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    Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods
    (Wiley, 2023) Depciuch, Joanna; Paja, Wieslaw; Pancerz, Krzysztof; Uzun, Ozgur; Bulut, Huri; Tarhan, Nevzat; Guleken, Zozan
    Oocytes 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.
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    Blood serum lipid profiling may improve the management of recurrent miscarriage: a combination of machine learning of mid-infrared spectra and biochemical assays
    (SPRINGER HEIDELBERG, 2022) Guleken, Zozan; Bahat, Pınar Yalçın; Toto, Ömer Faruk; Bulut, Huri; Jakubczyk, Pawel; Cebulski, Jozef; Paja, Wieslaw; Pancerz, Krzysztof; Wosiak, Agnieszka; Depciuch, Joanna
    The present article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood samples of women with recurrent miscarriage vs. those of healthy individuals who were followed in the Department of Obstetrics and Gynecology for 2 years. For this purpose, blood samples from a total of 120 participants, including healthy women (n=60) and women with diagnosed recurrent miscarriage (n=60), were obtained. The lipid profile (high-density lipoprotein, low-density lipoprotein, triglyceride, and total cholesterol levels) and lipid peroxidation (malondialdehyde and glutathione levels) were evaluated with a Beckman Coulter analyzer system for chemical analysis. Biomolecular structure and composition were determined using an attenuated total reflectance sampling methodology with Fourier transform infrared spectroscopy alongside machine learning technology to advance toward clinical translation. Here, we developed and validated instrumentation for the analysis of recurrent miscarriage patient serum that was able to differentiate recurrent miscarriage and control patients with an accuracy of 100% using a Fourier transform infrared region corresponding to lipids. We found that predictors of lipid profile abnormalities in maternal serum could significantly improve this patient pathway. The study also presents preliminary results from the first prospective clinical validation study of its kind.
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    The cognitive dynamics of small-sooner over large-later preferences during temporal discounting task through event-related oscillations (EROs)
    (Elsevier Science, 2021) Guleken, Zozan; Sütçübaşı, Bernis; Metin, Baris
    Event-related oscillations (ERO) may provide a useful tool for the identification of cognitive processes during economic decisions. In the present study, we investigate peak-to-peak amplitude of task event-related oscillations of healthy subjects during delay discounting task. The study included forty-seven consecutive volunteers with mean 22 age- and matched education and socioeconomic condition. We used two temporal discounting (TD) tasks: the first was used to find individual indifference points for a set of delays and in the second, we recorded EEG as the participants made now vs delay decisions for the indifferent options. The EEG activity were recorded from 24 electrodes placed on the head surface according to the international 10–20 system. EEG activity for each choice (now and future) was averaged separately. The ERO responses were calculated for delta, theta, alpha and beta bands by the peak-to-peak measures. After Bonferroni correction, we found a significant effect of the de-cision process on the left frontal theta, left centroparietal delta, and frontoparietal beta oscillations. These were significantly greater during future decisions compared to now condition. These results indicate that a widespread frontoparietal network is implicated during delay discounting.
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    FTIR, RAMAN and biochemical tools to detect reveal of oxidative Stress-Related lipid and protein changes in fibromyalgia
    (Elsevier, 2023) Guleken, Zozan; Suna, Gizem; Karaca, Sahika Burcu; Bulut, Huri; Ayada, Ceylan; Pancerz, Krzysztof; Paja, Wieslaw
    In this study, our aim was to investigate the pathogenesis and diagnosis of fibromyalgia (FM), a complex disorder with poorly understood causes. We focused on examining the role of oxidative stress and associated lipid and protein alterations in FM patients. To achieve this, we conducted a comprehensive analysis of serum samples obtained from 60 FM patients and 40 healthy individuals. In our analysis, we employed various biochemical assays and spectroscopic techniques including Fourier Transform Infra-Red (FTIR) and Raman spectroscopy. Moreover, we applied advanced statistical methods such as chemometrics and machine learning algorithms to analyze the collected data.The obtained results showed higher levels of oxidative stress, around 113% on the visual analogue scale score and around 5800% higher when C = O vibrations from lipids visible in FTIR spectra were analyzed. Also, lower levels of total antioxidants and oxidants in FM patients were observed compared with the healthy group.Moreover, FTIR spectra of serum collected from FM patients showed significantly higher absorbance of bands corresponding to polysaccharides, proteins, and lipids, while differences were not found in the Raman spectra. The principal component analysis (PCA) of the obtained spectroscopic data showed that it is possible to distinguish patients suffering from FM and healthy control groups with 100% accuracy using FTIR spectroscopy. PLS analysis showed significance in the differentiation of lipid vibrations among groups. In summary, FTIR coupled with chemometrics has the potential for fibromyalgia diagnosis.
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    Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. machine learning applied to FT-raman spectra study
    (Springer, 2023) Depciuch, Joanna; Jakubczyk, Pawel; Paja, Wieslaw; Pancerz, Krzysztof; Wosiak, Agnieszka; Bahat, Pinar Yalcin; Toto, Omer Faruk; Bulut, Huri; Guleken, Zozan
    The presented article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood serum samples in patients with diagnosed recurrent pregnancy loss (RPL) versus healthy individuals who were followed at the Gynecology department. A total of 120 participants, RPL disease (n = 60) and healthy individuals (n = 60), participated in the study. First, we investigated the effect of circulating nerve growth factor (NGF) in RPL and healthy groups. To show NGF's effect, we measured the level of oxidative loads such as Total Antioxidant Level (TAS), Total Oxidant Level (TOS), and Oxidative Stress Index (OSI) with Beckman Coulter AU system and biochemical assays. We find a correlation between oxidative load and NGF level. Oxidative load mainly causes structural changes in the blood. Therefore, we obtained Raman measurements of the participant's serum. Then we selected two Raman regions, 800 and 1800 cm(-1), and between 2700 cm(-1) and 3000 cm(-1), to see chemical changes. We noted that Raman spectra obtained for RPL and healthy women differed. The findings confirm that the imbalance between reactive oxygen species and antioxidants has important implications for the pathogenesis of RPL and that NGF levels accompany the level of oxidative load in the RPL state. Biomolecular structure and composition were determined using Raman spectroscopy and machine learning methods, and the correlation of these parameters was studied alongside machine learning technologies to advance toward clinical translation. Here we determined and validated the development of instrumentation for the Analysis of RPL patients' serum that can differentiate from control individuals with an accuracy of 100% using the Raman region corresponding to structural changes. Furthermore, this study found a correlation between traditional biochemical parameters and Raman data. This suggests that Raman spectroscopy is a sensitive tool for detecting biochemical changes in serum caused by RPL or other diseases.