{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T06:58:01Z","timestamp":1785481081742,"version":"3.56.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Delta University for Science and Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system, underscoring the importance of early and accurate diagnosis. In this study investigates the predictive modelling of MS progression in patients with Clinically Isolated Syndrome (CIS), privacy-preserving for a federated and explainable Machine Learning (ML) framework. To address missing data while preserving inter-feature dependencies, Multivariate Imputation by Chained Equations (MICE) with iterative imputers was employed. Classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model interpretability was developed through Explainable Artificial Intelligence (XAI) techniques, specifically Shapley Additive Explanations (SHAP). To ensure data confidentiality and simulate decentralized clinical environments, an in silico federated learning framework was applied. Experimental results demonstrated strong predictive performance, achieving 96.7% accuracy and 99% ROC\u2013AUC during training, 92.5% accuracy in validation, and 81.8% accuracy with an AUC of 88% on the test set. For the Federated Learning (FL) simulation, the model maintained competitive performance, yielding an accuracy of 76.3% and an AUC of 83.9%. The proposed approach supports early diagnosis, enhances clinical trust through interpretability, and promotes secure data collaboration, thereby contributing to more informed and transparent clinical decision-making and improved patient care.<\/jats:p>","DOI":"10.1038\/s41598-026-63991-1","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:04:47Z","timestamp":1785431087000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3297-0204","authenticated-orcid":false,"given":"Hagar E.","family":"Ghazy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zainab H.","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamer","family":"Medhat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"63991_CR1","doi-asserted-by":"crossref","unstructured":"Xu, W., Huang, Y. & Zhou, R. NLRP3 inflammasome in neuroinflammation and central nervous system diseases. Cell. Mol. Immunol. 1\u201315 (2025).","DOI":"10.1038\/s41423-025-01275-w"},{"issue":"19","key":"63991_CR2","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.1056\/NEJMoa2415988","volume":"392","author":"RJ Fox","year":"2025","unstructured":"Fox, R. J. et al. Tolebrutinib in nonrelapsing secondary progressive multiple sclerosis. N. Engl. J. Med. 392 (19), 1883\u20131892 (2025).","journal-title":"N. Engl. J. Med."},{"key":"63991_CR3","doi-asserted-by":"publisher","first-page":"106366","DOI":"10.1016\/j.msard.2025.106366","volume":"96","author":"S Darvishi","year":"2025","unstructured":"Darvishi, S. et al. Antibiotic drug use in the five years preceding the diagnosis of multiple sclerosis. Multiple Scler. Relat. Disorders. 96, 106366 (2025).","journal-title":"Multiple Scler. Relat. Disorders"},{"issue":"1","key":"63991_CR4","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1186\/s43055-025-01653-4","volume":"56","author":"AJ Ferrao","year":"2025","unstructured":"Ferrao, A. J., Sandeep, S., Fernando, A. & Sangeeth, S. Atrophy patterns and imaging biomarkers in Indian patients with early relapsing remitting multiple sclerosis: addressing geographic gaps. Egypt. J. Radiol. Nuclear Med. 56 (1), 233 (2025).","journal-title":"Egypt. J. Radiol. Nuclear Med."},{"issue":"1","key":"63991_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/08164622.2021.1947745","volume":"105","author":"R Dhanapalaratnam","year":"2022","unstructured":"Dhanapalaratnam, R., Markoulli, M. & Krishnan, A. V. Disorders of vision in multiple sclerosis. Clin. Experimental Optometry. 105 (1), 3\u201312 (2022).","journal-title":"Clin. Experimental Optometry"},{"issue":"15","key":"63991_CR6","doi-asserted-by":"publisher","first-page":"2582","DOI":"10.3390\/diagnostics13152582","volume":"13","author":"J Kufel","year":"2023","unstructured":"Kufel, J. et al. What is machine learning, artificial neural networks and deep learning?\u2014Examples of practical applications in medicine. Diagnostics 13 (15), 2582 (2023).","journal-title":"Diagnostics"},{"issue":"2","key":"63991_CR7","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/JBHI.2022.3185673","volume":"27","author":"O Aouedi","year":"2022","unstructured":"Aouedi, O., Sacco, A., Piamrat, K. & Marchetto, G. Handling privacy-sensitive medical data with federated learning: challenges and future directions. IEEE J. biomedical health Inf. 27 (2), 790\u2013803 (2022).","journal-title":"IEEE J. biomedical health Inf."},{"key":"63991_CR8","doi-asserted-by":"publisher","first-page":"102872","DOI":"10.1016\/j.artmed.2024.102872","volume":"152","author":"L Bai","year":"2024","unstructured":"Bai, L. et al. Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training. Artif. Intell. Med. 152, 102872 (2024).","journal-title":"Artif. Intell. Med."},{"key":"63991_CR9","doi-asserted-by":"publisher","first-page":"108571","DOI":"10.1016\/j.cmpb.2024.108571","volume":"260","author":"Y Liu","year":"2025","unstructured":"Liu, Y., Acharya, U. R. & Tan, J. H. Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation. Comput. Methods Programs Biomed. 260, 108571 (2025).","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1","key":"63991_CR10","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s00607-024-01352-4","volume":"107","author":"TE Ali","year":"2025","unstructured":"Ali, T. E., Ali, F. I., Daki\u0107, P. & Zoltan, A. D. Trends, prospects, challenges, and security in the healthcare internet of things. Computing 107 (1), 28 (2025).","journal-title":"Computing"},{"issue":"1","key":"63991_CR11","doi-asserted-by":"publisher","first-page":"13061","DOI":"10.1038\/s41598-025-95858-2","volume":"15","author":"S Shukla","year":"2025","unstructured":"Shukla, S. et al. Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity. Sci. Rep. 15 (1), 13061 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"63991_CR12","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s00415-025-13537-8","volume":"273","author":"G Bsteh","year":"2026","unstructured":"Bsteh, G. et al. Application of optical coherence tomography in multiple sclerosis: consensus recommendations of the Austrian network (AN-OCT-MS). J. Neurol. 273 (1), 24 (2026).","journal-title":"J. Neurol."},{"issue":"1","key":"63991_CR13","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/s00415-025-13499-x","volume":"273","author":"G Restuccia","year":"2026","unstructured":"Restuccia, G. et al. Serum and cerebrospinal fluid biomarkers as predictors of cognitive impairment in multiple sclerosis: a systematic review of longitudinal studies. J. Neurol. 273 (1), 36 (2026).","journal-title":"J. Neurol."},{"issue":"1","key":"63991_CR14","doi-asserted-by":"publisher","first-page":"101406","DOI":"10.1016\/j.neurad.2025.101406","volume":"53","author":"M Mastilovi\u0107","year":"2026","unstructured":"Mastilovi\u0107, M. et al. Artificial intelligence in the detection of multiple sclerosis plaques: Can it influence the treatment decision? J. Neuroradiol. 53 (1), 101406 (2026).","journal-title":"J. Neuroradiol."},{"key":"63991_CR15","doi-asserted-by":"publisher","first-page":"108329","DOI":"10.1016\/j.bspc.2025.108329","volume":"111","author":"D Dhinakaran","year":"2026","unstructured":"Dhinakaran, D., Ramani, R., Raja, S. E. & Selvaraj, D. Enhancing clinical data security with the contextual polynomial-based data protection model (CPDPM). Biomed. Signal Process. Control. 111, 108329 (2026).","journal-title":"Biomed. Signal Process. Control"},{"key":"63991_CR16","doi-asserted-by":"publisher","first-page":"110924","DOI":"10.1016\/j.compeleceng.2025.110924","volume":"131","author":"A Miloudi","year":"2026","unstructured":"Miloudi, A. et al. Federated learning in healthcare: Recent progress and challenges. Comput. Electr. Eng. 131, 110924 (2026).","journal-title":"Comput. Electr. Eng."},{"issue":"1","key":"63991_CR17","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10238-025-01965-9","volume":"26","author":"CY Hsu","year":"2026","unstructured":"Hsu, C. Y. et al. AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions. Clin. experimental Med. 26 (1), 29 (2026).","journal-title":"Clin. experimental Med."},{"key":"63991_CR18","doi-asserted-by":"publisher","first-page":"105614","DOI":"10.1016\/j.msard.2024.105614","volume":"86","author":"S Rasouli","year":"2024","unstructured":"Rasouli, S. et al. Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach. Multiple Scler. Relat. Disorders. 86, 105614 (2024).","journal-title":"Multiple Scler. Relat. Disorders"},{"issue":"1","key":"63991_CR19","first-page":"1","volume":"5","author":"H Emad","year":"2026","unstructured":"Emad, H., Ali, Z. H. & Medhat, T. Optimized Machine Learning Model of Clinical Isolated Syndrome to Multiple Sclerosis Conversion Prediction. Egypt. J. Artif. Intell. 5 (1), 1\u201310 (2026).","journal-title":"Egypt. J. Artif. Intell."},{"key":"63991_CR20","doi-asserted-by":"publisher","first-page":"100533","DOI":"10.1016\/j.rico.2025.100533","volume":"18","author":"RM Haggag","year":"2025","unstructured":"Haggag, R. M., Ali, E. M., Khalifa, M. E. & Taha, M. Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach. Results Control Optim. 18, 100533 (2025).","journal-title":"Results Control Optim."},{"issue":"2","key":"63991_CR21","doi-asserted-by":"publisher","first-page":"103241","DOI":"10.1016\/j.asej.2024.103241","volume":"16","author":"MG Khattap","year":"2025","unstructured":"Khattap, M. G. et al. Advancing multiple sclerosis diagnosis through an innovative hybrid AI framework incorporating Multi-view ResNet and quantum RIME-inspired metaheuristics. Ain Shams Eng. J. 16 (2), 103241 (2025).","journal-title":"Ain Shams Eng. J."},{"key":"63991_CR22","doi-asserted-by":"crossref","unstructured":"Amini, A. et al. STRIKE-MS: Synergistic texture and raw intensity knowledge extraction for active MS lesion detection on non-contrast MRI. IEEE Access (2025).","DOI":"10.1109\/ACCESS.2025.3609133"},{"key":"63991_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, H. et al. Predicting conversion from clinically isolated syndrome to multiple sclerosis\u2013An imaging-based machine learning approach. Clinical. 21, 101593 (2019).","DOI":"10.1016\/j.nicl.2018.11.003"},{"key":"63991_CR24","doi-asserted-by":"publisher","first-page":"108530","DOI":"10.1016\/j.compbiomed.2024.108530","volume":"176","author":"W Xu","year":"2024","unstructured":"Xu, W. et al. Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network. Comput. Biol. Med. 176, 108530 (2024).","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"63991_CR25","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1007\/s10687-023-00473-x","volume":"26","author":"J Velthoen","year":"2023","unstructured":"Velthoen, J., Dombry, C., Cai, J. J. & Engelke, S. Gradient boosting for extreme quantile regression. Extremes 26 (4), 639\u2013667 (2023).","journal-title":"Extremes"},{"issue":"22","key":"63991_CR26","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s12665-021-10054-5","volume":"80","author":"H Sahour","year":"2021","unstructured":"Sahour, H., Gholami, V., Torkaman, J., Vazifedan, M. & Saeedi, S. Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features and tree-rings. Environ. Earth Sci. 80 (22), 747 (2021).","journal-title":"Environ. Earth Sci."},{"issue":"Suppl 2","key":"63991_CR27","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1007\/s00366-020-01260-z","volume":"38","author":"H Nguyen","year":"2022","unstructured":"Nguyen, H., Nguyen, N. M., Cao, M. T., Hoang, N. D. & Tran, X. L. Prediction of long-term deflections of reinforced-concrete members using a novel swarm optimized extreme gradient boosting machine. Eng. Comput. 38 (Suppl 2), 1255\u20131267 (2022).","journal-title":"Eng. Comput."},{"issue":"1","key":"63991_CR28","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/S1474-4422(22)00184-3","volume":"22","author":"JS Graves","year":"2023","unstructured":"Graves, J. S. et al. Ageing and multiple sclerosis. Lancet Neurol. 22 (1), 66\u201377 (2023).","journal-title":"Lancet Neurol."},{"issue":"1","key":"63991_CR29","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1186\/s12905-023-02501-1","volume":"23","author":"A Yazdani","year":"2023","unstructured":"Yazdani, A., Ebrahimi, N., Mirmosayyeb, O. & Ghajarzadeh, M. Prevalence and risk of developing sexual dysfunction in women with multiple sclerosis (MS): a systematic review and meta-analysis. BMC Women\u2019s Health. 23 (1), 352 (2023).","journal-title":"BMC Women\u2019s Health"},{"issue":"11","key":"63991_CR30","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1016\/S1474-4422(23)00281-8","volume":"22","author":"C Lebrun-Frenay","year":"2023","unstructured":"Lebrun-Frenay, C. et al. Radiologically isolated syndrome. Lancet Neurol. 22 (11), 1075\u20131086 (2023).","journal-title":"Lancet Neurol."},{"key":"63991_CR31","doi-asserted-by":"crossref","unstructured":"Zaman, W., Siddique, M. F., Khan, S. U., Kim, J. & Kim, J. M. Advanced fault diagnosis in rotary machines using optimized transfer learning. IEEE Access (2026).","DOI":"10.1109\/ACCESS.2026.3675976"},{"key":"63991_CR32","doi-asserted-by":"crossref","unstructured":"Siddique, M. F., Zaman, W., Khalid, M., Hamdan, B. & Kim, J. M. A multistage transfer learning framework for intelligent fault diagnosis of rotating machinery under variable operating conditions. Sci. Rep. (2026).","DOI":"10.1038\/s41598-026-47377-x"},{"key":"63991_CR33","doi-asserted-by":"crossref","unstructured":"Arteaga-Noriega, A. et al. Factors associated with the progression of disability (EDSS) among MS patients. eNeurologicalSci, 100602. (2025).","DOI":"10.1016\/j.ensci.2025.100602"},{"key":"63991_CR34","doi-asserted-by":"publisher","unstructured":"Pineda, B., De Flores Rivera, J. & Jesus Conversion predictors of clinically isolated syndrome to multiple sclerosis in Mexican patients: A prospective study. Mendeley Data. V1 https:\/\/doi.org\/10.17632\/8wk5hjx7x2.1 (2023).","DOI":"10.17632\/8wk5hjx7x2.1"},{"issue":"5","key":"63991_CR35","doi-asserted-by":"publisher","first-page":"102843","DOI":"10.1016\/j.arcmed.2023.102843","volume":"54","author":"V Chavarria","year":"2023","unstructured":"Chavarria, V. et al. Conversion predictors of clinically isolated syndrome to multiple sclerosis in Mexican patients: a prospective study. Arch. Med. Res. 54 (5), 102843 (2023).","journal-title":"Arch. Med. Res."},{"issue":"1","key":"63991_CR36","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1002\/mpr.329","volume":"20","author":"MJ Azur","year":"2011","unstructured":"Azur, M. J., Stuart, E. A., Frangakis, C. & Leaf, P. J. Multiple imputation by chained equations: what is it and how does it work? Int. J. Methods Psychiatr. Res. 20 (1), 40\u201349 (2011).","journal-title":"Int. J. Methods Psychiatr. Res."},{"issue":"1","key":"63991_CR37","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13040-023-00322-4","volume":"16","author":"D Chicco","year":"2023","unstructured":"Chicco, D. & Jurman, G. The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Min. 16 (1), 4 (2023).","journal-title":"BioData Min."},{"key":"63991_CR38","doi-asserted-by":"crossref","unstructured":"Pandi, S. S. & Chiranjeevi, V. R. Improvement of classification accuracy in machine learning algorithm by hyper-parameter optimization. In 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) (pp. 1\u20135). IEEE. (2023), November.","DOI":"10.1109\/RMKMATE59243.2023.10369177"},{"key":"63991_CR39","doi-asserted-by":"crossref","unstructured":"Ekanayake, I. U., Meddage, D. P. P. & Rathnayake, U. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Stud. Constr. Mater., 16, e01059. (2022).","DOI":"10.1016\/j.cscm.2022.e01059"},{"key":"63991_CR40","doi-asserted-by":"publisher","first-page":"126641","DOI":"10.1016\/j.apenergy.2025.126641","volume":"401","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y. et al. Dominant factor identification and fast optimization of carnot battery by integrating SHAP and physics-guided neural network. Appl. Energy. 401, 126641 (2025).","journal-title":"Appl. Energy"},{"issue":"1","key":"63991_CR41","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1080\/13880209.2023.2229379","volume":"61","author":"E Hu","year":"2023","unstructured":"Hu, E. et al. Metabolomics reveals the effects of hydroxysafflor yellow A on neurogenesis and axon regeneration after experimental traumatic brain injury. Pharm. Biol. 61 (1), 1054\u20131064 (2023).","journal-title":"Pharm. Biol."},{"issue":"1","key":"63991_CR42","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1109\/TPWRS.2023.3248941","volume":"39","author":"RI Hamilton","year":"2023","unstructured":"Hamilton, R. I. & Papadopoulos, P. N. Using SHAP values and machine learning to understand trends in the transient stability limit. IEEE Trans. Power Syst. 39 (1), 1384\u20131397 (2023).","journal-title":"IEEE Trans. Power Syst."},{"key":"63991_CR43","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1016\/j.ast.2022.107993","volume":"131","author":"Y Shen","year":"2022","unstructured":"Shen, Y., Huang, W., Yan, L., Wang, Z. G. & Xu, D. F. An automatic visible explainer of geometric knowledge for aeroshape design optimization based on SHAP. Aerosp. Sci. Technol. 131, 1079 (2022).","journal-title":"Aerosp. Sci. Technol."},{"issue":"1","key":"63991_CR44","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s13755-025-00370-6","volume":"13","author":"Y Qian","year":"2025","unstructured":"Qian, Y., Wang, Q., Yin, L. & Lu, A. Y. CF-DTI: coarse-to-fine feature extraction for enhanced drug-target interaction prediction. Health Inform. Sci. Syst. 13 (1), 55 (2025).","journal-title":"Health Inform. Sci. Syst."},{"issue":"7","key":"63991_CR45","doi-asserted-by":"publisher","first-page":"610","DOI":"10.2174\/1574893618666230504143647","volume":"18","author":"X Hu","year":"2023","unstructured":"Hu, X. et al. Predicting herb-disease associations through graph convolutional network. Curr. Bioinform. 18 (7), 610\u2013619 (2023).","journal-title":"Curr. Bioinform."},{"key":"63991_CR46","doi-asserted-by":"crossref","unstructured":"Su, Q. et al. LeF-MTP: Prioritizing GNN test cases by fusing model uncertainty and feature\u2010space confusability. Comput. Intell. 42(1), e70199. (2026).","DOI":"10.1111\/coin.70199"},{"key":"63991_CR47","doi-asserted-by":"publisher","first-page":"100604","DOI":"10.1109\/ACCESS.2023.3312343","volume":"11","author":"M Alabdulhafith","year":"2023","unstructured":"Alabdulhafith, M. et al. A clinical decision support system for edge\/cloud ICU readmission model based on particle swarm optimization, ensemble machine learning, and explainable artificial intelligence. IEEE Access. 11, 100604\u2013100621 (2023).","journal-title":"IEEE Access."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-63991-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-63991-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-63991-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T06:03:42Z","timestamp":1785477822000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-63991-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,30]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["63991"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-63991-1","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,30]]},"assertion":[{"value":"20 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2026","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","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The original database was approved by the National Institute of Neurology and Neurosurgery (NINN) Institutional Review Board (Approval No. 28\/11), and written informed consent was obtained from all participants, as stated by V\u00edctor Chavarria et al.\n                      35\n                      . Therefore, when dealing with the data in this study, no additional institutional ethical approval was required.","order":2,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"23654"}}