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Öğe Acute and Subacute Effects of Low Versus High Doses of Standardized Panax ginseng Extract on the Heart: An Experimental Study(Humana Press Inc, 2019) Parlakpinar, Hakan; Ozhan, Onural; Ermis, Necip; Vardi, Nigar; Cigremis, Yilmaz; Tanriverdi, Lokman H.; Colak, CemilPanax ginseng is commonly used in Chinese medicine and Western herbal preparations. However, it has also been recently noted to be associated with some cardiac pathologies-including cardiogenic shock due to acute anterior myocardial infarction, trans-ischemic attack, and stent thrombosis. This study was aimed to elucidate acute and subacute effects of the low and high doses of standardized Panax ginseng extract (sPGe) on cardiac functions. Rats were randomly assigned to control group, acute low-dose group (ALD), subacute low-dose group (SALD), acute high-dose group (AHD), and subacute high-dose group (SAHD). The cardiac effects of sPGe were evaluated using hemodynamic, biochemical, echocardiographic, genetic, and immunohistopathologic parameters. Mean blood pressures were significantly lower in all sPGe-treated groups compared with the control group. Troponin I and myoglobin levels were increased in the SALD, AHD, and SAHD groups. Mitral E-wave velocity was reduced after sPGe administration in all the groups. Acidophilic cytoplasm and pyknotic nucleus in myocardial fibers were observed in AHD and SAHD groups. Cu/Zn-SOD1 gene expressions were significantly higher in the sPGe-treated groups whereas caveolin 1 and VEGF-A gene expressions were not changed. According to our results, sPGe may have a potential effect to cause cardiac damage including diastolic dysfunction, heart failure with preserved ejection fraction, and reduction of blood pressure depending on the dose and duration of usage. Healthcare professionals must be aware of adverse reactions stemming from the supplementation use, particularly with cardiac symptoms.Öğe Adenosine deaminase level in the serum of the patients Toxoplasma gondii seropositive and Giardia intestinalis(Academic Journals, 2009) Karaman, Uelkue; Beytur, Leyla; Kiran, Tugba Raika; Colak, CemilAdenosine deaminase (ADA) is an aminohydrolase making adenosine, deoksiadenozini inozin, and deocsiniozine deaminise irreversibly and plays role in the catabolism of purine nucleotids. Toxoplasma gondii is a zoonoses intracellular parasite that causes infection in animals and humans. This parasite encompasses enzymes that produce free radicals such as superoxide and hydrogen peroxide. In addition, Giardia intestinalis is another parasite that causes irritations in mucosa, over mucus discharge, aggravating former inflammations, and various absorption defects. In the present study, it has been aimed to compare ADA levels between T. gondii seropositive (IgG seropositive but symptomless patients), G. intestinalis positive patients, and control group. Thus, ADA levels between 32 patients being T. gondii seropositive and 29 controls and between 50 patients' G. intestinalis positive and 40 controls have been evaluated. The results were analyzed using independent samples t-test at the level of p < 0.05. According to this, in the statistical comparison between the parameters of patient and control groups, a meaningful decrease could be determined in ADA levels. This situation can be commented in the way that toxoplasmosis infection being inactive does not necessarily cause an increase in T lymphocytes. In addition, this decrease can be due to increasing oxidative stress in parasitic infections.Öğe Alpha lipoic acid decreases neuronal damage on brain tissue of STZ-induced diabetic rats(Pergamon-Elsevier Science Ltd, 2022) Tanbek, Kevser; Ozerol, Elif; Yilmaz, Umit; Yilmaz, Nesibe; Gul, Mehmet; Colak, CemilNeuropathy that develops due to diabetic complications causes cognitive impairment due to functional and structural damage. The aim of this study was to evaluate the biochemical, histological and physiological effects of Alpha Lipoic Acid (ALA) against brain tissue damage caused by diabetes. Fourty male Wistar albino rats were separated into four groups as control, diabetes mellitus (DM), ALA and DM+ALA. Single dose of 50 mg/kg intraperitonal streptozotocin (STZ) was used to induce DM. For six weeks, ALA (100 mg/kg/day) was administered to the ALA and DM+ALA groups. At the end of the six week rats were sacrificed by collecting blood samples and collected brain tissues (hippocampus, cortex, hippotalamus and striatum) were histologically evaluated in addition to the oxidant-antioxidant parameters. ALA administration showed significant improvement in cognitive functions evaluated by MWM in rats with diabetes mellitus (p < 0.05). SOD, CAT, GSH-Px activities, which were decreased in the DM group compared to the control group, increased statistically significantly in rats in DM+ALA group (p < 0.05). While MDA and PC levels increased in the DM group, they decreased statistically significantly in the DM+ALA group (p < 0.05). According to the histological examinations made by light and electron microscopies, it was determined that the ultrastructural damage and degeneration findings observed in the sections of the DM group were significantly ameliorated in the sections of rats in the DM+ALA group. ALA may be effective in restoring cell damage and cognitive functions in brain tissue with its antioxidant and neuroprotective effects without showing antidiabetic effects.Öğe The alterations in serum ghrelin and leptin levels after intracerebroventricularly irisin infusion in rats(Wiley-Blackwell, 2015) Tekin, Suat; Erden, Yavuz; Sandal, Suleyman; Ozyalin, Fatma; Colak, Cemil[Abstract Not Available]Öğe Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah AliIntroduction Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF. Methods In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model's incorporated risk factors. Results White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF. Conclusion The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.Öğe Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study (vol 11, 1285067, 2024)(Frontiers Media Sa, 2024) Yilmaz, Rustem; Yagin, Fatma Hilal; Colak, Cemil; Toprak, Kenan; Samee, Nagwan Abdel; Mahmoud, Noha F.; Alshahrani, Amnah Ali[Abstract Not Available]Öğe Angiotensin II type 2 receptor agonist treatment of doxorubicin induced heart failure(Taylor & Francis Ltd, 2023) Ermis, Necip; Ulutas, Zeynep; Ozhan, Onural; Yildiz, Azibe; Vardi, Nigar; Colak, Cemil; Parlakpinar, HakanDoxorubicin (DOX) is an anthracycline derivative used for treatment of malignancies; however, its clinical use is limited by its cardiotoxicity. We investigated the effects of angiotensin II type 2 receptor agonist compound 21 (C21) on DOX induced heart failure in rat heart. We compared C21 with losartan (LOS), an AT 1 receptor antagonist used for treating heart failure. We allocated 40 rats into five groups of eight: saline treated control group, DOX group administered a single 20 mg/kg dose of DOX, DOX + C21 group administered 0.3 mg/kg C21 for 21 days following the 20 mg/kg dose of DOX, DOX + losartan (LOS) group administered a 21 day regimen of 20 mg/kg LOS following the single dose of DOX, and a DOX + LOS + C21 group administered 0.3 mg/kg C21 and 20 mg/kg LOS for 21 days following the single dose of DOX. We assessed histopathology and conducted echocardiograpic and hemodynamic measurements. Left ventricular ejection fraction (EF) was reduced only in the DOX treated group. C21, LOS and C21 + LOS therapy prevented decreased EF due to DOX. Less histopathology was observed in the DOX + LOS + C21 group than for the other treatment groups. Application of C21 decreased DOX induced cardiac injury similar to LOS. Combined use of C21 and LOS was most beneficial for DOX induced heart failure.Öğe Application of knowledge discovery process on the prediction of stroke(Elsevier Ireland Ltd, 2015) Colak, Cemil; Karaman, Esra; Turtay, M. GokhanObjective: Stroke is a prominent life-threatening disease in the world. The current study was performed to predict the outcome of stroke using knowledge discovery process (KDP) methods, artificial neural networks (ANN) and support vector machine (SVM) models. Materials and methods: The records of 297 (130 sick and 167 healthy) individuals were acquired from the databases of the department of emergency medicine. Nine predictors (coronary artery disease, diabetes mellitus, hypertension, history of cerebrovascular disease, atrial fibrillation, smoking, the findings of carotid Doppler ultrasonography [normal, plaque, plaque + stenosis >= 50%], the levels of cholesterol and C-reactive protein) were used for predicting the stroke. Feature selection based on the Cramer's V test was carried out for reducing the predictors. Multilayer perceptron (MLP) ANN and SVM with radial basis function (RBF) kernel were used for the prediction based on the selected predictors. Results: The accuracy values were 81.82% for ANN and 80.38% for SVM in the training dataset (n = 209), and 85.9% for ANN and 84.62% for SVM in the testing dataset (n = 78), respectively. ANN and SVM models yielded area under curve (AUC) values of 0.905 and 0.899 in the training dataset, and 0.928 and 0.91 in the testing dataset, consecutively. Conclusion: The findings of the current study pointed out that ANN had more predictive performance when compared with SVM in predicting stroke. The proposed ANN model would be useful when making clinical decisions regarding stroke. (C) 2015 Elsevier Ireland Ltd. All rights reserved.Öğe ARTIFICIAL INTELLIGENCE-ASSISTED PREDICTION OF COVID-19 STATUS BASED ON THORAX CT SCANS USING A PROPOSED META-LEARNING STRATEGY(Carbone Editore, 2022) Guldogan, Emek; Yilderim, Ismail Okan; Sevgi, Serkan; Colak, CemilBackground: Radiological techniques integrated with artificial intelligence (AI) are a promising diagnostic tool for the rapidly increasing number of COVID-19 cases today. In this study, we intended to construct an artificial intelligence-assisted prediction of COVID-19 status based on thorax computed tomography (CT) scans using a proposed meta-learning strategy. Methods: A public dataset including 1252 positive and 1230 negative thorax CT scans of SARS-CoV-2 was used in the current study. The CT images for COVID-19 status were analyzed by 26 transfer learning (TL) models. The stacking ensemble learning was used to obtain more consistent and high-performance prediction results by combining the prediction results of 26 TL models with an Results: Mobile had the best prediction with an accuracy of 0.946 (95% CI: 0.93-0.962) among the TL models. The Meta-learning model yielded the best classification accuracy of 0.993 (0.98-1), which outperformed MobileNet, the most successful architecture Conclusions: The proposed meta-model that can distinguish CT images between COVID-19 positive and abnormal/normal conditions due to other etiology of COVID-19 negative may be beneficial in such pandemics. The AI application in this study can be used in mobile, desktop, and web-based platforms to have facilitating and complementary effects on classical reporting and the current workload in radiology departments.Öğe Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling(Elsevier Ireland Ltd, 2021) Yasar, Seyma; Colak, Cemil; Yologlu, SaimBackground: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). Methods: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. Results: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. Conclusions: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. Background: COVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). Methods: The dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. Results: The accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. Conclusions: The proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease. ? 2021 Elsevier B.V. All rights reserved.Öğe Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma(Lippincott Williams & Wilkins, 2023) Colak, Cemil; Kucukakcali, Zeynep; Akbulut, SamiBackground:Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC.Methods:The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment.Results:AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC.Conclusion:A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.Öğe Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set(Ieee, 2019) Akbas, Kubra Elif; Kivrak, Mehmet; Arslan, A. Kadir; Colak, CemilAssociation rules mining is one of the uttermost applied techniques in data mining and artificial intelligence. Support and confidence are two basic measures employed in the evaluation of association rules. The rules obtained with these two values are often correct; however, they are not strong rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. In this study it is aimed to establish strong association rules with variables in open sourced heart data set. In the current study, Apriori algorithm was used to obtain the rules. As a result of the analysis, only 55 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measure proposed in the literature, and it was concluded that only 26 of these rules were strong. As a result of the analysis of the findings obtained in the context of the research, it can be inferred that stronger rules can be obtained by using the certainty factor in association rules mining.Öğe Assessment of clinical and pathological features of patients who underwent thyroid surgery: A retrospective clinical study(Baishideng Publishing Group Inc, 2018) Emre, Arif; Akbulut, Sami; Sertkaya, Mehmet; Bitiren, Muharrem; Kale, Ilhami Taner; Bulbuloglu, Ertan; Colak, CemilAIM To evaluate whether there was any correlation between the clinical parameters and final pathological results among patients who underwent thyroid surgery. METHODS We retrospectively analyzed parameters, including age, sex, complete blood cell count parameters, nodule diameter, nodule localization, thyroid function testing, and pathology reports, in patients who underwent thyroid surgery. The patients were divided into malignant (n = 92) and benign (n = 413) groups depending on the final pathological results. Both groups were compared for demographic and clinical parameters. The Kolmogorov-Smirnov normality test was used to determine if the quantitative variables had a normal distribution. The nonparametric Mann-Whitney U test was used to compare quantitative data that were not normally distributed, and Pearson's chi-squared test was used to compare the qualitative data. The correlation between the final pathological results and fine-needle aspiration biopsy findings was calculated using the cross-tabulation method. RESULTS This study included 406 women and 99 men aged between 15 and 85 years. No significant differences were found between the groups with respect to age, sex, white blood cell count, neutrophil count, lymphocyte count, thrombocyte count, red cell distribution width, platelet distribution width, mean platelet volume, platecrit, nodule localization, and thyroid function testing. On the other hand, there were significant differences between the groups with respect to nodule size (P = 0.001), cervical lymphadenopathy (P = 0.0001) and nodular calcification (P = 0.0001). Compared with the malignant group, the benign group had a significantly greater nodule size (35.4 mm vs 27.6 mm). The best cut-off point (<= 28 mm) for nodule size, as determined by the receiver operating characteristic curve, had a sensitivity and specificity of 67.7% and 64.4%, respectively. The correlation between fine-needle aspiration biopsy and the final pathological results was assessed using the cross-table method. The sensitivity and specificity of fine-needle aspiration biopsy were 60% and 98%, respectively. CONCLUSION This study showed that significant differences existed between the malignant and benign groups with regard to nodule size, cervical lymphadenopathy, and nodular calcification.Öğe Assessment of COVID-19-Related Genes Through Associative Classification Techniques(Duzce Univ, Fac Medicine, 2022) Cicek, Ipek Balikci; Kaya, Mehmet Onur; Colak, CemilObjective: This study aims to classify COVID-19 by applying the associative classification method on the gene data set consisting of open access COVID-19 negative and positive patients and revealing the disease relationship with these genes by identifying the genes that cause COVID-19. Methods: In the study, an associative classification model was applied to the gene data set of patients with and without open access COVID-19. In this open-access data set used, 15979 genes are belonging to 234 individuals. Out of 234 people, 141 (60.3%) were COVID-19 negative and 93 (39.7%) were COVID-19 positives. In this study, LASSO, one of the feature selection methods, was performed to choose the relevant predictors. The models' performance was evaluated with accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results: According to the study findings, the performance metrics from the associative classification model were accuracy of 92.70%, balanced accuracy of 91.80%, the sensitivity of 87.10%, the specificity of 96.50%, the positive predictive value of 94.20%, the negative predictive value of 91.90%, and F1-score of 90.50%. Conclusions: The proposed associative classification model achieved very high performances in classifying COVID-19. The extracted association rules related to the genes can help diagnose and treat the disease.Öğe Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Yilmaz, Rustem; Yagin, Fatma Hilal; Raza, Ali; Colak, Cemil; Akinci, Tahir CetinAcute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.Öğe Assessment of myocardial changes in athletes with native T1 mapping and cardiac functional evaluation using 3 T MRI(Springer, 2016) Gormeli, Cemile Ayse; Gormeli, Gokay; Yagmur, Julide; Ozdemir, Zeynep Maras; Kahraman, Aysegul Sagar; Colak, Cemil; Ozdemir, RamazanIntensive physical exercise leads to increases in left ventricular muscle mass and wall thickness. Cardiac magnetic resonance imaging allows the assessment of functional and morphological changes in an athlete's heart. In addition, a native T1 mapping technique has been suggested as a non-contrast method to detect myocardial fibrosis. The aim of this study was to show the correlation between athletes' cardiac modifications and myocardial fibrosis with a native T1 mapping technique. A total of 41 healthy non-athletic control subjects and 46 athletes underwent CMR imaging. After the functional and morphological assessments, native T1 mapping was performed in all subjects using 3.0 T magnetic resonance imaging. Most of the CMR findings were significantly higher in athletes who had a parts per thousand yen5 years of sports activity when compared with non-athletic controls and athletes who had < 5 years of sports activity. Significantly higher results were shown in native T1 values in athletes who had < 5 years of sports activity, but there were no significant differences in the left ventricular end-diastolic volume, left ventricular end-diastolic mass, or interventricular septal wall thickness between non-athletic controls and athletes who had < 5 years of sports activity. The native T1 mapping technique has the potential to discriminate myocardial fibrotic changes in athletes when compared to a normal myocardium. The T1 mapping method might be a feasible technique to evaluate athletes because it does not involve contrast, is non-invasive and allows for easy evaluation of myocardial remodeling.Öğe Assessment of Patient Safety Attitude Levels Among Healthcare Professionals Working in the Operating Room(Erciyes Univ Sch Medicine, 2023) Tamer, Murat; Akbulut, Sami; Cicek, Ipek Balikci; Saritas, Hasan; Akbulut, Mehmet Serdar; Ozer, Ali; Colak, CemilObjective: This study aims to determine the factors affecting the perception levels of operating room (OR) nurses and nurse anesthetists working in the OR regarding patient safety attitudes. Materials and Methods: This study was conducted using face-to-face interviews with 117 healthcare professionals working as OR nurses (n=60) and nurse anesthetists (n=57). The patient safety attitude questionnaire (SAQ), where the reliability analysis was also performed for the SAQ scale. and sociodemographic characteristics were used for this study. Qualitative variables were given as numbers and percentages (%), and the dataset belonging to quantitative variables that met the normal distribution criteria was given as mean (standard deviation), and data of quantitative variables that did not comply with nor-mality were given as median, IQR, and 95% CI of the median.Results: There were significant differences between OR nurses and nurse anesthetists regarding job satisfaction (p=0.015) and total SAQ score (p=0.040). Significant differences were detected between men and women participants regarding smoking (p=0.020) and stress recognition (p=0.040). The reliability analysis of the scale was as follows: total (alpha: 0.791), job satisfaction (alpha: 0.883), teamwork climate (alpha: 0.856), safety climate (alpha: 0.864), perceptions of management (alpha: 0.881), stress recognition (alpha: 0.791), and working conditions (alpha: 0.530).Conclusion: It was shown that the patient safety attitudes of the healthcare professionals participating in this study are above average, although it is still insufficient, where the stress identification score of the female participant was higher, and it was also found that the nurses' job satisfaction and SAQ score were higher.Öğe Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model(Mdpi, 2024) Aygun, Umran; Yagin, Fatma Hilal; Yagin, Burak; Yasar, Seyma; Colak, Cemil; Ozkan, Ahmet Selim; Ardigo, Luca PaoloThis study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.Öğe Assessment of urinary incontinence in the women in Eastern Turkey(Springer London Ltd, 2013) Altintas, Ramazan; Beytur, Ali; Oguz, Fatih; Tasdemir, Cemal; Kati, Bulent; Cimen, Serhan; Colak, CemilThe aims of the present study were to determine the types of UI among women visiting the urology department, to identify the potential risk factors associated with each type of UI, and to identify healthcare-seeking behaviors of affected women in our region. The data of 617 community-dwelling women, who were at least 18 years of age or older and who presented with a complaint of UI ongoing over a year, and those without UI, who were admitted for any other reason, from June 2010 to April 2012, were evaluated. Mean age was 51.29 years (range 18-110 years); median parity was 3.54 (range 0-11) and 88.2 % of the women were married. Mean BMI was 28.01 kg/m(2). Very few women (18.5 %) accepted UI as a disease and searched for medical help by themselves; however, the remaining women (81.5 %) were brought or directed for evaluation by someone else. Stress UI was reported by 43 women (10.5 %), urge UI and mixed UI were noted by 153 (37.5 %) and 212 (52 %) women respectively. The most frequent type of UI was mixed UI in our region. Age, BMI, multiparity, and hypertension were identified to have a different importance for each type of UI, but diabetes mellitus, birth trauma, gynecological surgery, lumbar disc hernia (LDH), and multiple sclerosis (MS) were the other important related factors. However, a small number of patients accepted UI as a disease and searched for therapy. This reveals that the public should be informed in detail about female UI in developing countries.Öğe Automated Classification of Brain Tumors by Deep Learning-Based Models on Magnetic Resonance Images Using a Developed Web-Based Interface(Duzce Univ, Fac Medicine, 2021) Tetik, Bora; Ucuzal, Hasan; Yasar, Seyma; Colak, CemilObjective: Primary central nervous system tumors (PCNSTs) compose nearly 3% of newly diagnosed cancers worldwide and are more common in men. The incidence of brain tumors and PCNSTs-related deaths are gradually increasing all over the world. Recently, many studies have focused on automated machine learning (AutoML) algorithms which are developed using deep learning algorithms on medical imaging applications. The main purposes of this study are -to demonstrate the use of artificial intelligence-based techniques to predict medical images of different brain tumors (glioma, meningioma, pituitary adenoma) to provide techicalsupport to radiologists and -to develop a user-friendly and free web-based software to classify brain tumors for making quick and accurate clinical decisions. Methods: Open-sourced T1-weighted magnetic resonance brain tumor images were achieved from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, To construct the proposed system which web-based interface and the deep learning-based models, the Keras/Auto-Keras library, which is employed in Python's programming language, is used. Accuracy, sensitivity, specificity, G-mean, F-score, and Matthews correlation coefficient metrics were used for performance evaluations. Results: While 80% (2599 instances) of the dataset was used in the training phase, 20% (465 instances) was employed in the testing phase. All the performance metrics were higher than 98% for the classification of brain tumors on the training data set. Similarly, all the evaluation metrics were higher than 91% except for sensitivity and MCC for meningioma on the testing dataset. Conclusions: The results from the experiment reveal that the proposed software can be used to detect and diagnose three types of brain tumors. This developed web-based software can be accessed freely in both English and Turkish at http://biostatapps.inonu.edu.tr/BTSY/.
