{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:28:50Z","timestamp":1772782130626,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["SFRH\/BD\/144966\/2019"],"award-info":[{"award-number":["SFRH\/BD\/144966\/2019"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["2021.03416.CEECIND"],"award-info":[{"award-number":["2021.03416.CEECIND"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["DSAIPA\/DS\/0118\/2020"],"award-info":[{"award-number":["DSAIPA\/DS\/0118\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia,Portugal","award":["2022.12479.BD"],"award-info":[{"award-number":["2022.12479.BD"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo<jats:italic>\/<\/jats:italic>in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an open-access and up-to-date curated database that provides experimental quantitative uptake values for over 2,300 entries and physicochemical properties of 1,315 peptides. POSEIDON also offers physicochemical properties, such as cell line, cargo, and sequence, among others. By leveraging this database along with cell line genomic features, we processed a dataset of over 1,200 entries to develop an ML regression CPP uptake predictor. Our results demonstrated that POSEIDON accurately predicted peptide cell line uptake, achieving a Pearson correlation of 0.87, Spearman correlation of 0.88, and r<jats:sup>2<\/jats:sup> score of 0.76, on an independent test set. With its comprehensive and novel dataset, along with its potent predictive capabilities, the POSEIDON database and its associated ML predictor signify a significant leap forward in CPP research and development. The POSEIDON database and ML Predictor are available for free and with a user-friendly interface at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/moreiralab.com\/resources\/poseidon\/\">https:\/\/moreiralab.com\/resources\/poseidon\/<\/jats:ext-link>, making them valuable resources for advancing research on CPP-related topics. Scientific Contribution Statement: Our research addresses the critical need for more efficient and cost-effective methodologies in Cell-Penetrating Peptide (CPP) research. We introduced POSEIDON, a comprehensive and freely accessible database that delivers quantitative uptake values for over 2,300 entries, along with detailed physicochemical profiles for 1,315 peptides. Recognizing the limitations of current Machine Learning (ML) models for CPP design, our work leveraged the rich dataset provided by POSEIDON to develop a highly accurate ML regression model for predicting CPP uptake.<\/jats:p>\n                <jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1186\/s13321-024-00810-7","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T23:01:52Z","timestamp":1708124512000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["POSEIDON: Peptidic Objects SEquence-based Interaction with cellular DOmaiNs: a new database and predictor"],"prefix":"10.1186","volume":"16","author":[{"given":"Ant\u00f3nio J.","family":"Preto","sequence":"first","affiliation":[]},{"given":"Ana B.","family":"Caniceiro","sequence":"additional","affiliation":[]},{"given":"Francisco","family":"Duarte","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Fernandes","sequence":"additional","affiliation":[]},{"given":"Lino","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Joana","family":"Mour\u00e3o","sequence":"additional","affiliation":[]},{"given":"Irina S.","family":"Moreira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"810_CR1","doi-asserted-by":"publisher","first-page":"697","DOI":"10.3389\/fphar.2020.00697","volume":"11","author":"J Xie","year":"2020","unstructured":"Xie J, Bi Y, Zhang H et al (2020) Cell-penetrating peptides in diagnosis and treatment of human diseases: from preclinical research to clinical application. Front Pharmacol 11:697","journal-title":"Front Pharmacol"},{"key":"810_CR2","doi-asserted-by":"publisher","first-page":"D1098","DOI":"10.1093\/nar\/gkv1266","volume":"44","author":"P Agrawal","year":"2016","unstructured":"Agrawal P, Bhalla S, Usmani SS et al (2016) CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides. Nucleic Acids Res 44:D1098\u2013D1103","journal-title":"Nucleic Acids Res"},{"key":"810_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3390\/ijms17020185","volume":"17","author":"M Kristensen","year":"2016","unstructured":"Kristensen M, Birch D, M\u00f8rck Nielsen H (2016) Applications and challenges for use of cell-penetrating peptides as delivery vectors for peptide and protein cargos. Int J Mol Sci 17:185","journal-title":"Int J Mol Sci"},{"key":"810_CR4","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.jconrel.2019.07.020","volume":"309","author":"J Xu","year":"2019","unstructured":"Xu J, Khan AR, Fu M et al (2019) Cell-penetrating peptide: a means of breaking through the physiological barriers of different tissues and organs. J Control Release 309:106\u2013124","journal-title":"J Control Release"},{"key":"810_CR5","doi-asserted-by":"publisher","first-page":"927","DOI":"10.3390\/molecules24050927","volume":"24","author":"J Habault","year":"2019","unstructured":"Habault J, Poyet J-L (2019) Recent advances in cell penetrating peptide-based anticancer therapies. Molecules 24:927","journal-title":"Molecules"},{"key":"810_CR6","doi-asserted-by":"publisher","first-page":"414729","DOI":"10.1155\/2011\/414729","volume":"2011","author":"F Madani","year":"2011","unstructured":"Madani F, Lindberg S, Langel U et al (2011) Mechanisms of cellular uptake of cell-penetrating peptides. J Biophys 2011:414729","journal-title":"J Biophys"},{"key":"810_CR7","doi-asserted-by":"publisher","first-page":"430","DOI":"10.2174\/1567201816666190123120915","volume":"16","author":"J Yang","year":"2019","unstructured":"Yang J, Luo Y, Shibu MA et al (2019) Cell-penetrating peptides: efficient vectors for vaccine delivery. Curr Drug Deliv 16:430\u2013443","journal-title":"Curr Drug Deliv"},{"key":"810_CR8","doi-asserted-by":"publisher","first-page":"716226","DOI":"10.3389\/fphar.2021.716226","volume":"12","author":"L Porosk","year":"2021","unstructured":"Porosk L, P\u00f5hako K, Arukuusk P, Langel \u00dc (2021) Cell-penetrating peptides predicted from CASC3, AKIP1, and AHRR proteins. Front Pharmacol 12:716226","journal-title":"Front Pharmacol"},{"key":"810_CR9","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1016\/j.biopha.2018.09.097","volume":"108","author":"H Derakhshankhah","year":"2018","unstructured":"Derakhshankhah H, Jafari S (2018) Cell penetrating peptides: a concise review with emphasis on biomedical applications. Biomed Pharmacother 108:1090\u20131096","journal-title":"Biomed Pharmacother"},{"key":"810_CR10","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1016\/j.drudis.2012.03.002","volume":"17","author":"F Milletti","year":"2012","unstructured":"Milletti F (2012) Cell-penetrating peptides: classes, origin, and current landscape. Drug Discov Today 17:850\u2013860","journal-title":"Drug Discov Today"},{"key":"810_CR11","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.molmed.2022.03.010","volume":"28","author":"J-H Koo","year":"2022","unstructured":"Koo J-H, Kim G-R, Nam K-H, Choi J-M (2022) Unleashing cell-penetrating peptide applications for immunotherapy. Trends Mol Med 28:482\u2013496","journal-title":"Trends Mol Med"},{"key":"810_CR12","doi-asserted-by":"publisher","first-page":"12585","DOI":"10.3390\/ijms222212585","volume":"22","author":"L Ugalde-Trivi\u00f1o","year":"2021","unstructured":"Ugalde-Trivi\u00f1o L, D\u00edaz-Guerra M (2021) PSD-95: an effective target for stroke therapy using neuroprotective peptides. Int J Mol Sci 22:12585","journal-title":"Int J Mol Sci"},{"key":"810_CR13","doi-asserted-by":"publisher","first-page":"100248","DOI":"10.1016\/j.mtbio.2022.100248","volume":"14","author":"T Samec","year":"2022","unstructured":"Samec T, Boulos J, Gilmore S et al (2022) Peptide-based delivery of therapeutics in cancer treatment. Mater Today Bio 14:100248","journal-title":"Mater Today Bio"},{"key":"810_CR14","unstructured":"European Medicines Agency (2020) EU\/3\/20\/2328 - orphan designation for treatment of Friedreich\u2019s ataxia. https:\/\/www.ema.europa.eu\/en\/medicines\/human\/orphan-designations\/eu-3-20-2328. Accessed 30 Aug 2023"},{"key":"810_CR15","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1021\/cb100423u","volume":"6","author":"S Gao","year":"2011","unstructured":"Gao S, Simon MJ, Hue CD et al (2011) An unusual cell penetrating peptide identified using a plasmid display-based functional selection platform. ACS Chem Biol 6:484\u2013491","journal-title":"ACS Chem Biol"},{"key":"810_CR16","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1002\/biot.201100220","volume":"7","author":"J-H Lee","year":"2012","unstructured":"Lee J-H, Song HS, Park TH et al (2012) Screening of cell-penetrating peptides using mRNA display. Biotechnol J 7:387\u2013396","journal-title":"Biotechnol J"},{"key":"810_CR17","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.bbrc.2016.06.035","volume":"477","author":"H Tang","year":"2016","unstructured":"Tang H, Su Z-D, Wei H-H et al (2016) Prediction of cell-penetrating peptides with feature selection techniques. Biochem Biophys Res Commun 477:150\u2013154","journal-title":"Biochem Biophys Res Commun"},{"key":"810_CR18","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1021\/acs.jproteome.7b00019","volume":"16","author":"L Wei","year":"2017","unstructured":"Wei L, Xing P, Su R et al (2017) CPPred-RF: a sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. J Proteome Res 16:2044\u20132053","journal-title":"J Proteome Res"},{"key":"810_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-017-4128-1","author":"L Wei","year":"2017","unstructured":"Wei L, Tang J, Zou Q (2017) SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides. BMC Genomics. https:\/\/doi.org\/10.1186\/s12864-017-4128-1","journal-title":"BMC Genomics"},{"key":"810_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2018.00725","author":"V Kumar","year":"2018","unstructured":"Kumar V, Agrawal P, Kumar R et al (2018) Prediction of cell-penetrating potential of modified peptides containing natural and chemically modified residues. Front Microbiol. https:\/\/doi.org\/10.3389\/fmicb.2018.00725","journal-title":"Front Microbiol"},{"key":"810_CR21","doi-asserted-by":"publisher","first-page":"2715","DOI":"10.1021\/acs.jproteome.8b00148","volume":"17","author":"B Manavalan","year":"2018","unstructured":"Manavalan B, Subramaniyam S, Shin TH et al (2018) Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy. J Proteome Res 17:2715\u20132726","journal-title":"J Proteome Res"},{"key":"810_CR22","doi-asserted-by":"publisher","first-page":"167604","DOI":"10.1016\/j.jmb.2022.167604","volume":"434","author":"B Manavalan","year":"2022","unstructured":"Manavalan B, Patra MC (2022) MLCPP 2.0: an updated cell-penetrating peptides and their uptake efficiency predictor. J Mol Biol 434:167604","journal-title":"J Mol Biol"},{"key":"810_CR23","doi-asserted-by":"publisher","first-page":"3214","DOI":"10.1021\/acs.jproteome.8b00322","volume":"17","author":"P Pandey","year":"2018","unstructured":"Pandey P, Patel V, George NV, Mallajosyula SS (2018) KELM-CPPpred: Kernel extreme learning machine based prediction model for cell-penetrating peptides. J Proteome Res 17:3214\u20133222","journal-title":"J Proteome Res"},{"key":"810_CR24","doi-asserted-by":"publisher","first-page":"3028","DOI":"10.1093\/bioinformatics\/btaa131","volume":"36","author":"X Fu","year":"2020","unstructured":"Fu X, Cai L, Zeng X, Zou Q (2020) StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency. Bioinformatics 36:3028\u20133034","journal-title":"Bioinformatics"},{"key":"810_CR25","doi-asserted-by":"publisher","first-page":"4","DOI":"10.32614\/RJ-2015-001","volume":"7","author":"D Osorio","year":"2015","unstructured":"Osorio D, Rond\u00f3n-Villarreal P, Torres R (2015) Peptides: a package for data mining of antimicrobial peptides. R J 7:4","journal-title":"R J"},{"key":"810_CR26","doi-asserted-by":"publisher","first-page":"3909","DOI":"10.1093\/bioinformatics\/btx496","volume":"33","author":"D Wang","year":"2017","unstructured":"Wang D, Zeng S, Xu C et al (2017) MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction. Bioinformatics 33:3909\u20133916","journal-title":"Bioinformatics"},{"key":"810_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.1036862","author":"J Zhao","year":"2022","unstructured":"Zhao J, Jiang H, Zou G et al (2022) CNNArginineMe: a CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence. Front Genet. https:\/\/doi.org\/10.3389\/fgene.2022.1036862","journal-title":"Front Genet"},{"key":"810_CR28","doi-asserted-by":"publisher","first-page":"D955","DOI":"10.1093\/nar\/gks1111","volume":"41","author":"W Yang","year":"2013","unstructured":"Yang W, Soares J, Greninger P et al (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41:D955\u2013D961","journal-title":"Nucleic Acids Res"},{"key":"810_CR29","unstructured":"R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https:\/\/www.R-project.org\/"},{"key":"810_CR30","unstructured":"RStudio. http:\/\/www.rstudio.com\/. Accessed 31 Jan 2022"},{"key":"810_CR31","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.21105\/joss.01686","volume":"4","author":"H Wickham","year":"2019","unstructured":"Wickham H, Averick M, Bryan J et al (2019) Welcome to the tidyverse. J Open Source Softw 4:1686","journal-title":"J Open Source Softw"},{"key":"810_CR32","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al (2012) Scikit-learn: Machine Learning in Python. arXiv [cs.LG]"},{"key":"810_CR33","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. arXiv [cs.LG]","DOI":"10.1145\/2939672.2939785"},{"key":"810_CR34","unstructured":"Abadi M, Barham P, Chen J, et al (2016) TensorFlow: A system for large-scale machine learning. arXiv [cs.DC]"},{"key":"810_CR35","unstructured":"Liaw R, Liang E, Nishihara R, et al (2018) Tune: A research platform for distributed model selection and training. arXiv [cs.LG]"},{"key":"810_CR36","unstructured":"Rinberg M (2018) Flask web development: developing web applications with python"},{"key":"810_CR37","volume-title":"Collaborative data science","author":"PT Inc","year":"2015","unstructured":"Inc PT (2015) Collaborative data science. Plotly Technologies Inc Montral, Montreal"},{"key":"810_CR38","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3390\/biom9010022","volume":"9","author":"S Silva","year":"2019","unstructured":"Silva S, Almeida A, Vale N (2019) Combination of cell-penetrating peptides with nanoparticles for therapeutic application: a review. Biomolecules 9:22","journal-title":"Biomolecules"},{"key":"810_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3390\/biomedicines6020051","volume":"6","author":"G McClorey","year":"2018","unstructured":"McClorey G, Banerjee S (2018) Cell-penetrating peptides to enhance delivery of oligonucleotide-based therapeutics. Biomedicines 6:51","journal-title":"Biomedicines"},{"key":"810_CR40","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.jconrel.2013.11.020","volume":"174","author":"F Wang","year":"2014","unstructured":"Wang F, Wang Y, Zhang X et al (2014) Recent progress of cell-penetrating peptides as new carriers for intracellular cargo delivery. J Control Release 174:126\u2013136","journal-title":"J Control Release"},{"key":"810_CR41","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3762\/bjnano.11.10","volume":"11","author":"I Ruseska","year":"2020","unstructured":"Ruseska I, Zimmer A (2020) Internalization mechanisms of cell-penetrating peptides. Beilstein J Nanotechnol 11:101\u2013123","journal-title":"Beilstein J Nanotechnol"},{"key":"810_CR42","doi-asserted-by":"publisher","first-page":"10241","DOI":"10.1021\/acs.chemrev.9b00008","volume":"119","author":"PG Dougherty","year":"2019","unstructured":"Dougherty PG, Sahni A, Pei D (2019) Understanding cell penetration of cyclic peptides. Chem Rev 119:10241\u201310287","journal-title":"Chem Rev"},{"key":"810_CR43","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1042\/BJ20061808","volume":"403","author":"MM Fretz","year":"2007","unstructured":"Fretz MM, Penning NA, Al-Taei S et al (2007) Temperature-, concentration- and cholesterol-dependent translocation of L- and D-octa-arginine across the plasma and nuclear membrane of CD34+ leukaemia cells. Biochem J 403:335\u2013342","journal-title":"Biochem J"},{"key":"810_CR44","doi-asserted-by":"publisher","first-page":"2363","DOI":"10.1021\/bc800194e","volume":"19","author":"J Mueller","year":"2008","unstructured":"Mueller J, Kretzschmar I, Volkmer R, Boisguerin P (2008) Comparison of cellular uptake using 22 CPPs in 4 different cell lines. Bioconjug Chem 19:2363\u20132374","journal-title":"Bioconjug Chem"},{"key":"810_CR45","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1096\/fj.05-5523com","volume":"20","author":"G T\u00fcnnemann","year":"2006","unstructured":"T\u00fcnnemann G, Martin RM, Haupt S et al (2006) Cargo-dependent mode of uptake and bioavailability of TAT-containing proteins and peptides in living cells. FASEB J 20:1775\u20131784","journal-title":"FASEB J"},{"key":"810_CR46","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10989-016-9542-8","volume":"23","author":"M Dowaidar","year":"2017","unstructured":"Dowaidar M, Regberg J, Dobchev DA et al (2017) Refinement of a quantitative structure\u2013activity relationship model for prediction of cell-penetrating peptide based transfection systems. Int J Pept Res Ther 23:91\u2013100","journal-title":"Int J Pept Res Ther"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00810-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-024-00810-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00810-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T23:01:59Z","timestamp":1708124519000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-024-00810-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["810"],"URL":"https:\/\/doi.org\/10.1186\/s13321-024-00810-7","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,16]]},"assertion":[{"value":"17 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18"}}