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An individual who has the ability to speak from birth continues the development of this skill in education and training environments after the family. In this regard, teachers have great responsibilities for the speaking skills to be acquired in educational environments. Teachers are expected to know about the development of children's speaking skills in order to transfer these skills to students. In addition, having a high level of knowledge about speaking skills is an indispensable element of effective teaching. Elementary school teachers, who spend the most time with students in the classroom, have a key role in helping students develop their speaking skills and easily transfer what they have learned to daily life skills. The study aims to determine the knowledge levels of elementary school teachers regarding speaking skills. The study was carried out with the survey model, one of the quantitative research methods. The study group of the research consists of 302 elementary school teachers. The data were collected with the "Knowledge Level Test on Speaking Skills" and analyzed with descriptive statistics, Independent Samples t-Test, One-way ANOVA. According to the results of the research, the arithmetic average of the elementary school teachers' knowledge of speaking skills was found to be 13.74 out of 18 total questions. In addition, while elementary school teachers' knowledge of speaking skills did not differ significantly according to the variables of gender, graduation, and location of the school, it showed a significant difference according to the professional experience variable.

With the urgent shift to distance learning due to Covid-19 measures, educational institutions around the world have started to adopt e-learning massively. According to many experts, artificial intelligence (AI) may provide both system-wide and pedagogical solutions to the problems which the administrators, educators, and students encounter during e-learning. The purpose of this study was to systematically review articles on AI applications in open and distance education between the years 2007 and 2021 in order to visualize the potential of intelligent educational systems. Thus, 171 selected articles regarding explicit inclusion and exclusion criteria, were analysed in terms of the year/ place of the paper, affiliation of the first author, the method applied, and the journals they published in. Then the research areas of AI applications in distance education were coded iteratively. The results indicated that most of the articles were published after 2018 mainly by Chinese, Spanish, Turkish, American, and Indian authors in computer science and STEM disciplines. Moreover, there is a sharp increase in publishing on this topic, especially during Covid-19 pandemics. Consequently, the analysis resulted in six areas of AI applications in open and distance education: 1. ITSs, 2. Adaptive systems and personalisation, 3. assessment and evaluation, 4. learner analytics, 5. affect recognition, and 6. Virtual learning environments, with 28 subcategories. In summary, our review results that intelligent systems can be effectively used for both system-wide solutions and pedagogical solutions to the problems such as the low level of adaptivity, collaboration, academic achievement, or engagement, and the high level of dropout that we usually experience in distance learning via the use of adaptive expert systems, intelligent tutors or agents, chatbots, data analytics, and recommender systems.

The aim of this study was to determine the attitudes of teachers towards using digital games in social studies courses. The sample of the study was consisted of social studies teachers and primary school teachers who attend social studies course in secondary schools affiliated to Van District National Education Directorate. Total 192 teachers voluntarily participated in the study. In the study, the Digital Games Utilization Scale (DGUS) was used to collect quantitative data. The data was analyzed using the SPSS 21.0 package program. For the analysis of quantitative data, arithmetic mean, standard deviation, independent sample t-test and One Way ANOVA tests were performed. As a result of the research and based on the findings obtained by using DGUS, it has been determined that there is no significant difference in terms of subdimensions of DGUS (attitudes related to the use of digital games in the courses, attitudes related to the negative aspects of digital games, attitudes related to the positive aspects of digital games, attitudes related to the impact of digital games on learning in the classroom on the part of students) and variables such as teachers' age groups, professional seniority, educational status and gender.

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 need for psychosocial intervention programmes to address the negative consequences of the COVID-19 pandemic and its associated lockdown restrictions on the mental health of young people is evident. Using a within subject pretest-posttest design, this study aimed to evaluate the effects of online Cognitive Behavioural Therapy (CBT)-based anxiety and depression management psychoeducation programmes on mental health and coping skills in youth ages 14-20. The Demographic Information Form, Revised Child Anxiety Depression Scale, and KidCope were administered before and after the psychoeducational programme to assess programme outcomes. The results indicate significant decreases in levels of anxiety, significant decreases in avoidance of anxiety-provoking situations and significant increases in coping skills following the online CBT Anxiety Management Psychoeducation Program. A significant decrease in depression scores was noted among the online CBT Depression Management Psychoeducation Program participants. Although these results should be interpreted cautiously due to limitations of the study (e.g., no control group, high attrition), they suggest that psychological prevention or intervention programmes may be beneficial for young people who are physically unable to go to school or who cannot interact face-to-face with social support networks.

Cortical thickness (CT) and brain-derived neurotrophic factor (BDNF) were widely investigated in bipolar disorder (BD). Previous studies focused on the association between the volume of subcortical regions and neurotrophic factor levels.

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.

Objective: The lack of self-rating multidimensional questionnaires to assess obsessive-compulsive disorder (OCD) in children and adolescents poses a problem for monitoring clinical practices and implementing academic research. This study aimed to empirically examine the psychometric properties of the OCI-CV in a Turkish clinical sample of children and adolescents diagnosed with OCD alongside a control group for comparative analysis. Method: The OCI-CV was administered alongside other measures to a clinical sample of 232 participants aged 8-18 years (mean±SD=13.35±2.68; female/male: 46.1%/53.9%) and a control group. Results: According to the results of the item analysis, corrected item-total correlation coefficients were found to be between 0.36 and 0.62. Confirmatory factor analysis confirmed the original six-factor model with acceptable fit indices (Minimum Discrepancy per Degree of Freedom (CMIN/df)=1.734, Root Mean Square Error of Approximation (RMSEA)=0.056, Comparative Fit Index (CFI)=0.919, Standardized Root Mean Square Residual (SRMR)=0.067, Incremental Fit Index (IFI)=0.921, Normed Fit Index (NFI)=0.835, Root Mean Residual (RMR)=0.034). The standardized factor loadings of the scale items varied between 0.39 and 0.90. According to the Pearson correlation results, a significant positive correlation (p<0.001) was found within the scope of the concurrent validity of the OCI-CV. The Cronbach a coefficient of the six-dimensional 21-item scale was found to be 0.88, and that of the sub-dimensions of the scale ranged between 0.63 and 0.81. Conclusion: The present study demonstrated the psychometric properties of the Child Version of the Obsessive-Compulsive Inventory (OCI-CV), and the results showed that the measure is valid and reliable for use in a clinical sample of Turkish children and adolescents. [ABSTRACT FROM AUTHOR]

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 aim of this study was to investigate the prevalence of and the factors associated with psychiatric symptoms (PS) and challenging behaviour (CB) in adults with intellectual disabilities, and the utilization of psychiatric services in Turkey. Method: Psychiatric Assessment Schedule for Adults with Developmental Disorders Checklist-Revised was used for PS and a structured form for other variables in 771 participants. Results: Of the participants, 50.1% had PS and 36.4% presented with CB. Multivariate analysis revealed that a higher level of needs, better verbal ability, residential living, incontinence and CB, and lifetime suicidal ideation/attempt were independently associated with PS. For CB, it emerged as male carer, PS, lifetime suicidal attempt/ideation, lower level of verbal ability and autism spectrum disorder. Barriers were experienced by 64.7% of participants within the previous year. Conclusions: Psychiatric symptoms and CB seem to be problems for a significant proportion of adults with intellectual disabilities in Turkey, and there are certain barriers to psychiatric services.

The demand of plant production product use has increased because of the current system of citrus production, which prioritizes high agricultural yields. Therefore, the monitoring of pesticide residues in citrus fruits and other agricultural products and their impacts on human health and food security are of great concern. This study aims to determine multi-class pesticides including highly polar residues in satsuma mandarins. A total of 226 mandarin samples were collected over three consecutive harvesting years from 2019 to 2021 in the Izmir region of Turkey. Targeted compounds included pesticides and metabolites with European Union (EU) regulatory levels, plus other non-approved residues and highly polar compounds. The residues excluding highly polar substances were analyzed by applying the quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction and liquid chromatography–triple quadrupole mass spectrometry (LC-MS/MS) determination for 434 analytes and gas chromatography–triple quadrupole mass spectrometry (GC-MS/MS) determination for 71 analytes. For six highly polar pesticides, sample preparation was based on Quick Polar Pesticides (QuPPe) extraction. The polar residues were determined by LC-MS/MS using internal standards. Forty different residues, including two highly polar substances, were recorded in mandarin samples through three harvesting years. In 8.4% of the samples, no quantifiable residues were detected, whereas 207 samples contained at least one residue. The maximum residue level (MRL) exceedances were recorded for 22.1% of the samples. The two most frequently found pesticides were phosphonic acid and spirotetramat, with an incidence rate of 48.7% and 46.5%, respectively. The concentration of phosphonic acid and spirotetramat in mandarin samples varied from 0.026 to 39.386 mg kg−1 and from 0.010 to 1.485 mg kg−1, respectively. The results will enable researchers and regulatory authorities to assess the extent of pesticide presence, identify potential risks, and take necessary measures to ensure the safety of satsuma mandarins for consumers.

IntroductionObstructive sleep apnea (OSA) severity is based on the apnea-hypopnea index (AHI). The AHI is a simplistic measure that is inadequate for capturing disease severity and its consequences in cardiovascular diseases (CVDs). Deleterious effects of OSA have been suggested to influence the prognosis of specific endotypes of patients with acute coronary syndrome (ACS). We aim to identify respiratory polygraphy (RP) patterns that contribute to identifying the risk of recurrent cardiovascular events in patients with ACS.MethodsPost hoc analysis of the ISAACC study, including 723 patients admitted for a first ACS (NCT01335087) in which RP was performed. To identify specific RP patterns, a principal component analysis (PCA) was performed using six RP parameters: AHI, oxygen desaturation index, mean and minimum oxygen saturation (SaO2), average duration of events and percentage of time with SaO2 < 90%. An independent HypnoLaus population-based cohort was used to validate the RP components.ResultsFrom the ISAACC study, PCA showed that two RP components accounted for 70% of the variance in the RP data. These components were validated in the HypnoLaus cohort, with two similar RP components that explained 71.3% of the variance in the RP data. The first component (component 1) was mainly characterized by low mean SaO2 and obstructive respiratory events with severe desaturation, and the second component (component 2) was characterized by high mean SaO2 and long-duration obstructive respiratory events without severe desaturation. In the ISAACC cohort, component 2 was associated with an increased risk of recurrent cardiovascular events in the third tertile with an adjusted hazard ratio (95% CI) of 2.44 (1.07 to 5.56; p-value = 0.03) compared to first tertile. For component 1, no significant association was found for the risk of recurrent cardiovascular events.ConclusionA RP component, mainly characterized by intermittent hypoxemia, is associated with a high risk of recurrent cardiovascular events in patients without previous CVD who have suffered a first ACS.

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.

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. [ABSTRACT FROM AUTHOR]

Ectopic decidua (deciduosis) is most commonly localized in the ovary, uterus, cervix, and tuba uterina. It can rarely be observed within the peritoneum in pregnant women during laparotomy. More rarely, it can be localized in the omentum, appendix, liver, and spleen. It is usually incidental. In a 23-year-old female patient, a biopsy was taken from the thickening in a 4x3 cm area on the omentum during cesarean section. Microscopic evaluation revealed decidualized cells the majority of which had large polygonal eosinophilic cytoplasm and a few of which had vacuolated cytoplasm, that formed small nodules in the adipose tissue. Immunohistochemically, the decidualized cells were positive for vimentin, progesterone receptor antibody and negative for S-100, HMB-45, calretinin, pancytokeratin. The case was reported as ectopic omental deciduosis. Although ectopic omental deciduosis is a benign lesion, it may be confused with malignant tumors. Therefore, differential diagnosis should be made carefully. [Med-Science 2020; 9(4.000): 1093-6]

Bladder pain syndrome/interstitial cystitis (BPS/IC) is a debilitating pain syndrome of unknown etiology that predominantly affects females. Clinically, BPS/IC presents in a wide spectrum where all patients report severe bladder pain together with one or more urinary tract symptoms. On bladder examination, some have normal-appearing bladders on cystoscopy, whereas others may have severely inflamed bladder walls with easily bleeding areas (glomerulations) and ulcerations (Hunner’s lesion). Thus, the reported prevalence of BPS/IC is also highly variable, between 0.06% and 30%. Nevertheless, it is rightly defined as a rare disease (ORPHA:37202). The aetiopathogenesis of BPS/IC remains largely unknown. Current treatment is mainly symptomatic and palliative, which certainly adds to the suffering of patients. BPS/IC is known to have a genetic component. However, the genes responsible are not defined yet. In addition to traditional genetic approaches, novel research methodologies involving bioinformatics are evaluated to elucidate the genetic basis of BPS/IC. This article aims to review the current evidence on the genetic basis of BPS/IC to determine the most promising targets for possible novel treatments.

Cherries are popular fruits due to their health benefits, organoleptic quality, and attractive appearance. Since highly polar pesticides are of low mass and amphoteric character, and are not amenable to traditional multi-residue extraction methods, they are more commonly not included in the pesticide monitoring program. This study aims to determine twelve highly polar pesticide residues in cherry samples intended for export from Turkey. A total of 16,022 cherry samples from 2018–2020 harvests in four production areas of Turkey were analyzed using a modification of the Quick Polar Pesticides method and liquid chromatography-tandem mass spectrometry. The method was validated at two fortification levels (0.01 and 0.05 mg kg−1), and good recoveries (87.4–111.4%) and relative standard deviations (−1. Overall, 28.4% of the analyzed cherry samples were detected with phosphonic acid, calculated as fosetyl aluminium (fosetyl-Al) in amounts up to 77.7 mg kg−1. For 2304 samples (14.4%), the residues exceeded the European Union maximum residue level of 2 mg kg−1. There is no reason to be concerned about long-term exposure to phosphonic acid/fosetyl-Al, and the other highly polar pesticides through the consumption of sweet cherry.

Meeting the needs of the whole child is the goal of community schools, which partner with other agencies to offer a range of services and opportunities. In this article the authors discuss community schools as a full-spectrum resource for families and children, and give an example of how community schools are able to provide for the needs of students, minimizing the impact on instructional time. They also discuss how the community schools approach is gaining renewed consideration as key to addressing the complexity of the achievement gap.