Blood consists of different cell populations with distinct functions and correspondingly, distinct gene expression profiles. In this study, global miRNA expression profiling was performed across a panel of nine human immune cell subsets (neutrophils, eosinophils, monocytes, B cells, NK cells, CD4 T cells, CD8 T cells, mDCs and pDCs) to identify cell-type specific miRNAs. mRNA expression profiling was performed on the same samples to determine if miRNAs specific to certain cell types down-regulated expression levels of their target genes. Six cell-type specific miRNAs (miR-143; neutrophil specific, miR-125; T cells and neutrophil specific, miR-500; monocyte and pDC specific, miR-150; lymphoid cell specific, miR-652 and miR-223; both myeloid cell specific) were negatively correlated with expression of their predicted target genes. These results were further validated using an independent cohort where similar immune cell subsets were isolated and profiled for both miRNA and mRNA expression. miRNAs which negatively correlated with target gene expression in both cohorts were identified as candidates for miRNA/mRNA regulatory pairs and were used to construct a cell-type specific regulatory network. miRNA/mRNA pairs formed two distinct clusters in the network corresponding to myeloid (nine miRNAs) and lymphoid lineages (two miRNAs). Several myeloid specific miRNAs targeted common genes including ABL2, EIF4A2, EPC1 and INO80D; these common targets were enriched for genes involved in the regulation of gene expression (p<9.0E-7). Those miRNA might therefore have significant further effect on gene expression by repressing the expression of genes involved in transcriptional regulation. The miRNA and mRNA expression profiles reported in this study form a comprehensive transcriptome database of various human blood cells and serve as a valuable resource for elucidating the role of miRNA mediated regulation in the establishment of immune cell identity.

Daniel Maeusezahl and colleagues conducted a cluster-randomized controlled trial in rural Bolivia of solar drinking water disinfection, and find only moderate compliance with the intervention and no evidence of reduction in diarrhea among children.

Many different methods have been proposed for the analysis of cluster randomized trials (CRTs) over the last 30 years. However, the evaluation of methods on overdispersed count data has been based mostly on the comparison of results using empiric data; i.e. when the true model parameters are not known. In this study, we assess via simulation the performance of five methods for the analysis of counts in situations similar to real community-intervention trials. We used the negative binomial distribution to simulate overdispersed counts of CRTs with two study arms, allowing the period of time under observation to vary among individuals. We assessed different sample sizes, degrees of clustering and degrees of cluster-size imbalance. The compared methods are: (i) the two-sample t-test of cluster-level rates, (ii) generalized estimating equations (GEE) with empirical covariance estimators, (iii) GEE with model-based covariance estimators, (iv) generalized linear mixed models (GLMM) and (v) Bayesian hierarchical models (Bayes-HM). Variation in sample size and clustering led to differences between the methods in terms of coverage, significance, power and random-effects estimation. GLMM and Bayes-HM performed better in general with Bayes-HM producing less dispersed results for random-effects estimates although upward biased when clustering was low. GEE showed higher power but anticonservative coverage and elevated type I error rates. Imbalance affected the overall performance of the cluster-level t-test and the GEE's coverage in small samples. Important effects arising from accounting for overdispersion are illustrated through the analysis of a community-intervention trial on Solar Water Disinfection in rural Bolivia.

BackgroundDiarrhoea is the second leading cause of childhood mortality, with an estimated 1.3 million deaths per year. Promotion of Solar Water Disinfection (SODIS) has been suggested as a strategy for reducing the global burden of diarrhoea by improving the microbiological quality of drinking water. Despite increasing support for the large-scale dissemination of SODIS, there are few reports describing the effectiveness of its implementation. It is, therefore, important to identify and understand the mechanisms that lead to adoption and regular use of SODIS.MethodsWe investigated the behaviours associated with SODIS adoption among households assigned to receive SODIS promotion during a cluster-randomized trial in rural Bolivia. Distinct groups of SODIS-users were identified on the basis of six compliance indicators using principal components and cluster analysis. The probability of adopting SODIS as a function of campaign exposure and household characteristics was evaluated using ordinal logistic regression models.ResultsStandardised, community-level SODIS-implementation in a rural Bolivian setting was associated with a median SODIS use of 32% (IQR: 17-50). Households that were more likely to use SODIS were those that participated more frequently in SODIS promotional events (OR = 1.07, 95%CI: 1.01-1.13), included women (OR = 1.18, 95%CI: 1.07-1.30), owned latrines (OR = 3.38, 95%CI: 1.07-10.70), and had severely wasted children living in the home (OR = 2.17, 95%CI: 1.34-3.49).ConclusionsMost of the observed household characteristics showed limited potential to predict compliance with a comprehensive, year-long SODIS-promotion campaign; this finding reflects the complexity of behaviour change in the context of household water treatment. However, our findings also suggest that the motivation to adopt new water treatment habits and to acquire new knowledge about drinking water treatment is associated with prior engagements in sanitary hygiene and with the experience of contemporary family health concerns.Household-level factors like the ownership of a latrine, a large proportion of females and the presence of a malnourished child living in a home are easily assessable indicators that SODIS-programme managers could use to identify early adopters in SODIS promotion campaigns.Trial RegistrationClinicalTrials.gov: NCT00731497