Huyut, Mehmet Tahir; Huyut, Zübeyir

In the original published version of this article, the ethics committee approval number was missing in the Ethical approval section: Institutional Review Board Statement: The dataset used in this study was collected to be used in various studies in the estimation of the diagnosis, prognosis, and mortality of COVID-19. The necessary permissions for the collected dataset were given by the Ministry of Health of the Republic of Turkey and the Ethics Committee of Erzincan Binali Yıldırım University. This study was conducted following the 1989 Declaration of Helsinki. Erzincan Binali Yıldırım University Human Research Health and Sports Sciences Ethics Committee. This has now been added and the Ethics approval section revised to be more concise. The correct version can be found below: Institutional Review Board Statement: The dataset used in this study was collected to be used in various studies in the estimation of the diagnosis, prognosis, and mortality of COVID-19. The necessary permissions for the collected data set were obtained from the Ministry of Health of the Republic of Turkey. This study was approved by Erzincan Binali Yıldırım University Clinical Research Ethics Committee (31.03.2022, Report No: 03–09). The authors apologize for the errors. Both the HTML and PDF versions of the article have been updated to correct the errors.

Bayram, Hüseyin; Pala, Şenol Mail

This research examined how effectively the social studies course achieves its intended purpose of socialization. The research was conducted using the Q-method. The participant group of the research was formed by the criterion sampling method. The participant group consisted of a total of forty-eight students, with sixteen from the fifth grade, sixteen from the sixth grade and sixteen from seventh grade, who were studying at secondary schools in the center of a province in Turkey. Data were collected with a list and a Q-matrix was developed by the researcher and analyzed using the PQ-Method 2.35 program. At the end of the research, it was determined that secondary school students' socialization perceptions were grouped under seven factors and were all different from each other. As a result, it was revealed that the purpose of the social studies course was not realized. Based on the results and limitations of the research, recommendations were developed to examine secondary school students' socialization perceptions with larger participant groups.

Saroughi, Mohsen; Mirzania, Ehsan; Achite, Mohammed; Katipoğlu, Okan Mert; Al-Ansari, Nadhir; Vishwakarma, Dinesh Kumar; Chung, Il-Moon; Alreshidi, Maha Awjan; Yadav, Krishna Kumar

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to −25.3%, −29.6% and −57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has −204.9 value for AIC which has grown by 5.23% (−194.7) compared to the state without any pre-processing method (ANN_Relu_25).

Tuncer, Seref Bugra; Celik, Betul; Kılıc Erciyas, Seda; Sukruoglu Erdogan, Ozge; Pasin, Ozge; Avsar, Mukaddes; Kurt Gultaslar, Busra; Adamnejad Ghafour, Arash; Uyaroglu, Gamze; Akdeniz Odemis, Demet

Ovarian cancer (OC) ranks as the eighth most prevalent malignancy among women globally. The short non-coding RNA molecules, microRNAs (miRNAs) target multiple mRNAs and regulate the gene expression. Here in this study, we aimed to validate miR-3135b and miR-1273g-3p as novel biomarkers for prognostic and diagnostic factor OC. After RNA isolation, we analyzed the miR-3135b and miR-1273g-3p expression in peripheral blood samples derived from 150 OC patients. Subsequently, we compared their expression levels with 100 healthy controls. The differences of miR-3135b and miR-1273g-3p expression were detected using the Quantitative Real Time-PCR (qRT-PCR) technique following miRNA-specific cDNA synthesis pursing miRNA separation. The miR-3135b and miR-1273g-3p were higher in OC patients who tested positive for BRCA1/2 compared to BRCA-negative patients, and healthy cases. The level of miR-3135b demonstrated a roughly 4.82-fold increase in OC patients in comparison to the healthy cases, while miR-1273g-3p expression exhibited a roughly 6.77-fold increase. The receiver operating characteristic (ROC) analysis has demonstrated the potential of miR-3135b and miR-1273g-3p as markers for distinguishing between OC patients and healthy controls. The higher expressions of miR-3135b and miR-1273g-3p could be associated with OC development. Moreover, miR-3135b may have a diagnostic potential and miR-1273g-3p may have both diagnostic and prognostic potential in OC cell differentiation. The string analysis has revealed an association between miR-1273g-3p and the MDM2 gene, suggesting a potential link to tumor formation through the proteasomal degradation of the TP53 tumor suppressor gene. Additionally, the analysis indicates an association of miR-1273g-3p with CHEK1, a gene involved in checkpoint-mediated cell cycle arrest. String analysis also indicates that miR-3135b is associated with the MAPK1 gene, causing activation of the oncogenesis cascade. In conclusion, miR-1273g-3p, and miR-3135b exhibit significant potential as diagnostic markers. However, further research is needed to comprehensively investigate these miRNAs diagnostic and predictive characteristics in a larger cohort.

Huyut, Mehmet Tahir; Huyut, Zübeyir

Background and objective: A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis. Material and method: This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease. Results: Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients. Conclusions: This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Akgül, İsmail

Crack detection is very important during the inspection of building structures to determine whether they are safe. Therefore, to ensure the reliability and longevity of buildings, it is necessary to have experts periodically carry out building inspections. Building inspection has traditionally been conducted using human-based visual inspection methods as well as artificial intelligence methods that have shown great success in computer vision in recent years. In this study, 9 different models (Xception, VGG16, ResNet101, InceptionV3, InceptionResNetV2, MobileNetV2, DenseNet169, NASNetMobile, and EfficientNetB6), which have shown significant success in the field of artificial intelligence, are discussed to detect and classify cracks in building structures. In addition, a new fusion model structure called Mobile-DenseNet has been proposed by making block cutting and adding auxiliary layers to the MobileNetV2 and DenseNet169 model structures. With this proposed model structure, cracks in concrete structures were classified. A dataset consisting of concrete surface images was used to detect and classify cracks occurring in concrete structures, and a 99.87 % success rate was achieved with the proposed Mobile-DenseNet model in classifying cracks occurring on the concrete surface. The proposed model outperformed the traditional pretrained model structures in the study in terms of the number of transactions, density, features, complexity, and success accuracy.

Bingol, Zeynebe; Kızıltaş, Hatice; Gören, Ahmet C.; Kose, Leyla Polat; Topal, Meryem; Durmaz, Lokman; Alwasel, Saleh H.; Gulcin, İlhami

In order to evaluate the antioxidant activity of evaporated ethanolic extract (EESB) and lyophilized water extract (WESB) of Shaggy bindweed (Convulvulus betonicifolia Mill. Subs), some putative antioxidant methods such as DPPH· scavenging activity, ABTS•+ scavenging effect, ferric ions (Fe3+) reduction method, cupric ions (Cu2+) reducing capacity, and ferrous ions (Fe2+) binding activities were separately performed. Also, ascorbic acid, α-tocopherol and BHT were used as the standard compounds. Additionally, some phenolic compounds that responsible for antioxidant abilities of EESB and WESB were screened by liquid chromatography-high resolution mass spectrometry (LC-HRMS). At the same concentration, EESB and WESB demonstrated effective antioxidant abilities when compared to standards. In addition, EESB demonstrated IC50 values of 1.946 μg/mL against acetylcholinesterase (AChE), 0.815 μg/mL against α-glycosidase and 0.675 μg/mL against α-amylase enzymes.