{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T23:28:15Z","timestamp":1793662095575,"version":"4.5.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Tuberculosis disease (TB), caused by <jats:italic>Mycobacterium tuberculosis<\/jats:italic> (<jats:italic>Mtb<\/jats:italic>), is a major global public health problem, resulting in &gt;\u20091\u00a0million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the <jats:italic>Mtb<\/jats:italic> genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 <jats:italic>Mtb<\/jats:italic> WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3\u201396.5) and second-line (AUC range 84.1\u201395.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide\u2009+\u200925%; ethionamide\u2009+\u200910%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., <jats:italic>ndh<\/jats:italic> 293dupG, <jats:italic>Rv3861<\/jats:italic> 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (<jats:italic>n<\/jats:italic>\u2009=\u200912,066), and identify mutations associated with highly resistant phenotypes (e.g., <jats:italic>inhA<\/jats:italic> \u2212\u2009779G\u2009&gt;\u2009T and 62T\u2009&gt;\u2009C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting <jats:italic>Mtb<\/jats:italic> binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.<\/jats:p>","DOI":"10.1038\/s41598-024-77947-w","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T11:04:00Z","timestamp":1730977440000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches"],"prefix":"10.1038","volume":"14","author":[{"given":"Siddharth Sanjay","family":"Pruthi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Billows","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Thorpe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susana","family":"Campino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jody E.","family":"Phelan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fady","family":"Mohareb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taane G.","family":"Clark","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"77947_CR1","unstructured":"Geneva World Health Organization. Global tuberculosis report. Licence: CC BY-NC-SA 3.0 IGO. (2022)."},{"key":"77947_CR2","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.ijid.2015.01.014","volume":"32","author":"JD Fonseca","year":"2015","unstructured":"Fonseca, J. D., Knight, G. M. & McHugh, T. D. The complex evolution of antibiotic resistance in Mycobacterium tuberculosis. Int. J. Infect. Dis. 32, 94\u2013100 (2015).","journal-title":"Int. J. Infect. Dis."},{"key":"77947_CR3","unstructured":"World Health Organization. Target Regimen Profiles for Tuberculosis Treatment, 2023 Update, in Target Regimen Profiles for Tuberculosis Treatment, 2023 Update. 2023. (2023)."},{"issue":"1","key":"77947_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s13073-019-0650-x","volume":"11","author":"J Phelan","year":"2019","unstructured":"Phelan, J. et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Med. 11 (1), 41 (2019).","journal-title":"Genome Med."},{"key":"77947_CR5","doi-asserted-by":"crossref","unstructured":"Gomes, L. C. et al. Whole genome sequencing reveals large deletions and other loss of function mutations in Mycobacterium tuberculosis drug resistance genes. Microb. Genom. 7(12). (2021).","DOI":"10.1099\/mgen.0.000724"},{"issue":"2","key":"77947_CR6","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1038\/s41588-017-0029-0","volume":"50","author":"F Coll","year":"2018","unstructured":"Coll, F. et al. Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nat. Genet. 50 (2), 307\u2013316 (2018).","journal-title":"Nat. Genet."},{"issue":"1","key":"77947_CR7","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.1038\/s41467-019-10110-6","volume":"10","author":"MR Farhat","year":"2019","unstructured":"Farhat, M. R. et al. GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions. Nat. Commun. 10 (1), 2128 (2019).","journal-title":"Nat. Commun."},{"issue":"1","key":"77947_CR8","doi-asserted-by":"publisher","first-page":"e20","DOI":"10.1016\/S2666-5247(22)00359-7","volume":"4","author":"S Bagcchi","year":"2023","unstructured":"Bagcchi, S. WHO\u2019s Global Tuberculosis Report 2022. Lancet Microbe. 4 (1), e20 (2023).","journal-title":"Lancet Microbe"},{"key":"77947_CR9","doi-asserted-by":"crossref","unstructured":"Libiseller-Egger, J. et al. Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers. PLoS Comput. Biol. (2020).","DOI":"10.1371\/journal.pcbi.1008518"},{"key":"77947_CR10","doi-asserted-by":"publisher","first-page":"922","DOI":"10.3389\/fgene.2019.00922","volume":"10","author":"W Deelder","year":"2019","unstructured":"Deelder, W. et al. Machine learning predicts accurately Mycobacterium tuberculosis drug resistance from whole genome sequencing data. Front. Genet. 10, 922 (2019).","journal-title":"Front. Genet."},{"issue":"1","key":"77947_CR11","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s12864-022-08291-4","volume":"23","author":"W Deelder","year":"2022","unstructured":"Deelder, W. et al. A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis. BMC Genom. 23 (1), 46 (2022).","journal-title":"BMC Genom."},{"issue":"7","key":"77947_CR12","doi-asserted-by":"publisher","first-page":"btad428","DOI":"10.1093\/bioinformatics\/btad428","volume":"39","author":"N Billows","year":"2023","unstructured":"Billows, N. et al. Feature weighted models to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences. Bioinformatics. 39 (7), btad428 (2023).","journal-title":"Bioinformatics"},{"issue":"10","key":"77947_CR13","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1093\/bioinformatics\/btx801","volume":"34","author":"Y Yang","year":"2018","unstructured":"Yang, Y. et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics. 34 (10), 1666\u20131671 (2018).","journal-title":"Bioinformatics"},{"issue":"1","key":"77947_CR14","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1038\/s41598-022-06449-4","volume":"12","author":"X Kuang","year":"2022","unstructured":"Kuang, X. et al. Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN. Sci. Rep. 12 (1), 2427 (2022).","journal-title":"Sci. Rep."},{"issue":"1","key":"77947_CR15","doi-asserted-by":"publisher","first-page":"3817","DOI":"10.1038\/s41467-022-31236-0","volume":"13","author":"AG Green","year":"2022","unstructured":"Green, A. G. et al. A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis. Nat. Commun. 13 (1), 3817 (2022).","journal-title":"Nat. Commun."},{"issue":"1","key":"77947_CR16","doi-asserted-by":"publisher","first-page":"vbad040","DOI":"10.1093\/bioadv\/vbad040","volume":"3","author":"J Libiseller-Egger","year":"2023","unstructured":"Libiseller-Egger, J. et al. TB-ML-a framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis. Bioinform Adv. 3 (1), vbad040 (2023).","journal-title":"Bioinform Adv."},{"key":"77947_CR17","unstructured":"Chen, T. et al. Xgboost: extreme gradient boosting. R package version 0.4-2, 2015. 1(4), 1\u20134 (2015)."},{"issue":"8","key":"77947_CR18","doi-asserted-by":"publisher","first-page":"e3001721","DOI":"10.1371\/journal.pbio.3001721","volume":"20","author":"The CRyPTIC Consortium","year":"2022","unstructured":"The CRyPTIC Consortium. A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics. PLoS Biol. 20 (8), e3001721 (2022).","journal-title":"PLoS Biol."},{"issue":"13","key":"77947_CR19","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.1093\/bioinformatics\/bty949","volume":"35","author":"S Kouchaki","year":"2019","unstructured":"Kouchaki, S. et al. Application of machine learning techniques to tuberculosis drug resistance analysis. Bioinformatics. 35 (13), 2276\u20132282 (2019).","journal-title":"Bioinformatics"},{"issue":"1","key":"77947_CR20","doi-asserted-by":"publisher","first-page":"7312","DOI":"10.1038\/s41467-021-27616-7","volume":"12","author":"A Torres Ortiz","year":"2021","unstructured":"Torres Ortiz, A. et al. Genomic signatures of pre-resistance in Mycobacterium tuberculosis. Nat. Commun. 12 (1), 7312 (2021).","journal-title":"Nat. Commun."},{"issue":"1","key":"77947_CR21","doi-asserted-by":"publisher","first-page":"41","DOI":"10.5588\/ijtld.22.0138","volume":"27","author":"H Mansoor","year":"2023","unstructured":"Mansoor, H. et al. Clinical utility of target-based next-generation sequencing for drug-resistant TB. Int. J. Tuberc. Lung Dis. 27 (1), 41\u201348 (2023).","journal-title":"Int. J. Tuberc. Lung Dis."},{"issue":"1","key":"77947_CR22","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.advms.2015.09.007","volume":"61","author":"M Njire","year":"2016","unstructured":"Njire, M. et al. Pyrazinamide resistance in Mycobacterium tuberculosis: Review and update. Adv. Med. Sci. 61 (1), 63\u201371 (2016).","journal-title":"Adv. Med. Sci."},{"issue":"3","key":"77947_CR23","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1128\/AAC.04541-14","volume":"59","author":"Q Xia","year":"2015","unstructured":"Xia, Q. et al. Phenotypic and genotypic characterization of pyrazinamide resistance among multidrug-resistant Mycobacterium tuberculosis isolates in Zhejiang, China. Antimicrob. Agents Chemother. 59 (3), 1690\u20131695 (2015).","journal-title":"Antimicrob. Agents Chemother."},{"key":"77947_CR24","doi-asserted-by":"crossref","unstructured":"Hicks, N. D. et al. Bacterial genome-wide association identifies novel factors that contribute to ethionamide and prothionamide susceptibility in Mycobacterium tuberculosis. mBio. 10(2). (2019).","DOI":"10.1128\/mBio.00616-19"},{"issue":"9","key":"77947_CR25","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1128\/AAC.43.9.2317","volume":"43","author":"M Mestdagh","year":"1999","unstructured":"Mestdagh, M. et al. Relationship between pyrazinamide resistance, loss of pyrazinamidase activity, and mutations in the pncA locus in multidrug-resistant clinical isolates of Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 43 (9), 2317\u20132319 (1999).","journal-title":"Antimicrob. Agents Chemother."},{"key":"77947_CR26","doi-asserted-by":"publisher","first-page":"710","DOI":"10.3389\/fmicb.2017.00710","volume":"8","author":"MLT Ang","year":"2017","unstructured":"Ang, M. L. T. et al. EthA\/R-independent killing of Mycobacterium tuberculosis by ethionamide. Front. Microbiol. 8, 710 (2017).","journal-title":"Front. Microbiol."},{"issue":"5","key":"77947_CR27","doi-asserted-by":"publisher","first-page":"e358","DOI":"10.1016\/S2666-5247(23)00002-2","volume":"4","author":"L Sonnenkalb","year":"2023","unstructured":"Sonnenkalb, L. et al. Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis. Lancet Microbe. 4 (5), e358\u2013e368 (2023).","journal-title":"Lancet Microbe"},{"issue":"1","key":"77947_CR28","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s12866-022-02475-4","volume":"22","author":"DK Saeed","year":"2022","unstructured":"Saeed, D. K. et al. Variants associated with Bedaquiline (BDQ) resistance identified in Rv0678 and efflux pump genes in Mycobacterium tuberculosis isolates from BDQ na\u00efve TB patients in Pakistan. BMC Microbiol. 22 (1), 62 (2022).","journal-title":"BMC Microbiol."},{"issue":"4","key":"77947_CR29","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1046\/j.1365-2958.2001.02427.x","volume":"40","author":"AS Pym","year":"2001","unstructured":"Pym, A. S. et al. Regulation of catalase-peroxidase (KatG) expression, isoniazid sensitivity and virulence by furA of Mycobacterium tuberculosis. Mol. Microbiol. 40 (4), 879\u2013889 (2001).","journal-title":"Mol. Microbiol."},{"key":"77947_CR30","doi-asserted-by":"publisher","first-page":"117693432094493","DOI":"10.1177\/1176934320944932","volume":"16","author":"YP Lai","year":"2020","unstructured":"Lai, Y. P. & Ioerger, T. R. Exploiting homoplasy in genome-wide association studies to enhance identification of antibiotic-resistance mutations in bacterial genomes. Evol. Bioinform Online. 16, 1176934320944932 (2020).","journal-title":"Evol. Bioinform Online"},{"key":"77947_CR31","doi-asserted-by":"crossref","unstructured":"Vilcheze, C. & Jacobs, W. R. Jr. Resistance to isoniazid and ethionamide in Mycobacterium tuberculosis: Genes, mutations, and causalities. Microbiol. Spectr. 2(4): MGM2-0014-2013 (2014).","DOI":"10.1128\/microbiolspec.MGM2-0014-2013"},{"issue":"8","key":"77947_CR32","doi-asserted-by":"publisher","first-page":"e3001755","DOI":"10.1371\/journal.pbio.3001755","volume":"20","author":"The CRyPTIC Consortium","year":"2022","unstructured":"The CRyPTIC Consortium. Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms. PLoS Biol. 20 (8), e3001755 (2022).","journal-title":"PLoS Biol."},{"issue":"10","key":"77947_CR33","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1016\/S1473-3099(15)00062-6","volume":"15","author":"TM Walker","year":"2015","unstructured":"Walker, T. M. et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet. Infect. Dis. 15 (10), 1193\u20131202 (2015).","journal-title":"Lancet. Infect. Dis"},{"issue":"1","key":"77947_CR34","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1186\/s12864-019-5615-3","volume":"20","author":"YEA Oppong","year":"2019","unstructured":"Oppong, Y. E. A. et al. Genome-wide analysis of Mycobacterium tuberculosis polymorphisms reveals lineage-specific associations with drug resistance. BMC Genom. 20 (1), 252 (2019).","journal-title":"BMC Genom."},{"issue":"1","key":"77947_CR35","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1038\/s41467-023-44325-5","volume":"15","author":"The CRyPTIC Consortium","year":"2024","unstructured":"The CRyPTIC Consortium. Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach. Nat. Commun. 15 (1), 488 (2024).","journal-title":"Nat. Commun."},{"key":"77947_CR36","doi-asserted-by":"publisher","first-page":"1204064","DOI":"10.3389\/fpubh.2023.1204064","volume":"11","author":"L de Araujo","year":"2023","unstructured":"de Araujo, L. et al. Implementation of targeted next-generation sequencing for the diagnosis of drug-resistant tuberculosis in low-resource settings: a programmatic model, challenges, and initial outcomes. Front. Public. Health. 11, 1204064 (2023).","journal-title":"Front. Public. Health"},{"issue":"14","key":"77947_CR37","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1093\/bioinformatics\/btp324","volume":"25","author":"H Li","year":"2009","unstructured":"Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows\u2013Wheeler transform. Bioinformatics. 25 (14), 1754\u20131760 (2009).","journal-title":"Bioinformatics"},{"issue":"15","key":"77947_CR38","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1093\/bioinformatics\/btu170","volume":"30","author":"AM Bolger","year":"2014","unstructured":"Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 30 (15), 2114\u20132120 (2014).","journal-title":"Bioinformatics"},{"issue":"9","key":"77947_CR39","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1101\/gr.107524.110","volume":"20","author":"A McKenna","year":"2010","unstructured":"McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20 (9), 1297\u20131303 (2010).","journal-title":"Genome Res."},{"issue":"6","key":"77947_CR40","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1093\/bioinformatics\/btq033","volume":"26","author":"AR Quinlan","year":"2010","unstructured":"Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 26 (6), 841\u2013842 (2010).","journal-title":"Bioinformatics"},{"issue":"null","key":"77947_CR41","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 (null), 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"77947_CR42","doi-asserted-by":"publisher","first-page":"80","DOI":"10.4161\/fly.19695","volume":"6","author":"P Cingolani","year":"2012","unstructured":"Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. (Austin). 6 (2), 80\u201392 (2012).","journal-title":"Fly. (Austin)"},{"key":"77947_CR43","doi-asserted-by":"crossref","unstructured":"Ripley, B. et al. Package \u2018mass\u2019. Cran r, 2013. 538, 113\u2013120 (2013).","DOI":"10.1109\/ICAwST.2013.6765420"},{"issue":"3-12","key":"77947_CR44","first-page":"p700","volume":"7","author":"B Ripley","year":"2016","unstructured":"Ripley, B. et al. Package \u2018nnet\u2019. R package version. 7 (3\u201312), 700 (2016).","journal-title":"R package version"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77947-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77947-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77947-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T11:08:10Z","timestamp":1730977690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77947-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["77947"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-77947-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"25 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","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":"27091"}}