Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods

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dc.authoridPaja, Wieslaw/0000-0002-6446-036X
dc.authoridGuleken, Zozan/0000-0002-4136-4447
dc.authoridPaja, Wieslaw/0000-0002-6446-036X
dc.authoridTarhan, Nevzat Kasif/0000-0002-6810-7096
dc.authorwosidPaja, Wieslaw/GQH-8014-2022
dc.authorwosidGuleken, Zozan/AAF-1789-2019
dc.authorwosidPaja, Wieslaw/I-2597-2016
dc.authorwosidUzun, Özgür/A-2361-2019
dc.authorwosidTarhan, Nevzat Kasif/AIB-8542-2022
dc.contributor.authorDepciuch, Joanna
dc.contributor.authorPaja, Wieslaw
dc.contributor.authorPancerz, Krzysztof
dc.contributor.authorUzun, Ozgur
dc.contributor.authorBulut, Huri
dc.contributor.authorTarhan, Nevzat
dc.contributor.authorGuleken, Zozan
dc.date.accessioned2024-05-19T14:41:36Z
dc.date.available2024-05-19T14:41:36Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractOocytes are surrounded by a fluid called follicular fluid, which provides an essential microenvironment for developing oocytes in human fertility. Various molecules exist in antral follicles, including proteins, steroid hormones, polysaccharides, metabolites, reactive oxygen species, and antioxidants. Oxidative stress is involved in the etiology of defective oocyte development or poor oocyte and embryo quality. Raman spectroscopy, a noninvasive method, can be used for biological diagnostics and direct chemical identification of follicular fluid. Therefore, we measured the oxidative index of follicular fluids and then attempted Raman spectroscopy on the follicular fluids combined with machine learning techniques to identify, detect, and quantify follicular fluid of unexplained infertility-diagnosed women as a safe and effective tool to use as adjacent for clinical studies. This was a retrospective study set in an academic hospital where the patients were selected from an unexplained infertility-diagnosed population in the in vitro fertilization (IVF) center. Raman spectra of 128 follicular fluid samples (n = 63 control; and 65 unexplained infertility) were obtained. To profile Raman-based results of follicular fluid, oxidative load measurements, multivariate analysis, correlation tests, and six machine learning methods were used. Raman bands associated with oxidative load and amide III and lipids differed significantly. Classification using stacks of Raman signals was applied by random forest, C5.0 decision tree algorithm, k-nearest neighbors, deep neural networks, support vector machine, and XGBoost trees algorithms achieved an overall accuracy of 92.04% to 99.17% in assigned correctly. Group has an oxidative load in their follicle fluids consistent with clinical results and biochemical measurements and performing testing based on Raman spectra validated by kNN clustering and SVM object vector separation machine learning methods. The study suggests that Raman spectroscopy can detect changes in follicle fluid in unexplained infertility.en_US
dc.identifier.doi10.1002/jrs.6510
dc.identifier.endpage511en_US
dc.identifier.issn0377-0486
dc.identifier.issn1097-4555
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85149290749en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage501en_US
dc.identifier.urihttps://doi.org10.1002/jrs.6510
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5133
dc.identifier.volume54en_US
dc.identifier.wosWOS:000940725600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal of Raman Spectroscopyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFollicular Fluiden_US
dc.subjectMachine Learningen_US
dc.subjectOxidative Loaden_US
dc.subjectRaman Spectroscopyen_US
dc.subjectUnexplained Infertilityen_US
dc.titleAnalysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methodsen_US
dc.typeArticleen_US

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