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Background: Diabetes and hypertension have been identified as risk factors for HCV complications in previous studies. This has sparked the interest in the field of prevention by identifying at-risk individuals and increasing investments for screening among pharmacists. The aim of this study was to screen for risk factors, including age, gender, BMI, hypertension, diabetes, and obesity, in Egyptian patients with HCV. Methods: A prospective cross-sectional study was carried out from September 2018 to February 2019, with a total of 1,959 medical records collected. By comparing the patients’ characteristics, variables related to metabolic risk, and body composition measurements, regression models have been established to determine any confounding factors. Results: The prevalence of HCV antibody was 41.0% in men and 59.0% in women. Among the variables included in the regression analysis, age, BMI, and uncontrolled hypertension were found to have statistically significant associations with diabetes in HCV positive cases (p < 0.001). HCV patients ? 40 years old with high BMI were found to have significant associations with both, diabetes and hypertension (p < 0.001). Hypertensive HCV patients were found to have significant associations with gender, age ? 40, and DM (p < 0.001). Conclusion: HCV infection and metabolic disorders have a closed cycle relationship. Reducing the complications of DM has a promising prospective of limiting the complications of HCV.

AimsWe wished to explore associations between intrinsic religiosity, extrinsic (non-organizational (ENORG) and organizational (EORG)) religiosity and hallucinations phenomenology in a non-clinical Muslim population.MethodsWe selected full-time students at Qatar University using systematic random sampling and administered the Questionnaire of Psychotic Experiences online. We modelled the effects of sociodemographic variables, anxiety, depressive symptoms, and religiosity measures, delusions on hallucinations severity and distress/impact in the past week, using structural equation modelling.ResultsDirect-effects models supported ENORG religiosity's protective role against experiencing distress or negative impact on daily function from hallucinations. Intrinsic religiosity had positive indirect-effects on hallucinations distress/impact through depression, anxiety, and through EORG but negative (suppression) indirect-effects on hallucinations distress/impact through ENORG. Younger and married from lower socio-economic class participants had comparatively more severe hallucinations and more distress from them.ConclusionWe present evidence of differential associations between the religiosity types, socioeconomic and cultural groups, and past week distress/impact of hallucinations.Our data support the importance of alignment between religious education and mental health and well-being education.

Blastocystis sp. is an enteric protozoan that frequently colonizes humans and many animals. Despite impacting on human health, data on the prevalence and subtype (ST) distribution of Blastocystis sp. remain sparse in Africa. Accordingly, we performed the first multicenter and largest epidemiological survey ever conducted on Blastocystis sp. for this continent. A total of 731 stool samples collected from healthy school children living in 10 villages of the northwestern region of Senegal were tested for the presence of Blastocystis sp. by real-time polymerase chain reaction followed by subtyping of positive samples. Considerable variation in prevalence between villages (51.7 to 100%) was evident with the overall prevalence being 80.4%. Mixed infections were identified in 23% of positive individuals. Among 453 school children with a single infection, ST2 was predominant, followed by ST1, ST3, ST7, ST10, and ST14; this is the first report of ST10 and ST14 in humans. Genetic polymorphisms were evident at the intra-ST level with the identification of numerous ST1 to ST3 genotypes. ST1 showed the greatest intra-ST diversity followed by ST2 and ST3. The prevalence and distribution of STs and genotypes varied among target villages, pointing to several potential infection sources, including human-to-human, zoonotic, and waterborne transmission.

The early prediction of loan default in banks is a critical area nowadays. Machine learning techniques are being used to predict and classify the loan applicant as defaulter or not before granting him the loan. Several types of statistical and artificial intelligent classification techniques can be used to predict the loan default of bank's customers. However, ensemble machine learning techniques are introduced to perform better than traditional statistical and AI techniques as it combines several base models to produce one optimal predictive model with better performance and accuracy. A comparative study is applied on different machine learning techniques on customer's loans dataset obtained from a public bank's database that contains customer's loans and personal data. The data is processed and analyzed using Apache Spark, a machine learning tool for big data processing. The results of the proposed system is evaluated by seven performance metrics to compare the performance of each classifier and find out the best performing one among them. It is found out that the ensemble machine learning techniques has better performance than single base classifiers in predicting the loan default.