Comparison of real-time PCR assays for detection, quantification, and differentiation of campylobacter jejuni and campylobacter coli in broiler neck skin samples

We tested the use of multiplex real-time PCR for detection and quantification of Campylobacter jejuni and Campylobacter coli on broiler carcass neck skin samples collected during 2008 from slaughterhouses in Switzerland. Results from an established TaqMan assay based on two different targets (hipO and ceuE for C. jejuni and C. coli, respectively) were corroborated with data from a newly developed assay based on a single-nucleotide polymorphism in the fusA gene, which allows differentiation between C. jejuni and C. coli. Both multiplex real-time PCRs were applied simultaneously for direct detection, differentiation, and quantification of Campylobacter from 351 neck skin samples and compared with culture methods. There was good correlation in detection and enumeration between real-time PCR results and quantitative culture, with real-time PCR being more sensitive. Overall, 251 (71.5%) of the samples were PCR positive for Campylobacter, with 211 (60.1%) in the hipO-ceuE assays, 244 (69.5%) in the fusA assay, and 204 (58.1%) of them being positive in both PCR assays. Thus, the fusA assay was similarly sensitive to the enrichment culture (72.4% positive); however, it is faster and allows for quantification. In addition, real-time PCR allowed for species differentiation; roughly 60% of positive samples contained C. jejuni, less than 10% C. coli, and more than 30% contained both species. Real-time PCR proved to be a suitable method for direct detection, quantification, and differentiation of Campylobacter from carcasses, and could permit time-efficient surveillance of these zoonotic agents.

Haemodynamic variables and functional outcome in hypothermic patients following out-of-hospital cardiac arrest

To evaluate the association between haemodynamic variables during the first 24h after intensive care unit (ICU) admission and neurological outcome in out-of-hospital cardiac arrest (OHCA) victims undergoing therapeutic hypothermia.

Dynamics of Shifting Cultivation Landscapes in Northern Lao PDR Between 2000 and 2009 Based on an Analysis of MODIS Time Series and Landsat Images

The rotational nature of shifting cultivation poses several challenges to its detection by remote sensing. Consequently, there is a lack of spatial data on the dynamics of shifting cultivation landscapes on a regional, i.e. sub-national, or national level. We present an approach based on a time series of Landsat and MODIS data and landscape metrics to delineate the dynamics of shifting cultivation landscapes. Our results reveal that shifting cultivation is a land use system still widely and dynamically utilized in northern Laos. While there is an overall reduction in the areas dominated by shifting cultivation, some regions also show an expansion. A review of relevant reports and articles indicates that policies tend to lead to a reduction while market forces can result in both expansion and reduction. For a better understanding of the different factors affecting shifting cultivation landscapes in Laos, further research should focus on spatially explicit analyses.

DECOMPOSITION OF REGRESSION ESTIMATORS TO EXPLORE THE INFLUENCE OF "UNMEASURED" TIME-VARYING CONFOUNDERS

In environmental epidemiology, exposure X and health outcome Y vary in space and time. We present a method to diagnose the possible influence of unmeasured confounders U on the estimated effect of X on Y and to propose several approaches to robust estimation. The idea is to use space and time as proxy measures for the unmeasured factors U. We start with the time series case where X and Y are continuous variables at equally-spaced times and assume a linear model. We define matching estimator b(u)s that correspond to pairs of observations with specific lag u. Controlling for a smooth function of time, St, using a kernel estimator is roughly equivalent to estimating the association with a linear combination of the b(u)s with weights that involve two components: the assumptions about the smoothness of St and the normalized variogram of the X process. When an unmeasured confounder U exists, but the model otherwise correctly controls for measured confounders, the excess variation in b(u)s is evidence of confounding by U. We use the plot of b(u)s versus lag u, lagged-estimator-plot (LEP), to diagnose the influence of U on the effect of X on Y. We use appropriate linear combination of b(u)s or extrapolate to b(0) to obtain novel estimators that are more robust to the influence of smooth U. The methods are extended to time series log-linear models and to spatial analyses. The LEP plot gives us a direct view of the magnitude of the estimators for each lag u and provides evidence when models did not adequately describe the data.