{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:19:55Z","timestamp":1784762395282,"version":"3.55.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":25,"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\/s41598-025-34724-7","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T17:43:55Z","timestamp":1767894235000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The role of texture analysis of T1 weighted images in diagnosis of chronic kidney disease"],"prefix":"10.1038","volume":"16","author":[{"given":"Marcin","family":"Majos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artur","family":"Klepaczko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katarzyna","family":"Szychowska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ludomir","family":"Stefanczyk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilona","family":"Kurnatowska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"34724_CR1","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.kisu.2024.01.008","volume":"13","author":"M Pippias","year":"2024","unstructured":"Pippias, M. et al. Capacity for the management of kidney failure in the international society of nephrology Western Europe region: report from the 2023 ISN global kidney health atlas (ISN-GKHA). Kidney Int. Suppl. 13, 136 (2024).","journal-title":"Kidney Int. Suppl."},{"key":"34724_CR2","doi-asserted-by":"publisher","first-page":"381","DOI":"10.7326\/M22-2904","volume":"176","author":"SD Navaneethan","year":"2023","unstructured":"Navaneethan, S. D. et al. Diabetes management in chronic kidney disease: synopsis of the KDIGO 2022 clinical practice guideline update. Ann. Intern. Med. 176, 381\u2013387 (2023).","journal-title":"Ann. Intern. Med."},{"key":"34724_CR3","doi-asserted-by":"publisher","first-page":"3452","DOI":"10.1681\/ASN.2017060647","volume":"28","author":"TD Hull","year":"2017","unstructured":"Hull, T. D., Agarwal, A. & Hoyt, K. New ultrasound techniques promise further advances in AKI and CKD. J. Am. Soc. Nephrol. 28, 3452\u20133460 (2017).","journal-title":"J. Am. Soc. Nephrol."},{"key":"34724_CR4","doi-asserted-by":"publisher","first-page":"5211","DOI":"10.1007\/s00330-023-09674-1","volume":"33","author":"C Hua","year":"2023","unstructured":"Hua, C. et al. Value of multiparametric magnetic resonance imaging for evaluating chronic kidney disease and renal fibrosis. Eur. Radiol. 33, 5211\u20135221 (2023).","journal-title":"Eur. Radiol."},{"key":"34724_CR5","doi-asserted-by":"publisher","first-page":"894","DOI":"10.3348\/kjr.2018.0540","volume":"20","author":"YM Yu","year":"2019","unstructured":"Yu, Y. M., Ni, Q. Q., Wang, Z. J., Chen, M. L. & Zhang, L. J. Multiparametric functional magnetic resonance imaging for evaluating renal allograft injury. Korean J. Radiol. 20, 894\u2013908 (2019).","journal-title":"Korean J. Radiol."},{"key":"34724_CR6","doi-asserted-by":"publisher","first-page":"4138","DOI":"10.1007\/s00330-022-09329-7","volume":"33","author":"W Mao","year":"2023","unstructured":"Mao, W. et al. Evaluation of interstitial fibrosis in chronic kidney disease by multiparametric functional MRI and histopathologic analysis. Eur. Radiol. 33, 4138\u20134147 (2023).","journal-title":"Eur. Radiol."},{"key":"34724_CR7","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1111\/j.1523-1755.2005.00230.x","volume":"67","author":"S Moghazi","year":"2005","unstructured":"Moghazi, S. et al. Correlation of renal histopathology with sonographic findings. Kidney Int. 67, 1515\u20131520 (2005).","journal-title":"Kidney Int."},{"key":"34724_CR8","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1016\/S0140-6736(16)32064-5","volume":"389","author":"AC Webster","year":"2017","unstructured":"Webster, A. C., Nagler, E. V., Morton, R. L. & Masson, P. Chronic kidney disease. Lancet (London England). 389, 1238\u20131252 (2017).","journal-title":"Lancet (London England)"},{"key":"34724_CR9","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1681\/ASN.2017121260","volume":"29","author":"A Srivastava","year":"2018","unstructured":"Srivastava, A. et al. The prognostic value of histopathologic lesions in native kidney biopsy specimens: results from the Boston kidney biopsy cohort study. J. Am. Soc. Nephrol. 29, 2213\u20132224 (2018).","journal-title":"J. Am. Soc. Nephrol."},{"key":"34724_CR10","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/j.kint.2017.01.002","volume":"91","author":"S Sethi","year":"2017","unstructured":"Sethi, S. et al. A proposal for standardized grading of chronic changes in native kidney biopsy specimens. Kidney Int. 91, 787\u2013789 (2017).","journal-title":"Kidney Int."},{"key":"34724_CR11","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1159\/000515709","volume":"46","author":"F Chen","year":"2021","unstructured":"Chen, F. et al. Evaluation of renal tissue oxygenation using blood oxygen Level-Dependent magnetic resonance imaging in chronic kidney disease. Kidney Blood Press. Res. 46, 441\u2013451 (2021).","journal-title":"Kidney Blood Press. Res."},{"key":"34724_CR12","doi-asserted-by":"publisher","first-page":"e13765","DOI":"10.1111\/eci.13765","volume":"52","author":"S Copur","year":"2022","unstructured":"Copur, S., Yavuz, F., Sag, A. A., Tuttle, K. R. & Kanbay, M. Future of kidney imaging: functional magnetic resonance imaging and kidney disease progression. Eur. J. Clin. Invest. 52, e13765 (2022).","journal-title":"Eur. J. Clin. Invest."},{"key":"34724_CR13","doi-asserted-by":"publisher","first-page":"20170952","DOI":"10.1259\/bjr.20170952","volume":"91","author":"H Liu","year":"2018","unstructured":"Liu, H. et al. Diffusion-weighted imaging for staging chronic kidney disease: A meta-analysis. Br. J. Radiol. 91, 20170952 (2018).","journal-title":"Br. J. Radiol."},{"key":"34724_CR14","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1002\/jmri.29000","volume":"59","author":"K Zhao","year":"2024","unstructured":"Zhao, K., Seeliger, E., Niendorf, T. & Liu, Z. Noninvasive assessment of diabetic kidney disease with MRI: hype or hope? J. Magn. Reson. Imaging. 59, 1494\u20131513 (2024).","journal-title":"J. Magn. Reson. Imaging"},{"key":"34724_CR15","doi-asserted-by":"publisher","DOI":"10.2463\/MRMS.MP.2023-0027","author":"W Mao","year":"2023","unstructured":"Mao, W. et al. Value of T1 mapping in the Non-invasive assessment of renal pathologic injury for chronic kidney disease patients. Magn. Reson. Med. Sci. https:\/\/doi.org\/10.2463\/MRMS.MP.2023-0027 (2023).","journal-title":"Magn. Reson. Med. Sci."},{"key":"34724_CR16","doi-asserted-by":"publisher","first-page":"17361","DOI":"10.1038\/s41598-023-44539-z","volume":"13","author":"K Inoue","year":"2023","unstructured":"Inoue, K. et al. The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network. Sci. Rep. 13, 17361 (2023).","journal-title":"Sci. Rep."},{"key":"34724_CR17","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1097\/RLI.0000000000000633","volume":"55","author":"F Siedek","year":"2020","unstructured":"Siedek, F. et al. Magnetic resonance kidney Parenchyma-T2 as a novel imaging biomarker for autosomal dominant polycystic kidney disease. Invest. Radiol. 55, 217\u2013225 (2020).","journal-title":"Invest. Radiol."},{"key":"34724_CR18","doi-asserted-by":"publisher","unstructured":"Depeursinge, A., Fageot, J. & Al-Kadi, O. S. Fundamentals of texture processing for biomedical image analysis: A general definition and problem formulation. Biomed. Texture Anal. Fundam Tools Challenges. 1\u201327 https:\/\/doi.org\/10.1016\/B978-0-12-812133-7.00001-6 (2017).","DOI":"10.1016\/B978-0-12-812133-7.00001-6"},{"key":"34724_CR19","doi-asserted-by":"publisher","first-page":"520","DOI":"10.2214\/AJR.18.20624","volume":"212","author":"BA Varghese","year":"2019","unstructured":"Varghese, B. A., Cen, S. Y., Hwang, D. H. & Duddalwar, V. A. Texture analysis of imaging: what radiologists need to know. Am. J. Roentgenol. 212, 520\u2013528 (2019).","journal-title":"Am. J. Roentgenol."},{"key":"34724_CR20","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1016\/j.kint.2017.03.026","volume":"92","author":"TL Kline","year":"2017","unstructured":"Kline, T. L. et al. Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease. Kidney Int. 92, 1206\u20131216 (2017).","journal-title":"Kidney Int."},{"key":"34724_CR21","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.mri.2021.05.002","volume":"80","author":"W Mao","year":"2021","unstructured":"Mao, W. et al. Diffusion kurtosis imaging for the assessment of renal fibrosis of chronic kidney disease: A preliminary study. Magn. Reson. Imaging. 80, 113\u2013120 (2021).","journal-title":"Magn. Reson. Imaging"},{"key":"34724_CR22","doi-asserted-by":"publisher","first-page":"14776","DOI":"10.1038\/s41598-022-19009-7","volume":"12","author":"Y Hara","year":"2022","unstructured":"Hara, Y. et al. The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model. Sci. Rep. 12, 14776 (2022).","journal-title":"Sci. Rep."},{"key":"34724_CR23","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s13244-023-01370-4","volume":"14","author":"X. Mo","year":"2023","unstructured":"Mo, X. et al. MRI texture-based machine learning models for the evaluation of renal function on different segmentations: A proof-of-concept study. Insights Imaging 14, 28 (2023).","journal-title":"Insights Imaging"},{"key":"34724_CR24","doi-asserted-by":"publisher","unstructured":"Deng, J. et al. ImageNet: A large-scale hierarchical image database 248\u2013255 (2010). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"34724_CR25","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks."},{"key":"34724_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA54004.2022.9786351","author":"SS Boudouh","year":"2022","unstructured":"Boudouh, S. S. & Bouakkaz, M. Breast cancer: using deep transfer learning techniques AlexNet convolutional neural network for breast tumor detection in mammography images. 2022 7th Int. Conf. Image Signal. Process. Their Appl. ISPA 2022 - Proc. https:\/\/doi.org\/10.1109\/ISPA54004.2022.9786351 (2022).","journal-title":"2022 7th Int. Conf. Image Signal. Process. Their Appl. ISPA 2022 - Proc."},{"key":"34724_CR27","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.compeleceng.2019.03.004","volume":"76","author":"T Shanthi","year":"2019","unstructured":"Shanthi, T. & Sabeenian, R. S. Modified Alexnet architecture for classification of diabetic retinopathy images. Comput. Electr. Eng. 76, 56\u201364 (2019).","journal-title":"Comput. Electr. Eng."},{"key":"34724_CR28","first-page":"101490","volume":"45","author":"H Eldem","year":"2023","unstructured":"Eldem, H., \u00dclker, E. & I\u015f\u0131kl\u0131, O. Y. Alexnet architecture variations with transfer learning for classification of wound images. Eng. Sci. Technol. Int. J. 45, 101490 (2023).","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"34724_CR29","first-page":"770","volume":"2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016, 770\u2013778 (2016).","journal-title":"Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit."},{"key":"34724_CR30","unstructured":"Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. ICLR 2021 9th Int. Conf. Learn. Represent. (2020)."},{"key":"34724_CR31","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.1007\/s00330-014-3250-6","volume":"24","author":"K Hueper","year":"2014","unstructured":"Hueper, K. et al. T1-mapping for assessment of ischemia-induced acute kidney injury and prediction of chronic kidney disease in mice. Eur. Radiol. 24, 2252\u20132260 (2014).","journal-title":"Eur. Radiol."},{"key":"34724_CR32","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.transproceed.2016.10.014","volume":"49","author":"JY Kaimori","year":"2017","unstructured":"Kaimori, J. Y. et al. Diffusion tensor imaging MRI with spin-echo sequence and long-duration measurement for evaluation of renal fibrosis in a rat fibrosis model. Transplant. Proc. 49, 145\u2013152 (2017).","journal-title":"Transplant. Proc."},{"key":"34724_CR33","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1109\/TBME.2018.2879362","volume":"66","author":"E Hodneland","year":"2019","unstructured":"Hodneland, E. et al. In vivo detection of chronic kidney disease using tissue deformation fields from dynamic MR imaging. IEEE Trans. Biomed. Eng. 66, 1779\u20131790 (2019).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"34724_CR34","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10157-017-1413-x","volume":"22","author":"T Otsuka","year":"2018","unstructured":"Otsuka, T. et al. Kidney morphological parameters measured using noncontrast-enhanced steady-state free precession MRI with spatially selective inversion recovery pulse correlate with eGFR in patients with advanced CKD. Clin. Exp. Nephrol. 22, 45\u201354 (2018).","journal-title":"Clin. Exp. Nephrol."},{"key":"34724_CR35","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1093\/ckj\/sfac054","volume":"15","author":"K Makvandi","year":"2022","unstructured":"Makvandi, K. et al. Multiparametric magnetic resonance imaging allows non-invasive functional and structural evaluation of diabetic kidney disease. Clin. Kidney J. 15, 1387\u20131402 (2022).","journal-title":"Clin. Kidney J."},{"key":"34724_CR36","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1148\/radiology.146.2.6849088","volume":"146","author":"H Hricak","year":"1983","unstructured":"Hricak, H., Crooks, L., Sheldon, P. & Kaufman, L. Nuclear magnetic resonance imaging of the kidney. Radiology 146, 425\u2013432 (1983).","journal-title":"Radiology"},{"key":"34724_CR37","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1002\/mrm.1910050207","volume":"5","author":"M Marotti","year":"1987","unstructured":"Marotti, M., Hricak, H., Terrier, F., McAninch, J. W. & Thuroff, J. W. MR in renal disease: importance of cortical-medullary distinction. Magn. Reson. Med. 5, 160\u2013172 (1987).","journal-title":"Magn. Reson. Med."},{"key":"34724_CR38","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1148\/radiology.179.1.2006305","volume":"179","author":"JTS Liou","year":"1991","unstructured":"Liou, J. T. S. et al. Renal transplants: can acute rejection and acute tubular necrosis be differentiated with MR imaging? Radiology 179, 61\u201365 (1991).","journal-title":"Radiology"},{"key":"34724_CR39","doi-asserted-by":"publisher","first-page":"846407","DOI":"10.3389\/fendo.2022.846407","volume":"13","author":"B. Yu","year":"2022","unstructured":"Yu, B. et al. Application of MR imaging features in differentiation of renal changes in patients with stage III type 2 diabetic nephropathy and normal subjects. Front. Endocrinol. (Lausanne) 13, 846407 (2022).","journal-title":"Front. Endocrinol. (Lausanne)"},{"key":"34724_CR40","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.21037\/qims-20-842","volume":"11","author":"G Zhang","year":"2021","unstructured":"Zhang, G. et al. Texture analysis based on quantitative magnetic resonance imaging to assess kidney function: A preliminary study. Quant. Imaging Med. Surg. 11, 1256\u20131270 (2021).","journal-title":"Quant. Imaging Med. Surg."},{"key":"34724_CR41","doi-asserted-by":"publisher","first-page":"R1229","DOI":"10.1152\/ajpregu.00720.2010","volume":"301","author":"F. Palm","year":"2011","unstructured":"Palm, F. & Nordquist, L. Renal oxidative stress, oxygenation, and hypertension. Am. J. Physiol. Regul. Integr. Comp. Physiol. 301, R1229\u2013R1241 (2011).","journal-title":"Am. J. Physiol. Regul. Integr. Comp. Physiol."},{"key":"34724_CR42","doi-asserted-by":"publisher","first-page":"e12999","DOI":"10.1111\/apha.12999","volume":"222","author":"CPC Ow","year":"2018","unstructured":"Ow, C. P. C. et al. Renal hypoxia in kidney disease: Cause or consequence? Acta Physiol. (Oxford). 222, e12999 (2018).","journal-title":"Acta Physiol. (Oxford)"},{"key":"34724_CR43","doi-asserted-by":"publisher","first-page":"952","DOI":"10.2174\/138945007781386884","volume":"8","author":"Y S","year":"2007","unstructured":"S, Y. & K, F. Molecular mechanisms of diabetic nephropathy and its therapeutic intervention. Curr. Drug Targets 8, 952\u2013959 (2007).","journal-title":"Curr. Drug Targets"},{"key":"34724_CR44","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1379\/CSC-270.1","volume":"12","author":"V Calabrese","year":"2007","unstructured":"Calabrese, V. et al. Oxidative stress and cellular stress response in diabetic nephropathy. Cell. Stress Chaperones. 12, 299\u2013306 (2007).","journal-title":"Cell. Stress Chaperones"},{"key":"34724_CR45","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1038\/nrneph.2017.52","volume":"13","author":"C Zoccali","year":"2017","unstructured":"Zoccali, C. et al. The systemic nature of CKD. Nat. Rev. Nephrol. 13, 344\u2013358 (2017).","journal-title":"Nat. Rev. Nephrol."},{"key":"34724_CR46","first-page":"e76498","volume":"16","author":"WN Hauwanga","year":"2024","unstructured":"Hauwanga, W. N. et al. The pathophysiology and vascular complications of diabetes in chronic kidney disease: A comprehensive review. Cureus 16, e76498 (2024).","journal-title":"Cureus"},{"key":"34724_CR47","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1148\/radiology.190.1.8259395","volume":"190","author":"RC Semelka","year":"1994","unstructured":"Semelka, R. C., Corrigan, K., Ascher, S. M., Brown, J. J. & Colindres, R. E. Renal corticomedullary differentiation: observation in patients with differing serum creatinine levels. Radiology 190, 149\u2013152 (1994).","journal-title":"Radiology"},{"key":"34724_CR48","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.2214\/AJR.16.17758","volume":"208","author":"N Schieda","year":"2017","unstructured":"Schieda, N. & Siegelman, E. S. Update on CT and MRI of adrenal nodules. AJR Am. J. Roentgenol. 208, 1206\u20131217 (2017).","journal-title":"AJR Am. J. Roentgenol."},{"key":"34724_CR49","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.2214\/ajr.180.6.1801649","volume":"180","author":"F Fujiyoshi","year":"2003","unstructured":"Fujiyoshi, F., Nakajo, M., Fukukura, Y. & Tsuchimochi, S. Characterization of adrenal tumors by chemical shift fast low-angle shot MR imaging: comparison of four methods of quantitative evaluation. AJR Am. J. Roentgenol. 180, 1649\u20131657 (2003).","journal-title":"AJR Am. J. Roentgenol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34724-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34724-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34724-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T12:02:42Z","timestamp":1770033762000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-34724-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,8]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["34724"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-34724-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,8]]},"assertion":[{"value":"7 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","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":"4507"}}