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.

Examining the workload and working conditions of healthcare workers is crucial regarding the quality of the work life of employees and the quality of services provided. This study aims to determine the effects of the perceptions of workload on the quality of work life of health personnel working in two different tertiary hospitals affiliated with the Ministry of Health and the Higher Education Institution in Türkiye with structural equation modeling. This study was conducted in two hospitals in Malatya province: İnönü University Turgut Özal Medical Center affiliated with the Higher Education Institution and Malatya Training and Research Hospital affiliated with the Ministry of Health. The mean score of the Individual Workload Perception Scale was 106.18 ± 16.5, and the mean score of the Work-related Quality of Life Scale was 66.39 ± 15.33. Total workload scores had a statistically significant positive effect on total quality of work life scores (β1 = 0.98; p = 0.001). It was concluded that there was a statistically significant relationship between workload and quality of work life and age, unit of employment, working time in the profession, and hospital type.

Objectives: In this study, we aimed to examine the indirect and direct effects of the stress experienced by health workers after an earthquake on their psychological resilience. Methods: This study was conducted using the Structural Equation Model on 315 employees serving in hospitals in Kahramanmaraş, Gaziantep, Şanlıurfa, Diyarbakır, Adana, Adıyaman, Osmaniye, Hatay, Kilis, Malatya, and Elazığ (11 provinces) that were most affected by the earthquake. Results: The study group consisted of 58.4% (n = 184) women and 41.6% (n = 131) men. The mean score on the Psychological Resilience Scale was 71.74, the mean score on the Coping with Stress Scale was 87.17, the mean score on the Traumatic Stress Symptom Scale was 44.04, and the mean score on the Disability Scale was 3.75. Psychological resilience had a statistically significant negative effect on traumatic stress symptoms (β₁ = −0.26; p = 0.001 < 0.05). There was a statistically significant positive relationship between coping with stress and psychological resilience (β₁ = 0.52; p = 0.001 < 0.05). Conclusions: It was concluded that an increase in an individual’s psychological resilience will increase the level of coping with stress, and the traumatic stress symptoms of individuals with an increased level of coping with stress will decrease.

Background: The objective was to adapt the Fear of Missing Out Scale (FoMOs) Scale developed by Przybylski et al in 2013 to Turkish and to perform the validity and reliability study of this scale. Methods: This study was designed as a methodological study, and the adapted questionnaire was completed by 1142 dental students educating in 59 universities in Turkey. The answers given were evaluated according to the original scale and its validity and reliability were tested. Explanatory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed to assess the construct validity of the form. The reliability of FoMOs was tested through the Cronbach’s α internal consistency coefficient, total item correlation, and test-retest analysis. Results: Different from the original FOMO obtained in the study, it consisted of 2 sub-dimensions and a total of 10 items. Item 10 was excluded owing to the low factor loads. Accordingly, the "10-item scale" with two sub-dimensions was obtained. The 2 sub-dimension scale structure found as a result of EFA was supported by the result obtained in DFA. The Cronbach alpha reliability coefficient was found to be 0.840 for the FoMOs total score, 0.919 for the "trait" sub-dimension, and 0.718 for the "state" sub-dimension. Conclusion: This scale was made to define and analyze the concept of FoMO in dentistry postgraduate career planning. The current scale is a valuable and reliable tool to guide the idea behind career planning and provides a great contribution to the literature.

Objective: The purpose of our study is to show how the relationship between marital adjustment and satisfaction will change in cases of chronic disease in either or both spouses of married couples using a multivariate statistical analysis method. Methods: Marriage adjustment ve marriage satisfaction scales were used. A structural equation modeling - multiple group analysis method was used in the study, which was designed as a relational screening model. Results: In the study, which included 898 participants, 56.6% of the participants were female and 43.4% were male. The mean age of the participants was 36.94 ± 8.72 standard deviations. First, the relationship between marital adjustment and satisfaction was analyzed using structural equation modeling, and the relationship between the scales was found to be statistically significant (p=0.001). In the model, which was significant and sufficient, the variable of chronic disease was coded on the arrow representing the regression coefficient between the scales, and multiple group analysis was applied. The relationship between marital adjustment and satisfaction was found to be weak among individuals with chronic diseases. The rate of marriage satisfaction explaining marriage adjustment was lower in individuals without chronic disease (R2=0.16) than in those without chronic disease (R2=0.10). While ego scores were not significant in individuals without chronic disease (p=0.237), they were statistically significant in individuals with chronic disease (p=0.017). Conclusion: Chronic diseases has been found to have a significant impact on the relationship between spouses. Many studies have examined the effects of chronic diseases on marriage. However, our study differs from other studies because of the analytical methods used. In the scales, it was determined whether the chronic disease showed a change in the relationship between the scales, not the scores in the chronic disease state.

Aim: In this study, the effects of the addiction level of smartphone use in university students on the range of motion and proprioception of the dominant hand, wrist range of motion (ROM), and thumb ROM were investigated. Material and Methods: Wrist and thumb ROMs were measured with a manual goniometer with a sensitivity of 1 degree, and hand grip strength was measured with a hand grip dynamometer in a total of 100 volunteer university students with a mean age of 18-25. Smartphone Addiction Scale-Short Form (SAS-SF) and Patient Rated Wrist and Hand Evaluation (PRWHE) questionnaire were applied to the students. Statistical analyzes were made using the SPSS 25 program. Results: The students’ usage time of smartphones and addiction levels do not affect the hand grip strength; the increase in smartphone addiction statistically correlates with the right thumb flexion (0.016) and abduction ROMs (0.015), statistically correlated with increased pain level in daily life and statistically correlated with the decrease of the wrist radial deviation ROM (0.009). As the duration of smartphone use increases, the error rate in right thumb abduction proprioception statistically increases (0.027). In addition, we determined the statistically correlations in both thumb flexion and abduction movements. Conclusion: We determined that the excessive usage of smartphones affects the ROM of thumb flexion and abduction, the ROM of wrist radial deviation, and the proprioception of thumb abduction, and it does not affect the hand grip strength. The findings of our research will be a source for future studies.