Influenza surveillance helps time prevention and control interventions especially where complex seasonal patterns exist. We assessed influenza surveillance sustainability in Africa where influenza activity varies and external funds for surveillance have decreased.
 
  We surveyed African Network for Influenza Surveillance and Epidemiology (ANISE) countries about 2011-2017 surveillance system characteristics. Data were summarized with descriptive statistics and analyzed with univariate and multivariable analyses to quantify sustained or expanded influenza surveillance capacity in Africa.
 
  Eighteen (75%) of 24 ANISE members participated in the survey; their cumulative population of 710751471 represent 56% of Africa's total population. All 18 countries scored a mean 95% on WHO laboratory quality assurance panels. The number of samples collected from severe acute respiratory infection case-patients remained consistent between 2011 and 2017 (13823 vs 13674 respectively) but decreased by 12% for influenza-like illness case-patients (16210 vs 14477). Nine (50%) gained capacity to lineage-type influenza B. The number of countries reporting each week to WHO FluNet increased from 15 (83%) in 2011 to 17 (94%) in 2017.
 
  Despite declines in external surveillance funding, ANISE countries gained additional laboratory testing capacity and continued influenza testing and reporting to WHO. These gains represent important achievements toward sustainable surveillance and epidemic/pandemic preparedness.
 Despite declines in external surveillance funding, ANISE countries gained additional laboratory testing capacity and continued influenza testing and reporting to WHO. These gains represent important achievements toward sustainable surveillance and epidemic/pandemic preparedness.In this study, four genes encoding secondary acyltransferases of lipid A in Vibrio parahaemolyticus ATCC33846 were identified. When the four genes were overexpressed in Escherichia coli MLK1067 that which produces the penta-acylated lipid A lacking the secondary acylation at the C3' position, a C120 secondary acyl chain was added at the C3' position of lipid A only in E. coli overexpressing VP_RS01045, but not VP_RS00880, VP_RS08405, or VP_RS12170. When the four genes were overexpressed in E. coli MKV15b that produces lipid IVA , a C120 secondary acyl chain was again added at the C3' position in E. coli overexpressing VP_RS01045, but a C140 secondary acyl chain was added at the C2' position of lipid A in E. coli overexpressing VP_RS00880, VP_RS08405, or VP_RS12170. The results indicate that four acyltransferases of lipid A are encoded by VP_RS01045, VP_RS00880, VP_RS08405, or VP_RS12170 in V. parahaemolyticus. The acyltransferase encoded by VP_RS01045 adds a C120 secondary acyl chain at the C3' position of lipid A, whereas the acyltransferase encoded by VP_RS00880, VP_RS08405, or VP_RS12170 adds a C140 secondary acyl chain at the C2' position of lipid A. This work contributes to understanding the biosynthetic pathway of lipid A in V.  https://www.selleckchem.com/Proteasome.html  parahaemolyticus.
  This study compared the capacity of strains of Salmonella enterica serovars Enteritidis and Dublin isolated in Brazil to invade epithelial cells, to be internalized by and survive within macrophages, and to stimulate cytokine release in vitro.
 
  Both serovars infected 75 and 73% Caco-2 (human) and MDBK (bovine) epithelial cells respectively. Salmonella Dublin and S. Enteritidis (i) were internalized at the respective rates of 79·6 and 65·0% (P≤0·05) by U937 (human) macrophages, and 70·4 and 66·9% by HD11 (chicken) macrophages; and (ii) multiplied at the respective rates of 3·2- and 2·7-fold within U937 cells, and 1·9- and 1·1-fold (P≤0·05) within HD11 cells respectively. Seventy per cent of 10 S. Dublin strains stimulated IL-8 production, while 70% of S. Enteritidis strains enhanced production of IL-1β, IL-6, IL-8, IL-10, IL-12p70 and TNF in Caco-2 cells.
 
  Compared with S. Enteritidis, S. Dublin had stronger ability to survive within macrophages and induced weak cytokine production, which may explain the higher incidence of invasive diseases caused by S. Dublin in humans.
 
  This study compared S. enterica serovars Enteritidis and Dublin to provide comparative data about the profile of the two serovars in cells from humans, the common host and their respective natural animal hosts and vice versa in order to check the differences between these two phylogenetically closely related serovars that share antigenic properties but present different phenotypic behaviours.
 This study compared S. enterica serovars Enteritidis and Dublin to provide comparative data about the profile of the two serovars in cells from humans, the common host and their respective natural animal hosts and vice versa in order to check the differences between these two phylogenetically closely related serovars that share antigenic properties but present different phenotypic behaviours.Inferring the causal effect of a treatment on an outcome in an observational study requires adjusting for observed baseline confounders to avoid bias. However, adjusting for all observed baseline covariates, when only a subset are confounders of the effect of interest, is known to yield potentially inefficient and unstable estimators of the treatment effect. Furthermore, it raises the risk of finite-sample bias and bias due to model misspecification. For these stated reasons, confounder (or covariate) selection is commonly used to determine a subset of the available covariates that is sufficient for confounding adjustment. In this article, we propose a confounder selection strategy that focuses on stable estimation of the treatment effect. In particular, when the propensity score (PS) model already includes covariates that are sufficient to adjust for confounding, then the addition of covariates that are associated with either treatment or outcome alone, but not both, should not systematically change the effect estimator. The proposal, therefore, entails first prioritizing covariates for inclusion in the PS model, then using a change-in-estimate approach to select the smallest adjustment set that yields a stable effect estimate. The ability of the proposal to correctly select confounders, and to ensure valid inference of the treatment effect following data-driven covariate selection, is assessed empirically and compared with existing methods using simulation studies. We demonstrate the procedure using three different publicly available datasets commonly used for causal inference.