The presence of a mutation in any sarcomere gene is associated with a number of clinical features. The heterogeneous nature of the disease and the inconsistency of study design precludes the establishment of more precise genotype-phenotype relationships. Large scale studies examining the relation between genotype, disease severity, and prognosis are required.

AFD is associated with a high burden of cardiac morbidity and mortality. Adverse cardiac outcomes are associated with age, global disease severity and advanced cardiac disease but not the presence of cardiac genetic variants.

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BackgroundTo quarantine the spreading possibility of HIV virus to general population boosting public awareness is must. But the proper awareness level is substantially low in Bangladesh. This paper aims to identify the factors associated with the awareness regarding HIV/AIDS through a bivariate and multivariate analysis using the data extracted from Bangladesh Demography and Health Survey (BDHS) 1999–2000.ResultsThe findings of both techniques show that education, occupation, socioeconomic status, status of household food consumption, area of residence and media exposure have significant (p < 0.001) contribution in determining HIV/AIDS awareness level. It also reveals that media, particularly TV, and education play the leading role regarding this issue while the others have an indirect relationship. The odds of awareness among higher educated women and men were 4.69 and 77.73 times of no educated women and men respectively. In addition, both women and men those who regularly watch TV were 8.6 times more likely to be aware about AIDS compared to those who never watch TV. This phenomenon holds true for both women and men.ConclusionAt this instant it is urgent to give emphasis on education, alleviation of poverty, ensuring electronic media exposure, head to head communication program, institutional based sex education and necessary information to learn about HIV/AIDS for the young, adult and adolescents all over the country.

BackgroundWhen developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likelihood due to separation. The problem of separation occurs commonly even if sample size is large but there is sufficient number of strong predictors. In the presence of separation, even if one develops the model, it produces overfitted model with poor predictive performance. Firth-and logF-type penalized regression methods are popular alternative to MLE, particularly for solving separation-problem. Despite the attractive advantages, their use in risk prediction is very limited. This paper evaluated these methods in risk prediction in comparison with MLE and other commonly used penalized methods such as ridge.MethodsThe predictive performance of the methods was evaluated through assessing calibration, discrimination and overall predictive performance using an extensive simulation study. Further an illustration of the methods were provided using a real data example with low prevalence of outcome.ResultsThe MLE showed poor performance in risk prediction in small or sparse data sets. All penalized methods offered some improvements in calibration, discrimination and overall predictive performance. Although the Firth-and logF-type methods showed almost equal amount of improvement, Firth-type penalization produces some bias in the average predicted probability, and the amount of bias is even larger than that produced by MLE. Of the logF(1,1) and logF(2,2) penalization, logF(2,2) provides slight bias in the estimate of regression coefficient of binary predictor and logF(1,1) performed better in all aspects. Similarly, ridge performed well in discrimination and overall predictive performance but it often produces underfitted model and has high rate of convergence failure (even the rate is higher than that for MLE), probably due to the separation problem.ConclusionsThe logF-type penalized method, particularly logF(1,1) could be used in practice when developing risk model for small or sparse data sets.