Bayesian Integration of Genetics and Epigenetics Detects Causal Regulatory SNPs Underlying Expression Variability

The standard expression quantitative trait loci (eQTL) detects polymorphisms associated with gene expression without revealing causality. We introduce a coupled Bayesian regression approach—eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combination of regulatory single-nucleotide polymorphisms (SNPs) that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance but also predicts gene expression more accurately than other methods. Based on realistic simulated data, we demonstrate that eQTeL accurately detects causal regulatory SNPs, including those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal

Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models

Pseudomonas aeruginosa (P. aeruginosa) is an important cause of healthcare-associated infections, particularly in immunocompromised patients. Understanding how this multi-drug resistant pathogen is transmitted within intensive care units (ICUs) is crucial for devising and evaluating successful control strategies. While it is known that moist environments serve as natural reservoirs for P. aeruginosa, there is little quantitative evidence regarding the contribution of environmental contamination to its transmission within ICUs. Previous studies on other nosocomial pathogens rely on deploying specific values for environmental parameters derived from costly and laborious genotyping. Using solely longitudinal surveillance data, we estimated the relative importance of P. aeruginosa transmission routes by exploiting the fact that different routes cause different pattern of fluctuations in the prevalence. We developed a mathematical model including background transmission, cross-transmission and environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we were able to disentangle environmental contamination during and after a patient's stay. Based on a data-augmented Markov Chain Monte Carlo method the relative importance of the considered acquisition routes is determined for two ICUs of the University hospital in Besançon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients. Both background and cross-transmission play a significant role in the transmission process in both ICUs. In contrast, only about 1% of the total transmissions were due to environmental contamination after discharge. Based on longitudinal surveillance data, we conclude that cleaning improvement of the environment after discharge might have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs of the University hospital in Besançon. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well

Bootstrapping complex time-to-event data without individual patient data, with a view toward time-dependent exposures

Development and application of statistical models for medical scientific researc

Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models

Pseudomonas aeruginosa (P. aeruginosa) is an important cause of healthcare-associated infections, particularly in immunocompromised patients. Understanding how this multi-drug resistant pathogen is transmitted within intensive care units (ICUs) is crucial for devising and evaluating successful control strategies. While it is known that moist environments serve as natural reservoirs for P. aeruginosa, there is little quantitative evidence regarding the contribution of environmental contamination to its transmission within ICUs. Previous studies on other nosocomial pathogens rely on deploying specific values for environmental parameters derived from costly and laborious genotyping. Using solely longitudinal surveillance data, we estimated the relative importance of P. aeruginosa transmission routes by exploiting the fact that different routes cause different pattern of fluctuations in the prevalence. We developed a mathematical model including background transmission, cross-transmission and environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we were able to disentangle environmental contamination during and after a patient's stay. Based on a data-augmented Markov Chain Monte Carlo method the relative importance of the considered acquisition routes is determined for two ICUs of the University hospital in Besançon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients. Both background and cross-transmission play a significant role in the transmission process in both ICUs. In contrast, only about 1% of the total transmissions were due to environmental contamination after discharge. Based on longitudinal surveillance data, we conclude that cleaning improvement of the environment after discharge might have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs of the University hospital in Besançon. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well

Mandatory surveillance and outbreaks reporting of the WHO priority pathogens for research & discovery of new antibiotics in European countries.

In 2017 the WHO published a global priority list of 12 antibiotic-resistant bacteria (ARB) in urgent need of new antibiotics. We aimed to identify and assess publicly accessible mandatory surveillance systems and outbreaks reporting for these pathogens in the 28 European Union and four European Free Trade Association member states. Compulsory reporting was mapped by reviewing national documents without applying language restrictions and through expert consultation. Information on surveillance targets, indicators, metrics and dissemination modalities was extracted and a qualitative assessment was performed for open access systems only. Twenty-one countries (66%) had a mandate to survey at least one among the 12 WHO priority pathogens; 15 provided access to surveillance frameworks. These systems covered most frequently carbapenem-resistant Enterobacteriales (12; 38%), methicillin-resistant Staphylococcus aureus (12; 38%), and vancomycin-resistant enterococci (8; 25%). None of the European countries required reporting of resistance in Salmonella, Campylobacter, Helicobacter pylori and Neisseria gonorrhoeae. High heterogeneity was observed in data collection, reporting and dissemination among countries with clinical outcomes and risk factors being reported in less than half (22% and 25%). Only six countries (19%) implemented mandatory surveillance of outbreaks due to at least one WHO priority pathogen. Our review shows that despite the increasing burden of ARB on the European population, very few countries implemented mandatory surveillance and outbreak reporting of the WHO priority pathogens. International efforts are needed to define the effectiveness of implementing mandatory reporting of these pathogens and to assess their role in reducing the spread of ARB in health-care and community settings

From study to work: methodological challenges of a graduate destination survey in the Western Cape, South Africa

Current literature proposes several strategies for improving response rates to student evaluation surveys. Graduate destination surveys pose the difficulty of tracing graduates years later when their contact details may have changed. This article discusses the methodology of one such a survey to maximise response rates. Compiling a sample frame with reliable contact details was foremost important, but may require using additional sources of information other than university records. In hindsight, graduates should have been contacted prior to introduce the survey and mention its importance, while email and postal reminders appeared to have a limited effect on non-respondents. Due to varying response rates between participating universities, online responses were augmented with a call centre administering the survey telephonically to nonrespondents. Although overall differences between online and telephonic responses appeared to be small, certain question items may need to be treated with caution when conducting telephonic surveys. The article concludes by highlighting some of the benefits of the Western Cape graduate destination survey.http://www.tandfonline.com/loi/caeh20hb201