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BackgroundDifferentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data.MethodsThe balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables. To address the class imbalance in recurrence status, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) was utilized. A hybrid model combining classification algorithms with association rule mining was developed. Two relational classification approaches, regularized class association rules (RCAR) and classification based on association rules (CBAR), were implemented. Binomial logistic regression analyzed independent predictors of recurrence. Model performance was assessed through accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score.ResultsThe RCAR model demonstrated superior performance over the CBAR model, achieving accuracy, sensitivity, and F1 score of 96.7%, 93.1%, and 96.7%, respectively. Association rules highlighted that papillary pathology with an incomplete response strongly predicted recurrence. The combination of incomplete response and lymphadenopathy was also a significant predictor. Conversely, the absence of adenopathy and complete response to treatment were linked to freedom from recurrence. Incomplete structural response was identified as a critical predictor of recurrence risk, even with other low-recurrence conditions.ConclusionThis study introduces a robust and interpretable predictive model that enhances personalized medicine in thyroid cancer care. The model effectively identifies high-risk individuals, allowing for tailored follow-up strategies that could improve patient outcomes and optimize resource allocation in DTC management.

Abstract Placenta accreta spectrum (PAS) presents a significant obstetric challenge, associated with considerable maternal and fetal-neonatal morbidity and mortality. Nevertheless, it is imperative to acknowledge that a noteworthy subset of PAS cases remains undetected until the time of delivery, thereby contributing to an augmented incidence of morbidity among the affected individuals. The delayed identification of PAS not only hinders timely intervention but also exacerbates the associated health risks for both the maternal and fetal outcomes. This underscores the urgency to innovate strategies for early PAS diagnosis. In this study, we aimed to explore plasma proteins as potential diagnostic biomarkers for PAS. Integrated transcriptome and proteomic analyses were conducted to establish a novel diagnostic approach. A cohort of 15 pregnant women diagnosed with PAS and delivering at Inonu University Faculty of Medicine between 01/04/2021 and 01/01/2023, along with a matched control group of 15 pregnant women without PAS complications, were enrolled. Plasma protein identification utilized enzymatic digestion and liquid chromatography-tandem mass spectrometry techniques. Proteomic analysis identified 228 plasma proteins, of which 85 showed significant differences (P

Abstract Cardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.

Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite’s individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model’s predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.

Background: The current study’s objective is to evaluate the molecular genetic mechanisms influencing the biological behavior of hepatocellular carcinoma (HCC) by analyzing the transcriptomic and epigenetic signatures of the tumors. Methods: Transcriptomic data were downloaded from the NCBI GEO database. We investigated the expression differences between the GSE46444 (48 cirrhotic tissues versus 88 HCC tissues) and GSE63898 (168 cirrhotic tissues versus 228 HCC tissues) data sets using GEO2R. Differentially expressed genes were evaluated using GO and KEGG metabolic pathway analysis websites. Whole genome bisulfite sequencing (WGBS) and Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) data sets (26 HCC tissues versus 26 adjacent non-tumoral tissues) were also downloaded from the NCBI SRA database. These data sets were analyzed using Bismark and QSEA, respectively. The methylation differences between the groups were assessed using functional enrichment analysis. Results: In the GSE46444 data set, 80 genes were upregulated, and 315 genes were downregulated in the tumor tissue (HCC tissue) compared to the non-tumor cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were downregulated in the cirrhotic tissue compared to the tumor tissues. WGBS revealed that 20 protein-coding loci were hypermethylated. while the hypomethylated regions were non-protein-coding. The methylated residues of the tumor tissue, non-tumorous cirrhotic tissue, and healthy tissue were comparable. MeDIP-Seq, conducted on tumoral and non-tumoral tissues, identified hypermethylated or hypomethylated areas as protein-coding regions. The functional enrichment analysis indicated that these genes were related to pathways including peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras, and PI3K/AKT signal transduction. Conclusions: The investigation of transcriptomic and epigenetic mechanisms identified several genes significant in the biological behavior of HCC. These genes present potential targets for the development of targeted therapy.

IntroductionAcute 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.MethodsIn 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.ResultsWhite 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

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequency-matched groups, were used to develop the XAI-based model. The dataset contained 832 metabolites, and after feature selection, the model was developed using only 50 metabolites, meaning less medical knowledge is required, thus reducing diagnostic costs and improving prognostic time. The computational method was developed using six different ML algorithms before and after feature selection. The final classification model was explained using the XAI approach, SHAP. The best-performing classification model (XGBoost) achieved an area under the receiver operating characteristic curve (AUCROC) value of 98.85%. SHAP results showed that decreased levels of alpha-CEHC sulfate, hypoxanthine, and phenylacetylglutamine, as well as increased levels of N-delta-acetylornithine and oleoyl-linoloyl-glycerol (18:1/18:2)[2], increased the risk of ME/CFS. Besides the robustness of the methodology used, the results showed that the combination of ML and XAI could explain the biomarker prediction of ME/CFS and provided a first step toward establishing prognostic models for ME/CFS.

Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro–Wilk test for normality, the Kruskal–Wallis H test for group differences, and the Mann–Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.

The quick and large development in the accumulation of medical data provides broad potential for the application of artificial intelligence technologies [...]

Acute 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.

This 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.

Introduction:Despite significant advances in breast cancer (BC) management, the prognosis for most patients with distant metastasis remains poor. We predicted distant metastasis in BC patients with artificial intelligence (AI) methods based on genomic biomarkers.Methods:The dataset used in the study included 97 patients with BC, of whom 46 (47%) developed distant metastases, and 51 (53%) did not develop distant metastases, and the expression level of 24,481 genes of these patients. An approach combining Boruta + LASSO methods was applied to identify biomarker genes associated with BC distant metastasis. Mann-Whitney U test was used to examine the difference between groups in terms of gene expression levels in statistical analyses, and Cohen d effect sizes and odds ratios were calculated. AdaBoost and XGBoost algorithms, which are tree-based methods, were used for BC distant metastasis prediction, and the results were compared by evaluating comprehensive performance criteria.Results:After Boruta + LASSO methods, 14 biomarker candidate genes were identified. These predictive genes were PIB5PA, SSX2, OR1F1, ALDH4A1, FGF18, WISP1, PRAME, CEGP1, AL080059, NMU, ATP5E, SMARCE1, FGD6, and SLC37A1. In effect size results; in particular, show that the AL080059 (Cohen’s D: 1.318) gene is clinically predictive of BC Metastasis. The accuracy, F1-score, positive predictive value, sensitivity, and area under the ROC Curve (AUC) values obtained with the AdaBoost algorithm for BC metastasis prediction was 95%, 96.3%, 100%, 92.6%, and 98.8%, respectively. The model created with the XGBoost algorithm, on the other hand, obtained 90%, 92.9%, 92.9%, 92.9%, 97.6% accuracy, F1-score, positive predictive value, sensitivity, and AUC values, respectively.Conclusion:Identifying genes that successfully predict BC distant metastasis with AI methods in the study may be decisive for future therapeutic targets and help clinicians better adapt adjuvant chemotherapy to their patients. Additionally, the AdaBoost prediction model created can discriminate patients at risk of BC distant metastases.

This study aims to classify NSCLC death status and consists of patient records of 24 variables created by the open-source dataset of the cancer data site. Besides, basic classifiers such as SMO (Sequential Minimal Optimization), K-NN (K-Nearest Neighbor), random forest, and XGBoost (Extreme Gradient Boosting), which are machine learning methods, and their performances, and voting, bagging, boosting, and stacking methods from ensemble learning methods were used. Performance evaluation of models was compared in terms of accuracy, specificity, sensitivity, precision, and Roc curve. The basic classifier performances of random forest, SMO, K-NN, and XGBoost classifiers, their performances in the bagging ensemble learning method, and their performances in the boosting ensemble learning method are evaluated. In addition, Model 1 (random forest + SMO), Model 2 (XGBoost + K-NN), Model 3 (random forest + K-NN), Model 4 (XGBoost+SMO), Model 5 (SMO+K-NN + random forest), Model 6 (SMO+K-NN+XGBoost) and Model 7 (SMO+K-NN + random forest + XGBoost) the performances of in different metrics were expressed. The boosting ensemble learning method, which provides the maximum classification performance with XGBoost, achieved a 0.982 accuracy value, 0.971 sensitivity value, 0.989 precision value, 0.989 specificity value, and 0.998 ROC curve. It is recommended to use ensemble learning methods for classification problems in patients with a high prevalence of cancer to achieve successful results. [Med-Science 2022; 11(2.000): 924-33]

Aim:The diagnosis of breast cancer can be accomplished using an algorithm or an early detection model of breast cancer risk via determining factors. In the present study, gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) models were applied and their performances were compared.Methods:The open-access Breast Cancer Wisconsin Dataset, which includes 10 features of breast tumors and results from 569 patients, was used for this study. The GBM, XGBoost, and LightGBM models for classifying breast cancer were established by a repeated stratified K-fold cross validation method. The performance of the model was evaluated with accuracy, recall, precision, and area under the curve (AUC).Results:Accuracy, recall, AUC, and precision values obtained from the GBM, XGBoost, and LightGBM models were as follows: (93.9%, 93.5%, 0.984, 93.8%), (94.6%, 94%, 0.985, 94.6%), and (95.3%, 94.8%, 0.987, 95.5%), respectively. According to these results, the best performance metrics were obtained from the LightGBM model. When the effects of the variables in the dataset on breast cancer were assessed in this study, the five most significant factors for the LightGBM model were the mean of concave points, texture mean, concavity mean, radius mean, and perimeter mean, respectively.Conclusion:According to the findings obtained from the study, the LightGBM model gave more successful predictions for breast cancer classification compared with other models. Unlike similar studies examining the same dataset, this study presented variable significance for breast cancer-related variables. Applying the LightGBM approach in the medical field can help doctors make a quick and precise diagnosis.

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with a significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. This study uses explainable artificial intelligence and machine learning techniques to identify discriminative metabolites for ME/CFS. Material and Methods: The model investigates a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22–72. The dataset encapsulated 768 metabolites into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics. Random forest methods together with other classifiers were applied to the data to classify individuals as ME/CFS patients and healthy individuals. The classification learning algorithms’ performance in the validation step was evaluated using a variety of methods, including the traditional hold-out validation method, as well as the more modern cross-validation and bootstrap methods. Explainable artificial intelligence approaches were applied to clinically explain the optimum model’s prediction decisions. Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis. The random forest model outperformed the other classifiers in ME/CFS prediction using the 1000-iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC. According to the obtained results, the bootstrap validation approach demonstrated the highest classification outcomes. Conclusion: The proposed model accurately classifies ME/CFS patients based on the selected biomarker candidate metabolites. It offers a clear interpretation of risk estimation for ME/CFS, aiding physicians in comprehending the significance of key metabolomic features within the model.

Aim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models’ predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the “black box” problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p ≤ 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.

Obesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels. The data included gender, age, height, weight, family history of being overweight, dietary patterns, physical activity frequency, and more. Data preprocessing involved addressing class imbalance using Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) and feature selection using Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost)) were used for obesity level prediction, and Bayesian optimization was employed for hyperparameter tuning. The performance of different models was evaluated using metrics such as accuracy, recall, precision, F1-score, area under the curve (AUC), and precision–recall curve. The LR model showed the best performance across most metrics, followed by RF and XGBoost. Feature selection improved the performance of LR and RF models, while XGBoost’s performance was mixed. The study contributes to the understanding of obesity classification using machine-learning techniques based on physical activity and nutritional habits. The LR model demonstrated the most robust performance, and feature selection was shown to enhance model efficiency. The findings underscore the importance of considering both physical activity and nutritional habits in addressing the obesity epidemic.

Background: The purpose of this study was to carry out the bioinformatic analysis of lncRNA data obtained from the genomic analysis of kidney tissue samples taken from rats with nephrotoxicity induced by methotrexate (MTX) and from rats without pathology and modeling with the tree-based machine learning method. Another aim of the study was to identify potential biomarkers for the diagnosis of nephrotoxicity and to provide a better understanding of the nephrotoxicity formation process by providing the interpretability of the model with explainable artificial intelligence methods as a result of the modeling. Methods: To identify potential indicators of drug-induced nephrotoxicity, 20 female Wistar albino rats were separated into two groups: MTX-treated and the control. Kidney tissue samples were collected from the rats, and genomic, histological, and immunohistochemical analyses were performed. The dataset obtained as a result of genomic analysis was modeled with random forest (RF), a tree-based method. Modeling results were evaluated with sensitivity (Se), specificity (Sp), balanced accuracy (B-Acc), negative predictive value (Npv), accuracy (Acc), positive predictive value (Ppv), and F1-score performance metrics. The local interpretable model-agnostic annotations (LIME) method was used to determine the lncRNAs that could be biomarkers for nephrotoxicity by providing the interpretability of the RF model. Results: The outcomes of the histological and immunohistochemical analyses conducted in the study support the conclusion that MTX use caused kidney injury. According to the results of the bioinformatics analysis, 52 lncRNAs showed different expressions in the groups. As a result of modeling with RF for lncRNAs selected with Boruta variable selection, the B-Acc, Acc, Sp, Se, Npv, Ppv, and F1-score were 88.9%, 90%, 90.9%, 88.9%, 90.9%, 88.9%, and 88.9%, respectively. lncRNAs with id rnaXR_591534.3 rnaXR_005503408.1, rnaXR_005495645.1, rnaXR_001839007.2, rnaXR_005492056.1, and rna_XR_005492522.1. The lncRNAs with the highest variable importance values produced from RF modeling can be used as nephrotoxicity biomarker candidates. Furthermore, according to the LIME results, the high level of lncRNAs with id rnaXR_591534.3 and rnaXR_005503408.1 particularly increased the possibility of nephrotoxicity. Conclusions: With the possible biomarkers resulting from the analyses in this study, it can be ensured that the procedures for the diagnosis of drug-induced nephrotoxicity can be carried out easily, quickly, and effectively.

In this study, the prevalence of Cyclospora cayetanensis and Cryptosporidium spp. were researched in children with parasitological investigations requested for a variety of reasons, regardless of immune status. The stool samples of 1057 pediatric patients with parasitological evaluation requested for a variety of reasons within two years were investigated under a microscopy using native-lugol, sedimentation and modified Kinyouns acid-fast stain methods. The mean age of the 1057 pediatric patients participating in the research were 8.07±4.32 years, 11.4% were identified to have coccidian parasites (Cyclospora cayetanensis and Cryptosporidium spp.). Evaluation according to age group found the majority of children positive for Cryptosporidium spp. were in the 6-12 year interval. These parasites were observed more frequently in the spring and summer seasons compared to other seasons. There was a significant correlation between parasite positivity and spring and summer seasons (p [Med-Science 2021; 10(2.000): 338-45]

Abstract Background There are doubts among anesthesiologists on the use of the Laryngeal Mask Airway (LMA) in nasal surgeries because of concerns about the occurrence of blood leakages to the airway. We hypothesized that the use of LMA-Supreme (LMA-S) in nasal surgery is comparable with endotracheal tube (ETT) according to airway protection against blood leakage through the fiberoptic bronchoscopy, oropharyngeal leakage pressure (OLP), heart rate (HR), mean arterial pressure (MAP), and postoperative adverse events. Methods The present study was conducted in a prospective, randomized, single-blind, controlled manner on 80 patients, who underwent septoplasty procedures under general anesthesia, after dividing them randomly into two groups according to the device used (LMA-S or ETT). The presence of blood in the airway (glottis/trachea, distal trachea) was analyzed with the fiberoptic bronchoscope and a four-point scale. Both groups were evaluated for OLP; HR; MAP; postoperative sore throat, nausea, and vomiting; dysphagia; and dysphonia. Results In the fiberoptic evaluation of the airway postoperatively, less blood leakage was detected in both anatomic areas in the LMA-S group than in the ETT group (glottis/trachea, p = 0.004; distal trachea, p = 0.034). Sore throat was detected less frequently in the LMA-S group at a significant level in the 2nd, 6th, and 12th hours of postoperative period; however, other adverse events were similar in both groups. Hemodynamic parameters were not different between the two groups. Conclusion The present findings demonstrate that the LMA-S provided more effective airway protection than the ETT in preventing blood leakage in the septoplasty procedures. We believe that the LMA-S can be used safely and as an alternative to the ETT in septoplasty cases. Trial registration This trial is registered at the US National Institutes of Health ( ClinicalTrials.gov ) # NCT03903679 on April 5, 2019.

Colorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700–0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94–1).

Background: The first aim of this study is to perform bioinformatic analysis of lncRNAs obtained from liver tissue samples from rats treated with cisplatin hepatotoxicity and without pathology. Another aim is to identify possible biomarkers for the diagnosis/early diagnosis of hepatotoxicity by modeling the data obtained from bioinformatics analysis with ensemble learning methods. Methods: In the study, 20 female Sprague-Dawley rats were divided into a control group and a hepatotoxicity group. Liver samples were taken from rats, and transcriptomic and histopathological analyses were performed. The dataset achieved from the transcriptomic analysis was modeled with ensemble learning methods (stacking, bagging, and boosting). Modeling results were evaluated with accuracy (Acc), balanced accuracy (B-Acc), sensitivity (Se), specificity (Sp), positive predictive value (Ppv), negative predictive value (Npv), and F1 score performance metrics. As a result of the modeling, lncRNAs that could be biomarkers were evaluated with variable importance values. Results: According to histopathological and immunohistochemical analyses, a significant increase was observed in the sinusoidal dilatation and Hsp60 immunoreactivity values in the hepatotoxicity group compared to the control group (p < 0.0001). According to the results of the bioinformatics analysis, 589 lncRNAs showed different expressions in the groups. The stacking model had the best classification performance among the applied ensemble learning models. The Acc, B-Acc, Se, Sp, Ppv, Npv, and F1-score values obtained from this model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. lncRNAs with id rna-XR_005492522.1, rna-XR_005492536.1, and rna-XR_005505831.1 with the highest three values according to the variable importance obtained as a result of stacking modeling can be used as predictive biomarker candidates for hepatotoxicity. Conclusions: Among the ensemble algorithms, the stacking technique yielded higher performance results as compared to the bagging and boosting methods on the transcriptomic data. More comprehensive studies can support the possible biomarkers determined due to the research and the decisive results for the diagnosis of drug-induced hepatotoxicity.

Background: The primary aim of this study was to compare liver transplant (LT) recipients with and without hepatocellular carcinoma (HCC) in terms of COVID-19-related depression, anxiety, and stress. Method: A total of 504 LT recipients with (HCC group; n = 252) and without HCC (non-HCC group; n = 252) were included in the present case–control study. Depression Anxiety Stress Scales (DASS-21) and Coronavirus Anxiety Scale (CAS) were used to evaluate the depression, stress, and anxiety levels of LT patients. DASS-21 total and CAS-SF scores were determined as the primary outcomes of the study. Poisson regression and negative binomial regression models were used to predict the DASS and CAS scores. The incidence rate ratio (IRR) was used as a coefficient. Both groups were also compared in terms of awareness of the COVID-19 vaccine. Results: Poisson regression and negative binomial regression analyses for DASS-21 total and CAS-SF scales showed that the negative binomial regression method was the appropriate model for both scales. According to this model, it was determined that the following independent variables increased the DASS-21 total score: non-HCC (IRR: 1.26; p = 0.031), female gender (IRR: 1.29; p = 0.036), presence of chronic disease (IRR: 1.65; p < 0.001), exposure to COVID-19 (IRR: 1.63; p < 0.001), and nonvaccination (IRR: 1.50; p = 0.002). On the other hand, it was determined that the following independent variables increased the CAS score: female gender (IRR:1.75; p = 0.014) and exposure to COVID-19 (IRR: 1.51; p = 0.048). Significant differences were found between the HCC and non-HCC groups in terms of median DASS-21 total (p < 0.001) and CAS-SF (p = 0.002) scores. Cronbach’s alpha internal consistency coefficients of DASS-21 total and CAS-SF scales were calculated to be 0.823 and 0.783, respectively. Conclusion: This study showed that the variables including patients without HCC, female gender, having a chronic disease, being exposed to COVID-19, and not being vaccinated against COVID-19 increased anxiety, depression, and stress. High internal consistency coefficients obtained from both scales indicate that these results are reliable.

Background: It is important to evaluate the attitude of society towards vaccines to understand the rates of acceptance and hesitance towards vaccination, which are essential components of public health and epidemiology. This study aimed to evaluate the perspective of the Turkish population on COVID-19 status, rate of vaccination, and also to evaluate the reasons for refusal to vaccinate, vaccine hesitancy, and related factors. Methods: A total of 4539 participants were included in this population-based descriptive and cross-sectional study. The Nomenclature of Territorial Units for Statistics (NUTS-II) was used to obtain a representative sample and for this purpose Turkey was divided into 26 regions. Participants were randomly selected based on the demographic features and population ratios of the selected regions. The following parameters were evaluated: sociodemographic characteristics and perspectives on COVID-19 vaccines, Vaccine Hesitancy Scale Adapted to Pandemics (VHS-P), and Anti-Vaccine Scale-Long Form (AVS-LF) questions. Results: A total of 4539 participants, 2303 (50.7%) male and 2236 (49.3%) female, aged between 18 and 73 years, were included in this study. It was observed that 58.4% of the participants had hesitations towards COVID-19 vaccination, and 19.6% were hesitant about all childhood vaccinations. Those who did not have the COVID-19 vaccine, who did not think that the COVID-19 vaccine was protective, and who had hesitation to vaccinate against COVID-19 had significantly higher median scores on the VHS-P and AVS-LF scales, respectively (all p < 0.01). Those who did not have their children vaccinated in childhood and who were hesitant about childhood vaccinations, had significantly higher median scores on the VHS-P and AVS-LF scales, respectively (all p < 0.01). Conclusion: Although the rate of vaccination for COVID-19 was 93.4% in the study, hesitation to vaccinate was 58.4%. The median score of the scales of those who were hesitant about childhood vaccinations was higher than individuals who did not have any hesitation. In general, the source of concerns about vaccines should be clearly seen, and precautions should be taken.

Background: The primary aim of this study was to create a machine learning (ML) model that can predict perforated and nonperforated acute appendicitis (AAp) with high accuracy and to demonstrate the clinical interpretability of the model with explainable artificial intelligence (XAI). Method: A total of 1797 patients who underwent appendectomy with a preliminary diagnosis of AAp between May 2009 and March 2022 were included in the study. Considering the histopathological examination, the patients were divided into two groups as AAp (n = 1465) and non-AAp (NA; n = 332); the non-AAp group is also referred to as negative appendectomy. Subsequently, patients confirmed to have AAp were divided into two subgroups: nonperforated (n = 1161) and perforated AAp (n = 304). The missing values in the data set were assigned using the Random Forest method. The Boruta variable selection method was used to identify the most important variables associated with AAp and perforated AAp. The class imbalance problem in the data set was resolved by the SMOTE method. The CatBoost model was used to classify AAp and non-AAp patients and perforated and nonperforated AAp patients. The performance of the model in the holdout test set was evaluated with accuracy, F1- score, sensitivity, specificity, and area under the receiver operator curve (AUC). The SHAP method, which is one of the XAI methods, was used to interpret the model results. Results: The CatBoost model could distinguish AAp patients from non-AAp individuals with an accuracy of 88.2% (85.6–90.8%), while distinguishing perforated AAp patients from nonperforated AAp individuals with an accuracy of 92% (89.6–94.5%). According to the results of the SHAP method applied to the CatBoost model, it was observed that high total bilirubin, WBC, Netrophil, WLR, NLR, CRP, and WNR values, and low PNR, PDW, and MCV values increased the prediction of AAp biochemically. On the other hand, high CRP, Age, Total Bilirubin, PLT, RDW, WBC, MCV, WLR, NLR, and Neutrophil values, and low Lymphocyte, PDW, MPV, and PNR values were observed to increase the prediction of perforated AAp. Conclusion: For the first time in the literature, a new approach combining ML and XAI methods was tried to predict AAp and perforated AAp, and both clinical conditions were predicted with high accuracy. This new approach proved successful in showing how well which demographic and biochemical parameters could explain the current clinical situation in predicting AAp and perforated AAp.

Background: Obesity, which causes physical and mental problems, is a global health problem with serious consequences. The prevalence of obesity is increasing steadily, and therefore, new research is needed that examines the influencing factors of obesity and how to predict the occurrence of the condition according to these factors. This study aimed to predict the level of obesity based on physical activity and eating habits using the trained neural network model. Methods: The chi-square, F-Classify, and mutual information classification algorithms were used to identify the most critical factors associated with obesity. The models’ performances were compared using a trained neural network with different feature sets. The hyperparameters of the models were optimized using Bayesian optimization techniques, which are faster and more effective than traditional techniques. Results: The results predicted the level of obesity with average accuracies of 93.06%, 89.04%, 90.32%, and 86.52% for all features using the neural network and for the features selected by the chi-square, F-Classify, and mutual information classification algorithms. The results showed that physical activity, alcohol consumption, use of technological devices, frequent consumption of high-calorie meals, and frequency of vegetable consumption were the most important factors affecting obesity. Conclusions: The F-Classify score algorithm identified the most essential features for obesity level estimation. Furthermore, physical activity and eating habits were the most critical factors for obesity prediction.

Abstract Background This study were designed to investigate the usefulness of the videolaryngoscope-guided insertion technique compared with the standard digital technique for the insertion success rate and insertion conditions of the Proseal™ laryngeal mask airway (PLMA). Methods Prospective, one hundred and nineteen patients (ASA I–II, aged 18–65 yr) were randomly divided for PLMA insertion using the videolaryngoscope-guided technique or the standard digital technique. The PLMA was inserted according to the manufacturer’s instructions in the standard digital technique group. The videolaryngoscope-guided technique was performed a C-MAC® videolaryngoscope with D-Blade, under gentle videolaryngoscope guidance, the epiglottis was lifted, and the PLMA was advanced until the tip of the distal cuff reached the oesophagus inlet. The number of insertion attempts, insertion time, oropharyngeal leak pressure, leak volume, fiberoptic bronchoscopic view, peak inspiratory pressure, ease of gastric tube placement, hemodynamic changes, visible blood on PLMA and postoperative airway morbidity were recorded. Results The first-attempt success rate (the primary outcome) was higher in the videolaryngoscope-guided technique than in the standard digital technique (p = 0.029). The effect size values with 95% confidence interval were 0.19 (0.01–0.36) for the first and second attempts, 0.09 (− 0.08–0.27) for the first and third attempts, and not computed for the second and third attempts by the groups, respectively. Conclusion Videolaryngoscope-guided insertion technique can be a help in case of difficult positioning of a PLMA and can improve the PLMA performance in some conditions. We suggest that the videolaryngoscope-guided technique may be a useful technique if the digital technique fails. Trial registration ClinicalTrials.gov NCT03852589 date of registration: February 22th 2019.

The effect of ketofol, a mixture of ketamine and propofol in various ratios, on hemodynamic, for intubation without the use of neuromuscular blockers, has not been elucidated in patients undergoing tympanomastoidectomy. We evaluated the effects of ketofol and propofol on intubation conditions and hemodynamic without the use of a neuromuscular blocker. The prospective randomized, double-blinded study was scheduled for tympanoplasty or mastoidectomy. The patients were divided randomly into a propofol group (Group P) and a ketofol group (Group KP). Intubation conditions, changes in hemodynamics, HR, MAP, systolic arterial pressure (SAP), and SpO2 values were recorded before induction, after induction, after intubation, and at 3-min intervals during the first 30 min, 5-min intervals for the next 30 min, and 10-min intervals after that. In the intragroup evaluation, SAP, DAP, MAP and HR values were lower in both groups compared to the baseline values. Hemodynamic values were significantly lower in Group P than in Group KP after intubation compared to baseline. DAP at 12 and 18 min, DAP and MAP at 24 min, SAP, DAP and MAP at 27 min, and SAP and MAP at 30 min after the start of the operation were significantly lower in Group P than in Group KP. The need for ephedrine and the number of patients who required ephedrine were significantly lower in Group KP than in Group P. Ketofol provided appropriate intubation conditions similar to propofol, without the use of a neuromuscular blocker, and contributed to better hemodynamic conditions in patients undergoing tympanomastoidectomy. [Med-Science 2019; 8(2.000): 381-4]

It is important to vaccinate individuals working in the field of health who are more at risk compared to society during the pandemic period. The aim of this study was to evaluate the vaccine hesitancy and anxiety levels of hospital cleaning staff and caregivers during the COVID-19 pandemic. This descriptive type cross-sectional study was conducted with 460 hospital cleaning staff and caregivers. Demographic and social characteristics form, Coronavirus Anxiety Scale (CAS), and Vaccine Hesitancy Scale (VHS) adapted to the pandemic were used in the questionnaire form used to collect the data of the study. It was determined that the rates of hesitation against the COVID-19 vaccine and childhood vaccine were 42.2% (n = 194) and 10.9% (n = 50), respectively. Less than half of the participants (44.6%) believe that the COVID-19 vaccine is protective. COVID-19 anxiety (CAS score ≥ 9 point) was detected in 19.6% of participants and statistically significant differences were found between patients with (n = 90) and without (n = 370) anxiety regarding gender (p < 0.001), working unit (p = 0.002), vaccination status (p = 0.023) and history of psychological disease (p = 0.023). It has been shown that the VHS-total scores of those who are not vaccinated, those who are hesitant about vaccination, those who do not think that the vaccine is protective, and those who state that there is no need for a legal obligation in vaccination are higher. When participants were asked about the most anxious situation during the COVID-19 period, the highest response rate was 62.4% for my parents’ exposure to COVID-19. The most anxious situation among participants is their parents’ exposure to COVID-19. Although participants are highly vaccinated, they have serious hesitancy about the COVID-19 vaccine. This study also showed that there was a parallel relationship between COVID-19 anxiety and vaccine hesitancy.

Background: Adenosine deaminase (ADA) is an aminohydrolase involved in the catabolism of purine nucleotides and irreversibly deaminizes adenosine and deoxyadenosine to inosine and deoxyinosine. ADA enzyme deficiency results in the loss of functional properties of B and T lymphocytes. Demodex species have been reported to be transmitted between humans through close contact and to play a role in the pathogenesis of rosacea, acne vulgaris, perioral dermatitis, seborrhoeic dermatitis, micropapillary-pruritic dermatitis and blepharitis. The present study aimed to compare serum ADA levels in D. folliculorum positive patients with the healthy control individuals. Methods: Serum ADA levels were examined for 30 patients diagnosed with erythematotelangiectatic rosacea and 40 healthy individuals in Malatya Inonu University in 2017. Standardized skin surface biopsy (SSSB) method was used to diagnose D. folliculorum. A significant decrease was found in the ADA levels of Demodex-positive rosacea patients when compared to the control group. Results: ADA levels were decreased in the Demodex-positive group. The mean ADA level in patient group was significantly lower than the mean in the control group (P

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. [Med-Science 2018; 7(2.000): 350-4]

The optimal management of moderate ischemic mitral regurgitation (MR) remains controversial. Some surgeons advocate coronary artery bypass alone, while others suggest concomitant mitral valve annuloplasty. We aimed to evaluate the early results of isolated coronary artery bypass operation on the cases with mild-to-moderate ischemic MR. Between May 2010 and May 2011, 59 patients (64% male, mean age: 50.5 years) with a preoperative diagnosis of mild-to-moderate ischemic MR underwent a coronary bypass operation. Patients evaluated with preoperative and postoperative (in twelve-month period with an average of five months) transthoracic echocardiogram (TTE). Postoperative mortality was not observed in study group. The preoperative functional capacity of the patients as well as the variables of mild MR and moderate MR showed a statistically significant difference in a positive way when compared with the postoperative functional capacity and MR variables. Postoperative TTE evaluation revealed that only 2 cases have severe MR (3,4%) also 62,7 % of patients have mild and 33.9% of patients have moderate MR. While there was a significant difference in a positive way between the preoperative and postoperative period in terms of left atrial diameter, no significant difference was found for the variables of ejection fraction and pulmonary artery pressure. Among the patients whom undergoing coronary bypass surgery, if there is mild or moderate MR revealed with the TTE prior to the operation, performing only coronary bypass operation will be adequate, and our early results in this matter are satisfactory. But, if severe MR revealed with TTE, performance of mitral valve repair or replacement should be evaluated additional to coronary bypass operation. [Med-Science 2018; 7(1.000): 114-117]

Alzheimers Disease (AD), which is the most common reason for dementia, is an irreversible neurodegenerative brain disease. FEZ1 is a protein expressed in the brain. High expressions of FEZ1 mRNA have been interpreted as an indicator of high neural plasticity for memory and learning. This study was planned to determine the effect of melatonin deficiency (pinealectomy; PnX) on AD and to reveal its association with FEZ1. 30 male rats were used in the study. The rats were divided into three as SHAM, PnX+streptozotocin (STZ) and PnX+STZ+melatonin (MLT) groups. The pineal glands of rats were surgically removed (except SHAM group) and STZ was intracerebroventricularly (icv) administrated at the first and third days. MLT (10 mg/kg/day) was intraperitoneally (ip) injected 1 hour before the first STZ application and it was continued for 14 days. STZ and MLT solvents were applied to the rats in the SHAM group. At the end of the applications, Morris water maze (MWM) test was carried out the rats. At the end of MWM tests, the rats were sacrificed and their blood and hippocampus tissues were taken. FEZ1 gene expression and protein levels were determined from hippocampus tissue, while serum nonadrenaline, dopamine and serotonin levels were detected from blood samples. FEZ1 protein levels of PnX+STZ+MLT group were found to be statistically significantly lower than those of SHAM and PnX+STZ groups (p [Med-Science 2017; 6(2.000): 217-23]

antioxidant which is known to have liver protective effects, against thioacetamide (TAA)-induced liver injury. Accordingly, we aimed to investigate the toxicity of a hepatotoxic agent, thioacetamide, compared the NSO and NAC liver preventive effects in experimental animal model. Wistar-albino rats were randomly allocated to five groups, each consisting of eight rats, and were subjected to different treatment regimens for 6 days. Alanine aminotransferase (ALT), malondialdehyde (MDA), and protein carbonyl (PC) levels decreased significantly by NSO treatment (P [Med-Science 2017; 6(1.000): 96-103]

Aims: This study aimed to evaluate the effect of the corneal changes following corneal cross-linking (CXL) on the anterior chamber in keratoconus patients. Materials and Methods: Forty-five eyes of 32 patients who had been diagnosed with progressive keratoconus and had undergone CXL were included in this retrospective study. The thinnest corneal thickness of the progressive keratoconus patients included in the study was >400 μ. The preoperative (T0), postoperative 6th month (T1), and postoperative 1st year (T2) anterior chamber volume (ACV), anterior chamber angle (ACA), and anterior chamber depth (ACD) scheimpflug imaging values were obtained for each eye. Results: The mean T0 ACV value was 182.79 ± 36.68 mmwhile the T1 value was 201.25 ± 41.73 mm3 and the T2 value was 208.40 ± 42.69 mm3 with a statistically significant difference between the periods (P = 0.001). The mean T0 ACA value was 38.64° ±5.85°, increasing to 41.45° ±4.83° in the T1 and 42.10° ± 4.84° in the T2. The T0 value was significantly lower than the post-CXL values (P = 0.003). The mean ACD value was 3.73 ± 0.29 mm at the T0 and 3.82 ± 0.38 mm at the T1 and 3.84 ± 0.36 mm at the T2. The pre-CXL values were significantly lower than the post-CXL values (P = 0.001). Conclusions: The improvement of corneal parameters by CXL in keratoconus patients can have a positive effect on anterior chamber parameters as well. This effect becomes marked at the postoperative first 6-month evaluation.

Introduction and Hypothesis: We retrospectively evaluated the prognostic impact of neutrophil-lymphocyte ratio (NLR) as a marker for inflammatory and immune state in men with prostate cancer. Materials and Methods: This retrospective study was conducted in a single urology clinic to review the medical data of 558 patients who underwent transrectal prostate needle biopsies between 2007 and 2014. Prior to transrectal prostate needle biopsies, patient histories, physical examinations, and routine laboratory tests including blood biochemistry, urinaly- sis and urine cultures, free PSA and total PSA, rectal examination, transrectal ultrasound findings, and pathology results were evaluated. Results: Benign biopsy results were found in 287 patients (Group 1) using neutrophil / lymphocyte (NLR) and platelet / lymphocyte ratio (PLR). When calculating malignancy in 271 patients (Group 2), there was no significantly difference in NLR and PLR values between benign and malign prostate diseases (p=0.14 and p=0.369, respectively). Conclusion: With reference to the survey prior to the biopsy, NLR and PLR ratio values do not appear to be helpful in the differentiation of benign prostatic hyperplasia and prostate cancer. [Arch Clin Exp Surg 2016; 5(4.000): 194-199]

The aim of this study was to investigate the possible protective effects of melatonin and β-D-glucan against AA-induced liver injury in rats. Forty (SpraqueDawley male) rats were randomly divided into 5 experimental groups: sham, acetaminophen only (AA, 900 mg/kg), melatonin (10 mg/kg) + AA (MLT), β-D-glucan (50 mg/kg) + AA (β), and melatonin + β-D-glucan + AA (MLT+β) groups. All of the rats were killed on day 11 of the experiment. Histopathological changes and biochemical parameters including levels of serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) and liver tissue malondialdehyde (MDA), activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) were determined to assess the liver function. MDA levels were the highest in the AA group whereas activities of SOD, CAT, and GPx in the liver tissue were found as lowest in this group. MDA activities were significantly lower in the MLT+β group than in the AA group. Only GPx activities in the MLT+β group were significantly higher than those in the MLT and β groups. The serum AST and ALT levels were increased significantly following treatment with AA (p < 0.001). Pretreatment with the antioxidant compounds decreased AST levels significantly but again, the levels were still significantly higher than the sham levels (p < 0.001). There were no statistically significant differences in the microscopic damage between the S, MLT, β, and MLT+β groups (p > 0.05). This finding can be attributed to the higher efficacy of the combination of melatonin and β-D-glucan in scavenging free radicals and stimulating the antioxidant enzymes. [Med-Science 2016; 5(2.000): 539-43]

The Acute Physiology and Chronic Health Evaluation (APACHE II) is a beneficial tool for the estimation of risk and the comparison of the patients who received care with similar risk properties. Machine learning based systems can assist clinicians in the early diagnosis of diseases. This research aimed at predicting the APACHE II score using Support Vector Machine (SVM) from Medical Data Mining (MDM). The records of 280 patients from intensive care unit included the dataset containing the target variable (the APACHE II score), and 23 demographical/clinical predictor variables. Genetic algorithm based feature selection and 10-fold cross validation method were employed. SVM with radial basis (RBF) was constructed. The performance of the proposed approach was assessed using root mean squared error (RMSE), mean absolute error (MAE), correlation (R) and coefficient of determination (R2). Mean age of the individuals was 51±23 years. 153 (54.6%) were females, and 127 (45.4%) were males. The proposed approach yielded the values of 1.037 for RMSE, 0.727 for MAE, 0.993 for R and 0.986 for R2, respectively. The results demonstrated that the proposed approach had an excellent predictive performance of the APACHE II score. Additionally, ensemble approaches such as bagging, boosting, voting etc. can improve markedly the performance of the prediction and classification tasks. [Med-Science 2015; 4(4.000): 2743-50]

It has been reported that the parasites can make the free oxygen radicals such as superoxide and hydrogen peroxide and they contain enzymes which produce them. The cytokines play role in the regulation of immune response in living beings. The synthesizing of IL-10 is increased in Enterobius vermicularis, too. The adenosine deaminase (ADA) is also an aminohydrolase which plays role in the catabolism of purine nucleotides, inosines and deoxyinosine deamidates the adenosine and deoxyadenosine irreversibly. In the study, the comparison of ADA levels between the serums of the control group and the group of patients with E. vermicularis was aimed. In the study, the experimental group included 40 E. vermicularis positive patients. The average age of the patients is 35.2±5.4, 20 of them are men and 10 of them are women. The average age of 40 healthy Enterobius vermicularis negative people is 40.23±7.01, 31 of them are male and 9 of them are female. A significant decrease between the Enterobius vermicularis positive patients and the control group was detected in the level of ADA (p=0.0001). It can be considered that the ADA level of E. vermicularis positive patients in the study may be decreased because of increasing oxidative stress due to the parasitic infection. [Med-Science 2014; 3(4.000): 1648-54]

A few prospective studies evaluated the oxidative stress variables and adenosine deaminase activity in patients with hyperthyroidism and hypothyroidism. Thus; we aimed to investigate oxidative stress variables and adenosine deaminase activity in patients with hyperthyroidism and hypothyroidism. Study populations consisted of 20 patients with hyperthyroidism, 20 patients with hypothyroidism and 20 age matched healthy volunteers as control groups. Plasma nitric oxide, malondialdehyde levels and glutathione, adenosine deaminase activities were measured in all subjects. plasma nitric oxide and adenosine deaminase levels were higher in patients with hyperthyroidism, but lower in patients with hypothyroidism compared to those of controls (P [Med-Science 2014; 3(4.000): 1595-604]

Prostate cancer is one of the most prevalent type of cancer in men. The exact solution for the treatment of different types of cancers has not been fully elucideted. Apelin secreted from adipose tissue reveals its effects by binding to APJ receptor. Obesity develops together with increased adipose tissue, and the increase of obesity related-apelin secretion is known. Obesity is a risk factor in the development of cancer, and considering both apelin and its receptor widely localized in the tissues of testis and prostate and apelin may have significant effects on prostate cancer. In the present study, 0.1, 1 and 10 nM concentrations of apelin-13 and three different testosterone concentrations (1, 10 and 100 nM) were separately aplied to human prostate cancer cells with LNCaP (androgen receptor positive) and DU-145 (androgen receptor negative). Then 1, 10 and 100 nM concentrations of testosterone hormone and 10 nM dose of apelin-13 incubated for 24 h in the prostate cancer cells were administrated and were allowed to incubate for 24 h. Effects of apelin-13 and testosterone on prostate cancer cells were determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. At the end of the experiment, DU-145 did not affected cell viability while the treatment with apelin -13 and testosterone increased the viability of LNCaP cells (p [Med-Science 2014; 3(3.000): 1427-41]

This is a methodological type research that was proceeded to evaluate the validity and reliability of the Scale for the Problematic Mobile Phone Use (PU) among the Turkish society. The related research was conducted within the Spring Semester of 2011-2012 Academic Year on 387 students attending Inonu University Medical Faculty. Besides the scale, a questionnaire for determining the descriptive features of the students was also used in the study. The German original scale was translated into Turkish by using the group translation and retranslation techniques. Content validity index were analyzed for the content validity, exploratory and confirmatory factor analyzes were conducted for the construct validity and of the scale, the stability over time and internal consistency for reliability analyzes were conducted. Turkish questionnaire of the scale was reviewed by 10 experts to determine for the content (theme) validity. The necessary corrections were done in accordance with the suggestions of the experts, and the value of the content validity index of the scale (KGI) was found 0.89. Exploratory and confirmatory factor analyses performed to test the structure validity of the scale were completed, and a three-factor structure explaining the 45% of the total scale variance was obtained. Cronbach Alpha value calculated for the reliability analyses of the scales was found 0.854, meanwhile the test-retest correlation coefficient of the scale for the total points was found 0.86. Additionally, the difference between the total average points of the pre-test and the re-test of the scale was not found statistically significant (p=0.30). As a result of the Turkish version of the scale of the values obtained from the PU is defined to be a reliable and valid scale. [Med-Science 2014; 3(3.000): 1361-81]

The objective of the present study was to investigate the possible impact of specific endothelin-A (ETA) receptor blockage by BQ-123 on myocardial ischemia-reperfusion (MI/R) induced apoptosis in rats. To produce MI/R, a branch of the descending left coronary artery was occluded for 30 min followed by 2 h reperfusion. Thirty-two rats were randomly assigned to four groups equally: (1) sham-operated group, (2) MI/R group, (3) MI/R+BQ-123-treated group, and (4) MI/R+ET-1+BQ-123-treated group. TUNEL staining, caspase-3 and caspase-9 activities were determined immunohistologically. MI/R group revealed extensive TUNEL positive cardiomyocytes especially in the free wall of the left ventricule, interventicular septum, and apex zone. Intensity of TUNEL-positive cardiomyocytes reduced as a result of BQ-123 treatment compared to the sham group in the same sections. Result of the caspase activity was found to correlate with TUNEL evaluation. BQ-123 administrations to MI/R group with or without ET-1 caused significant decrease both in lipid peroxidation and nitric oxide (NO) generation. Also, BQ-123 leads to augmentation of superoxide dismutase, catalase and glutathione contents. We propose that selective ETA antagonism by BQ-123 has a worthwhile effect on apoptotic cell death following MI/R, and that scavenging of free radicals by selective ETA antagonist is part of this beneficial effect. [Med-Science 2012; 1(4.000): 254-70]

Two Demodex species are known to live on people. Demodex folliculorum lives in the openings of hair follicles alone or in groups. D brevis lives in the depths of the sebaceous glands alone. There are different related on the epidemiology of Demodex species have been published. Method: In this study, taking into account that the parasite is transmitted through close contact from person to person, we aimed to evaluate the relation between the presence of Demodex spp with gender and age among the Health hazard bearing and sanitary establishment operators and workers who came for porter examination. Results: For this purpose, 862 male and 215 female patients with a total of 1077 volunteers from the face area of the standard superficial skin biopsy (SSSB) method and studied samples were taken. More than five mites in 1 cm2 density was defined as positive. In investigated samples 37.3% Demodex spp. positivity was detected. Only two were found to be D brevis and the others were D folliculorum. The study revealed statistically significant relationships between the positivity of parasites with the occupational group, age and sex. Conclusion: Since the prevalence of Demodex among healthy individuals without any complaint was found to be 37%, we believe Demodex spp should be investigated in porter examinations of people who have dermatological complaints.