RESUMO
Background and aim: Mental disorders are among the most common diseases in the world. Not only the lack of social support and health services but also the social stigma surrounding individuals with mental disorders and their family members has exacerbated the difficulty of living with and caring for these patients. The present study aimed to determine the effect of group psychological training on the attitudes of family caregivers taking care of patients with schizophrenia as a common mental disorder. Materials and methods: This interventional study was conducted on 60 caregivers of patients with schizophrenia who had a history of hospitalization in the psychiatric ward of Imam Reza Hospital of Birjand in 2020. The intervention group attended eight 90-minute sessions of psychological training in four weeks. However, the control group received no educational intervention during the study period. the opinions about mental illness scale (OMI) modified by Cohen and Struening was used to collect data before and after the intervention. Data were analyzed using SPSS16 by running descriptive statistics, Chi-square, independent t-test, and paired t-test at the significance level of 0.05. Results: The mean scores of attitudes toward mental illness were not significantly different between the intervention (87.37 ± 15.51) and control (89.48 ± 14.08) groups before the intervention (P > 0.05). However, the mean scores of the intervention group (156.44 ± 10.09) increased significantly compared to the mean scores of the control group (88.4 ± 16.15) followed by the intervention (P < 0.05). Conclusion: According to the study results, psychological group training is effective in improving the attitudes of family caregivers of schizophrenic patients. So, it can be applied method of this study as simple, low-cost, and effective in improving the life quality of schizophrenic patients and their families.
RESUMO
High-content screening (HCS) can generate large multidimensional datasets and when aligned with the appropriate data mining tools, it can yield valuable insights into the mechanism of action of bioactive molecules. However, easy-to-use data mining tools are not widely available, with the result that these datasets are frequently underutilized. Here, we present HC StratoMineR, a web-based tool for high-content data analysis. It is a decision-supportive platform that guides even non-expert users through a high-content data analysis workflow. HC StratoMineR is built by using My Structured Query Language for storage and querying, PHP: Hypertext Preprocessor as the main programming language, and jQuery for additional user interface functionality. R is used for statistical calculations, logic and data visualizations. Furthermore, C++ and graphical processor unit power is diffusely embedded in R by using the rcpp and rpud libraries for operations that are computationally highly intensive. We show that we can use HC StratoMineR for the analysis of multivariate data from a high-content siRNA knock-down screen and a small-molecule screen. It can be used to rapidly filter out undesirable data; to select relevant data; and to perform quality control, data reduction, data exploration, morphological hit picking, and data clustering. Our results demonstrate that HC StratoMineR can be used to functionally categorize HCS hits and, thus, provide valuable information for hit prioritization.
