Elucidating the difference in the kinetics of antibody titres of infants in Belgium and Vietnam

Protective efficacy of a recombinant enterotoxin antigen in a maternal immunization model and the inhibition of specific maternal antibodies to neonatal oral vaccination

ETEC (enterotoxigenic Escherichia coli) infection is the leading cause of profuse watery diarrhea in mammals, especially among pre-weaning and post-weaning piglets in swine industry. Maternal immunization is a current rational strategy for providing protection to susceptive piglets and reducing the incidence of ETEC-associated diarrhea. Here we evaluated the protective efficiency of a recombinant antigen (MBP-SLS) fused by major enterotoxin subunits (STa-LTB-STb) via a maternal immunization model, and the impacts of maternal antibodies to neonatal oral vaccination were also investigated in the neonates. The high titers of specific IgG and sIgA in pups shown that the maternal antibodies could be transferred passively. Furthermore, the increases of IL-6 and IL-10 cytokines in breast milk and pup serum indicated that immunization on mother could effectively boost the immune system of neonates. Newborn rats from immunized mothers showed a 70% survival rate after ETEC infection. However, the mucosal immune responses of neonates were inhibited by the high level of maternal specific antibodies. Among the oral-vaccinated neonates, born from mock-immunized rats reached the highest survival after ETEC challenge. Collectively, the fusion MBP-SLS antigen could effectively induce strong immune responses in rats during pregnancy and the pups could receive passive protection through specific antibodies transferred via milk and placenta. However, the specific maternal antibodies exhibited an inhibition effect on the mucosal immune responses in offspring.

Measuring association among censored antibody titer data

Censoring due to a limit of detection or limit of quantification happens quite often in many medical studies. Conventional approaches to deal with censoring when analyzing these data include, for example, the substitution method and the complete case (CC) analysis. More recently, maximum likelihood estimation (MLE) has been increasingly used. While the CC analysis and the substitution method usually lead to biased estimates, the MLE approach appears to perform well in many situations. This article proposes an MLE approach to estimate the association between two measurements in the presence of censoring in one or both quantities. The central idea is to use a copula function to join the marginal distributions of the two measurements. In various simulation studies, we show that our approach outperforms existing conventional methods (CC and substitution analyses). In addition, rank‐based measures of global association such as Kendall's tau or Spearman's rho can be studied, hence, attention is not only confined to Pearson's product‐moment correlation coefficient capturing solely linear association. We have shown in our simulations that our approach is robust to misspecification of the copula function or marginal distributions given a small association. Furthermore, we propose a straightforward MLE method to fit a (multiple) linear regression model in the presence of censoring in a covariate or both the covariate and the response. Given the marginal distribution of the censored covariate, our method outperforms conventional approaches. We also compare and discuss the performance of our method with multiple imputation and missing indicator model approaches.