CD8(+) T cell adjuvant effects of Salmonella FliCd flagellin in live vaccine vectors or as purified protein

Salmonella flagellin, the flagellum structural subunit, has received particular interest as a vaccine adjuvant conferring enhanced immunogenity to soluble proteins or peptides, both for activation of antibody and cellular immune responses. In the present study, we evaluated the Salmonella enterica FliCd flagellin as a T cell vaccine adjuvant using as model the 9-mer (SYVPSAEQI) synthetic H2(d)-restricted CD8(+) T cell-specific epitope (CS(280-288)) derived from the Plasmodium yoelii circumsporozoite (G) protein. The FliCd adjuvant effects were determined under two different conditions: (i) as recombinant flagella, expressed by orally delivered live S. Dublin vaccine strains expressing the target CS(280-288) peptide fused at the central hypervariable domain, and (ii) as purified protein in acellular vaccines in which flagellin was administered to mice either as a recombinant protein fused or admixed with the target CS(280-288) peptide. The results showed that CS(280-288)-specific cytotoxic CD8(+) T cells were primed when BALB/c mice were orally inoculated with the expressing the CS280-288 epitope S. Dublin vaccine strain. In contrast, mice immunized with purified FliCd admixed with the CS280-288 peptide and, to a lesser extent, fused with the target peptide developed specific cytotoxic CD8(+) T cell responses without the need of a heterologous booster immunization. The CD8(+) T cell adjuvant effects of flagellin, either fused or not with the target peptide, correlated with the in vivo activation of CD11c(+) dendritic cells. Taken together, the present results demonstrate that Salmonella flagellins are flexible adjuvant and induce adaptative immune responses when administered by different routes or vaccine formulations. (C) 2009 Elsevier Ltd. All rights reserved.

A Bayesian model for estimating the malaria transition probabilities considering individuals lost to follow-up

It is known that patients may cease participating in a longitudinal study and become lost to follow-up. The objective of this article is to present a Bayesian model to estimate the malaria transition probabilities considering individuals lost to follow-up. We consider a homogeneous population, and it is assumed that the considered period of time is small enough to avoid two or more transitions from one state of health to another. The proposed model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of the longitudinal study. To simulate the unknown number of individuals with positive and negative states of malaria at the end of the study and lost to follow-up, two latent variables were introduced in the model. We used a real data set and a simulated data to illustrate the application of the methodology. The proposed model showed a good fit to these data sets, and the algorithm did not show problems of convergence or lack of identifiability. We conclude that the proposed model is a good alternative to estimate probabilities of transitions from one state of health to the other in studies with low adherence to follow-up.