Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications

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dc.authoridHuri Bulut / 0000-0003-2706-9625en_US
dc.authorscopusidHuri Bulut / 57185264300en_US
dc.authorwosidHuri Bulut / AAM-1432-2020
dc.contributor.authorGüleken, Zozan
dc.contributor.authorJakubczyk, Pawe?
dc.contributor.authorPaja, W.
dc.contributor.authorKrzysztof, Pancerz
dc.contributor.authorBulut, Huri
dc.contributor.authorÖten, Esra
dc.contributor.authorDepciuch, Joanna
dc.contributor.authorTarhan, Nevzat K.
dc.date.accessioned2021-10-19T06:47:27Z
dc.date.available2021-10-19T06:47:27Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümüen_US
dc.description.abstractHerein, we show differences in blood serum of asymptomatic and symptomatic pregnant women infected with COVID-19 and correlate them with laboratory indexes, ATR FTIR and multivariate machine learning methods. We collected the sera of COVID-19 diagnosed pregnant women, in the second trimester (n = 12), third-trimester (n = 7), and second-trimester with severe symptoms (n = 7) compared to the healthy pregnant (n = 11) women, which makes a total of 37 participants. To assign the accuracy of FTIR spectra regions where peak shifts occurred, the Random Forest algorithm, traditional C5.0 single decision tree algorithm and deep neural network approach were used. We verified the correspondence between the FTIR results and the laboratory indexes such as: the count of peripheral blood cells, biochemical parameters, and coagulation indicators of pregnant women. CH2 scissoring, amide II, amide I vibrations could be used to differentiate the groups. The accuracy calculated by machine learning methods was higher than 90%. We also developed a method based on the dynamics of the absorbance spectra allowing to determine the differences between the spectra of healthy and COVID-19 patients. Laboratory indexes of biochemical parameters associated with COVID-19 validate changes in the total amount of proteins, albumin and lipase.en_US
dc.identifier.citationGuleken, Z., Jakubczyk, P., Wiesław, P., Krzysztof, P., Bulut, H., Öten, E., ... & Tarhan, N. (2021). Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications. Talanta, 122916.en_US
dc.identifier.doi10.1016/j.talanta.2021.122916en_US
dc.identifier.issn0039-9140en_US
dc.identifier.pmid34736654en_US
dc.identifier.scopus2-s2.0-85116686984en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.talanta.2021.122916
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2158
dc.identifier.volume237en_US
dc.identifier.wosWOS:000711461300008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorBulut, Huri
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofTalantaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectFTIRen_US
dc.subjectLaboratory Indexesen_US
dc.subjectMachine Learningen_US
dc.subjectPregnancyen_US
dc.titleCharacterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classificationsen_US
dc.typeArticleen_US

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