749838000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A deep learning model for detection of leukocytes under various interference factors"],"prefix":"10.1038","volume":"13","author":[{"given":"Meiyu","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanshan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuguo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"issue":"5","key":"29331_CR1","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1056\/NEJMra043442","volume":"353","author":"BJ Bain","year":"2005","unstructured":"Bain, B. J. Diagnosis from the blood smear. N. Engl. J. Med. 353(5), 498\u2013507 (2005).","journal-title":"N. Engl. J. Med."},{"key":"29331_CR2","first-page":"256","volume":"2019","author":"Y Wang","year":"2019","unstructured":"Wang, Y. & Cao, Y. Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation. Med. Phys. 2019, 256 (2019).","journal-title":"Med. Phys."},{"key":"29331_CR3","doi-asserted-by":"publisher","first-page":"105020","DOI":"10.1016\/j.cmpb.2019.105020","volume":"180","author":"A Acevedo","year":"2019","unstructured":"Acevedo, A., Alferez, S., Merino, A., Puigvi, L. & Rodellar, J. Recognition of peripheral blood cell images using convolutional neural networks. Comput. Methods Programs Biomed. 180, 105020 https:\/\/doi.org\/10.1016\/j.cmpb.2019.105020 (2019).","journal-title":"Comput. Methods Programs Biomed."},{"key":"29331_CR4","unstructured":"CellaVision. http:\/\/www.cellavision.com (2016)."},{"key":"29331_CR5","unstructured":"medicacorp. http:\/\/www.medicacorp.com\/products\/hematology-imaging-analyzers\/ (2016)."},{"key":"29331_CR6","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.cmpb.2017.11.015","volume":"168","author":"AI Shahin","year":"2019","unstructured":"Shahin, A. I., Guo, Y., Amin, K. M. & Sharawi, A. A. White blood cells identification system based on convolutional deep neural learning networks. Comput. Methods Programs Biomed. 168, 69\u201380 https:\/\/doi.org\/10.1016\/j.cmpb.2017.11.015 (2019).","journal-title":"Comput. Methods Programs Biomed."},{"key":"29331_CR7","doi-asserted-by":"publisher","first-page":"3732","DOI":"10.1002\/mp.14144","volume":"47","author":"C Zhang","year":"2020","unstructured":"Zhang, C. et al. Hybrid adversarial-discriminative network for leukocyte classification in leukemia. Med. Phys. 47, 3732\u20133744 https:\/\/doi.org\/10.1002\/mp.14144 (2020).","journal-title":"Med. Phys."},{"key":"29331_CR8","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390 https:\/\/doi.org\/10.1145\/3065386 (2017).","journal-title":"Commun. ACM"},{"key":"29331_CR9","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017).","journal-title":"Nature"},{"issue":"22","key":"29331_CR10","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402\u20132410 (2016).","journal-title":"JAMA"},{"key":"29331_CR11","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1038\/s41591-019-0715-9","volume":"26","author":"TC Hollon","year":"2020","unstructured":"Hollon, T. C. et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat. Med. 26, 52\u201358 (2020).","journal-title":"Nat. Med."},{"key":"29331_CR12","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","volume":"577","author":"S McKinney","year":"2020","unstructured":"McKinney, S. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89\u201394 (2020).","journal-title":"Nature"},{"key":"29331_CR13","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s41591-018-0147-y","volume":"24","author":"J Titano","year":"2018","unstructured":"Titano, J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337\u20131341 (2018).","journal-title":"Nat. Med."},{"key":"29331_CR14","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","volume":"141","author":"P Wang","year":"2021","unstructured":"Wang, P., Fan, E. & Wang, P. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recogn. Lett. 141, 61\u201367 (2021).","journal-title":"Pattern Recogn. Lett."},{"key":"29331_CR15","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1111\/ced.14029","volume":"45","author":"D Jakhar","year":"2020","unstructured":"Jakhar, D. & Kaur, I. Artificial intelligence, machine learning and deep learning: Definitions and differences. Clin. Exp. Dermatol. 45, 131\u2013132 (2020).","journal-title":"Clin. Exp. Dermatol."},{"key":"29331_CR16","doi-asserted-by":"publisher","first-page":"101699","DOI":"10.1016\/j.compmedimag.2020.101699","volume":"80","author":"YY Baydilli","year":"2020","unstructured":"Baydilli, Y. Y. & Atila, U. Classification of white blood cells using capsule networks. Comput. Med. Imaging Graph. 80, 101699 https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101699 (2020).","journal-title":"Comput. Med. Imaging Graph."},{"key":"29331_CR17","doi-asserted-by":"publisher","first-page":"105645","DOI":"10.1016\/j.cmpb.2020.105645","volume":"196","author":"YY Baydilli","year":"2020","unstructured":"Baydilli, Y. Y., Atila, U. & Elen, A. Learn from one data set to classify all\u2014a multi-target domain adaptation approach for white blood cell classification. Comput. Methods Programs Biomed. 196, 105645 https:\/\/doi.org\/10.1016\/j.cmpb.2020.105645 (2020).","journal-title":"Comput. Methods Programs Biomed."},{"key":"29331_CR18","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1002\/mp.13904","volume":"47","author":"Y Wang","year":"2020","unstructured":"Wang, Y. & Cao, Y. Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation. Med. Phys. 47, 142\u2013151 https:\/\/doi.org\/10.1002\/mp.13904 (2020).","journal-title":"Med. Phys."},{"key":"29331_CR19","doi-asserted-by":"crossref","unstructured":"Mohamed, M. M. A., Far, B. & Guaily, A. An efficient technique for white blood cells nuclei automatic segmentation. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 220\u2013225 (2012).","DOI":"10.1109\/ICSMC.2012.6377703"},{"key":"29331_CR20","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.compmedimag.2011.01.003","volume":"35","author":"SH Rezatofighi","year":"2011","unstructured":"Rezatofighi, S. H. & Soltanian-Zadeh, H. Automatic recognition of five types of white blood cells in peripheral blood. Comput. Med. Imaging Graph. 35, 333\u2013343 https:\/\/doi.org\/10.1016\/j.compmedimag.2011.01.003(2011).","journal-title":"Comput. Med. Imaging Graph."},{"key":"29331_CR21","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.micron.2018.01.010","volume":"107","author":"X Zheng","year":"2018","unstructured":"Zheng, X., Wang, Y., Wang, G. & Liu, J. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 107, 55\u201371 https:\/\/doi.org\/10.1016\/j.micron.2018.01.010 (2018).","journal-title":"Micron"},{"key":"29331_CR22","doi-asserted-by":"publisher","first-page":"81","DOI":"10.21833\/ijaas.2018.01.011","volume":"5","author":"A Elen","year":"2018","unstructured":"Elen, A. & Turan, M. A new approach for fully automated segmentation of peripheral blood smears. Int. J. Adv. Appl. Sci. 5, 81\u201393 (2018).","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"29331_CR23","doi-asserted-by":"crossref","unstructured":"Sarrafzadeh, O., Dehnavi, A., Rabbani, H. & Talebi, A. A simple and accurate method for white blood cells segmentation using K-means algorithm. In 2015 IEEE Workshop on Signal Processing Systems (SiPS) 1\u20136 (2015).","DOI":"10.1109\/SiPS.2015.7344978"},{"key":"29331_CR24","doi-asserted-by":"crossref","unstructured":"Sarrafzadeh, O., Dehnavi, A., Rabbani, H., Ghane, N. & Talebi, A. Circlet based framework for red blood cells segmentation and counting. In 2015 IEEE Workshop on Signal Processing Systems (SiPS) 1\u20136 (2015).","DOI":"10.1109\/SiPS.2015.7344979"},{"key":"29331_CR25","unstructured":"Mundhra, D., Cheluvaraju, B., Rampure, J. & Dastidar, T. R. In DLMIA\/ML-CDS@MICCAI (2017)."},{"key":"29331_CR26","doi-asserted-by":"publisher","first-page":"29","DOI":"10.4103\/2153-3539.100154","volume":"3","author":"M Rollins-Raval","year":"2012","unstructured":"Rollins-Raval, M., Raval, J. & Contis, L. Experience with CellaVision DM96 for peripheral blood differentials in a large multi-center academic hospital system. J. Pathol. Inf. 3, 29 (2012).","journal-title":"J. Pathol. Inf."},{"key":"29331_CR27","doi-asserted-by":"crossref","unstructured":"Labati, R. D., Piuri, V. & Scotti, F. All-IDB: The acute lymphoblastic leukemia image database for image processing. In 2011 18th IEEE International Conference on Image Processing 2045\u20132048 (2011).","DOI":"10.1109\/ICIP.2011.6115881"},{"key":"29331_CR28","doi-asserted-by":"publisher","first-page":"105474","DOI":"10.1016\/j.dib.2020.105474","volume":"30","author":"A Acevedo","year":"2020","unstructured":"Acevedo, A. et al. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief. 30, 105474 (2020).","journal-title":"Data Brief."},{"key":"29331_CR29","doi-asserted-by":"publisher","first-page":"103530","DOI":"10.1016\/j.compbiomed.2019.103530","volume":"116","author":"C Di-Ruberto","year":"2020","unstructured":"Di-Ruberto, C., Loddo, A. & Putzu, L. Detection of red and white blood cells from microscopic blood images using a region proposal approach. Comput. Biol. Med. 116, 103530 https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103530(2020).","journal-title":"Comput. Biol. Med."},{"key":"29331_CR30","doi-asserted-by":"publisher","first-page":"e0218808","DOI":"10.1371\/journal.pone.0218808","volume":"14","author":"Q Wang","year":"2019","unstructured":"Wang, Q. et al. Deep learning approach to peripheral leukocyte recognition. PLoS ONE 14, e0218808 https:\/\/doi.org\/10.1371\/journal.pone.0218808 (2019).","journal-title":"PLoS ONE"},{"key":"29331_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-19817-3","volume":"11","author":"D Li","year":"2020","unstructured":"Li, D. et al. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nature Commun. 11, 1 (2020).","journal-title":"Nature Commun."},{"key":"29331_CR32","first-page":"439","volume":"11","author":"M Ragab","year":"2022","unstructured":"Ragab, M., Albukhari, A., Alyami, J. & Mansour, R. F. Ensemble deep-learning-enabled clinical decision support system for breast cancer diagnosis and classification on ultrasound images. Biol. (Basel) 11, 439 (2022).","journal-title":"Biol. (Basel)"},{"key":"29331_CR33","doi-asserted-by":"publisher","first-page":"9276579","DOI":"10.1155\/2022\/9276579","volume":"2022","author":"AA Bahaddad","year":"2022","unstructured":"Bahaddad, A. A., Ragab, M., Ashary, E. B. & Khalil, E. M. Metaheuristics with deep learning-enabled Parkinson\u2019s disease diagnosis and classification model. J. Healthc. Eng. 2022, 9276579 https:\/\/doi.org\/10.1155\/2022\/9276579 (2022).","journal-title":"J. Healthc. Eng."},{"key":"29331_CR34","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s10916-018-0912-y","volume":"42","author":"E Abdulhay","year":"2018","unstructured":"Abdulhay, E., Mohammed, M. A., Ibrahim, D. A., Arunkumar, N. & Venkatraman, V. Computer aided solution for automatic segmenting and measurements of blood leucocytes using static microscope images. J. Med. Syst. 42, 58 https:\/\/doi.org\/10.1007\/s10916-018-0912-y (2018).","journal-title":"J. Med. Syst."},{"key":"29331_CR35","doi-asserted-by":"publisher","first-page":"7384131","DOI":"10.1155\/2022\/7384131","volume":"2022","author":"S Sharma","year":"2022","unstructured":"Sharma, S. et al. Deep learning model for the automatic classification of white blood cells. Comput. Intell. Neurosci. 2022, 7384131 https:\/\/doi.org\/10.1155\/2022\/7384131 (2022).","journal-title":"Comput. Intell. Neurosci."},{"key":"29331_CR36","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1080\/21691401.2021.1879823","volume":"49","author":"X Yao","year":"2021","unstructured":"Yao, X., Sun, K., Bu, X., Zhao, C. & Jin, Y. Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artif. Cells Nanomed. Biotechnol. 49, 147\u2013155 https:\/\/doi.org\/10.1080\/21691401.2021.1879823 (2021).","journal-title":"Artif. Cells Nanomed. Biotechnol."},{"key":"29331_CR37","doi-asserted-by":"crossref","unstructured":"Cai, Z. & Vasconcelos, N. Cascade R-CNN: Delving into high quality object detection. In 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 6154\u20136162 (2017).","DOI":"10.1109\/CVPR.2018.00644"},{"key":"29331_CR38","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R. B. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137\u20131149 (2015).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29331_CR39","doi-asserted-by":"crossref","unstructured":"Lu, X., Li, B., Yue, Y., Li, Q. & Yan, J. Grid R-CNN. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 7355\u20137364 (2018).","DOI":"10.1109\/CVPR.2019.00754"},{"key":"29331_CR40","first-page":"06493","volume":"1904","author":"Y Wu","year":"2019","unstructured":"Wu, Y. et al. Double-head RCNN: Rethinking classification and localization for object detection. arXiv 1904, 06493 (2019).","journal-title":"arXiv"},{"key":"29331_CR41","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y. & Savvides, M. Feature selective anchor-free module for single-shot object detection. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 840\u2013849 (2019).","DOI":"10.1109\/CVPR.2019.00093"},{"key":"29331_CR42","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H. & He, T. FCOS: Fully convolutional one-stage object detection. In 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) 9626\u20139635 (2019).","DOI":"10.1109\/ICCV.2019.00972"},{"key":"29331_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104117","volume":"107","author":"RA Solovyev","year":"2021","unstructured":"Solovyev, R. A., Wang, W. & Gabruseva, T. Weighted boxes fusion: Ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021).","journal-title":"Image Vis. Comput."},{"key":"29331_CR44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"29331_CR45","doi-asserted-by":"publisher","first-page":"20043","DOI":"10.1038\/s41598-022-21250-z","volume":"12","author":"MJ Fanous","year":"2022","unstructured":"Fanous, M. J. et al. White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS). Sci. Rep. 12, 20043 https:\/\/doi.org\/10.1038\/s41598-022-21250-z(2022).","journal-title":"Sci. Rep."},{"key":"29331_CR46","doi-asserted-by":"publisher","first-page":"1499546","DOI":"10.1155\/2022\/1499546","volume":"2022","author":"L Alhazmi","year":"2022","unstructured":"Alhazmi, L. Detection of WBC, RBC, and platelets in blood samples using deep learning. Biomed. Res. Int. 2022, 1499546 https:\/\/doi.org\/10.1155\/2022\/1499546 (2022).","journal-title":"Biomed. Res. Int."},{"key":"29331_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2019.109472","volume":"135","author":"H Kutlu","year":"2019","unstructured":"Kutlu, H., Avci, E. & \u00d6zyurt, F. White blood cells detection and classification based on regional convolutional neural networks. Med. Hypotheses 135, 109472 (2019).","journal-title":"Med. Hypotheses"},{"key":"29331_CR48","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.cell.2018.03.040","volume":"173","author":"EM Christiansen","year":"2018","unstructured":"Christiansen, E. M. et al. In silico labeling: Predicting fluorescent labels in unlabeled images. Cell 173, 792-803.e719 (2018).","journal-title":"Cell"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-29331-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-29331-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-29331-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T06:08:52Z","timestamp":1675750132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-29331-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,7]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["29331"],"URL":"https:\/\/doi.org\/10.1038\/s41598-023-29331-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,7]]},"assertion":[{"value":"1 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2023","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"}}],"article-number":"2160"}}