{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T08:18:54Z","timestamp":1794471534370,"version":"4.9.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"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\/s41598-025-21352-4","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T09:27:42Z","timestamp":1761298062000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence strategies based on random forests for detecting ischemia\u2013reperfusion injury changes in kidney tissue during intravital imaging"],"prefix":"10.1038","volume":"15","author":[{"given":"Igor","family":"Pantic","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jovana","family":"Paunovic Pantic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Valjarevic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jelena","family":"Cumic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiwu","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6796-4301","authenticated-orcid":false,"given":"Peter R.","family":"Corridon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"21352_CR1","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1097\/MNH.0000000000000313","volume":"26","author":"AM Hall","year":"2017","unstructured":"Hall, A. M., Schuh, C. D. & Haenni, D. New frontiers in intravital microscopy of the kidney. Curr. Opin. Nephrol. Hypertens. 26, 172\u2013178 (2017).","journal-title":"Curr. Opin. Nephrol. Hypertens."},{"key":"21352_CR2","doi-asserted-by":"crossref","unstructured":"Scott, C., Neira Agonh, D., White, H., Sultana, S. & Lehmann, C. Intravital microscopy of lipopolysaccharide-induced inflammatory changes in different organ systems-A scoping review. Int. J. Mol. Sci. 24 (2023).","DOI":"10.3390\/ijms242216345"},{"key":"21352_CR3","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/ki.2012.328","volume":"83","author":"AM Hall","year":"2013","unstructured":"Hall, A. M., Rhodes, G. J., Sandoval, R. M., Corridon, P. R. & Molitoris, B. A. In vivo multiphoton imaging of mitochondrial structure and function during acute kidney injury. Kidney Int. 83, 72\u201383 (2013).","journal-title":"Kidney Int."},{"key":"21352_CR4","doi-asserted-by":"crossref","unstructured":"Molitoris, B. A., Sandoval, R. M. & Wagner, M. C. Intravital multiphoton microscopy as a tool for studying renal physiology, pathophysiology and therapeutics. Front. Physiol. 13 (2022).","DOI":"10.3389\/fphys.2022.827280"},{"key":"21352_CR5","doi-asserted-by":"publisher","first-page":"2310","DOI":"10.1021\/acs.biochem.3c00344","volume":"63","author":"AJ Kolb","year":"2024","unstructured":"Kolb, A. J. et al. Sulfotransferase 1C2 increases mitochondrial respiration by converting mitochondrial membrane cholesterol to cholesterol sulfate. Biochemistry 63, 2310\u20132322 (2024).","journal-title":"Biochemistry"},{"key":"21352_CR6","doi-asserted-by":"publisher","first-page":"5289","DOI":"10.1038\/s41598-023-31747-w","volume":"13","author":"PR Corridon","year":"2023","unstructured":"Corridon, P. R. Capturing effects of blood flow on the transplanted decellularized nephron with intravital microscopy. Sci. Rep. 13, 5289 (2023).","journal-title":"Sci. Rep."},{"key":"21352_CR7","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1038\/s41597-022-01685-9","volume":"9","author":"PR Corridon","year":"2022","unstructured":"Corridon, P. R. Intravital microscopy datasets examining key nephron segments of transplanted decellularized kidneys. Sci. Data. 9, 561 (2022).","journal-title":"Sci. Data"},{"key":"21352_CR8","doi-asserted-by":"publisher","first-page":"8280","DOI":"10.1038\/s41598-021-87807-6","volume":"11","author":"PR Corridon","year":"2021","unstructured":"Corridon, P. R., Karam, S. H., Khraibi, A. A., Khan, A. A. & Alhashmi, M. A. Intravital imaging of real-time endogenous actin dysregulation in proximal and distal tubules at the onset of severe ischemia-reperfusion injury. Sci. Rep. 11, 8280 (2021).","journal-title":"Sci. Rep."},{"key":"21352_CR9","doi-asserted-by":"publisher","first-page":"1024238","DOI":"10.3389\/fphys.2023.1024238","volume":"14","author":"PR Corridon","year":"2023","unstructured":"Corridon, P. R. Enhancing the expression of a key mitochondrial enzyme at the inception of ischemia-reperfusion injury can boost recovery and halt the progression of acute kidney injury. Front. Physiol. 14, 1024238 (2023).","journal-title":"Front. Physiol."},{"key":"21352_CR10","doi-asserted-by":"publisher","first-page":"10047","DOI":"10.1038\/s41598-021-89469-w","volume":"11","author":"O Mahmoud","year":"2021","unstructured":"Mahmoud, O., El-Sakka, M. & Janssen, B. G. H. Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy. Sci. Rep. 11, 10047 (2021).","journal-title":"Sci. Rep."},{"key":"21352_CR11","doi-asserted-by":"publisher","first-page":"1105377","DOI":"10.3389\/fbioe.2023.1105377","volume":"11","author":"IV Pantic","year":"2023","unstructured":"Pantic, I. V. et al. Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization. Front. Bioeng. Biotechnol. 11, 1105377 (2023).","journal-title":"Front. Bioeng. Biotechnol."},{"key":"21352_CR12","doi-asserted-by":"publisher","first-page":"4025","DOI":"10.1038\/s41598-023-31205-7","volume":"13","author":"I Pantic","year":"2023","unstructured":"Pantic, I., Cumic, J., Dugalic, S., Petroianu, G. A. & Corridon, P. R. Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury. Sci. Rep. 13, 4025 (2023).","journal-title":"Sci. Rep."},{"key":"21352_CR13","doi-asserted-by":"publisher","first-page":"4025","DOI":"10.1038\/s41598-023-31205-7","volume":"13","author":"I Pantic","year":"2022","unstructured":"Pantic, I., Cumic, J., Dugalic, S., Petroianu, G. & Corridon, P. Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury. Sci. Rep. 13, 4025 (2022).","journal-title":"Sci. Rep."},{"key":"21352_CR14","doi-asserted-by":"publisher","first-page":"2143","DOI":"10.2147\/CMAR.S370477","volume":"14","author":"L Qin","year":"2022","unstructured":"Qin, L. et al. Machine learning-based gray-level co-occurrence matrix (GLCM) models for predicting the depth of myometrial invasion in patients with stage I endometrial cancer. Cancer Manag. Res. 14, 2143\u20132154 (2022).","journal-title":"Cancer Manage. Res."},{"key":"21352_CR15","doi-asserted-by":"publisher","first-page":"809560","DOI":"10.3389\/fcimb.2021.809560","volume":"11","author":"SJ Buckley","year":"2021","unstructured":"Buckley, S. J. & Harvey, R. J. Lessons learnt from using the machine learning random forest algorithm to predict virulence in Streptococcus pyogenes. Front. Cell. Infect. Microbiol. 11, 809560 (2021).","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"21352_CR16","doi-asserted-by":"publisher","first-page":"100347","DOI":"10.1016\/j.jpi.2023.100347","volume":"15","author":"R Gonzalez","year":"2024","unstructured":"Gonzalez, R. et al. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: challenges and opportunities. J. Pathol. Inf. 15, 100347 (2024).","journal-title":"J. Pathol. Inf."},{"key":"21352_CR17","doi-asserted-by":"crossref","unstructured":"Hu, J. & Szymczak, S. A review on longitudinal data analysis with random forest. Brief. Bioinform. 24 (2023).","DOI":"10.1093\/bib\/bbad002"},{"key":"21352_CR18","doi-asserted-by":"publisher","first-page":"109533","DOI":"10.1016\/j.cbi.2021.109533","volume":"345","author":"LM Davidovic","year":"2021","unstructured":"Davidovic, L. M., Laketic, D., Cumic, J., Jordanova, E. & Pantic, I. Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage. Chem. Biol. Interact. 345, 109533 (2021).","journal-title":"Chemico-Biol. Interact."},{"key":"21352_CR19","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.nima.2012.09.006","volume":"702","author":"M Strzelecki","year":"2013","unstructured":"Strzelecki, M., Szczypinski, P., Materka, A. & Klepaczko, A. A software tool for automatic classification and segmentation of 2D\/3D medical images. Nucl. Instrum. Methods Phys. Res. A 702, 137\u2013140 (2013).","journal-title":"Nuclear Instruments Methods Phys. Res. A"},{"key":"21352_CR20","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.cmpb.2008.08.005","volume":"94","author":"P Szczypinski","year":"2009","unstructured":"Szczypinski, P., Strzelecki, M., Materka, A. & Klepaczko, A. MaZda-A software package for image texture analysis. Comput. Methods Programs Biomed. 94, 66\u201376 (2009).","journal-title":"Comput. Methods Programs Biomed."},{"key":"21352_CR21","doi-asserted-by":"crossref","unstructured":"Szczypinski, P., Strzelecki, M. & Materka, A. MaZda-a software for texture analysis. In Proc. of ISITC, November 23\u201323, 2007 245\u2013249 (2007).","DOI":"10.1109\/ISITC.2007.15"},{"key":"21352_CR22","unstructured":"Kocio\u0142ek, M., Materka, A., Strzelecki, M. & Szczypinski, P. Discrete wavelet transform-derived features for digital image texture analysis. In Proc. of Interational Conference onSignals and Electronic Systems, 18\u201321 September 163\u2013168 (Lodz, 2001)."},{"key":"21352_CR23","doi-asserted-by":"crossref","unstructured":"Zheng, Z. et al. The combine of GLCM and group, focuses on the grayscale of medical images. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Vol. 2024, 1\u20134 (2024).","DOI":"10.1109\/EMBC53108.2024.10782101"},{"key":"21352_CR24","doi-asserted-by":"crossref","unstructured":"Oliveira, M. J., Ribeiro, P. & Rodrigues, P. M. Machine learning-driven GLCM analysis of structural MRI for Alzheimer\u2019s disease diagnosis. Bioengineering 11 (2024).","DOI":"10.3390\/bioengineering11111153"},{"key":"21352_CR25","doi-asserted-by":"crossref","unstructured":"Karakas, M. F. & Latifoglu, F. Distinguishing Parkinson\u2019s disease with GLCm features from the Hankelization of EEG signals. Diagnostics 13 (2023).","DOI":"10.3390\/diagnostics13101769"},{"issue":"6","key":"21352_CR26","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"SMC-3","author":"RM Haralick","year":"1973","unstructured":"Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man. Cybern. SMC-3(6), 610\u2013621 (1973).","journal-title":"IEEE Trans. Syst. Man. Cybernetics"},{"key":"21352_CR27","doi-asserted-by":"publisher","first-page":"e14268","DOI":"10.1002\/acm2.14268","volume":"25","author":"C Liu","year":"2024","unstructured":"Liu, C. et al. Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification. J. Appl. Clin. Med. Phys. 25, e14268 (2024).","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"21352_CR28","doi-asserted-by":"publisher","first-page":"e21697","DOI":"10.1016\/j.heliyon.2023.e21697","volume":"9","author":"C Nyasulu","year":"2023","unstructured":"Nyasulu, C. et al. A comparative study of machine learning-based classification of tomato fungal diseases: Application of GLCM texture features. Heliyon 9, e21697 (2023).","journal-title":"Heliyon"},{"key":"21352_CR29","doi-asserted-by":"publisher","first-page":"17368","DOI":"10.1038\/s41598-022-19209-1","volume":"12","author":"SW Fei","year":"2022","unstructured":"Fei, S. W. & Liu, Y. Z. Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM. Sci. Rep. 12, 17368 (2022).","journal-title":"Sci. Rep."},{"key":"21352_CR30","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.mri.2024.03.039","volume":"110","author":"X Zhao","year":"2024","unstructured":"Zhao, X. et al. Three-dimensional texture analyses of multi-quantitative relaxation time maps for evaluating cartilage repair with the treatment of allogeneic human adipose-derived mesenchymal progenitor cells. Magn. Reson. Imaging. 110, 7\u201316 (2024).","journal-title":"Magn. Reson. Imaging"},{"key":"21352_CR31","doi-asserted-by":"publisher","first-page":"111405","DOI":"10.1016\/j.mehy.2024.111405","volume":"189","author":"J Paunovic Pantic","year":"2024","unstructured":"Paunovic Pantic, J., Valjarevic, S., Cumic, J. & Pantic, I. AI-enhanced EEG signal interpretation: A novel approach using texture analysis with random forests. Med. Hypotheses 189, 111405 (2024).","journal-title":"Med. Hypotheses"},{"key":"21352_CR32","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/S0146-664X(75)80008-6","volume":"4","author":"MM Galloway","year":"1975","unstructured":"Galloway, M. M. Texture analysis using Gray level run lengths. Comput. Graph. Image Process. 4, 172\u2013179 (1975).","journal-title":"Comput. Graphics Image Process."},{"key":"21352_CR33","doi-asserted-by":"publisher","first-page":"797283","DOI":"10.3389\/fcvm.2022.797283","volume":"9","author":"IV Pantic","year":"2022","unstructured":"Pantic, I. V., Shakeel, A., Petroianu, G. A. & Corridon, P. R. Analysis of vascular architecture and parenchymal damage generated by reduced blood perfusion in decellularized Porcine kidneys using a Gray level Co-occurrence matrix. Front. Cardiovasc. Med. 9, 797283 (2022).","journal-title":"Front. Cardiovasc. Med."},{"key":"21352_CR34","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40644-020-0296-3","volume":"20","author":"WH Fang","year":"2020","unstructured":"Fang, W. H. et al. Resectable pancreatic ductal adenocarcinoma: Association between preoperative CT texture features and metastatic nodal involvement. Cancer Imaging 20, 17 (2020).","journal-title":"Cancer Imaging: Official Publication Int. Cancer Imaging Soc."},{"key":"21352_CR35","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"S Mallat","year":"1989","unstructured":"Mallat, S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674\u2013693 (1989).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21352_CR36","doi-asserted-by":"publisher","first-page":"e0317414","DOI":"10.1371\/journal.pone.0317414","volume":"20","author":"G Cui","year":"2025","unstructured":"Cui, G. & Wang, C. The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. PloS One 20, e0317414 (2025).","journal-title":"PloS One"},{"key":"21352_CR37","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1186\/s12889-025-22007-9","volume":"25","author":"EA Taye","year":"2025","unstructured":"Taye, E. A. et al. Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis. BMC Public Health 25, 901 (2025).","journal-title":"BMC public. Health"},{"key":"21352_CR38","doi-asserted-by":"publisher","first-page":"7564","DOI":"10.1038\/s41598-025-92248-6","volume":"15","author":"H Liu","year":"2025","unstructured":"Liu, H., Zhou, Y. & Jiang, C. Classifying metro drivers\u2019 cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination. Sci. Rep. 15, 7564 (2025).","journal-title":"Sci. Rep."},{"key":"21352_CR39","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"21352_CR40","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1186\/s12951-025-03392-0","volume":"23","author":"K Ye","year":"2025","unstructured":"Ye, K. et al. Ultrasmall platinum single-atom enzyme alleviates oxidative stress and macrophage polarization induced by acute kidney ischemia-reperfusion injury through Inhibition of cell death storm. J. Nanobiotechnol. 23, 320 (2025).","journal-title":"J. Nanobiotechnol."},{"key":"21352_CR41","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s10735-024-10281-7","volume":"56","author":"ZE Ozgen","year":"2024","unstructured":"Ozgen, Z. E. et al. Involvement of necroptosis and apoptosis in protective effects of cyclosporin a on ischemia-reperfusion injury in rat kidney. J. Mol. Histol. 56, 30 (2024).","journal-title":"J. Mol. Histol."},{"key":"21352_CR42","doi-asserted-by":"publisher","first-page":"175564","DOI":"10.1016\/j.ejphar.2023.175564","volume":"943","author":"S Emre Aydingoz","year":"2023","unstructured":"Emre Aydingoz, S. et al. Effect of hydrogen sulfide on ischemia-reperfusion injury of kidney: A systematic review and meta-analysis of in vivo animal studies. Eur. J. Pharmacol. 943, 175564 (2023).","journal-title":"Eur. J. Pharmacol."},{"key":"21352_CR43","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1017\/S1431927619014594","volume":"25","author":"D Nikolovski","year":"2019","unstructured":"Nikolovski, D., Cumic, J. & Pantic, I. Application of gray level co-occurrence matrix algorithm for detection of discrete structural changes in cell nuclei after exposure to iron oxide nanoparticles and 6-hydroxydopamine. Microsc. Microanal. 25, 982\u2013988 (2019).","journal-title":"Microscopy Microanalysis: Official J. Microscopy Soc. Am. Microbeam Anal. Soc. Microscopical Soc. Can."},{"key":"21352_CR44","doi-asserted-by":"publisher","first-page":"272","DOI":"10.3390\/fractalfract7030272","volume":"7","author":"I Pantic","year":"2023","unstructured":"Pantic, I. et al. Gray level co-occurrence matrix, fractal and wavelet analyses of discrete changes in cell nuclear structure following osmotic stress: focus on machine learning methods. Fractal Fract. 7, 272 (2023).","journal-title":"Fractal Fract."},{"key":"21352_CR45","doi-asserted-by":"publisher","first-page":"490","DOI":"10.3390\/fractalfract8080490","volume":"8","author":"I Pantic","year":"2024","unstructured":"Pantic, I. & Paunovic Pantic, J. Artificial intelligence in chromatin analysis: A random forest model enhanced by fractal and wavelet features. Fractal Fract. 8, 490 (2024).","journal-title":"Fractal Fract."},{"key":"21352_CR46","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.jtbi.2016.02.038","volume":"397","author":"I Pantic","year":"2016","unstructured":"Pantic, I. et al. Fractal analysis and gray level co-occurrence matrix method for evaluation of reperfusion injury in kidney medulla. J. Theor. Biol. 397, 61\u201367 (2016).","journal-title":"J. Theor. Biol."},{"key":"21352_CR47","doi-asserted-by":"publisher","first-page":"e004629","DOI":"10.1161\/CIRCGEN.124.004629","volume":"18","author":"JB Lusk","year":"2025","unstructured":"Lusk, J. B. et al. Random survival forest machine learning for the prediction of cardiovascular events among patients with a measured Lipoprotein(a) level: A model development study. Circ. Genomic Precis. Med. 18, e004629 (2025).","journal-title":"Circulation Genomic Precision Med."},{"key":"21352_CR48","doi-asserted-by":"publisher","first-page":"e37065","DOI":"10.1016\/j.heliyon.2024.e37065","volume":"10","author":"E Asamoah","year":"2024","unstructured":"Asamoah, E., Heuvelink, G. B. M., Chairi, I., Bindraban, P. S. & Logah, V. Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana. Heliyon 10, e37065 (2024).","journal-title":"Heliyon"},{"key":"21352_CR49","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1080\/1061186X.2023.2284096","volume":"32","author":"K Ita","year":"2024","unstructured":"Ita, K. & Prinze, J. Machine learning for skin permeability prediction: Random forest and XG boost regression. J. Drug Target. 32, 57\u201365 (2024).","journal-title":"J. Drug Target."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21352-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21352-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21352-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T10:03:19Z","timestamp":1761300199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21352-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,24]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["21352"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-21352-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,24]]},"assertion":[{"value":"20 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 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":"37334"}}