{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:08:37Z","timestamp":1792807717171,"version":"4.5.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Genomics"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s12920-018-0333-2","type":"journal-article","created":{"date-parts":[[2018,2,13]],"date-time":"2018-02-13T13:06:34Z","timestamp":1518527194000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics"],"prefix":"10.1186","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7009-6947","authenticated-orcid":false,"given":"Aida","family":"Catic","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lejla","family":"Gurbeta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amina","family":"Kurtovic-Kozaric","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senad","family":"Mehmedbasic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Almir","family":"Badnjevic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,2,13]]},"reference":[{"key":"333_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1601-5223.1956.tb03010.x","volume":"42","author":"JH Tjio","year":"1956","unstructured":"Tjio JH, Levan A. The chromosome number in man. Hereditas. 1956;42:1.","journal-title":"Hereditas"},{"key":"333_CR2","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1038\/1781020a0","volume":"178","author":"CE Ford","year":"1956","unstructured":"Ford CE, Hamerton JL. The chromosomes of man. Nature. 1956;178:1020\u20133.","journal-title":"Nature"},{"key":"333_CR3","first-page":"99","volume-title":"Prenatal diagnosis and screening","author":"M Mikkelsen","year":"1992","unstructured":"Mikkelsen M, Brondum-Nielsen K. Karyotype analysis and chromosome disorders. In: Brock DJH, Rodeck CH, Ferguson-Smith MA, editors. Prenatal diagnosis and screening. London: Churchill Livingstone; 1992. p. 99\u2013125."},{"key":"333_CR4","first-page":"360","volume":"159","author":"SA Schonberg","year":"1993","unstructured":"Schonberg SA. Cytogenetic analysis in prenatal diagnosis. West J Med. 1993;159:360\u20135.","journal-title":"West J Med"},{"key":"333_CR5","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00439-008-0603-8","volume":"125","author":"EG Allen","year":"2009","unstructured":"Allen EG, Freeman SB, Druschel C, et al. Maternal age and risk for trisomy 21 assessed by the origin of chromosome nondisjunction: a report from the Atlanta and National down Syndrome Projects. Hum Genet. 2009;125:41\u201352.","journal-title":"Hum Genet"},{"issue":"5","key":"333_CR6","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1177\/2211068214564595","volume":"20","author":"L Hixson","year":"2015","unstructured":"Hixson L, Goel S, Schuber P, Faltas V, Lee J, Narayakkadan A, Leung H, Osborne J. An overview on prenatal screening for chromosomal aberrations. J Lab Automat. 2015;20(5):562\u201373.","journal-title":"J Lab Automat"},{"issue":"6","key":"333_CR7","doi-asserted-by":"publisher","first-page":"828","DOI":"10.2353\/jmoldx.2010.090224","volume":"12","author":"C Badenas","year":"2010","unstructured":"Badenas C, Rodr\u00edguez-Revenga L, Morales C, Mediano C, Plaja A, P\u00e9rez-Iribarne MM, Soler A, Clusellas N, Borrell A, S\u00e1nchez M\u00c1, Mir\u00f3 E. Assessment of QF-PCR as the first approach in prenatal diagnosis. J Mol Diagn. 2010;12(6):828\u201334.","journal-title":"J Mol Diagn"},{"issue":"51","key":"333_CR8","doi-asserted-by":"publisher","first-page":"20458","DOI":"10.1073\/pnas.0810641105","volume":"105","author":"RW Chiu","year":"2008","unstructured":"Chiu RW, Chan KA, Gao Y, Lau VY, Zheng W, Leung TY, Foo CH, Xie B, Tsui NB, Lun FM, Zee BC. Noninvasive prenatal diagnosis of fetal chromosomal aneuploidy by massively parallel genomic sequencing of DNA in maternal plasma. Proc Natl Acad Sci. 2008;105(51):20458\u201363.","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"333_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s10897-012-9545-3","volume":"22","author":"KL Wilson","year":"2013","unstructured":"Wilson KL, Czerwinski JL, Hoskovec JM, Noblin SJ, Sullivan CM, Harbison A, Campion MW, Devary K, Devers P, Singletary CN. NSGC practice guideline: prenatal screening and diagnostic testing options for chromosome aneuploidy. J Genet Couns. 2013;22(1):4\u201315.","journal-title":"J Genet Couns"},{"issue":"8","key":"333_CR10","first-page":"1164","volume":"124","author":"CW Jia","year":"2011","unstructured":"Jia CW, Wang SY, Ma YM, Lan YL, Si YM, Yu L, Zhou LY. Fluorescence in situ hybridization in uncultured amniocytes for detection of aneuploidy in 4210 prenatal cases. Chin Med J. 2011;124(8):1164\u20138.","journal-title":"Chin Med J"},{"key":"333_CR11","unstructured":"Shaffer LG, Tommerup N, editors. ISCN 2005: An international system for human cytogenetic nomenclature. Basel: S. Karger; 2005."},{"key":"333_CR12","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003:1157\u201382."},{"key":"333_CR13","doi-asserted-by":"crossref","unstructured":"Guenther FH. Neural networks: Biological models and applications. In: Smel- ser NJ, Baltes PB, editors. Oxford: International Encyclopedia of the Social & Behavioral Sciences; 2001. p. 10534\u20137.","DOI":"10.1016\/B0-08-043076-7\/03667-6"},{"key":"333_CR14","first-page":"36","volume":"2","author":"OS Eluyode","year":"2013","unstructured":"Eluyode OS, Akomolafe AT. Comparative study of biological and artificial neural networks. Eur J Appl Eng Sci Res. 2013;2:36\u201346.","journal-title":"Eur J Appl Eng Sci Res"},{"issue":"Suppl 3","key":"333_CR15","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1186\/1472-6947-15-S3-S1","volume":"15","author":"A Badnjevic","year":"2015","unstructured":"Badnjevic A, Cifrek M, Koruga D, Osmankovic D. Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease. BMC Med Inform Decis Mak. 2015;15(Suppl 3):S1. https:\/\/doi.org\/10.1186\/1472-6947-15-S2-S1 .","journal-title":"BMC Med Inform Decis Mak"},{"key":"333_CR16","doi-asserted-by":"crossref","unstructured":"Badnjevic A, Cifrek M, Koruga D. Classification of Chronic Obstructive Pulmonary Disease (COPD) using integrated software suite. IFMBE XIII Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON), 25\u201328. Sevilla; 2013.","DOI":"10.1007\/978-3-319-00846-2_225"},{"key":"333_CR17","doi-asserted-by":"crossref","unstructured":"Badnjevic A, Cifrek M. Classification of asthma utilizing integrated software suite. 6th European Conference of the International Federation for Medical and Biological Engineering (MBEC), 07\u201311. Dubrovnik; 2014.","DOI":"10.1007\/978-3-319-11128-5_104"},{"key":"333_CR18","doi-asserted-by":"crossref","unstructured":"Aljovic A, Badnjevic A, Gurbeta L. Artificial Neural Networks in the Discrimination of Alzheimer's disease Using Biomarkers Data. IEEE 5th Mediterranean Conference on Embedded Computing (MECO), 12\u201316. 2016. Bar, Montenegro.","DOI":"10.1109\/MECO.2016.7525762"},{"key":"333_CR19","doi-asserted-by":"crossref","unstructured":"Alic B, Sejdinovic D, Gurbeta L, Badnjevic A. Classification of Stress Recognition using Artificial Neural Network. IEEE 5th Mediterranean Conference on Embedded Computing (MECO), 12\u201316 June 2016, Bar, Montenegro.","DOI":"10.1109\/MECO.2016.7525765"},{"key":"333_CR20","doi-asserted-by":"crossref","unstructured":"Fojnica A, Osmanovic A, Badnjevic A. Dynamical Model of Tuberculosis-Multiple Strain Prediction based on Artificial Neural Network. IEEE 5th Mediterranean Conference on Embedded Computing (MECO), 12\u201316 June 2016, Bar, Montenegro.","DOI":"10.1109\/MECO.2016.7525763"},{"key":"333_CR21","unstructured":"Badnjevic A, Koruga D, Cifrek M, Smith HJ, Bego T. \u201eInterpretation of pulmonary function test results in relation to asthma classification using integrated software suite\u201c, IEEE 36. International convention on information and communication technology, electronics and microelectronics (MIPRO), pp: 140\u2013144, 21.-25. May 2013. Opatija."},{"key":"333_CR22","doi-asserted-by":"crossref","unstructured":"Badnjevic A, Gurbeta L, Cifrek M, Marjanovic D, Classification of Asthma Using Artificial Neural Network\u201c, IEEE 39th International convention on information and communication technology, electronics and microelectronics (MIPRO), 30. May to 03. June 2016. Opatija.","DOI":"10.1109\/MIPRO.2016.7522173"},{"key":"333_CR23","doi-asserted-by":"crossref","unstructured":"Granulo E, Becar L, Gurbeta L, Badnjevic A, \u201cTelemetry system for diagnosis of Asthma and Chronical Obstructive Pulmonary Disease (COPD)\u201d, The 3rd EAI International Conference on IoT Technologies for HealthCare, 18\u201319 October 2016, Vasteras.","DOI":"10.1007\/978-3-319-51234-1_18"},{"key":"333_CR24","doi-asserted-by":"publisher","unstructured":"Veljovic E, Spirtovic Halilovic S, Muratovic S, Osmanovic A, Badnjevic A, et al. Artificial Neural Network and Docking Study in Design and Synthesis of Xanthenes as Antimicrobial Agents, CMBEBIH 2017. IFMBE Proceedings, vol 62. pp 617\u2013626, Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-10-4166-2_93","DOI":"10.1007\/978-981-10-4166-2_93"},{"key":"333_CR25","doi-asserted-by":"publisher","unstructured":"Alic B, Gurbeta L, Badnjevic A, et al. Classification of Metabolic Syndrome patients using implemented Expert System\u201d, CMBEBIH 2017. IFMBE Proceedings, vol 62. pp 601\u2013607, Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-10-4166-2_91","DOI":"10.1007\/978-981-10-4166-2_91"},{"key":"333_CR26","doi-asserted-by":"publisher","unstructured":"Badnjevic A, Gurbeta L, Cifrek M, Pecchia L. Pre-classification process symptom questionnaire based on fuzzy logic for pulmonary function test cost reduction. CMBEBIH 2017. IFMBE Proceedings, vol 62. pp 608\u2013616, Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-10-4166-2_92","DOI":"10.1007\/978-981-10-4166-2_92"},{"key":"333_CR27","doi-asserted-by":"publisher","unstructured":"Sejdinovic D, Gurbeta L, Badnjevic A, Malenica M, Dujic T, Causevic A, Bego T, Divovic L. Classification of prediabetes and Type 2 Diabetes using Artificial NeuralNetwork. CMBEBIH 2017. IFMBE Proceedings, vol 62. pp 685\u2013689, Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-10-4166-2_103","DOI":"10.1007\/978-981-10-4166-2_103"},{"key":"333_CR28","doi-asserted-by":"publisher","unstructured":"Gianola D, Okut H, Weigel KA, Rosa GJM. Predicting complex quantitative traits with neural networks: a case study with Jersey cows and wheat. BMC Genetics. 2011. https:\/\/doi.org\/10.1186\/1471-2156-12-87 .","DOI":"10.1186\/1471-2156-12-87"},{"key":"333_CR29","doi-asserted-by":"publisher","unstructured":"Ehret A, Hochstuhl D, Gianola D, Thaller G. Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle. Genetics Selection Evolution. 2015. https:\/\/doi.org\/10.1186\/s12711-015-0097-5 .","DOI":"10.1186\/s12711-015-0097-5"},{"key":"333_CR30","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.jbi.2008.05.004","volume":"42","author":"X Wang","year":"2009","unstructured":"Wang X, Zheng B, Li S, Mulvihill JJ, Wood MC, Liu H. Automated classification of metaphase chromosomes: optimization of an adaptive computerized scheme. J Biomed Inform. 2009;42:22\u201331.","journal-title":"J Biomed Inform"},{"key":"333_CR31","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1088\/0022-3727\/38\/15\/003","volume":"38","author":"X Wang","year":"2005","unstructured":"Wang X, Zheng B, Wood M, Li S, Chen W, Liu H. Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives. J Phys D Appl Phys. 2005;38:2536.","journal-title":"J Phys D Appl Phys"},{"key":"333_CR32","doi-asserted-by":"crossref","unstructured":"Beksa\u00e7 MS, et al. an expert diagnostic system based on neural networks and image analysis techniques in the field of automated cytogenetics. Technol Health Care 1996;3:217-229.","DOI":"10.3233\/THC-1996-3403"},{"key":"333_CR33","doi-asserted-by":"crossref","unstructured":"Eskiizmirliler S, Erkmen AM, Basaran F, Cakar AN. A hybrid intelligent diagnostic system based on neural networks and image analysis techniques in the field of automated cytogenetics. In Image Processing 1996. Proceedings.International Conference on. Vol. 1. IEEE. 1996:315\u2013318.","DOI":"10.1109\/ICIP.1996.559496"},{"key":"333_CR34","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1088\/0031-9155\/38\/7\/006","volume":"38","author":"AM Jennings","year":"1993","unstructured":"Jennings AM, Graham J. A neural network approach to automatic chromosome classification. Phys Med Biol. 1993;38:959.","journal-title":"Phys Med Biol"},{"key":"333_CR35","doi-asserted-by":"publisher","first-page":"149","DOI":"10.2174\/1874431100802010149","volume":"2","author":"O Braaten","year":"2008","unstructured":"Braaten O, Friestad J. Syndrome diagnosis: human intuition or machine intelligence? Open Med Inform J. 2008;2:149.","journal-title":"Open Med Inform J"},{"key":"333_CR36","unstructured":"Dagna-Bricarelli F, Hastings RJ, Kristoffersson U, Cavani S. Cytogenetic Guidelines and Quality Assurance. A common European framework for quality assessment for constitutional and acquired cytogenetic investigations. ECA Permanent Working Group for Cytogenetics and Society. Guidelines Version 1.2. http:\/\/www.iss.it\/binary\/tege4\/cont\/Linee_Guida_ECA.pdf"},{"key":"333_CR37","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/sj.ejhg.5201809","volume":"15","author":"RJ Hastings","year":"2007","unstructured":"Hastings RJ, Cavani S, Bricarelli FD, Patsalis PC, Kristoffersson U. Cytogenetic guidelines and quality assurance: a common European framework for quality assessment for constitutional and acquired cytogenetic investigations. Eur J Hum Genet. 2007;15:525\u20137.","journal-title":"Eur J Hum Genet"},{"key":"333_CR38","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1159\/000484791","volume":"5","author":"PM Kroisel","year":"1997","unstructured":"Kroisel PM, Fryns JP, Matthijs G, Brondum-Nielsen K, Vejerslev L, von Koskull H, Giraudon E, Saura R, Held K, Muller-Reible CR, Metaxotou C. Quality guidelines and standards for genetic laboratories\/clinics in prenatal diagnosis on fetal samples obtained by invasive procedures-an attempt to establish a common European framework for quality assessment. Eur J Hum Genet. 1997;5:342\u201350.","journal-title":"Eur J Hum Genet"},{"key":"333_CR39","doi-asserted-by":"publisher","unstructured":"GnanaSheela K, Deepa SN. Review on Methods to Fix Number of Hidden Neurons in Neural Networks. Math Probl Eng. 2013. https:\/\/doi.org\/10.1155\/2013\/425740 .","DOI":"10.1155\/2013\/425740"},{"issue":"6","key":"333_CR40","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1021\/ci0341161","volume":"43","author":"E Byvatov","year":"2003","unstructured":"Byvatov E, Fechner U, Sadowski J, Schneider G. Comparison of support vector machine and artificial neural network systems for drug\/nondrug classification. J Chem Inf Comput Sci. 2003;43(6):1882\u20139.","journal-title":"J Chem Inf Comput Sci"},{"key":"333_CR41","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/89044","volume":"7","author":"J Khan","year":"2001","unstructured":"Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673\u20139.","journal-title":"Nat Med"},{"key":"333_CR42","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.eswa.2005.04.011","volume":"29","author":"NF G\u00fcler","year":"2005","unstructured":"G\u00fcler NF, \u00dcbeyli ED, G\u00fcler \u0130. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl. 2005;29:506\u201314.","journal-title":"Expert Syst Appl"},{"key":"333_CR43","doi-asserted-by":"crossref","unstructured":"Lin F-J, Kung Y-S, Chen S-Y, Liu Y-H. Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors. IET Electric Power Applications. 2010;4(5):314\u2013332.IEPAAN000004000005000314000001.","DOI":"10.1049\/iet-epa.2009.0144"},{"issue":"5","key":"333_CR44","first-page":"953","volume":"7","author":"A Pasini","year":"2015","unstructured":"Pasini A. Artificial neural networks for small dataset analysis. J Thorac Dis. 2015;7(5):953.","journal-title":"J Thorac Dis"},{"issue":"2","key":"333_CR45","first-page":"1137","volume":"14","author":"R Kohavi","year":"1995","unstructured":"Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai. 1995;14(2):1137\u201345.","journal-title":"Ijcai"},{"key":"333_CR46","unstructured":"Hao Y, Wilamowski M. Levenberg-Marquardt Training. 2011, 1\u201316, eBook ISBN: 978\u20131\u20134398-0284-7, Print ISBN: 978\u20131\u20134398-0283-0."},{"key":"333_CR47","doi-asserted-by":"crossref","unstructured":"Huang SC, Huang YF. Bounds on the Number of Hidden Neurons in Multilayer Perceptrons. IEEE Trans. Neural Netw. 1991.","DOI":"10.1109\/72.80290"},{"key":"333_CR48","doi-asserted-by":"crossref","unstructured":"Sheela KG, Deepa SN. Review on Methods to Fix Number of Hidden Neurons in Neural Networks. J Math Probl Eng. 2013;425740.","DOI":"10.1155\/2013\/425740"},{"key":"333_CR49","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1016\/S0895-4356(96)00002-9","volume":"49","author":"J Tu","year":"1996","unstructured":"Tu J. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996;49:1225\u201331.","journal-title":"J Clin Epidemiol"},{"key":"333_CR50","doi-asserted-by":"crossref","unstructured":"Wojtowicz H, Wajs W. Intelligent information system for interpretation of Dermatoglyphic patterns of Down\u2019s syndrome in infants. Intell Inf Database Sys. 2012:284\u201393.","DOI":"10.1007\/978-3-642-28490-8_30"},{"key":"333_CR51","doi-asserted-by":"crossref","unstructured":"Wojtowicz H, Wajs W. Medical decision support system for assessment of dermatoglyphic indices and diagnosis of Down\u2019s syndrome. InIntelligent decision technologies. Berlin Heidelberg: Springer; 2012. p. 69\u201378.","DOI":"10.1007\/978-3-642-29920-9_8"},{"key":"333_CR52","doi-asserted-by":"crossref","unstructured":"Pereira H, Leite F. Applicability of Data Mining Technique using Bayesians Network in Diagnosis of Genetic Diseases. 2013.","DOI":"10.14569\/IJACSA.2013.040107"},{"key":"333_CR53","unstructured":"Soleimani F, Teymouri R, Biglarian A. Predicting developmental disorder in infants using an artificial neural network. Acta Med Iran. 2013;51:347."},{"key":"333_CR54","volume-title":"The 11\u201313+6 weeks scan","author":"K Nicolaides","year":"2004","unstructured":"Nicolaides K. The 11\u201313+6 weeks scan. London: Fetal Medicine Foundation; 2004."},{"key":"333_CR55","doi-asserted-by":"crossref","unstructured":"Badnjevic A., Cifrek M., Classification of asthma utilizing integrated software suite. 6th European Conference of the International Federation for Medical and Biological Engineering (MBEC). Dubrovnik; 2014. p. 7\u201311.","DOI":"10.1007\/978-3-319-11128-5_104"}],"container-title":["BMC Medical Genomics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12920-018-0333-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T23:42:10Z","timestamp":1693525330000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedgenomics.biomedcentral.com\/articles\/10.1186\/s12920-018-0333-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,13]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["333"],"URL":"https:\/\/doi.org\/10.1186\/s12920-018-0333-2","relation":{},"ISSN":["1755-8794"],"issn-type":[{"value":"1755-8794","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,13]]},"assertion":[{"value":"21 June 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Because this was a retrospective chart review study no ethics committee approval was required. By the rules of the private clinic \u201cMehmedbasic\u201d informed consent was obtained from all participants included in the study prior to amniocentesis procedure.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"All authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"19"}}