{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:14:11Z","timestamp":1740158051895,"version":"3.37.3"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T00:00:00Z","timestamp":1561334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T00:00:00Z","timestamp":1561334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s12652-019-01374-3","type":"journal-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T18:48:49Z","timestamp":1561402129000},"page":"15523-15533","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Lipid profile prediction based on artificial neural networks"],"prefix":"10.1007","volume":"14","author":[{"given":"Milan","family":"Vrba\u0161ki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9020-9494","authenticated-orcid":false,"given":"Rade","family":"Doroslova\u010dki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandar","family":"Kupusinac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edita","family":"Stoki\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dragan","family":"Iveti\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,24]]},"reference":[{"key":"1374_CR1","volume-title":"Learning from data","author":"YS Abu-Mostafa","year":"2012","unstructured":"Abu-Mostafa YS, Magdon-Ismail M, Lin HT (2012) Learning from data. AMLBook, Pasadena"},{"issue":"8","key":"1374_CR2","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1111\/j.1745-7599.2004.tb00456.x","volume":"16","author":"SJ Appel","year":"2004","unstructured":"Appel SJ, Jones ED, Kennedy-Malone L (2004) Central obesity and the metabolic syndrome: implications for primary care providers. J Am Acad Nurse Pract 16(8):335\u2013342","journal-title":"J Am Acad Nurse Pract"},{"key":"1374_CR3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.cmpb.2017.01.004","volume":"141","author":"Z Arabasadi","year":"2017","unstructured":"Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Comput Methods Programs Biomed 141:19\u201326. https:\/\/doi.org\/10.1016\/j.cmpb.2017.01.004","journal-title":"Comput Methods Programs Biomed"},{"issue":"5","key":"1374_CR4","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1079\/BJN20041273","volume":"92","author":"M Arroyo","year":"2004","unstructured":"Arroyo M, Rocandio AM, Ansotegui L, Herrera H, Salces I, Rebato E (2004) Comparison of predicted body fat percentage from anthropometric methods and from impedance in university students. Br J Nutr 92(5):827\u2013832","journal-title":"Br J Nutr"},{"issue":"7027","key":"1374_CR5","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1136\/bmj.312.7027.377","volume":"312","author":"M Ashwell","year":"1996","unstructured":"Ashwell M, Lejeune S, McPherson K (1996) Ratio of waist circumference to height may be better indicator of need for weight management. Br Med J 312(7027):377","journal-title":"Br Med J"},{"issue":"3","key":"1374_CR6","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1111\/j.1467-789X.2011.00952.x","volume":"13","author":"M Ashwell","year":"2012","unstructured":"Ashwell M, Gunn P, Gibson S (2012) Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev 13(3):275\u2013286. https:\/\/doi.org\/10.1111\/j.1467-789X.2011.00952.x","journal-title":"Obes Rev"},{"issue":"2","key":"1374_CR7","first-page":"45","volume":"8","author":"W Beeson","year":"2010","unstructured":"Beeson W, Batech M, Schultz E, Salto L, Firek A, Deleon M, Balcazar H, Cordero-Macintyre Z (2010) Comparison of body composition by bioelectrical impedance analysis and dual-energy X-ray absorptiometry in hispanic diabetics. Int J Body Compos Res 8(2):45\u201350","journal-title":"Int J Body Compos Res"},{"issue":"1","key":"1374_CR8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1475-2840-9-7","volume":"9","author":"DA Belletti","year":"2010","unstructured":"Belletti DA, Zacker C, Wogen J (2010) Effect of cardiometabolic risk factors on hypertension management: a cross-sectional study among 28 physician practices in the United States. Cardiovasc Diabetol 9(1):7","journal-title":"Cardiovasc Diabetol"},{"issue":"1","key":"1374_CR9","first-page":"31","volume":"13","author":"MS Bhatti","year":"2001","unstructured":"Bhatti MS, Akbri MZA, Shakoor M (2001) Lipid profile in obesity. J Ayub Med Coll Abbottabad 13(1):31\u20133","journal-title":"J Ayub Med Coll Abbottabad"},{"issue":"6","key":"1374_CR10","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.exger.2007.03.003","volume":"42","author":"MJ Cartwright","year":"2007","unstructured":"Cartwright MJ, Tchkonia T, Kirkland JL (2007) Aging in adipocytes: potential impact of inherent, depot-specific mechanisms. Exp Gerontol 42(6):463\u2013471","journal-title":"Exp Gerontol"},{"key":"1374_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1033-7","author":"K Chung","year":"2018","unstructured":"Chung K, Yoo H, Choe DE (2018) Ambient context-based modeling for health risk assessment using deep neural network. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-1033-7","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"4","key":"1374_CR12","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303\u2013314","journal-title":"Math Control Signals Syst"},{"issue":"6","key":"1374_CR13","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","volume":"117","author":"RB D\u2019Agostino","year":"2008","unstructured":"D\u2019Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB (2008) General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation 117(6):743\u2013753. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.107.699579","journal-title":"Circulation"},{"issue":"4","key":"1374_CR14","doi-asserted-by":"publisher","first-page":"7675","DOI":"10.1016\/j.eswa.2008.09.013","volume":"36","author":"R Das","year":"2009","unstructured":"Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36(4):7675\u20137680","journal-title":"Expert Syst Appl"},{"issue":"4","key":"1374_CR15","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1161\/01.ATV.10.4.497","volume":"10","author":"JP Despres","year":"1990","unstructured":"Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C (1990) Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 10(4):497\u2013511","journal-title":"Arteriosclerosis"},{"issue":"22","key":"1374_CR16","doi-asserted-by":"publisher","first-page":"1766","DOI":"10.1001\/2013.jamainternmed.327","volume":"172","author":"D Faeh","year":"2012","unstructured":"Faeh D, Braun J, Bopp M (2012) Body mass index vs cholesterol in cardiovascular disease risk prediction models. JAMA Intern Med 172(22):1766\u20131768","journal-title":"JAMA Intern Med"},{"issue":"3","key":"1374_CR17","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1093\/oxfordjournals.aje.a008733","volume":"143","author":"D Gallagher","year":"1996","unstructured":"Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB (1996) How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 143(3):228\u2013239","journal-title":"Am J Epidemiol"},{"issue":"9616","key":"1374_CR18","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/S0140-6736(08)60418-3","volume":"371","author":"TA Gaziano","year":"2008","unstructured":"Gaziano TA, Young CR, Fitzmaurice G, Atwood S, Gaziano JM (2008) Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I follow-up study cohort. The Lancet 371(9616):923\u2013931","journal-title":"The Lancet"},{"issue":"4","key":"1374_CR19","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.amepre.2011.12.009","volume":"42","author":"BB Green","year":"2012","unstructured":"Green BB, Anderson ML, Cook AJ, Catz S, Fishman PA, McClure JB, Reid R (2012) Using body mass index data in the electronic health record to calculate cardiovascular risk. Am J Prev Med 42(4):342\u2013347","journal-title":"Am J Prev Med"},{"key":"1374_CR20","volume-title":"Applied statistics for the behavioral sciences","author":"DE Hinkle","year":"1988","unstructured":"Hinkle DE, Wiersma W, Jurs SG et al (1988) Applied statistics for the behavioral sciences, 2nd edn. Houghton Mifflin, Boston","edition":"2"},{"issue":"1","key":"1374_CR21","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1620\/tjem.188.55","volume":"188","author":"SD Hsieh","year":"1999","unstructured":"Hsieh SD, Yoshinaga H (1999) Do people with similar waist circumference share similar health risks irrespective of height? Tohoku J Exp Med 188(1):55\u201360","journal-title":"Tohoku J Exp Med"},{"issue":"6","key":"1374_CR22","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1038\/sj.ijo.0802006","volume":"26","author":"AS Jackson","year":"2002","unstructured":"Jackson AS, Stanforth P, Gagnon J, Rankinen T, Leon AS, Rao D, Skinner J, Bouchard C, Wilmore J (2002) The effect of sex, age and race on estimating percentage body fat from body mass index: the heritage family study. Int J Obes 26(6):789\u2013796","journal-title":"Int J Obes"},{"issue":"1","key":"1374_CR23","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.amjmed.2015.08.010","volume":"129","author":"HS Kahn","year":"2016","unstructured":"Kahn HS, Bullard KM (2016) Beyond body mass index: advantages of abdominal measurements for recognizing cardiometabolic disorders. Am J Med 129(1):74\u201381. https:\/\/doi.org\/10.1016\/j.amjmed.2015.08.010","journal-title":"Am J Med"},{"issue":"3","key":"1374_CR24","doi-asserted-by":"publisher","first-page":"e0172245","DOI":"10.1371\/journal.pone.0172245","volume":"12","author":"HS Kahn","year":"2017","unstructured":"Kahn HS, Bullard KM (2017) Indicators of abdominal size relative to height associated with sex, age, socioeconomic position and ancestry among us adults. PloS ONE 12(3):e0172245. https:\/\/doi.org\/10.1371\/journal.pone.0172245","journal-title":"PloS ONE"},{"key":"1374_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01195-4","author":"MG Kim","year":"2019","unstructured":"Kim MG, Ko H, Pan SB (2019) A study on user recognition using 2D ECG based on ensemble of deep convolutional neural networks. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-019-01195-4","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"1374_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","volume":"23","author":"I Kononenko","year":"2001","unstructured":"Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23(1):89\u2013109. https:\/\/doi.org\/10.1016\/S0933-3657(01)00077-X","journal-title":"Artif Intell Med"},{"issue":"6","key":"1374_CR27","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1093\/ajcn\/81.6.1330","volume":"81","author":"JL Kuk","year":"2005","unstructured":"Kuk JL, Lee S, Heymsfield SB, Ross R (2005) Waist circumference and abdominal adipose tissue distribution: influence of age and sex. Am J Clin Nutr 81(6):1330\u20131334","journal-title":"Am J Clin Nutr"},{"issue":"4","key":"1374_CR28","first-page":"270","volume":"1","author":"A Kupusinac","year":"2012","unstructured":"Kupusinac A, Stoki\u0107 E, Srdi\u0107 B (2012) Determination of WHtR limit for predicting hyperglycemia in obese persons by using artificial neural networks. TEM J 1(4):270\u2013272","journal-title":"TEM J"},{"issue":"6","key":"1374_CR29","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1016\/j.compbiomed.2013.04.001","volume":"43","author":"A Kupusinac","year":"2013","unstructured":"Kupusinac A, Doroslova\u010dki R, Malba\u0161ki D, Srdi\u0107 B, Stoki\u0107 E (2013) A primary estimation of the cardiometabolic risk by using artificial neural networks. Comput Biol Med 43(6):751\u2013757. https:\/\/doi.org\/10.1016\/j.compbiomed.2013.04.001","journal-title":"Comput Biol Med"},{"issue":"2","key":"1374_CR30","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1016\/j.cmpb.2013.10.013","volume":"113","author":"A Kupusinac","year":"2014","unstructured":"Kupusinac A, Stoki\u0107 E, Doroslova\u010dki R (2014) Predicting body fat percentage based on gender, age and BMI by using artificial neural networks. Comput Methods Programs Biomed 113(2):610\u2013619. https:\/\/doi.org\/10.1016\/j.cmpb.2013.10.013","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"1374_CR31","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/s40846-015-0090-z","volume":"35","author":"A Kupusinac","year":"2015","unstructured":"Kupusinac A, Stoki\u0107 E, Le\u010di\u0107 D, Tomi\u0107-Nagli\u0107 D, Srdi\u0107-Gali\u0107 B (2015) Gender-, age-, and BMI-specific threshold values of sagittal abdominal diameter obtained by artificial neural networks. J Med Biol Eng 35(6):783\u2013788. https:\/\/doi.org\/10.1007\/s40846-015-0090-z","journal-title":"J Med Biol Eng"},{"issue":"1","key":"1374_CR32","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s12652-017-0598-x","volume":"10","author":"LP Malasinghe","year":"2019","unstructured":"Malasinghe LP, Ramzan N, Dahal K (2019) Remote patient monitoring: a comprehensive study. J Ambient Intell Human Comput 10(1):57\u201376. https:\/\/doi.org\/10.1007\/s12652-017-0598-x","journal-title":"J Ambient Intell Human Comput"},{"issue":"5","key":"1374_CR33","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.clnu.2009.12.011","volume":"29","author":"S Meeuwsen","year":"2010","unstructured":"Meeuwsen S, Horgan G, Elia M (2010) The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr 29(5):560\u2013566","journal-title":"Clin Nutr"},{"issue":"5","key":"1374_CR34","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/S0899-9007(02)01003-1","volume":"19","author":"A Misra","year":"2003","unstructured":"Misra A, Vikram NK (2003) Clinical and pathophysiological consequences of abdominal adiposity and abdominal adipose tissue depots. Nutrition 19(5):457\u2013466","journal-title":"Nutrition"},{"issue":"10","key":"1374_CR35","doi-asserted-by":"publisher","first-page":"e0186196","DOI":"10.1371\/journal.pone.0186196","volume":"12","author":"D Orozco-Beltran","year":"2017","unstructured":"Orozco-Beltran D, Gil-Guillen VF, Redon J, Martin-Moreno JM, Pallares-Carratala V, Navarro-Perez J, Valls-Roca F, Sanchis-Domenech C, Fernandez-Gimenez A, Perez-Navarro A et al (2017) Lipid profile, cardiovascular disease and mortality in a mediterranean high-risk population: the ESCARVAL-RISK study. PLoS ONE 12(10):e0186196. https:\/\/doi.org\/10.1371\/journal.pone.0186196","journal-title":"PLoS ONE"},{"issue":"387","key":"1374_CR36","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1080\/01621459.1984.10478083","volume":"79","author":"RR Picard","year":"1984","unstructured":"Picard RR, Cook RD (1984) Cross-validation of regression models. J Am Stat Assoc 79(387):575\u2013583","journal-title":"J Am Stat Assoc"},{"key":"1374_CR37","doi-asserted-by":"publisher","DOI":"10.1155\/2010\/757939","author":"U Ris\u00e9rus","year":"2010","unstructured":"Ris\u00e9rus U, De Faire U, Berglund L, Hell\u00e9nius ML (2010) Sagittal abdominal diameter as a screening tool in clinical research: cutoffs for cardiometabolic risk. J Obes. https:\/\/doi.org\/10.1155\/2010\/757939","journal-title":"J Obes"},{"issue":"422","key":"1374_CR38","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1080\/01621459.1993.10476299","volume":"88","author":"J Shao","year":"1993","unstructured":"Shao J (1993) Linear model selection by cross-validation. J Am Stat Assoc 88(422):486\u2013494","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"1374_CR39","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/ejcn.2009.101","volume":"64","author":"J Stevens","year":"2010","unstructured":"Stevens J, Katz EG, Huxley RR (2010) Associations between gender, age and waist circumference. Eur J Clin Nutr 64(1):6\u201315","journal-title":"Eur J Clin Nutr"},{"issue":"2","key":"1374_CR40","first-page":"115","volume":"2","author":"E Stoki\u0107","year":"2013","unstructured":"Stoki\u0107 E, Gali\u0107 BS, Kupusinac A, Doroslova\u010dki R (2013) Estimating SAD low-limits for the adverse metabolic profile by using artificial neural networks. TEM J 2(2):115\u2013119","journal-title":"TEM J"},{"issue":"2","key":"1374_CR41","first-page":"343","volume":"58","author":"A Szczygielska","year":"2003","unstructured":"Szczygielska A, Widomska S, Jaraszkiewicz M, Knera P, Muc K (2003) Blood lipids profile in obese or overweight patients. Ann Univ Mariae Curie-Sklodowska Sect D Med 58(2):343\u20139","journal-title":"Ann Univ Mariae Curie-Sklodowska Sect D Med"},{"key":"1374_CR42","volume-title":"Using multivariate statistics","author":"BG Tabachnick","year":"2007","unstructured":"Tabachnick BG, Fidell LS, Ullman JB (2007) Using multivariate statistics, 5th edn. Pearson, London","edition":"5"},{"issue":"6","key":"1374_CR43","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1093\/ije\/31.6.1253","volume":"31","author":"R Voss","year":"2002","unstructured":"Voss R, Cullen P, Schulte H, Assmann G (2002) Prediction of risk of coronary events in middle-aged men in the prospective cardiovascular M\u00fcnster study (PROCAM) using neural networks. Int J Epidemiol 31(6):1253\u20131262. https:\/\/doi.org\/10.1093\/ije\/31.6.1253","journal-title":"Int J Epidemiol"},{"issue":"4","key":"1374_CR44","doi-asserted-by":"publisher","first-page":"e0174944","DOI":"10.1371\/journal.pone.0174944","volume":"12","author":"SF Weng","year":"2017","unstructured":"Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE 12(4):e0174944","journal-title":"PLoS ONE"},{"key":"1374_CR45","unstructured":"World Health Organization (2000) Obesity: preventing and managing the global epidemic: report of a WHO consultation. Technical report 894"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-019-01374-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-019-01374-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-019-01374-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T10:32:57Z","timestamp":1707215577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-019-01374-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,24]]},"references-count":45,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["1374"],"URL":"https:\/\/doi.org\/10.1007\/s12652-019-01374-3","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"type":"print","value":"1868-5137"},{"type":"electronic","value":"1868-5145"}],"subject":[],"published":{"date-parts":[[2019,6,24]]},"assertion":[{"value":"9 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors hereby declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research was approved by Ethical Committee of the Clinical Centre of Vojvodina, Republic of Serbia (No. 00\u201320\/354). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}