Evaluation of the T-SPOT(RTM).TB test in the diagnosis of latent tuberculosis infection in HIV-infected children in Uganda

Background. About a third of the world’s population is infected with tuberculosis (TB) with sub-Saharan Africa being the worst hit. Uganda is ranked 16th among the countries with the biggest TB burden. The burden in children however has not been determined. The burden of TB has been worsened by the advent of HIV and TB is the leading cause of mortality in HIV infected individuals. Development of TB disease can be prevented if TB is diagnosed during its latent stage and treated with isoniazid. For over a century, latent TB infection (LTBI) was diagnosed using the Tuberculin Skin Test (TST). New interferon gamma release assays (IGRA) have been approved by FDA for the diagnosis of LTBI and adult studies have shown that IGRAs are superior to the TST but there have been few studies in children especially in areas of high TB and HIV endemicity. ^ Objective. The objective of this study was to examine whether the IGRAs had a role in LTBI diagnosis in HIV infected children in Uganda. ^ Methods. Three hundred and eighty one (381) children were recruited at the Baylor College of Medicine-Bristol Meyers Squibb Children’s Clinical Center of Excellence at Mulago Hospital, Kampala, Uganda between March and August 2010. All the children were subjected to a TST and T-SPOT ®.TB test which was the IGRA chosen for this study. Sputum examination and chest x-rays were also done to rule out active TB. ^ Results. There was no statistically significant difference between the tests. The agreement between the two assays was 95.9% and the kappa statistic was 0.7 (95% CI: 0.55–0.85, p-value<0.05) indicating a substantial or good agreement. The TST was associated with older age and higher weight for age z-scores but the T-SPOT®. TB was not. Both tests were associated with history of taking anti-retroviral therapy (ART). ^ Conclusion. Before promoting use of IGRAs in children living in HIV/TB endemic countries, more research needs to be done. ^

Multivariate methods for correlated effects sizes

Current statistical methods for estimation of parametric effect sizes from a series of experiments are generally restricted to univariate comparisons of standardized mean differences between two treatments. Multivariate methods are presented for the case in which effect size is a vector of standardized multivariate mean differences and the number of treatment groups is two or more. The proposed methods employ a vector of independent sample means for each response variable that leads to a covariance structure which depends only on correlations among the $p$ responses on each subject. Using weighted least squares theory and the assumption that the observations are from normally distributed populations, multivariate hypotheses analogous to common hypotheses used for testing effect sizes were formulated and tested for treatment effects which are correlated through a common control group, through multiple response variables observed on each subject, or both conditions.^ The asymptotic multivariate distribution for correlated effect sizes is obtained by extending univariate methods for estimating effect sizes which are correlated through common control groups. The joint distribution of vectors of effect sizes (from $p$ responses on each subject) from one treatment and one control group and from several treatment groups sharing a common control group are derived. Methods are given for estimation of linear combinations of effect sizes when certain homogeneity conditions are met, and for estimation of vectors of effect sizes and confidence intervals from $p$ responses on each subject. Computational illustrations are provided using data from studies of effects of electric field exposure on small laboratory animals. ^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

Functional analysis of p53 phosphorylation and a p53 hot spot mutation

The tumor suppressor p53 is a phosphoprotein which functions as a transcriptional activator. By monitoring the transcriptional activity, we studied how p53 functions is regulated in relation to cell growth and contact inhibition. When cells were arrested at G1 phase of the cell cycle by contact inhibition, we found that p53 transactivation function was suppressed. When contact inhibition was overridden by cyclin E overexpression which stimulates cell cycle progression, p53 function was restored. This observation led to the development of a cell density assay to study the regulation of p53 function during cell cycle for the functional significance of p53 phosphorylation. The murine p53 is phosphorylated at serines 7, 9, 12, 18, 37, 312 and 389. To understand the role of p53 phosphorylation, we generated p53 constructs encoding serine-to-alanine or serine-to-glutamate mutations at these codons. The transcriptional activity were measured in cells capable of contact inhibition. In low-density cycling cells, no difference in transcriptional activity was found between wild type p53 and any of the mutants. In contact-inhibited cells, however, only mutations of p53 at serine 389 resulted in altered responses to cell cycle arrest and to cyclin E overexpression. The mutant with serine-to-glutamate substitution at codon 389 retained its function in contact inhibited cells. Cyclin E overexpression in these cells induced p53 phosphorylation at serine 389. Furthermore, we showed that phosphorylation at serine 389 regulates p53 DNA binding activity. Our findings implicate that phosphorylation is an important mechanism for p53 activation.^ p53 is the most frequently mutated gene in human tumors. To study the mechanism of p53 inactivation by mutations, we carried out detailed analysis of a murine p53 mutation with an arginine-to-tryptophane substitution at codon 245. The corresponding human p53 mutation at amino acid 248 is the most frequently mutated codon in tumors. We showed that this mutant is inactive in suppressing focus formation, binding to DNA and transactivation. Structural analysis revealed that this mutant assumes the wild type protein conformation. These findings define a novel class of p53 mutations and help to understand structure-function relationship of p53. ^