Medicinal plants are the major sources of traditional treatment of disease in Indian subcontinent due to abundant presence of plants and vast side effects of synthetic drug. The present study was subjected to observe in vitro thrombolytic, antibacterial, and in vivo antidiarrheal activities of methanol soluble fraction of fruits of Annona muricata. In thrombolytic activity assay, various concentrations (2 ─ 10 mg/ml) of methanol soluble fraction was used and dose dependently less potent activity was found. The maximum clot lysis 18.33% (p* < 0.05) was achieved at 10 mg/ml of methanolic fruit extract, whereas standard drug streptokinase showed 55.50% (p*** < 0.001) clot lysis. In antibacterial assay, disc diffusion method was used comprising two gram positive (S. aureus and Micrococcus luteus) and two gram negative (E. coli and P. aeruginosa) bacteria. None of four (0.25, 0.5, 1, and 5 mg/disc) concentration of fruit extract showed antibacterial potentiality, whereas standard amikacin (3 mg/disc) revealed strong antibacterial activities (=~ 23 ─ 24 mm of MIC). To evaluate antidiarrheal activity, castor oil induced diarrhea was created in Swiss albino mice and different doses (100, 200, and 400 mg/kgbw) of fruit extract was introduced post orally. All of three different doses of fruit extract showed significant (p < 0.05 ─ 0.001) antidiarrheal activities. Notably, the percent inhibition of diarrhea by methanolic extract of fruits of Annona muricata was found to be 58.38% at a dose of 400 mg/kgbw. The effect of vehicle saline (10 ml/kgbw) was considered as control and loperamide (5 mg/kgbw) as standard that provided 67.01% inhibition of diarrhea. The results suggest that, the fruits of Annona muricata possess potent antidiarrheal properties, providing scientific basis of using the plant parts in the treatment of diarrheal disease.

Prior information about a financial market is very essential for investor to invest money on parches share from the stock market which can strengthen the economy. The study examines the relative ability of various models to forecast daily stock indexes future volatility. The forecasting models that employed from simple to relatively complex ARCH-class models. It is found that among linear models of stock indexes volatility, the moving average model ranks first using root mean square error, mean absolute percent error, Theil-U and Linex loss function criteria. We also examine five nonlinear models. These models are ARCH, GARCH, EGARCH, TGARCH and restricted GARCH models. We find that nonlinear models failed to dominate linear models utilizing different error measurement criteria and moving average model appears to be the best. Then we forecast the next two months future stock index price volatility by the best (moving average) model.

Aim To estimate the prevalence of COVID‐19 pandemic and its transmission rates among people in both community and household levels of Bangladesh. Methods We use the cross‐sectional online survey data of 2080 individuals, collected from 442 households during June to September 2020 in Bangladesh. The Longini and Koopman stochastic epidemic modelling approach was adapted for analysing the data. To validate the results, a simulation study was conducted using the Markov Chain Monte Carlo (MCMC) method via the Metropolis‐Hastings algorithm in the context of the Bayesian framework. Results Overall, the prevalence of COVID‐19 pandemic was 15.1% (315 out of 2080) among people in Bangladesh. This proportion was higher in smaller households (size one: 40.0%, two: 35.7% and three: 25.9%) than larger (four: 15.8%, five: 13.3%, six: 14.1%, seven: 12.5% eight: 8.7%, nine: 14.8% and ten or eleven: 5.7%). The transmission rate of COVID‐19 in community people was higher (12.0%, 95% CI: 10.0% to 13.0%) than household members (9.0%, 95% CI: 6.0% to 11.0%). Conclusion The susceptible individuals have a higher risk of community infection than the household and the community transmission is more responsible than the household for COVID‐19 pandemic in Bangladesh.

Background Previous studies have shown a relationship between socio-demographic variables and the mental health of children and adolescents. However, no research has been found on a model-based cluster analysis of socio-demographic characteristics with mental health. This study aimed to identify the cluster of the items representing the socio-demographic characteristics of Australian children and adolescents aged 11–17 years by using latent class analysis (LCA) and examining the associations with their mental health. Methods Children and adolescents aged 11–17 years (n = 3152) were considered from the 2013–2014 Young Minds Matter: The Second Australian Child and Adolescent Survey of Mental Health and Wellbeing. LCA was performed based on relevant socio-demographic factors from three levels. Due to the high prevalence of mental and behavioral disorders, the generalized linear model with log-link binomial family (log-binomial regression model) was used to examine the associations between identified classes, and the mental and behavioral disorders of children and adolescents. Results This study identified five classes based on various model selection criteria. Classes 1 and 4 presented the vulnerable class carrying the characteristics of “lowest socio-economic status and non-intact family structure” and “good socio-economic status and non-intact family structure” respectively. By contrast, class 5 indicated the most privileged class carrying the characteristics of “highest socio-economic status and intact family structure”. Results from the log-binomial regression model (unadjusted and adjusted models) showed that children and adolescents belonging to classes 1 and 4 were about 1.60 and 1.35 times more prevalent to be suffering from mental and behavioral disorders compared to their class 5 counterparts (95% CI of PR [prevalence ratio]: 1.41–1.82 for class 1; 95% CI of PR [prevalence ratio]: 1.16–1.57 for class 4). Although children and adolescents from class 4 belong to a socio-economically advantaged group and shared the lowest class membership (only 12.7%), the class had a greater prevalence (44.1%) of mental and behavioral disorders than did class 2 (“worst education and occupational attainment and intact family structure”) (35.2%) and class 3 (“average socio-economic status and intact family structure”) (32.9%). Conclusions Among the five latent classes, children and adolescents from classes 1 and 4 have a higher risk of developing mental and behavioral disorders. The findings suggest that health promotion and prevention as well as combating poverty are needed to improve mental health in particular among children and adolescents living in non-intact families and in families with a low socio-economic status.