{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T03:49:10Z","timestamp":1792036150197,"version":"4.4.0"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing Municipality","doi-asserted-by":"publisher","award":["CSTB2024NSCQ-MSX1087"],"award-info":[{"award-number":["CSTB2024NSCQ-MSX1087"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018571","name":"Specific Research Project of Guangxi for Research Bases and Talents","doi-asserted-by":"publisher","award":["AD23023001"],"award-info":[{"award-number":["AD23023001"]}],"id":[{"id":"10.13039\/501100018571","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072126"],"award-info":[{"award-number":["62072126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Machine learning shows great potential in science but struggles with complex, high-dimensional multi-omics data. PD progression is long, diagnosed mainly by clinical signs. This paper proposes a novel decision fusion method to improve the precision of the classification of progression of PD using imaging with clinical data.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    A Cross-Modal Fusion Prediction Model (CMFP) is proposed, with key steps that involve data preparation, modelling, and prediction. The data encompasses three modalities: clinical, DTI (diffusion tensor imaging), and DAT (dopamine transporter), with Lasso used for the selection of features. Individual modalities are classified using AdaBoost and the results are integrated into the new fusion strategy, CMF, to obtain a novel model. Finally, this model is used for predictions.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    The predictive performance of CMFP on the progression of PD achieved an AUC of 77.91%. This represents improvements of 24.48%, 30.78%, and 32.7% in AUC compared to predictions solely with clinical data, DTI data and DAT data, respectively. The combined prediction of clinical and DTI data demonstrated statistical significance compared to predictions based solely on clinical data, with a p-value of 9.183e-4. Additionally, this method identified crucial brain regions and important clinical metrics associated with PD. It should be noted that using the DTI metric along the perivascular space (DTI-ALPS) to predict and evaluate the progression of PD has relatively more advantages compared to DTI-clinical fusion prediction. Among them, the ACC can increase by 3.85%.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    The results indicate that CMFP is effective, contributing to overcoming the limitations of low predictive performance in single-modal data and enhancing the accuracy of the PD progression predictions.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0333822","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:34:55Z","timestamp":1763663695000},"page":"e0333822","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cross-modal fusion of brain imaging and clinical data for Parkinson\u2019s disease progression prediction"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9312-8289","authenticated-orcid":true,"given":"Jinyu","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meie","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhua","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"pone.0333822.ref001","doi-asserted-by":"crossref","unstructured":"Kumar T, Sharma P, Prakash N. Comparison of machine learning models for Parkinson\u2019s Disease prediction. In: 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). 2020. p. 195\u20139. https:\/\/doi.org\/10.1109\/uemcon51285.2020.9298033","DOI":"10.1109\/UEMCON51285.2020.9298033"},{"issue":"10","key":"pone.0333822.ref002","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1111\/cns.12410","article-title":"Propagation of pathology through brain networks in neurodegenerative diseases: from molecules to clinical phenotypes","volume":"21","author":"F Agosta","year":"2015","journal-title":"CNS Neurosci Ther."},{"issue":"2","key":"pone.0333822.ref003","first-page":"74","article-title":"Neuroprotection and classification of neurologic dysfunction in aortic arch surgery: a narrative review","volume":"8","author":"JZ Qu","year":"2023","journal-title":"Heart and Mind."},{"issue":"5","key":"pone.0333822.ref004","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1007\/s00415-008-0782-1","article-title":"Clinical heterogeneity in newly diagnosed Parkinson\u2019s disease","volume":"255","author":"B Post","year":"2008","journal-title":"J Neurol."},{"issue":"1","key":"pone.0333822.ref005","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41531-022-00439-z","article-title":"Identification and prediction of Parkinson\u2019s disease subtypes and progression using machine learning in two cohorts","volume":"8","author":"A Dadu","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"issue":"4","key":"pone.0333822.ref006","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s12311-021-01355-3","article-title":"Cerebellar microstructural abnormalities in Parkinson\u2019s disease: a systematic review of diffusion tensor imaging studies","volume":"21","author":"M Haghshomar","year":"2022","journal-title":"Cerebellum."},{"key":"pone.0333822.ref007","doi-asserted-by":"crossref","first-page":"103548","DOI":"10.1016\/j.nicl.2023.103548","article-title":"Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson\u2019s disease","volume":"41","author":"X Huang","year":"2024","journal-title":"Neuroimage Clin."},{"issue":"1","key":"pone.0333822.ref008","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1038\/s41531-023-00592-z","article-title":"White matter changes in Parkinson\u2019s disease","volume":"9","author":"K Yang","year":"2023","journal-title":"NPJ Parkinsons Dis."},{"key":"pone.0333822.ref009","first-page":"481","article-title":"Diffusion tensor imaging of nigral degeneration in Parkinson\u2019s disease: a region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis. NeuroImage:","volume":"3","author":"ST Schwarz","year":"2013","journal-title":"Clinical."},{"issue":"1","key":"pone.0333822.ref010","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/s41531-022-00372-1","article-title":"Longitudinal corpus callosum microstructural decline in early-stage Parkinson\u2019s disease in association with akinetic-rigid symptom severity","volume":"8","author":"M Amandola","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"issue":"3","key":"pone.0333822.ref011","doi-asserted-by":"crossref","first-page":"165","DOI":"10.4103\/hm.HM-D-23-00061","article-title":"The utility of artificial intelligence and machine learning in the diagnosis of Takotsubo cardiomyopathy: a systematic review","volume":"8","author":"H Huang","year":"2024","journal-title":"Heart and Mind."},{"key":"pone.0333822.ref012","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.still.2014.11.002","article-title":"Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content","volume":"146","author":"B Kuang","year":"2015","journal-title":"Soil and Tillage Research."},{"key":"pone.0333822.ref013","doi-asserted-by":"crossref","first-page":"107788","DOI":"10.1016\/j.compbiolchem.2022.107788","article-title":"Early diagnosis of Parkinson\u2019s disease: a combined method using deep learning and neuro-fuzzy techniques","volume":"102","author":"M Nilashi","year":"2023","journal-title":"Comput Biol Chem."},{"key":"pone.0333822.ref014","article-title":"Hereditary-Genealogical features of Parkinson\u2019s disease and their early detection of the disease","author":"EI Abdukodirov","year":"2022","journal-title":"IJHS."},{"key":"pone.0333822.ref015","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.ejmp.2019.12.022","article-title":"Machine learning methods for optimal prediction of motor outcome in Parkinson\u2019s disease","volume":"69","author":"MR Salmanpour","year":"2020","journal-title":"Phys Med."},{"issue":"1","key":"pone.0333822.ref016","first-page":"1","article-title":"Parkinson\u2019s Disease Prediction Using Artificial Neural Network","volume":"3","author":"RM Sadek","year":"2019","journal-title":"International Journal of Academic Health and Medical Research (IJAHMR)."},{"key":"pone.0333822.ref017","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.procs.2023.01.007","article-title":"Early detection of Parkinson\u2019s disease using machine learning","volume":"218","author":"A Govindu","year":"2023","journal-title":"Procedia Computer Science."},{"issue":"1","key":"pone.0333822.ref018","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1186\/s12874-017-0415-4","article-title":"Predicting the multi-domain progression of Parkinson\u2019s disease: a Bayesian multivariate generalized linear mixed-effect model","volume":"17","author":"M Wang","year":"2017","journal-title":"BMC Med Res Methodol."},{"key":"pone.0333822.ref019","article-title":"Automated methods for diagnosis of Parkinson\u2019s disease and predicting severity level","author":"Z Ayaz","year":"2022","journal-title":"Neural Comput & Applic."},{"issue":"1","key":"pone.0333822.ref020","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/s41531-022-00288-w","article-title":"Multi-modality machine learning predicting Parkinson\u2019s disease","volume":"8","author":"MB Makarious","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"key":"pone.0333822.ref021","doi-asserted-by":"crossref","first-page":"102715","DOI":"10.1016\/j.nicl.2021.102715","article-title":"Alteration of brain structural connectivity in progression of Parkinson\u2019s disease: a connectome-wide network analysis","volume":"31","author":"Y Yang","year":"2021","journal-title":"Neuroimage Clin."},{"key":"pone.0333822.ref022","doi-asserted-by":"crossref","unstructured":"Jain S, Shetty S. Improving accuracy in noninvasive telemonitoring of progression of Parkinson\u2019s disease using two-step predictive model. In: 2016 Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA); 2016. p. 104\u20139.","DOI":"10.1109\/EECEA.2016.7470774"},{"key":"pone.0333822.ref023","doi-asserted-by":"crossref","unstructured":"Anisha CD, Arulanand N. Early Prediction of Parkinson\u2019s Disease (PD) using ensemble classifiers. In: 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), 2020. https:\/\/doi.org\/10.1109\/icitiit49094.2020.9071562","DOI":"10.1109\/ICITIIT49094.2020.9071562"},{"key":"pone.0333822.ref024","doi-asserted-by":"crossref","first-page":"1142558","DOI":"10.3389\/fnagi.2023.1142558","article-title":"Predictors of cognitive impairment in newly diagnosed Parkinson\u2019s disease with normal cognition at baseline: a 5-year cohort study","volume":"15","author":"J Chen","year":"2023","journal-title":"Front Aging Neurosci."},{"key":"pone.0333822.ref025","doi-asserted-by":"crossref","first-page":"731109","DOI":"10.3389\/fnins.2021.731109","article-title":"Predicting motor outcome of subthalamic nucleus deep brain stimulation for Parkinson\u2019s disease using quantitative susceptibility mapping and radiomics: a pilot study","volume":"15","author":"Y Liu","year":"2021","journal-title":"Front Neurosci."},{"key":"pone.0333822.ref026","doi-asserted-by":"crossref","first-page":"461","DOI":"10.3389\/fgene.2019.00461","article-title":"Clinical-pharmacogenetic predictive models for time to occurrence of levodopa related motor complications in Parkinson\u2019s disease","volume":"10","author":"S Reden\u0161ek","year":"2019","journal-title":"Front Genet."},{"issue":"6","key":"pone.0333822.ref027","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0233296","article-title":"Identifying and predicting Parkinson\u2019s disease subtypes through trajectory clustering via bipartite networks","volume":"15","author":"S Krishnagopal","year":"2020","journal-title":"PLoS One."},{"key":"pone.0333822.ref028","doi-asserted-by":"crossref","first-page":"765765","DOI":"10.3389\/fnins.2021.765765","article-title":"Hoehn and yahr stage and striatal dat-SPECT uptake are predictors of Parkinson\u2019s disease motor progression","volume":"15","author":"H Jackson","year":"2021","journal-title":"Front Neurosci."},{"issue":"6","key":"pone.0333822.ref029","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s11307-019-01334-5","article-title":"Artificial neural network-based prediction of outcome in Parkinson\u2019s Disease patients using DaTscan SPECT imaging features","volume":"21","author":"J Tang","year":"2019","journal-title":"Mol Imaging Biol."},{"issue":"2","key":"pone.0333822.ref030","doi-asserted-by":"crossref","first-page":"906","DOI":"10.21037\/qims-21-425","article-title":"Longitudinal clustering analysis and prediction of Parkinson\u2019s disease progression using radiomics and hybrid machine learning","volume":"12","author":"MR Salmanpour","year":"2022","journal-title":"Quant Imaging Med Surg."},{"issue":"3","key":"pone.0333822.ref031","article-title":"Retinal age gap as a predictive biomarker of future risk of Parkinson\u2019s disease","volume":"51","author":"W Hu","year":"2022","journal-title":"Age Ageing."},{"issue":"1","key":"pone.0333822.ref032","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41531-022-00412-w","article-title":"Genetically-informed prediction of short-term Parkinson\u2019s disease progression","volume":"8","author":"HJ Sadaei","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"issue":"1","key":"pone.0333822.ref033","article-title":"CSF \u03b2-amyloid42 and risk of freezing of gait in early Parkinson disease","volume":"92","author":"R Kim","year":"2019","journal-title":"Neurology."},{"issue":"1","key":"pone.0333822.ref034","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1038\/s41531-022-00442-4","article-title":"Baseline cerebral structural morphology predict freezing of gait in early drug-na\u00efve Parkinson\u2019s disease","volume":"8","author":"Y Li","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"issue":"3","key":"pone.0333822.ref035","doi-asserted-by":"crossref","DOI":"10.1111\/cns.14651","article-title":"Predictors for early-onset psychotic symptoms in patients newly diagnosed with Parkinson\u2019s disease without psychosis at baseline: a 5-year cohort study","volume":"30","author":"J Chen","year":"2024","journal-title":"CNS Neurosci Ther."},{"issue":"1","key":"pone.0333822.ref036","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/s41531-021-00208-4","article-title":"Clinical classification systems and long-term outcome in mid- and late-stage Parkinson\u2019s disease","volume":"7","author":"E Ygland R\u00f6dstr\u00f6m","year":"2021","journal-title":"NPJ Parkinsons Dis."},{"issue":"4","key":"pone.0333822.ref037","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1002\/mdc3.12476","article-title":"Differences in MDS-UPDRS scores based on Hoehn and Yahr stage and disease duration","volume":"4","author":"M Skorvanek","year":"2017","journal-title":"Mov Disord Clin Pract."},{"issue":"4","key":"pone.0333822.ref038","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s00415-019-09645-x","article-title":"Changes of brain structural network connection in Parkinson\u2019s disease patients with mild cognitive dysfunction: a study based on diffusion tensor imaging","volume":"267","author":"W Wang","year":"2020","journal-title":"J Neurol."},{"issue":"1","key":"pone.0333822.ref039","doi-asserted-by":"crossref","first-page":"5","DOI":"10.4103\/hm.hm_50_22","article-title":"Exercise and the brain in cardiovascular disease: a narrative review","volume":"7","author":"JL Taylor","year":"2023","journal-title":"Heart and Mind."},{"issue":"8","key":"pone.0333822.ref040","first-page":"1460","article-title":"Comparative study of MRI biomarkers in the substantia Nigra to discriminate idiopathic Parkinson Disease","volume":"39","author":"N Pyatigorskaya","year":"2018","journal-title":"AJNR Am J Neuroradiol."},{"key":"pone.0333822.ref041","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3389\/fnins.2016.00131","article-title":"Multimodal imaging signatures of Parkinson\u2019s Disease","volume":"10","author":"FD Bowman","year":"2016","journal-title":"Front Neurosci."},{"key":"pone.0333822.ref042","doi-asserted-by":"crossref","first-page":"1018017","DOI":"10.3389\/fnagi.2022.1018017","article-title":"Recent advances in using diffusion tensor imaging to study white matter alterations in Parkinson\u2019s disease: a mini review","volume":"14","author":"Y-C Shih","year":"2023","journal-title":"Front Aging Neurosci."},{"key":"pone.0333822.ref043","doi-asserted-by":"crossref","unstructured":"Lei H, Huang Z, Elazab A, Li H, Lei B. Longitudinal and multi-modal data learning via joint embedding and sparse regression for Parkinson\u2019s disease diagnosis. In: International Workshop on Machine Learning in Medical Imaging. 2018. p. 310\u20138.","DOI":"10.1007\/978-3-030-00919-9_36"},{"key":"pone.0333822.ref044","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.eswa.2017.03.038","article-title":"Joint detection and clinical score prediction in Parkinson\u2019s disease via multi-modal sparse learning","volume":"80","author":"H Lei","year":"2017","journal-title":"Expert Systems with Applications."},{"issue":"2","key":"pone.0333822.ref045","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1002\/mds.28348","article-title":"Automated categorization of Parkinsonian syndromes using magnetic resonance imaging in a clinical setting","volume":"36","author":"L Chougar","year":"2021","journal-title":"Mov Disord."},{"issue":"2","key":"pone.0333822.ref046","doi-asserted-by":"crossref","first-page":"227","DOI":"10.3390\/brainsci12020227","article-title":"White matter microstructural alterations in newly diagnosed Parkinson\u2019s Disease: a whole-brain analysis using dMRI","volume":"12","author":"J-Y Kim","year":"2022","journal-title":"Brain Sci."},{"issue":"2","key":"pone.0333822.ref047","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1148\/radiol.2021203383","article-title":"Cortical thickness from MRI to predict conversion from mild cognitive impairment to dementia in Parkinson Disease: a machine learning-based model","volume":"300","author":"N-Y Shin","year":"2021","journal-title":"Radiology."},{"issue":"3","key":"pone.0333822.ref048","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1016\/j.neuroimage.2010.10.026","article-title":"Enriched white matter connectivity networks for accurate identification of MCI patients","volume":"54","author":"C-Y Wee","year":"2011","journal-title":"Neuroimage."},{"issue":"4","key":"pone.0333822.ref049","doi-asserted-by":"crossref","first-page":"3086","DOI":"10.21037\/qims-23-1478","article-title":"White matter biomarker for predicting de novo Parkinson\u2019s disease using tract-based spatial statistics: a machine learning-based model","volume":"14","author":"Q Zhang","year":"2024","journal-title":"Quant Imaging Med Surg."},{"key":"pone.0333822.ref050","doi-asserted-by":"crossref","first-page":"107089","DOI":"10.1016\/j.parkreldis.2024.107089","article-title":"Early detection of dopaminergic dysfunction and glymphatic system impairment in Parkinson\u2019s disease","volume":"127","author":"J Yao","year":"2024","journal-title":"Parkinsonism Relat Disord."},{"issue":"4","key":"pone.0333822.ref051","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s11604-017-0617-z","article-title":"Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer\u2019s disease cases","volume":"35","author":"T Taoka","year":"2017","journal-title":"Jpn J Radiol."},{"issue":"1","key":"pone.0333822.ref052","article-title":"Associations among Cognitive functions, plasma DNA, and diffusion tensor image along the perivascular space (DTI-ALPS) in patients with Parkinson\u2019s Disease","volume":"2021","author":"HL Chen","year":"2021","journal-title":"Oxidative Medicine and Cellular Longevity."},{"issue":"1","key":"pone.0333822.ref053","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s00415-023-12004-6","article-title":"Correlation of glymphatic system abnormalities with Parkinson\u2019s disease progression: a clinical study based on non-invasive fMRI","volume":"271","author":"J-C Meng","year":"2024","journal-title":"J Neurol."},{"issue":"9","key":"pone.0333822.ref054","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1002\/mds.29908","article-title":"Diffusion tensor imaging-along the perivascular-space index is associated with disease progression in Parkinson\u2019s Disease","volume":"39","author":"KH Wood","year":"2024","journal-title":"Mov Disord."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0333822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:35:04Z","timestamp":1763663704000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0333822"}},"subtitle":[],"editor":[{"given":"Nima","family":"Broomand Lomer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":54,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11,20]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0333822","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]}}}