Aims: Hyperechoic/echo-rich periportal cuffing (ErPC) is defined as an increase in echogenicity relative to the adjacentliver parenchyma. Thickening in the periportal area may occur with proliferation of bile ducts, hemorrhage, oedema, fibrosis,inflammatory changes or a combination of these. The aim of this study is to determine which intraabdominal inflammatory diseases are associated with the presence of ErPC in the pediatric population and to calculate the sensitivity and specificity of this finding.Material and methods: In this prospective study 200 consecutive children who underwent abdominal ultrasonography (US) were included: group 1, the patient group (100 children with appendicitis, gastroenteritis, mesenteric lymphadenitis, intestinal infection, terminal ileitis and invagination as cause of intra-abdominal inflammation) and group 2, the control group (100 children).Results: The ErPC was positive in 74 (74%) cases in the patient group and in 3 (3%) in the control group. According to final diagnoses, we found ErPC in most of patients with gastroenteritis (16/17), perforated appendicitis (10/11), mesenteric lymphadenitis (5/6) and acute appendicitis (27/37). The sensitivity of ErPC in indicating intra-abdominal inflammation was 0.80 and its specificity was 0.87. No significant correlation between ErPC and age, gender and CRP was found but a moderate and significant positive correlation between ErPC and WBC (p=0,010; r=0.255) was detected. Very good concordance between observers in terms of the presence of ErPC on abdominal US was found (concordance 97% and kappa 0.93).Conclusions: We consider that the presence of ErPC in pediatric patients, when evaluated alongside clinical and laboratory findings, has a high sensitivity and specificity for inflammatory intra-abdominal pathology.

Parental stress is a critical problem because it affects both the mental health of the mother and children's development. In addition to many factors related to birth and marriage, mother - infant bonding and psychological inflexibility are essential factors that can affect stress. In this study, we examined the effects of the psychological processes of mothers and factors related to pregnancy, mother, environment on parental stress, and their relationships. A sociodemographic variables scale, Acceptance and Action Questionnaire-II, Postpartum Bonding Questionnaire, and Parental Stress Scale were completed by 115 mothers in their first postpartum year. The model created with the correlation and regression results was subjected to path analysis. Breastfeeding, psychological inflexibility, and mother - infant bonding are related to parental stress. The bonding problem is the mediator of the relationship between psychological inflexibility and parental stress. The entire effect of sleep quality on parental stress occurred through psychological inflexibility. Efforts should be directed towards improving the bond between the mother and infant and enhancing the mother's psychological flexibility to lessen the negative impacts of stress. Breastfeeding should not be treated categorically, and its potential adverse effects should not be ignored.

Objective: The aim of this study is to examine the effectiveness of linear mixed-effects (LME) model, one of the traditional models used in the classification of clustered data, and mixed-effects machine learning models, which are the latest approaches. Material and Methods: For the simulation, various data sets were created with different number of groups (250, 500, 1000) and different sample sizes (5000, 10000, 15000). Within the scope of the simulation, LME model, mixed-effects random forest (MERF) and Gaussian process boosting (GPBoost) models were compared in terms of root mean square error (RMSE) on two functions. Results: When the error variance (EV) is 4 for the linear function, sample size is small and the number of groups is high, RMSE of MERF model is smaller. In all other scenarios, RMSE of the linear model was smaller and. In cases where EV for the nonlinear function is 1, the sample size is small and the number of groups is high, RMSE of MERF is smaller. In all other scenarios (while EV was 1), RMSE of GPBoost model was small, p0.05 for MERF. In cases where EV is 4 for the nonlinear function and the sample size and groups is high, RMSE of MERF is smaller. In all other scenarios (while EV was 4), RMSE of GPBoost model was smaller. Conclusion: As a conclusion, for a nonlinear function, GPBoost performed better than MERF and LME methods in terms of RMSE and time. However, when a linear function is considered, LME gives a better result.

Objectives: Life satisfaction is an essential indicator of quality of life, and enhancing it can contribute to individual well‐being strategies. Because it is a complex concept, a comprehensive approach is needed to address it effectively. Machine learning offers a unique statistical opportunity to address this challenge effectively. In this study, we examined how lifestyle parameters, psychological issues, and psychological processes predict life satisfaction. Methods: The study included 1366 participants, representing the general population. Lifestyle factors were self‐reported, and included exercise frequency, alcohol consumption, smoking, body mass index, and regularity of social rhythms. The participants also completed several assessment scales, such as the Life Satisfaction Scale, the Hospital Anxiety and Depression Scale, the Acceptance and Action Questionnaire–II, the Tuckman Procrastination Scale, the Big Three Perfectionism Scale–Short Form, and the Brief Social Rhythm Scale. Machine‐learning methods were used to evaluate the statistical parameters, with root mean square error values of 3.9, 3.6, and 3.7 for gradient boosting, extreme gradient boosting, and light gradient‐boosting machine, respectively. Results: The top five factors influencing life satisfaction were identified as depression scores, psychological inflexibility, marital status, social rhythm, and procrastination. Psychological inflexibility influences the impact of depression on life satisfaction. Factors that are difficult or impossible to change, such as age, gender, education, and chronic disease, ranked lower on the list. By contrast, psychological and environmental factors that can be improved had strong predictive power. Conclusions: These findings offer opportunities for enhancing life satisfaction and underscore the responsibility to address these factors.

Amaç: Kronik ruhsal rahatsızlığa sahip bireylere sağlanan hizmetler hastaların katılımı düşük olduğunda tedavinin istenilen düzeyde etki etmemesiyle sonuçlanabildiğinden önem verilmesi gereken bir durumdur. Bu araştırmanın amacı, hastaların toplum ruh sağlığı hizmetlerine katılımı ölçmek için Vaka Yöneticileri için Toplum Ruh Sağlığı Hizmetlerine Uyum Ölçeği'nin (VİTU) geçerlilik ve güvenilirliğini değerlendirmektir. Yöntem: Araştırma, Toplum Ruh Sağlığı Merkezi'nde en az bir yıl süreyle izlenen şizofreni tanısı almış 119 hasta örneklemiyle tamamlanmıştır. Bulgular: Toplanan verilere doğrulayıcı faktör analizi yapılmıştır. Güvenirlik ölçümleri iç tutarlılık ve test-tekrar test ölçümleri ile yapılmıştır. Ölçeğin DFA sonuçlarına göre RMSEA;0.081, GFI; 0.872, AGFI;0.809, RMR;0.035, NFI;0.926, CFI;0.966 değerlerine sahip olduğu, Ölçeğin toplam Cronbach's Alpha değeri 0.957, genel test – tekrar test korelasyon değeri(r) 0.684’dir. Sonuç: Bulgular, Vaka Yöneticileri için Toplum Ruh Sağlığı Hizmetlerine Uyum Ölçeği’nin geçerli ve güvenilir bir ölçme aracı olduğunu göstermektedir.