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Background . Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method . The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The aim of this research was to conduct a risk assessment and management of psychological, structural, social and economic determinants (PSSED) in a suicide attempt. The sample consisted of 353 individuals who had a recorded history of suicidal attempt; and 20 professional individuals by purposive sampling method within a descriptive cross-sectional design. Worksheets for RAM and AHP were used for data collection in this study. The rate of suicide attempt was 7.21 per 100,000 population in this study. Analysis showed that depression and mental disorders; personality disorders; family problems; socio-cultural and economic problems; lack of awareness; and low level of education have a high level of risk for suicide attempts. Psychiatric and psychological services; awareness and knowledge of life skills; medical services to dysfunctional families; development of community-based planning for PSSED of suicide; and employment and entrepreneurship services may lower suicide attempt risk.

Introduction: Breast cancer is the most common malignancy in the world. Screening is the basis for early detection. However, the mortality rate is still high in Iranian women related to not screening and timely check-ups. We offered a theory-based intervention program to improve breast cancer screening behavior in women., Methods: This interventional study was conducted in 135 employed women in 2019. Their screening behavior was investigated using a questionnaire based on the Protection Motivation and Social Support Theories. We compared the efficacy of 2 educational interventions (a workshop and an E-learning program) between 2 intervention groups and a control group. The results were collected 3 months after the interventions had taken place. Data were analyzed in SPSS 23 using descriptive statistics, chi-square, analysis of variance (ANOVA), and the paired sample t -test., Results: We found a significant difference between the mean score of knowledge and the theoretical constructs ( P value < .001) before and after the interventions. Our results also showed that both the intervention methods had a similar effect and that there was a significant difference in the performance of breast self-examinations between the intervention and control groups after the intervention ( P value < .001)., Conclusion: Given the cost-effectiveness and feasibility of implementing an E-learning program, we would recommend that health care planners assist in designing and implementing this effective form of intervention to encourage many more women to perform self-examinations to aid breast cancer screening., Competing Interests: Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

Background: Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory-II scores)., Methods: Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal., Results: Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band ( r = 0.839; P < .0001, root mean square error score of 7.69)., Conclusion: We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.