GA Pearson P. Burton, MJ Phillips and D. Field
Abstract: Conventional measurements of systolic cardiac function and cardiac output may be influenced by loading conditions. The presence of extra-cardiac shunts in the transitional circulation limits the application of such measures in a neonatal population. An accurate method to assess neonatal myocardial function is required that takes account of cardiac loading conditions. One option is to correlate conventional echocardiographic indices with end systolic stress, an index of afterload derived from measurements of the thickness of the ventricular wall and the end systolic blood pressure. We hypothesised that this could be done on-invasively and sought to compare such a method to a gold standard. In this study, end systolic blood pressure was derived from calibrated axillary artery pulse wave traces, made using hand held external pressure transducers. These were compared to pressures simultaneously recorded directly from a catheter in the aortic arch. 195 synchronous measurements were analysed (by assessment of bias and components of variance) in a population of 17 infants (<4 months). The results show a fixed bias of 1.23mmHg and 95% confidence limits of +/-5.56mmHg if five consecutive beats are analysed. These levels of agreement validate the method and demonstrate a degree of accuracy and precision that suggest that the technique may be clinically useful when used in a clinical setting without simultaneous cardiac catheterisation.
Kristan A. Schneider*
Abstract: The number of co-infections with a pathogen (multiplicity of infection or MOI) is a relevant parameter in epidemiology. It relates to transmission intensity and can be built into a metric in the context of disease control and prevention. Here, a maximum-likelihood framework to estimate MOI and the frequency spectrum of pathogen variants is reviewed. The method is applicable to infectious diseases such as malaria, to which it has been successfully applied previously.
Surjya Kumar Saikia*
Abstract: Reports on misuse of statistical methods in biological research are increasing at alarming rate. These reports highlight the irreproducibility of results as the major problem of laboratory experiments in many scientific communications. The basic problem is the lack of aptness and interest of researchers on the appropriate use of statistical methods for data analysis in biological research. Such limitations result improper interpretation and false occurrence of the tested hypothesis. The present article, therefore, focused on some fundamental statistical concepts that biologists often misunderstand and skip, but must cling to while performing exploratory or confirmatory studies.
Pablo Emilio Verd* and Anika Rottmann
Abstract: The comet assay is a simple, rapid and sensitive tool for direct visualization of DNA damage in individual cells. This assay is commonly applied in regulatory genotoxicological studies and environmental monitoring. It is well known that data generated by comet assays are difficult to analyze. Typically the distributions of outcomes are asymmetric and they don't follow standard parametric distributions. To complicate matters, some data may present an excess of zeros and observations are nested within two to three levels in each experimental unit. During the last years a series of innovative statistical models have been used to analyze comet assay data. In this review we bring together these models by highlighting their advantages and disadvantages.
Ao Yuan, Xi Zhang and Gang Han*
Abstract: Incorporating both Bayesian and frequentist methods in the same model the hybrid Bayesian statistical inference possesses some advantages over either of the methods used individually. It has been increasingly applied to biomedical research including genomics, viral load modeling, biomedical engineering, and survey. In this article we briefly describe the theoretical framework of hybrid Bayesian inference and review its applications. We also discuss the potential future research topics in this area.