The month of July: an early experience with pandemic influenza A (H1N1) in adults with cystic fibrosis

<p>Abstract</p> <p>Background</p> <p>Pandemic Influenza A (H1N1) 2009 is a novel viral infection that emerged in March 2009. This is the first report addressing the clinical course of patients with cystic fibrosis (CF) and H1N1 infection.</p> <p>Methods</p> <p>All patients with an influenza-like illness (ILI) attending our adult centre during July 2009 were identified. Baseline respiratory function, nutritional status, approach to management and short-term clinical course were recorded.</p> <p>Results</p> <p>Most patients experienced a mild course and were able to be managed with antiviral agents as an outpatient. Robust infection control policies were implemented to limit transmission of H1N1 infection within our CF centre. Patients with severe lung disease, poor baseline nutritional reserve and presenting with more than 48 hours of ILI experienced a more severe course. Prompt antiviral therapy within the first 48 hours of illness may have been important in improving outcomes.</p> <p>Conclusions</p> <p>This observational study demonstrates that most adults with CF with H1N1 infection had mild clinical courses and recovered rapidly.</p

Microbial Source Tracking for Identification of Fecal Pollution

Fecal pollution is a serious environmental problem that affects many coastal and inland waters worldwide. Both human and animal fecal pollution impose risks to human health from exposure to pathogenic bacteria, viruses, and protozoa. To assist authorities with the implementation of the changes suggested by more restricted legislation concering water quality in Europe, methods are needed which can identify the sources of fecal pollution. Management of fecal contamination of water would be improved if the origin of the fecal pollution could be correctly identified since remediation efforts could then be allocated in a more effective manner. The concept that the origin of fecal pollution can be traced has been termed microbial source tracking. In microbial source tracking (MST) endogenous markers of fecal sources are used for identification of the fecal pollution in aquatic environments. Chemical MST-methods can be used to trace mainly sewage pollution, but the used chemical targets have no direct relationship with pathogenic bacteria. This is not the case in microbial MST-methods where source-specific bacteria or viruses are cultured to identify fecal pollution sources. However, sometimes these microbial targets can be present in too low numbers to be detected. This is circumvented by using molecular assays for host-specific marker detection. Phenotypic and genotypic library-based methods can be used to discriminate among different fecal sources. However, the isolation step makes this procedure very labour-intensive, and issues as temporal and geographical variability remain unresolved. The underlying assumptions will be discussed and the methods mostly used in microbial source tracking will be described in more detai

The promise of metabolic phenotyping in gastroenterology and hepatology

Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management