Cultural perceptions of psychological disturbances : the folklore beliefs of South African Muslim and Hindu community members.

Culture shapes the expression and understanding of psychological disorders and plays a role in the emergence of culture-specific syndromes. In particular, certain cultures endorse beliefs in witchcraft, spells and spirits, which fall under the concept „folklore‟. Folklore beliefs like witchcraft and spirit possession and their assumed impact on the manifestation of psychological disturbances persist today. It thus becomes apparent that these cultural aspects will have an impact on how psychological disturbances are experienced and treated in different communities. Given this, the concept of psychological disturbance needs to be aligned with the culture of the afflicted individual if one is to holistically understand and treat him or her. In addition, considering that many cultures include a belief in the spiritual self, a need to understand its‟ alleged role in psychopathology exists (Ashy, 1999; Eldam, 2001; Smith, 2005). 6 Consequently, if one is to effectively understand diverse communities, an exploration of the impact that spiritual beliefs have on community members‟ perceptions of psychological disturbance is imperative. By focusing on the folklore beliefs of South African Muslim and Hindu community members, this study aims to promote a deeper understanding of the impact that these beliefs have on perceptions of psychological disturbances. Data was collected from four focus group discussions with two Muslim and two Hindu groups, comprising a total of 22 individuals. The interview schedule based on the salient themes from the literature guided the direction of the interview. This also allowed for clarification and exploration of new information. The data was analysed using thematic content analysis after the researcher had „cross-tabulated‟ participant responses. This enabled the researcher to sift through the data in a systematic manner, identifying themes that were indicative of the research questions. Responses to the questions fell into three broad categories: the participants‟ understanding of psychological disturbances, the participants‟ understanding of spiritual illnesses, and the impact of religious and/or cultural beliefs on the participants. Perceptions of psychological disturbances were found to reflect religious and cultural beliefs. A lay understanding of psychological disturbances was also reflected by the participants

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity in the Arabian Gulf. In this study, the in‐hospital mortality amongst patients admitted with ACS to Arabian Gulf hospitals is predicted using a comprehensive modelling framework that combines powerful machine‐learning methods such as support‐vector machine (SVM), Naïve Bayes (NB), artificial neural networks (NN), and decision trees (DT). The performance of the machine‐learning methods is compared with that of the performance of a commonly used statistical method, namely, logistic regression (LR). The study follows the current practise of computing mortality risk using risk scores such as the Global Registry of Acute Coronary Events (GRACE) score, which has not been validated for Arabian Gulf patients. Cardiac registry data of 7,000 patients from 65 hospitals located in Arabian Gulf countries are used for the study. This study is unique as it uses a contemporary data analytics framework. A k‐fold (k = 10) crossvalidation is utilized to generate training and validation samples from the GRACE dataset. The machine‐learning‐based predictive models often incur prejudgments for imbalanced training data patterns. To mitigate the data imbalance due to scarce observations for in‐hospital mortalities, we have utilized specialized methods such as random undersampling (RUS) and synthetic minority over sampling technique (SMOTE). A detailed simulation experimentation is carried out to build models with each of the five predictive methods (LR, NN, NB, SVM, and DT) for the each of the three datasets k‐fold subsamples generated. The predictive models are developed under three schemes of the k‐fold samples that include no data imbalance, RUS, and SMOTE. We have implemented an information fusion method rooted in computing weighted impact scores obtain for an individual medical history attributes from each of the predictive models simulated for a collective recommendation based on an impact score specific to a predictor. Finally, we grouped the predictors using fuzzy c‐mean clustering method into three categories, high‐, medium‐, and low‐risk factors for in‐hospital mortality due to ACS. Our study revealed that patients with medical history related to the presences of peripheral artery disease, congestive heart failure, cardiovascular transient ischemic attack valvular disease, and coronary artery bypass grafting amongst others have the most risk for in‐hospita

A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines

To understand the structure-activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17 beta-hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron-conformational matrix of contiguity (ECMC). A chemical property-based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D-QSAR (Rtraining2=0.856,Rtest2=0.851 and q(2) = 0.650) with good external set prediction was obtained