The influence of the accessory genome on bacterial pathogen evolution

Bacterial pathogens exhibit significant variation in their genomic content of virulence factors. This reflects the abundance of strategies pathogens evolved to infect host organisms by suppressing host immunity. Molecular arms-races have been a strong driving force for the evolution of pathogenicity, with pathogens often encoding overlapping or redundant functions, such as type III protein secretion effectors and hosts encoding ever more sophisticated immune systems. The pathogens’ frequent exposure to other microbes, either in their host or in the environment, provides opportunities for the acquisition or interchange of mobile genetic elements. These DNA elements accessorise the core genome and can play major roles in shaping genome structure and altering the complement of virulence factors. Here, we review the different mobile genetic elements focusing on the more recent discoveries and highlighting their role in shaping bacterial pathogen evolution

Seasonality of MRSA Infections

Using MRSA isolates submitted to our hospital microbiology laboratory January 2001–March 2010 and the number of our emergency department (ED) visits, quarterly community-associated (CA) and hospital-associated (HA) MRSA infections were modeled using Poisson regressions. For pediatric patients, approximately 1.85x (95% CI 1.45x–2.36x, adj. p<0.0001) as many CA-MRSA infections per ED visit occurred in the second two quarters as occurred in the first two quarters. For adult patients, 1.14x (95% CI 1.01x–1.29x, adj.p = 0.03) as many infections per ED visit occurred in the second two quarters as in the first two quarters. Approximately 2.94x (95% CI 1.39x–6.21x, adj.p = 0.015) as many HA-MRSA infections per hospital admission occurred in the second two quarters as occurred in the first two quarters for pediatric patients. No seasonal variation was observed among adult HA-MRSA infections per hospital admission. We demonstrated seasonality of MRSA infections and provide a summary table of similar observations in other studies

Multi-level analysis of electronic health record adoption by health care professionals: A study protocol

<p>Abstract</p> <p>Background</p> <p>The electronic health record (EHR) is an important application of information and communication technologies to the healthcare sector. EHR implementation is expected to produce benefits for patients, professionals, organisations, and the population as a whole. These benefits cannot be achieved without the adoption of EHR by healthcare professionals. Nevertheless, the influence of individual and organisational factors in determining EHR adoption is still unclear. This study aims to assess the unique contribution of individual and organisational factors on EHR adoption in healthcare settings, as well as possible interrelations between these factors.</p> <p>Methods</p> <p>A prospective study will be conducted. A stratified random sampling method will be used to select 50 healthcare organisations in the Quebec City Health Region (Canada). At the individual level, a sample of 15 to 30 health professionals will be chosen within each organisation depending on its size. A semi-structured questionnaire will be administered to two key informants in each organisation to collect organisational data. A composite adoption score of EHR adoption will be developed based on a Delphi process and will be used as the outcome variable. Twelve to eighteen months after the first contact, depending on the pace of EHR implementation, key informants and clinicians will be contacted once again to monitor the evolution of EHR adoption. A multilevel regression model will be applied to identify the organisational and individual determinants of EHR adoption in clinical settings. Alternative analytical models would be applied if necessary.</p> <p>Results</p> <p>The study will assess the contribution of organisational and individual factors, as well as their interactions, to the implementation of EHR in clinical settings.</p> <p>Conclusions</p> <p>These results will be very relevant for decision makers and managers who are facing the challenge of implementing EHR in the healthcare system. In addition, this research constitutes a major contribution to the field of knowledge transfer and implementation science.</p

Challenges and opportunities for integrating lake ecosystem modelling approaches

A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others (‘reinventing the wheel’). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available (‘having tunnel vision’). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its ‘leading principle’, there are many opportunities for combining approaches. We take the point of view that a single ‘right’ approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem model