{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:15:12Z","timestamp":1778645712560,"version":"3.51.4"},"reference-count":62,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Latent tuberculosis infection (LTBi) affects nearly a quarter of the global population, yet current diagnostic methods are limited by low sensitivity and specificity. This study applied an integrative bioinformatics framework, incorporating machine learning techniques, to identify robust gene expression biomarkers associated with LTBi. We analyzed four publicly available transcriptomic datasets from peripheral blood mononuclear cells (PBMCs), representing latent, active, and healthy states. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO) enrichment, functional clustering, and miRNA interaction analysis. Semantic similarity, unsupervised clustering, and pathway enrichment were applied to refine the gene list. Key biomarkers were prioritized using receiver operating characteristic (ROC) curve analysis, with CCL2 and CXCL10 emerging as top candidates (AUC &gt; 0.85). This multi-step approach demonstrates the potential of combining transcriptomic profiling with established machine learning and bioinformatics tools to uncover candidate biomarkers for improved LTBi detection, and it also provides a foundation for future experimental validation.<\/jats:p>","DOI":"10.3390\/genes16060715","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:08:06Z","timestamp":1750219686000},"page":"715","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identification of Gene Expression Biomarkers Predictive of Latent Tuberculosis Infection Using Machine Learning Approaches"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9398-1942","authenticated-orcid":false,"given":"Youssra","family":"Boumait","sequence":"first","affiliation":[{"name":"Biology Molecular Unit, Life and Health Sciences Laboratory, Faculty of Medicine and Pharmacy, Abdelmalek Essa\u00e2di University, Tangier 93000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6630-6382","authenticated-orcid":false,"given":"Boutaina","family":"Ettetuani","sequence":"additional","affiliation":[{"name":"Systems and Data Engineering Team, National School of Applied Sciences, Abdelmalek Essa\u00e2di University, Tangier 93000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manal","family":"Chrairi","sequence":"additional","affiliation":[{"name":"Biology Molecular Unit, Life and Health Sciences Laboratory, Faculty of Medicine and Pharmacy, Abdelmalek Essa\u00e2di University, Tangier 93000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Afaf","family":"Lamzouri","sequence":"additional","affiliation":[{"name":"Genetic Unit, Life and Health Sciences Laboratory, Faculty of Medicine and Pharmacy, Abdelmalek Essa\u00e2di University, Tangier 93000, Morocco"},{"name":"Department of Medical Genetics and Oncogenetics, Mohammed VI University Hospital, Tangier 90000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6063-4048","authenticated-orcid":false,"given":"Rajaa","family":"Chahboune","sequence":"additional","affiliation":[{"name":"Biology Molecular Unit, Life and Health Sciences Laboratory, Faculty of Medicine and Pharmacy, Abdelmalek Essa\u00e2di University, Tangier 93000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.1016\/S2666-5247(22)00359-7","article-title":"WHO\u2019s global tuberculosis report 2022","volume":"4","author":"Bagcchi","year":"2023","journal-title":"Lancet Microbe"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1016\/j.cmi.2023.07.014","article-title":"Update on the diagnosis of tuberculosis","volume":"30","author":"Kontsevaya","year":"2023","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Esmail, H., Barry, C., Young, D., and Wilkinson, R. (2014). The ongoing challenge of latent tuberculosis. Philos. Trans. R. Soc. Biol. Sci., 369.","DOI":"10.1098\/rstb.2013.0437"},{"key":"ref_4","first-page":"28","article-title":"Controlled chemoprophylaxis trials in tuberculosis","volume":"17","author":"Ferebee","year":"1969","journal-title":"Adv. Tuberc. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.2217\/fmb-2016-0185","article-title":"Mechanisms of mycobacterial transmission: How does Mycobacterium tuberculosis enter and escape from the human host","volume":"11","author":"Shiloh","year":"2016","journal-title":"Future Microbiol."},{"key":"ref_6","unstructured":"WHO (2023). Global Tuberculosis Report 2023."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Houben, R., and Dodd, P. (2016). The global burden of latent tuberculosis infection: A re-estimation using mathematical modelling. PLoS Med., 13.","DOI":"10.1371\/journal.pmed.1002152"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1056\/NEJMra1405427","article-title":"Latent Mycobacterium tuberculosis infection","volume":"372","author":"Getahun","year":"2015","journal-title":"N. Engl. J. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1038\/nrmicro2236","article-title":"The spectrum of latent tuberculosis: Rethinking the biology and intervention strategies","volume":"7","author":"Barry","year":"2009","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kwan, P.K.W., Periaswamy, B., De Sessions, P.F., Lin, W., Molton, J.S., Naftalin, C.M., Naim, A.N.M., Hibberd, M.L., and Paton, N.I. (2020). A blood RNA transcript signature for TB exposure in household contacts. BMC Infect. Dis., 20.","DOI":"10.1186\/s12879-020-05116-1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sambarey, A., Devaprasad, A., Baloni, P., Mishra, M., Mohan, A., Tyagi, P., Singh, A., Akshata, J., Sultana, R., and Buggi, S. (2017). Meta-analysis of host response networks identifies a common core in tuberculosis. NPJ Syst. Biol. Appl., 3.","DOI":"10.1038\/s41540-017-0005-4"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.7326\/0003-4819-149-3-200808050-00241","article-title":"Systematic review: T-cell-based assays for the diagnosis of latent tuberculosis infection: An update","volume":"149","author":"Pai","year":"2008","journal-title":"Ann. Intern. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1093\/cid\/ciw778","article-title":"Official American Thoracic Society\/Infectious Diseases Society of America\/Centers for Disease Control and Prevention clinical practice guidelines: Diagnosis of tuberculosis in adults and children","volume":"64","author":"Lewinsohn","year":"2017","journal-title":"Clin. Infect. Dis."},{"key":"ref_14","first-page":"11202","article-title":"Multi-cohort analysis of whole-blood gene expression data reveals a 3-gene signature for diagnosing pediatric tuberculosis","volume":"7","author":"Sweeney","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1086\/510397","article-title":"Gene-expression patterns in whole blood identify subjects at risk for recurrent tuberculosis","volume":"195","author":"Mistry","year":"2007","journal-title":"J. Infect. Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1016\/S0140-6736(15)01316-1","article-title":"A blood RNA signature for tuberculosis disease risk: A prospective cohort study","volume":"387","author":"Zak","year":"2016","journal-title":"Lancet"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1038\/nature09247","article-title":"An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis","volume":"466","author":"Berry","year":"2010","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nrg3920","article-title":"Machine learning applications in genetics and genomics","volume":"16","author":"Libbrecht","year":"2015","journal-title":"Nat. Rev. Genet."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s12038-015-9552-2","article-title":"pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts","volume":"40","author":"Rani","year":"2015","journal-title":"J. Biosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/jrsm.1378","article-title":"Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources","volume":"11","author":"Gusenbauer","year":"2020","journal-title":"Res. Synth. Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1111\/hir.12428","article-title":"A systematic review case study of urgent and emergency care configuration found citation searching of Web of Science and Google Scholar of similar value","volume":"41","author":"Cantrell","year":"2024","journal-title":"Health Inf. Libr. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"520","DOI":"10.32614\/RJ-2017-058","article-title":"Rentrez: An R package for the NCBI eUtils API","volume":"9","author":"Winter","year":"2017","journal-title":"R J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1093\/bioinformatics\/btg405","article-title":"affy\u2014Analysis of Affymetrix GeneChip data at the probe level","volume":"20","author":"Gautier","year":"2004","journal-title":"Bioinformatics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Leek, J.T., and Storey, J.D. (2007). Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet., 3.","DOI":"10.1371\/journal.pgen.0030161"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, G. (2020). Gene ontology semantic similarity analysis using GOSemSim. Stem Cell Transcriptional Networks: Methods and Protocols, Springer.","DOI":"10.1007\/978-1-0716-0301-7_11"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.gpb.2022.04.008","article-title":"simplifyEnrichment: A Bioconductor package for clustering and visualizing functional enrichment results","volume":"21","author":"Gu","year":"2023","journal-title":"Genom. Proteom. Bioinform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"D331","DOI":"10.1093\/nar\/gkw1108","article-title":"Expansion of the Gene Ontology knowledgebase and resources","volume":"45","author":"Consortium","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"D353","DOI":"10.1093\/nar\/gkw1092","article-title":"KEGG: New perspectives on genomes, pathways, diseases and drugs","volume":"45","author":"Kanehisa","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_31","first-page":"D845","article-title":"The DisGeNET knowledge platform for disease genomics: 2019 update","volume":"48","author":"Ronzano","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Marchesin, S., and Silvello, G. (2022). TBGA: A large-scale gene-disease association dataset for biomedical relation extraction. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04646-6"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chandan, K., Gupta, M., and Sarwat, M. (2020). Role of host and pathogen-derived microRNAs in immune regulation during infectious and inflammatory diseases. Front. Immunol., 10.","DOI":"10.3389\/fimmu.2019.03081"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.canlet.2018.05.028","article-title":"miRNAs in immune responses to Mycobacterium tuberculosis infection","volume":"431","author":"Yang","year":"2018","journal-title":"Cancer Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13244-022-01172-0","article-title":"Extrapulmonary tuberculosis: An old but resurgent problem","volume":"13","author":"Baykan","year":"2022","journal-title":"Insights Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"a017863","DOI":"10.1101\/cshperspect.a017863","article-title":"Multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis","volume":"5","author":"Seung","year":"2015","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sichen, L., Rui, W., Yue, Y., Xin, L., Youbin, C., Ze, T., and Hongfei, C. (2023). Analysis of drug resistance in pulmonary tuberculosis patients with positive sputum tuberculosis culture in Northeast China. Front. Pharmacol., 14.","DOI":"10.3389\/fphar.2023.1263726"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hussain, R., Ansari, A., Talat, N., Hasan, Z., and Dawood, G. (2011). CCL2\/MCP-I genotype-phenotype relationship in latent tuberculosis infection. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0025803"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1019639","DOI":"10.1155\/2020\/1019639","article-title":"Exploring the role of C-C motif chemokine ligand-2 single nucleotide polymorphism in pulmonary tuberculosis: A genetic association study from North India","volume":"2020","author":"Biswas","year":"2020","journal-title":"J. Immunol. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kumar, N.P., Moideen, K., Nancy, A., Viswanathan, V., Shruthi, B.S., Sivakumar, S., Natarajan, M., Kornfeld, H., and Babu, S. (2019). Plasma chemokines are biomarkers of disease severity, higher bacterial burden and delayed sputum culture conversion in pulmonary tuberculosis. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-54803-w"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hasan, Z., Cliff, J.M., Dockrell, H.M., Jamil, B., Irfan, M., Ashraf, M., and Hussain, R. (2009). CCL2 responses to Mycobacterium tuberculosis are associated with disease severity in tuberculosis. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0008459"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.5588\/ijtld.16.0017","article-title":"Genetic polymorphisms of CCL2 associated with susceptibility to latent tuberculous infection in Thailand","volume":"20","author":"Nonghanphithak","year":"2016","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, B., Wang, Y., Wang, Z., He, X., Wang, L., Yuan, D., He, Y., Jin, T., and He, S. (2022). Association of SLC11A1 polymorphisms with tuberculosis susceptibility in the Chinese Han population. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.899124"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7","DOI":"10.4238\/vol10-1gmr980","article-title":"Association of TIRAP(MAL) gene polymorphisms with susceptibility to tuberculosis in a Chinese population","volume":"10","author":"Zhang","year":"2011","journal-title":"Genet. Mol. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1111\/tan.14880","article-title":"Expression of specific HLA class II alleles is associated with an increased risk for active tuberculosis and a distinct gene expression profile","volume":"101","author":"Chihab","year":"2023","journal-title":"HLA"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0092-8674(04)00045-5","article-title":"MicroRNAs: Genomics, biogenesis, mechanism, and function","volume":"116","author":"Bartel","year":"2004","journal-title":"Cell"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Castro, I., Carvajal, P., Jara, D., Aguilera, S., Heathcote, B., Barrera, M.J., Aliaga-Tobar, V., Maracaja-Coutinho, V., Urz\u00faa, U., and Quest, A.F.G. (2022). Small RNA Expression Profiling Reveals hsa-miR-181d-5p Downregulation Associated With TNF-a Overexpression in Sj\u00f6gren\u2019s Syndrome Patients. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.870094"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yao, S., Liu, B., Hu, X., Tan, Y., Liu, K., He, M., Wu, B., Ahmad, N., Su, X., and Zhang, Y. (2024). Diagnostic value of microRNAs in active tuberculosis based on quantitative and enrichment analyses. Diagn. Microbiol. Infect. Dis., 108.","DOI":"10.1016\/j.diagmicrobio.2024.116172"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1016\/j.molimm.2011.02.001","article-title":"Modulation of T cell cytokine production by miR-144* with elevated expression in patients with pulmonary tuberculosis","volume":"48","author":"Liu","year":"2011","journal-title":"Mol. Immunol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1111\/imr.13147","article-title":"C3-dependent effector functions of complement","volume":"313","author":"Zarantonello","year":"2023","journal-title":"Immunol. Rev."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.humimm.2010.11.002","article-title":"Major histocompatibility complex class III (C2, C4, factor B) and C3 gene variants in patients with pulmonary tuberculosis","volume":"72","author":"Senbagavalli","year":"2011","journal-title":"Hum. Immunol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105","DOI":"10.18097\/pbmc20216702105","article-title":"Haptoglobin as a biomarker","volume":"67","author":"Naryzhny","year":"2021","journal-title":"Biomeditsinskaya Khimiya"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3076","DOI":"10.1128\/IAI.71.6.3076-3087.2003","article-title":"T-cell responses to CD1-presented lipid antigens in humans with Mycobacterium tuberculosis infection","volume":"71","author":"Ulrichs","year":"2003","journal-title":"Infect. Immun."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4092","DOI":"10.1038\/s41467-023-39642-8","article-title":"Immunoproteasome-specific subunit PSMB9 induction is required to regulate cellular proteostasis upon mitochondrial dysfunction","volume":"14","author":"Kim","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tellier, M. (2021). Structure, activity, and function of SETMAR protein lysine methyltransferase. Life, 11.","DOI":"10.20944\/preprints202111.0164.v1"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Domingo-Gonzalez, R., Prince, O., Cooper, A., and Khader, S. (2016). Cytokines and Chemokines in Mycobacterium tuberculosis Infection. Microbiol. Spectr., 4.","DOI":"10.1128\/microbiolspec.TBTB2-0018-2016"},{"key":"ref_57","unstructured":"Sigal, G., Segal, M., Mathew, A., Jarlsberg, L., Wang, Y., Baruch, N., Honda, E., Weiner, M., Davis, J., and Nahid, P. (2021). CXCL10 is elevated in latent tuberculosis and associated with increased immune activation: A meta-analysis of transcriptomic studies. PLoS ONE, 16."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e4","DOI":"10.4110\/in.2024.24.e4","article-title":"TNF in Human Tuberculosis: A Double-Edged Sword","volume":"24","author":"Yuk","year":"2024","journal-title":"Immune Netw."},{"key":"ref_59","first-page":"12","article-title":"HLA class II allele expression associated with tuberculosis susceptibility in a Moroccan population","volume":"84","author":"Chihab","year":"2023","journal-title":"Hum. Immunol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"104649","DOI":"10.1016\/j.meegid.2020.104649","article-title":"Human gene expression profiling identifies key therapeutic targets in tuberculosis infection: A systematic network meta-analysis","volume":"87","author":"Alam","year":"2021","journal-title":"Infect. Genet. Evol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.tube.2011.02.006","article-title":"Discriminating between latent and active tuberculosis with multiple biomarker responses","volume":"91","author":"Frahm","year":"2011","journal-title":"Tuberculosis"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s00109-007-0157-6","article-title":"Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis","volume":"85","author":"Jacobsen","year":"2007","journal-title":"J. Mol. Med."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/16\/6\/715\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:53:57Z","timestamp":1760032437000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/16\/6\/715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":62,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["genes16060715"],"URL":"https:\/\/doi.org\/10.3390\/genes16060715","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,18]]}}}