{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T03:06:57Z","timestamp":1779246417514,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Microbiol"],"DOI":"10.1186\/s12866-026-05028-1","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T01:14:53Z","timestamp":1779066893000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative evaluation of deep learning architectures for microbial colony classification in microbiological imaging"],"prefix":"10.1186","volume":"26","author":[{"given":"Mohamed Abdelmoaty","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rawaf","family":"Alenazy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"AbdelMoety","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hend A.","family":"Kamel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shokri M.","family":"Shafik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"5028_CR1","doi-asserted-by":"publisher","unstructured":"Garc\u00eda-Soriano DA, Andersen FD, Nygaard JV, T\u00f8rring T, ColFeatures. Automated data extraction and classification of bacterial colonies. bioRxiv: 2021.05.27.445853 [Preprint]. https:\/\/doi.org\/10.1101\/2021.05.27.445853.","DOI":"10.1101\/2021.05.27.445853"},{"key":"5028_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2022.1087074","volume":"12","author":"AK Bhunia","year":"2022","unstructured":"Bhunia AK, Singh AK, Parker K, Applegate BM. Petri-plate, bacteria, and laser optical scattering sensor. Front Cell Infect Microbiol. 2022;12:1087074.","journal-title":"Front Cell Infect Microbiol"},{"issue":"15","key":"5028_CR3","doi-asserted-by":"publisher","first-page":"8896","DOI":"10.1021\/acs.analchem.8b01024","volume":"90","author":"P Lasch","year":"2018","unstructured":"Lasch P, Stammler M, Zhang M, Baranska M, Bosch A, Majzner K. FT-IR hyperspectral imaging and artificial neural network analysis for identification of pathogenic bacteria. Anal Chem. 2018;90(15):8896\u2013904.","journal-title":"Anal Chem"},{"key":"5028_CR4","doi-asserted-by":"crossref","unstructured":"Nakada M, Inagaki T, Ariizumi R, Maeta S, Ozaki H, Fujisawa A et al. 590. A New Thin-Film Transistor Image Sensor for Estimation of Bacterial Colony Species on Agar Plates. In: Open Forum Infectious Diseases. 2023;10:ofad500. 659.","DOI":"10.1093\/ofid\/ofad500.659"},{"key":"5028_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2021.689476","volume":"12","author":"P Smith","year":"2021","unstructured":"Smith P, Schuster M. Inexpensive apparatus for high-quality imaging of microbial growth on agar plates. Front Microbiol. 2021;12:689476.","journal-title":"Front Microbiol"},{"key":"5028_CR6","doi-asserted-by":"publisher","unstructured":"Majchrowska S, Paw\u0142owski J, Gu\u0142a G, Bonus T, Hanas A, Loch A et al. AGAR a microbial colony dataset for deep learning detection. arXiv:210801234. 2021 [Preprint]. https:\/\/doi.org\/10.48550\/arXiv.2108.01234.","DOI":"10.48550\/arXiv.2108.01234"},{"issue":"4","key":"5028_CR7","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3358\/shokueishi.60.88","volume":"60","author":"H Fujikawa","year":"2019","unstructured":"Fujikawa H, Tsubaki H. Characteristics of microbial colony counts on agar plates for food and microbial culture samples. Food Hygiene Saf Sci (Shokuhin Eiseigaku Zasshi). 2019;60(4):88\u201395.","journal-title":"Food Hygiene Saf Sci (Shokuhin Eiseigaku Zasshi)"},{"key":"5028_CR8","doi-asserted-by":"crossref","unstructured":"Vera S, Perez F, Lara L, Ceresa M, Carranza N, Jover JH et al. Automated annotation removal in agar plates. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2013;2013:3016\u20139.","DOI":"10.1109\/EMBC.2013.6610175"},{"key":"5028_CR9","doi-asserted-by":"crossref","unstructured":"Bunkum M, Visitsattapongse S. Automated Bacterial Colony Counting on Agar Plate. In: 2023 15th Biomedical Engineering International Conference (BMEiCON). IEEE; 2023;2023:1\u20135.","DOI":"10.1109\/BMEiCON60347.2023.10322069"},{"key":"5028_CR10","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.mimet.2018.09.004","volume":"153","author":"G Zhu","year":"2018","unstructured":"Zhu G, Yan B, Xing M, Tian C. Automated counting of bacterial colonies on agar plates based on images captured at near-infrared light. J Microbiol Methods. 2018;153:66\u201373.","journal-title":"J Microbiol Methods"},{"key":"5028_CR11","doi-asserted-by":"crossref","unstructured":"Dukovski I, Golden L, Zhang J, Osborne M, Segr\u00e8 D, Korolev KS. Biophysical metabolic modeling of complex bacterial colony morphology. Cell Syst. 2025;16(8).","DOI":"10.1016\/j.cels.2025.101352"},{"issue":"12","key":"5028_CR12","doi-asserted-by":"publisher","first-page":"e1011699","DOI":"10.1371\/journal.pcbi.1011699","volume":"19","author":"JB Rattray","year":"2023","unstructured":"Rattray JB, Lowhorn RJ, Walden R, M\u00e1rquez-Zacar\u00edas P, Molotkova E, Perron G, et al. Machine learning identification of Pseudomonas aeruginosa strains from colony image data. PLoS Comput Biol. 2023;19(12):e1011699.","journal-title":"PLoS Comput Biol"},{"key":"5028_CR13","first-page":"504","volume":"77","author":"M Calgin","year":"2024","unstructured":"Calgin M, Kalayci H. Early classification of Gram-negative bacteria with colony imaging and deep learning without coding experience. Proc Bulg Acad Sci. 2024;77:504\u201312--12.","journal-title":"Proc Bulg Acad Sci"},{"key":"5028_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2024.116006","volume":"249","author":"H Tago","year":"2024","unstructured":"Tago H, Maeda Y, Tanaka Y, Kohketsu H, Lim T-K, Harada M, et al. Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification. Biosens Bioelectron. 2024;249:116006.","journal-title":"Biosens Bioelectron"},{"issue":"21","key":"5028_CR15","doi-asserted-by":"publisher","DOI":"10.21769\/BioProtoc.4862","volume":"13","author":"K Choe","year":"2023","unstructured":"Choe K, Sweedler JV. Workflow for high-throughput screening of enzyme mutant libraries using matrix-assisted laser desorption\/ionization mass spectrometry analysis of Escherichia coli colonies. Bio-protocol. 2023;13(21):e4862.","journal-title":"Bio-protocol"},{"issue":"4","key":"5028_CR16","doi-asserted-by":"publisher","first-page":"e00673","DOI":"10.1128\/spectrum.00673-23","volume":"11","author":"E Heuser","year":"2023","unstructured":"Heuser E, Becker K, Idelevich EA. Evaluation of an automated system for the counting of microbial colonies. Microbiol Spectr. 2023;11(4):e00673\u201323.","journal-title":"Microbiol Spectr"},{"issue":"3","key":"5028_CR17","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13510","volume":"41","author":"S Ms","year":"2024","unstructured":"Ms S, Martis JE, Krivic S, Kb S, Nazareth P. A swarm-optimized microbial colony counter. Expert Syst. 2024;41(3):e13510.","journal-title":"Expert Syst"},{"issue":"3","key":"5028_CR18","doi-asserted-by":"publisher","first-page":"e0311242","DOI":"10.1371\/journal.pone.0311242","volume":"20","author":"S Chen","year":"2025","unstructured":"Chen S, Huang P-H, Kim H, Cui Y, Buie CR. Mcount: an automated colony counting tool for high-throughput microbiology. PLoS One. 2025;20(3):e0311242.","journal-title":"PLoS One"},{"key":"5028_CR19","doi-asserted-by":"crossref","unstructured":"Park S, Yeoh JW, Choo CT, Lim CK, Dao VL, Poh CL. Automated Microbial Classification System based on Deep Convolutional Neural Networks using Images from Colony Picker. bioRxiv. 2023:2023.06. 29.547155.","DOI":"10.1101\/2023.06.29.547155"},{"key":"5028_CR20","doi-asserted-by":"crossref","unstructured":"Nagro SA. A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies. Int J Adv Comput Sci Appl. 2022;13(10).","DOI":"10.14569\/IJACSA.2022.0131024"},{"key":"5028_CR21","doi-asserted-by":"crossref","unstructured":"Siangchin A, Ploysuwan T. Reducing the Counting Time of Colonies Using Image Processing Techniques. In: 2024 24th International Conference on Control, Automation and Systems (ICCAS). IEEE; 2024;2024:1276\u201380.","DOI":"10.23919\/ICCAS63016.2024.10773099"},{"issue":"19","key":"5028_CR22","doi-asserted-by":"publisher","first-page":"10568","DOI":"10.3390\/app131910568","volume":"13","author":"F Yang","year":"2023","unstructured":"Yang F, Zhong Y, Yang H, Wan Y, Hu Z, Peng S. Microbial colony detection based on deep learning. Appl Sci. 2023;13(19):10568.","journal-title":"Appl Sci"},{"key":"5028_CR23","doi-asserted-by":"crossref","unstructured":"Whipp J, Dong A. Yolo-based deep learning to automated bacterial colony counting. In: 2022 IEEE Eighth International Conference on Multimedia Big Data (BigMM). IEEE; 2022;2022:120\u20134.","DOI":"10.1109\/BigMM55396.2022.00028"},{"key":"5028_CR24","doi-asserted-by":"crossref","unstructured":"Majchrowska S, Paw\u0142owski J, Czerep N, G\u00f3recki A, Kuci\u0144ski J, Golan T. Deep neural networks approach to microbial colony detection\u2014a comparative analysis. In: Conference on multimedia, interaction, design and innovation. Springer; 2021;440:98\u2013106.","DOI":"10.1007\/978-3-031-11432-8_9"},{"issue":"9","key":"5028_CR25","doi-asserted-by":"publisher","first-page":"e02885","DOI":"10.1128\/spectrum.02885-24","volume":"13","author":"F Kong","year":"2025","unstructured":"Kong F, Su M, Guo J, Wu H, Chen H, Qiu J, et al. Implementation of convolutional neural networks for microbial colony recognition. Microbiol Spectr. 2025;13(9):e02885\u201324.","journal-title":"Microbiol Spectr"},{"key":"5028_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110141","volume":"191","author":"N Ebert","year":"2025","unstructured":"Ebert N, Stricker D, Wasenm\u00fcller O. Enhancing robustness and generalization in microbiological few-shot detection through synthetic data generation and contrastive learning. Comput Biol Med. 2025;191:110141.","journal-title":"Comput Biol Med"},{"key":"5028_CR27","doi-asserted-by":"crossref","unstructured":"Mallela S, Gates A, Bhattacharya S, Okeke B, Kursun O. Ai-based detection of coliform colonies using CNN transfer learning for application to cultured plate analysis in water quality research. bioRxiv. 2025:2025.06.29.662231.","DOI":"10.1101\/2025.06.29.662231"},{"issue":"6","key":"5028_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/jobm.70018","volume":"65","author":"HNH Topsakal","year":"2025","unstructured":"Topsakal HNH, Asarkaya MC, Akcan E, \u00c7if\u00e7i M, Karaman R, Do\u011fan S, et al. Microbial art fixation on agar plate: micart fixation for microbiology teaching. J Basic Microbiol. 2025;65(6):e70018.","journal-title":"J Basic Microbiol"},{"key":"5028_CR29","doi-asserted-by":"crossref","unstructured":"Tasneem N, Babu DR. Escherichia Coli Bacterial Identification using Deep Learning Approach. In: 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE; 2024;2024:1\u20136.","DOI":"10.1109\/SSITCON62437.2024.10796386"},{"key":"5028_CR30","doi-asserted-by":"crossref","unstructured":"Inchara B, Disha V, Divya Shree K, GC SM. Classification of Bacteria from Agar Plate Using Deep Learning and Image Processing. In: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE; 2024;2024:1\u20136.","DOI":"10.1109\/ADICS58448.2024.10533650"},{"issue":"5","key":"5028_CR31","doi-asserted-by":"publisher","first-page":"e04209","DOI":"10.1128\/spectrum.04209-23","volume":"12","author":"A Gerada","year":"2024","unstructured":"Gerada A, Harper N, Howard A, Reza N, Hope W. Determination of minimum inhibitory concentrations using machine-learning-assisted agar dilution. Microbiol Spectr. 2024;12(5):e04209\u201323.","journal-title":"Microbiol Spectr"},{"issue":"7","key":"5028_CR32","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1021\/acsphotonics.2c00572","volume":"9","author":"Y Li","year":"2022","unstructured":"Li Y, Liu T, Koydemir HC, Wang H, O\u2019Riordan K, Bai B, et al. Deep learning-enabled detection and classification of bacterial colonies using a thin-film transistor (TFT) image sensor. ACS Photonics. 2022;9(7):2455\u201366.","journal-title":"ACS Photonics"},{"key":"5028_CR33","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neucom.2021.04.130","volume":"470","author":"T Beznik","year":"2022","unstructured":"Beznik T, Smyth P, de Lannoy G, Lee JA. Deep learning to detect bacterial colonies for the production of vaccines. Neurocomputing. 2022;470:427\u201331.","journal-title":"Neurocomputing"},{"key":"5028_CR34","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25."},{"key":"5028_CR35","doi-asserted-by":"publisher","unstructured":"Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS et al. The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv:180301164. 2018 [Preprint]. https:\/\/doi.org\/10.48550\/arXiv.1803.01164.","DOI":"10.48550\/arXiv.1803.01164"},{"key":"5028_CR36","doi-asserted-by":"publisher","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <\u20090.5\u00a0MB model size. arXiv:160207360. 2016 [Preprint]. https:\/\/doi.org\/10.48550\/arXiv.1602.07360.","DOI":"10.48550\/arXiv.1602.07360"},{"key":"5028_CR37","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;2016:770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"5028_CR38","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T, Sun J. Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV). 2018;2018:116\u201331.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"5028_CR39","unstructured":"Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR; 2019;97:6105\u201314."},{"key":"5028_CR40","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018;2018:4510\u201320.","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"1","key":"5028_CR41","doi-asserted-by":"publisher","first-page":"6874","DOI":"10.1038\/s41467-023-42563-1","volume":"14","author":"A Signoroni","year":"2023","unstructured":"Signoroni A, Ferrari A, Lombardi S, Savardi M, Fontana S, Culbreath K. Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology. Nat Commun. 2023;14(1):6874.","journal-title":"Nat Commun"},{"issue":"09","key":"5028_CR42","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.3855\/jidc.3623","volume":"8","author":"E De Carolis","year":"2014","unstructured":"De Carolis E, Vella A, Vaccaro L, Torelli R, Spanu T, Fiori B, et al. Application of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. J Infect Developing Ctries. 2014;8(09):1081\u20138.","journal-title":"J Infect Developing Ctries"},{"issue":"4","key":"5028_CR43","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.3390\/microorganisms11041071","volume":"11","author":"N Mohammad","year":"2023","unstructured":"Mohammad N, Normand A-C, Nabet C, Godmer A, Brossas J-Y, Blaize M, et al. Improving the detection of epidemic clones in Candida parapsilosis outbreaks by combining MALDI-TOF mass spectrometry and deep learning approaches. Microorganisms. 2023;11(4):1071.","journal-title":"Microorganisms"},{"issue":"1","key":"5028_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-05647-4","volume":"12","author":"A-C Normand","year":"2022","unstructured":"Normand A-C, Chaline A, Mohammad N, Godmer A, Acherar A, Huguenin A, et al. Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning. Sci Rep. 2022;12(1):1575.","journal-title":"Sci Rep"},{"issue":"1","key":"5028_CR45","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10010026","volume":"10","author":"PM Rodrigues","year":"2022","unstructured":"Rodrigues PM, Ribeiro P, Tavaria FK. Distinction of different colony types by a smart-data-driven tool. Bioengineering. 2022;10(1):26.","journal-title":"Bioengineering"},{"issue":"23","key":"5028_CR46","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1128\/JB.184.23.6481-6489.2002","volume":"184","author":"DA D\u2019Argenio","year":"2002","unstructured":"D\u2019Argenio DA, Calfee MW, Rainey PB, Pesci EC. Autolysis and autoaggregation in Pseudomonas aeruginosa colony morphology mutants. J Bacteriol. 2002;184(23):6481\u20139.","journal-title":"J Bacteriol"},{"issue":"1","key":"5028_CR47","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2180-12-30","volume":"12","author":"R Gupta","year":"2012","unstructured":"Gupta R, Schuster M. Quorum sensing modulates colony morphology through alkyl quinolones in Pseudomonas aeruginosa. BMC Microbiol. 2012;12(1):30.","journal-title":"BMC Microbiol"},{"issue":"10","key":"5028_CR48","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1016\/j.sab.2007.07.008","volume":"62","author":"SJ Rehse","year":"2007","unstructured":"Rehse SJ, Diedrich J, Palchaudhuri S. Identification and discrimination of Pseudomonas aeruginosa bacteria grown in blood and bile by laser-induced breakdown spectroscopy. Spectrochim Acta B At Spectrosc. 2007;62(10):1169\u201376.","journal-title":"Spectrochim Acta B At Spectrosc"}],"container-title":["BMC Microbiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12866-026-05028-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12866-026-05028-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12866-026-05028-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T02:32:23Z","timestamp":1779244343000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12866-026-05028-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["5028"],"URL":"https:\/\/doi.org\/10.1186\/s12866-026-05028-1","relation":{},"ISSN":["1471-2180"],"issn-type":[{"value":"1471-2180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,18]]},"assertion":[{"value":"5 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study involved only secondary analysis of a publicly available, fully anonymized image dataset of microbial colonies (AGAR dataset; Majchrowska et al., 2021; DOI:\n                      \n                      ). No experiments on human participants, human data\/tissue, or live vertebrate animals were conducted by the authors. Accordingly, approval by an Institutional Review Board (IRB) or Institutional Animal Care and Use Committee (IACUC)\/equivalent ethics committee was not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"467"}}