Objective: Religious coping is known as one of the successful manners to cure depressed infertile women; however, research findings show that demographic factors (e.g., education level) have played an important role on the relationship between depression and religious coping scores. The goal of this study is to measure the influence of age, job status, and education level on both scores within Iranian infertile women. Method: In this cross sectional study, 1000 women (mean age, 35.96; range, 26-45), who are recruited from different hospitals of Shiraz (Iran), are selected via multistage cluster sampling method. The reliability and validity of the translated versions of the questionnaires have been confirmed. The correlation coefficient (Spearman method), adjusted linear regression coefficient, and ordinal regression coefficient of demographic features with the depression scores/levels (minimal, mild, moderate, and severe) and religious coping scores are determined. Results: A significant negative correlation is found between depression and religious compatibility scores in 1000 infertile women (ρ = -0.318, P = 0.000). In addition, the results have implied the existence of a significant correlation and linear relationship between religious coping and age and job status (P < 0.05). Furthermore, both correlation and ordinal regression of depression intensity with both job status and education level are found to be statistically meaningful (P < 0.05). Conclusion: The negative correlation between religious coping and depression scores has implied the positive role of religious coping in protecting infertile women from depression, especially among employed women. Nevertheless, the correlation of religious coping with education level is not strong enough due to the nonuniform distribution of variables through their range.

No abstract

Background: Psychiatrists diagnose schizophrenia based on clinical symptoms such as disordered thinking, delusions, hallucinations, and severe distortion of daily functions. However, some of these symptoms are common with other mental illnesses such as bipolar mood disorder. Therefore, quantitative assessment of schizophrenia by analyzing a physiological-based data such as the electroencephalogram (EEG) signal is of interest. In this study, we analyze the spectrum and time-frequency distribution (TFD) of EEG signals to understand how schizophrenia affects these signals. Methods: In this regard, EEG signals of 20 patients with schizophrenia and 20 age-matched participants (control group) were investigated. Several features including spectral flux, spectral flatness, spectral entropy, time-frequency (TF)-flux, TF-flatness, and TF-entropy were extracted from the EEG signals. Results: Spectral flux (1.5388±0.0038 and 1.5497±0.0058 for the control and case groups, respectively, P=0.0000), spectral entropy (0.8526±0.0386 and 0.9018±0.0428 for the control and case groups, respectively, P=0.0004), spectral roll-off (0.3896±0.0434 and 0.4245±0.0410 for the control and case groups, respectively, P=0.0129), spectral flatness (0.1401±0.0063 and 0.1467±0.0077 for the control and case groups, respectively, P=0.0055), TF-flux (1.2675±0.1806 and 1.5284±0.2057 for the control and case groups, respectively, P=0.0001) and TF-flatness (0.9980±0.0000 and 0.9981±0.0000 for the control and case groups, respectively, P=0.0000) values in patients with schizophrenia were significantly greater than the control group in most EEG channels. This prominent irregularity may be caused by decreasing the synchronization of neurons in the frontal lobe. Conclusion: Spectral and time frequency distribution analysis of EEG signals can be used as quantitative indexes for neurodynamic investigation in schizophrenia.

Specialists mostly assess the skeletal maturity of short-height children by observing their left hand X-Ray image (radiograph), whereas precise separation of areas capturing the bones and growing plates is always not possible by visual inspection. Although a few attempts are made to estimate a suitable threshold for segmenting digitized radiograph images, their results are not still promising. To finely estimate segmentation thresholds, this paper presents the quantumized genetic algorithm (QGA) that is the integration of quantum representation scheme in the basic genetic algorithm (GA). This hybridization between quantum inspired computing and GA has led to an efficient hybrid framework that achieves better balance between the exploration and the exploitation capabilities. To assess the performance of the proposed quantitative bone maturity assessment framework, we have collected an exclusive dataset including 65 left-hand digitized images, aged from 3 to 13 years. Thresholds are estimated by the proposed method and the results are compared to harmony search algorithm (HSA), particle swarm optimization (PSO), quantumized PSO and standard GA. In addition, for more comparison of the proposed method and the other mentioned evolutionary algorithms, ten known benchmarks of complex functions are considered for optimization task. Our results in both segmentation and optimization tasks show that QGA and GA provide the best optimization results in comparison with the other mentioned algorithms. Moreover, the empirical results demonstrate that QGA is able to provide better diversity than that of GA.

Background: The precise differentiation of schizophrenic patients with positive and negative symptoms is still challenging; hence, psychiatrists mainly focus on diagnosing schizophrenic patients with positive symptoms. However, schizophrenic patients with negative symptoms have revealed remarkably poor outcomes. Objectives: This study aimed to differentiate schizophrenic patients with positive and negative dominant symptoms quantitatively by classifying their electroencephalography (EEG) features. Methods: In this study, 36 patients with schizophrenia and 26 age-matched control subjects voluntarily participated. Their EEG signals were captured and characterized by elicited multiscale entropy to decode the number of irregularities captured in each EEG channel. The principal component analysis (PCA) was deployed to decrease the dimension of elicited features, and the reduced features were applied to three Gaussian Naive Bayes classifiers, each of which was trained for a specific class. Results: The classification of the three groups resulted in 77.86% accuracy, while this accuracy in the schizophrenic groups provided 65% accuracy. In the resting state, the normal and schizophrenic subjects were differentiated by a high rate (95.43%). Conclusions: Exploiting information-theoretic features of the EEG signals over the scalp and automatic classification of these features, we can well-differentiate schizophrenic patients with different dominant symptoms. Moreover, better classification results can be achieved by passing the EEG features through PCA.