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Öğe Dose dependent cytotoxic activity of patulin on neuroblastoma, colon and breast cancer cell line(2021) Türkmen, Neşe Başak; Yüce, Hande; Ozek, Dilan Askin; Aslan, Sumeyye; Yaşar, Şeyma; Ünüvar, SongülAim: Patulin, a mycotoxin, is an organic compound classified as a polypeptide. Patulin, which is generally detected in moldy fruits and their derivatives, has been suggested to have anticancer activity. Some studies have shown that it induces apoptosis in the cell. This study aims to investigate the anticancer activity of patulin in SH-SY5Y (human neuroblastoma cell line), HCT116 (human colon cancer cell line), and MCF-7 (human breast cancer cell line) cell lines. Materials and Methods: SH-SY5Y, HCT116, MCF-7, and L929 (healthy fibroblast) cell lines were used for cytotoxicity experiments. Cells were added in 96-well plates at 5x103 cells per well. Serial dilutions of patulin at a dose of 1, 2.5, 5, 10, 25, 50, and 100 µM were added to the waiting cells in 24 hours incubation. All cell lines were exposed to patulin for 24 and 48 hours. The cytotoxic activity of patulin in cancer and healthy cell lines was determined in vitro by the MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2- (4-sulphophenyl)-2H-tetrazolium) cell viability test. The results of the toxicity tests were measured spectrophotometrically (450 nm) in ELISA at intervals of 24 hours for 2 days. Results: Patulin caused cytotoxic activity in all cell lines at a concentration of 100 µM. Patulin showed cytotoxic activity at low doses only in the SH-SY5Y cell line. At doses of 25 and 50 µM, HCT116 caused more than 50% death in the cell line, while higher concentrations induced cell death in the MCF-7 cell line. Conclusion: Patulin showed anticancer activity at high concentrations in colon and breast cancer cell lines, and both low and high concentrations in the SH-SY5Y cell line. Patulin may be a new candidate molecule in the treatment of neuroblastoma, colon, and breast cancers, depending on the dose.Öğe The estimating of hypothyroidism with the bagged CART model based on clinical dataset and identify of risk factors(2022) Evren, Bahri; Yaşar, ŞeymaAim: The purpose of this study is to use machine learning techniques, Bagged CART, to classify hypothyroidism, which typically results from insufficient thyroid hormone synthesis in the body or seldom affects target tissues, and to identify potential risk factors. Materials and Methods: In this study, the open source data set obtained from the UCI database was used. The 10-fold cross-validation technique was used in the creation of the Bagged CART model from the Decision Tree Ensembles class to classify hypothyroid, and the performance criteria of this model were accuracy, balanced accuracy, sensitiv- ity, specificity, positive predictive value, negative predictive value, F1-Score, G-mean and Matthews Correlation Coefficient ( MCC) was given. Then, the significance of the vari- able was calculated through the model created and possible risk factors for hypothyroidism were determined. Results: The accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-Score, G-mean and Matthews Correlation Coefficient (MCC) performance criteria for the model created for the classification of hypothyroidism were 99.9%, 99.2%, 98.3%, 100.0%, 100.0%, 99.9%, 99.2%, 99.9%, and 99.1%, respectively. According to the created XGBoost model, the three most important factors that could be associated with hypothyroidism were determined as TBG, TSH, T4U, TT4, age, FTI, Query hypothroid, on thyroxine, on antithyroid medication, thyroid surgery, sex, TBG measured, sick, T3 mesured, Query hyperthyroid, goitre. Conclusion: In conclusion, considering the results of the machine learning model created in this study, the hypothyroidism classification performance was quite high and the signif- icance of the variables and possible risk factors for hypothyroidism were determined. In the light of the findings, it is predicted that these risk factors may be useful in the clinic.Öğe Estimation of total prostate specific antigen values through artificial intelligence modelling(2018) Çolak, Cemil; Beytur, Ali; Yaşar, ŞeymaAbstract: It has been indicated that total prostate specific antigen (PSA) screening, one of the serum markers used for the diagnosis of prostate cancer, has been clinically beneficial. In this research, it was aimed to estimate the total PSA values by Multilayer Perceptron (MLP) artificial neural network (ANN) model. Data on total PSA values in this study (n = 1422) were randomly selected using the structured query language (SQL) from the database of patients records of Urology Department of Medical School at Inonu University. Total PSA values as a target/dependent variable, and age (year), blood group (A/B/0/AB), Exitus (EX) status (alive/death), Lymphocyte (LY) (%), Hemoglobin (HGB) (g / dL), Neutrophil (NE) (%), Albumin (g / dL), Calcium (mg / dL), Mean Corpuscular Hemoglobin (MCH), Leukocyte count (WBC) (103 / ml), Platelet (PLT) (103/ ml) as predictor variables were evaluated in the analyses. Outlier/extreme observations were analysed, and quantitative variables were rescaled by the transformation of Z-score or Box-Cox, and the MLP ANN model was constructed to estimate the total PSA values after variable selection method was used. Estimation performance of the model was examined by the values of mean absolute error, standard deviation and correlation coefficient. The MLP ANN model was created using a total of 1422 data sets as 993 of which were in training and 429 in the testing. Values of the mean absolute error, standard deviation, and correlation coefficient were calculated for training data set as; 0.744, 0.895 and 0.452; for the test data set as; 0.773, 0.935 and 0.355. The estimated accuracy of the generated model is predicted as 20.3%. In the MLP ANN model, the importance levels of the variables were obtained as 0.33 for HGB, 0.22 for NE, 0.16 for Calcium, 0.13 for PLT, 0.10 for age and 0.06 for EX. The MLP ANN model was established for the estimation of the total PSA values based on the selected variables, and calculated the importance levels of the related variables. Better prediction results in the estimation of total PSA values can be provided by using different additional variables, various resampling methods and alternative models.Öğe The evaluation of agreement between the measurement methods used in the diagnosis of prostate cancer with the statistical methods(2024) Yaşar, Şeyma; Yoloğlu, Saim; Altıntaş, Ramazan; Akatlı, Ayşe Nur; Karaca, LeylaThe aim of this study is to evaluate the compatibility of the "Computed Tomography (CT)" and "Magnetic Resonance (MR)" methods which are imaging techniques used to describe prostate cancer with the pathology accepted as the reference method. In this study, the concordance between CT and MR results and pathology results of 37 prostate cancer patients was evaluated using the Bland-Altman, Interclass Correlation Coefficient, Concordance Correlation Coefficient, Deming Regression and Passing-Bablok method comparison methods. Inter-class correlation coefficient and Concordance Correlation Coefficient values for CT-Pathology results were 0.62 and 0.62, respectively. Inter-class correlation coefficient and Concordance Correlation Coefficient values for MR-Pathology results were 0.74 and 0.75, respectively. The regression equation for the Deming regression method is y=-6.21+1.03x for CT-Pathology, whereas for the MR-Pathology y=-6.86+1.11x. When the CT-Pathology measurement values are evaluated by the Bland-Altman statistical method, the mean values of the measurement differences are -2.42 and the standard deviation is 33.50. When the agreement between MR-Pathology measurement values is examined by the Bland-Altman method, the mean values for the difference between the measurement values are 4.14 and the standard deviation is 26.78. Among the applied methods, the Bland-Altman method is the most suitable method for data structure. According to the results of the Bland-Altman method, the tumor size obtained by the pathology method can be found to be 2.4 smaller than the mean values obtained from the CT results and an average of 4.1 cc from the measurement values obtained by the MR imaging technique.Öğe Evaluation of delivery room resuscitation in late preterm and term newborns according to the intensity of resuscitation(2022) Melekoğlu, N. Aslı; Yaşar, ŞeymaThe transition from intrauterine to extrauterine life is not achieved smoothly by all newborns; approximately 10% of all births require additional assistance to initiate breathing. We aimed to describe the frequency and extent of delivery room resuscitation in late preterm and term infants with short term neonatal outcomes and we also compared our results according to the intensity of resuscitation. In this single-centre retrospective study, maternal and neonatal data were collected from medical records at Malatya Turgut Ozal University Hospital between January 2021-2022. A total of 181 infants resuscitated at birth were included. The mean gestational age of 38 ± 2 week and mean birth weights of 3169±629 grams. The majority of the newborns were male , and 28 (15.5%) were late premature. Free flow oxygen / CPAP and bag mask ventilation were the most common maneuvers with a frequency of 45.3% and 35.3% respectively. There was no significant difference between the need for advanced delivery room resuscitation and gender, prematurity and presence of small for gestational age or large for gestational age (p>0.05). But when maternal factors were evaluated, the mode of delivery, chorioamnionitis, adolescent mother subgroup, and presence of maternal diabetes were significantly different between the two groups (p<0.05). In addition, the advanced resuscitation group had lower Apgar scores, and also the rate of asphyxia, birth trauma, pneumothorax and death were higher in this group (p<0.05). In late preterm and term newborns, neonatal outcomes worsen as the intensity of delivery room resuscitation increases.Öğe Investigation of the effects of ısorhamnetin on motor function, sedation and analgesia in the diabeticrats(KARGER, ALLSCHWILERSTRASSE 10, CH-4009 BASEL, SWITZERLAND, 2018) Kurukafa, Diğdem; Köse, Evren; Parlakpınar, Hakan; Özhan, Onural; Yaşar, ŞeymaÖğe Makine öğrenimi yöntemlerine dayali bilgisayar destekli tani sisteminin geliştirilmesi: Proteomik teknolojileri üzerine uygulaması(İnönü Üniversitesi, 2023) Yaşar, Şeyma; Yoloğlu, Saı?mAmaç: Bu çalışmanın birinci temel amacı proteomik veriler kullanılarak, Plasenta Akreata sendromu hastalığının tanısına ilişkin uygun biyobelirteçlerinin saptanması ile makine öğrenme yöntemleri kullanılarak hastalığın sınıflandırılmasıdır. İkinci amacı ise, kütle spektrometrisi ile etiketsiz kantitasyon proteomik deneyleri için kullanılan çeşitli yazılım çıktıları üzerinden biyoinformatik analizleri yapmaya imkan sağlayan web tabanlı yazılım geliştirilmesidir. Materyal ve Metot: Çalışmada kullanılan veri seti, 10 kontrol ve 10 Plasenta Akreata sendromu hastasına ilişkin maternal serum örneklerine uygulanan proteomik analizler sonucu elde edilen 214 proteinden oluşmaktadır. Biyoinformatik analizler ile iki grup arasında farklı ekspresyona sahip proteinler belirlenmiştir. Çalışmada Plasenta Akreata sendromunun sınıflandırılması 16 proteine dayalı olarak gerçekleştirilmiştir. Sınıflandırma algoritmaları olarak Random Forest, Gradyan Artırılmış Ağaçlar, Destek Vektör Makineleri ve Extreme Gradient Boosting modelleri uygulanmıştır. Bulgular: Deneysel proteomik verilerine uygulanan biyoinformatik analizler sonrasında 98 proteinin iki grup arasında farklı ekspresyona sahip olduğu bulunmuştur. Bu 98 protein arasından RF-RFE değişken seçim yöntemiyle 16 protein seçilmiş ve en iyi sınıflandırma model performansının Extreme Gradient Boosting yöntemine ait olduğu belirlenmiş ve bu modele ilişkin doğruluk, seçicilik, duyarlılık, G-ortalama, Matthews'in Korelasyon Katsayısı, F1-skor değerleri sırasıyla, 99.9, 99.8, 100, 99.9, 99.9 ve 99.7 olarak hesaplanmıştır. Sonuç: Gerçekleştirilen proteomik biyoinformatik analizler ve makine öğrenme yöntemleri sonuçları gözönüne alındığında, P01703, Q96IY4, P06312 kodlu proteinler plasenta akreata sendromu tanı ve tedavisinde olası biyobelirteç olarak kullanılabilir. Öte yandan geliştirilen web tabanlı yazılım sayesinde çeşitli yazılımlardan elde edilen deneysel proteomik verilerinin biyoinformatik analizleri gerçekleştirilebilecektir.Öğe Peripheral facial paralysis during the COVID-19 pandemic(2022) Aydın, Şükrü; Fırat Koca, Çiğdem; Çelik, Turgut; Kelleş, Mehmet; Yaşar, ŞeymaThe mechanism surrounding idiopathic peripheral facial nerve paralysis remains unclear, though viral infections and even immunizations have been suspected of its origin. Thus, the relationship between COVID-19 and facial paralysis should be studied. With this research, we aimed to investigate the characteristics of facial paralysis during the COVID-19 illness as well as the relationship between facial paralysis and COVID-19, the length of time needed for recovery, concurrence with COVID-19 infection, and whether facial paralysis is a late complication or initial symptom of the disease. Forty-five patients thought to have had idiopathic peripheral facial paralysis were included in the study. Pure tone audiometry, COVID-19 PCR tests, and contrast-enhanced ear MRIs were performed on all participants. A standard prednisolone treatment protocol was followed. Participants were monitored for one month; we recorded whether they had COVID-19 previously, initially, or contracted it within the one-month testing period. At the same time, facial paralysis recovery rates were recorded and used in statistical analyses. PCR test at initial admission was reported as positive for COVID-19 in only one participant (2.2%). We discovered an improvement delay regarding facial paralysis in participants who had had COVID-19 previously (p<0.001). Prednisolone therapy used for peripheral facial paralysis did not pose an additional risk for COVID-19. Having had COVID-19 previously may cause delayed recovery of peripheral facial paralysis. Peripheral facial paralysis may be both a late manifestation as well as an early symptom of COVID-19.Öğe Prediction of breast cancer subtypes based on proteomic data with deep learning(2020) Yaşar, Şeyma; Çolak, Cemil; Yoloğlu, SaimAim: Although new advances in diagnosis and treatment have increased, breast cancer is still an important cause of morbidity and mortality today. Proteomics, which collectively deals with relevant information about proteins, is one of the important areas of study that has been emphasized recently. It is a machine learning class that uses many layers of nonlinear processing units for deep learning, feature extraction and conversion. The aim of this study is to classify the molecular subtypes (Basal-like, human epidermal growth factor receptor 2 (HER2)-enriched, Luminal A, Luminal B) of breast cancer with the deep learning algorithm designed by using proteomic data.Material and Methods: The data set used in this study consists of published Isobaric tags for relative and absolute quantitation (iTRAQ) proteome profiling of 77 breast cancer samples by the Clinical Proteomic Tumor Analysis Consortium (NCI/NIH). The missing values in the data were completed with the mean substitution method. “Lasso Regression Model” was used in the selection of variables and after repeating 100 times with 10 times cross-validation method. Finally, the deep learning algorithm has been used to classify the molecular subtypes of breast cancer.Results: The overall accuracy rate of the proposed model in classifying breast cancer are found to be 91.53%. The performance of this model for classifying molecular subtypes of breast cancer was calculated as accuracy %96.43, F-score %93.33, MCC %91.29, G-mean %93.54 for Basal-like, accuracy %94.74, F-score %84.21, MCC %81.23, G-mean %92.30 for HER2-enriched, accuracy %98.18, F-score %96.97, MCC %95.76, G-mean %98.71 for Luminal A and accuracy 93.10%, F-score 88.89%, MCC 83.89%, G-mean 91.89% for Luminal B, respectively.Conclusion: The model designed using the deep learning algorithm has been found to perform quite well in classifying the molecular subtypes of breast cancer. In further studies, different deep learning architectures can be used to classify the molecular subtypes of breast cancer with higher accuracy.Öğe Prostat kanseri tanısı koymada kullanılan ölçüm yöntemleri arası uyumun istatistiksel yöntemler ile değerlendirilmesi(İnönü Üniversitesi, 2018) Yaşar, ŞeymaAmaç: Teknolojik gelişmelerle birlikte geliştirilen her yeni yöntemin doğruluğunu ve kesinliğini tespit etmek için var olan referans yöntemi ile uyumunun karşılaştırıldığı yöntemler de ortaya çıkmıştır. Bu çalışmanın amacı, prostat kanserini tanımlamada kullanılan “Bilgisayarlı Tomografi” ve “Manyetik Rezonans” görüntüleme yöntemlerinin referans yöntem olan patoloji ile uyumunu bu yöntemler ile değerlendirmektir. Materyal ve Metot: Bu çalışmada erkeklerde en yaygın kanser türü olan 37 prostat kanseri hastasına ait veriler İnönü Üniversitesi Turgut Özal Tıp Merkezi Üroloji Anabilim Dalı'ndan alınmıştır. Prostat kanseri hastalarına ait Bilgisayarlı Tomografi ve Manyetik Rezonans sonuçları ile referans yöntem olan patoloji sonuçlarının arasındaki uyumu değerlendirmede Bland-Altman, Sınıf-içi Korelasyon Katsayısı, Uyum İlişki (Konkordans Korelasyon) Katsayısı, Deming Regresyon ve Passing-Bablok yöntem karşılaştırma yöntemleri kullanıldı. Bulgular: Yapılan analizler sonrasında veri yapısına en uygun olan Bland-Altman yöntemi ile Bilgisayarlı Tomografi-Patoloji ve Manyetik Rezonans-Patoloji arasındaki uyum değerlendirilmiştir. Sonuç olarak Bilgisayarlı Tomografi-Patoloji ölçüm farklarına ilişkin ortalama değerleri -2.42 ve standart sapması 33.50’dir. Benzer şekilde, Manyetik Rezonans-Patoloji ölçüm değerlerinin farklarına ilişkin ortalama değerleri 4.14 ve standart sapması 26.78’dir. Sonuçlar: Bland-Altman yöntemi sonuçlarına göre patoloji yöntemi ile elde edilen tümör boyutları Bilgisayarlı Tomografi sonucu elde edilen ölçüm değerlerinden ortalama 2.4 daha küçük, MR görüntüleme tekniği ile elde edilen ölçüm değerlerinden ise ortalama 4.1 cc daha büyük bulunabilir. Sınıf içi Korelasyon Katsayısı ve Uyum İlişki (Konkordans Korelasyon) Katsayısı değerleri incelendiğinde Manyetik Rezonans’ın Bilgisayarlı Tomografi’ye göre patoloji sonuçları ile daha uyumlu olduğu gözlenmektedir. Anahtar Kelimeler: Bland-Altman, Deming regresyon, Passing-Bablok regresyon, Prostat kanseri, Sınıfiçi korelasyon katsayısı, Uyum İlişki (Konkordans Korelasyon)katsayısı.Öğe WSSPAS: An Interactive Web Application for Sample Size and Power Analysis with R Using Shiny(2018) Yoloğlu, Saim; Çolak, Cemil; Yaşar, Şeyma; Arslan, Ahmet KadirAbstract: Objective: The calculation of sample size and power analysis plays an important role in biomedical research. The most general definition of the calculation of the sample size and power analysis is to determine the minimum number of individuals that have the ability to represent the population during the planning phase of the study. Since the statistical methods for each research plan are different, the calculation of sample size and power analysis will be different. Therefore, it is difficult to calculate the sample size and power analysis manually for each clinical trial. The aim of this research is to develop a new user-friendly web-based tool that calculates sample size and power analysis for hypothesis testing, diagnostic tests, correlation and regression analysis using the open source software R Shiny package and guides the researchers with examples. Material and Method: This web tool will be updated upon the updated R software packages, including shiny, shinydashboard, pwr, powerAnalysis, powerMediation, MKmisc and rhandsontable. Scripts were written for calculations that could not be done by these packages. Results: Hypothetical samples were created to introduce menus in the web-based software developed for the calculation of sample size and power analysis, and screen images of the results of these samples were given. Conclusion: The designed interactive web application is freely accessible through http: //biostatapps.inonu.edu.tr/WSSPAS. In the future studies, it is aimed to further strengthen the software by adding modules that can calculate sample size and power analysis for different multivariate statistical and machine learning methods.
