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Tucker S.,University of Iowa | Poland G.A.,Mayo Vaccine Research Group
Workplace Health and Safety | Year: 2013

Health care reform calls for the nursing profession, with a focus on disease prevention and health restoration, to innovate and create new models of care that are client-centric, evidence-based, and cost-effective. To do so, nurses must develop a fundamentally different paradigm and epistemology. New care models are required that focus on issues such as evidence-based prevention. Among the prevention foci for hospitals are hospital-acquired infections, including influenza, which kills 36,000 Americans annually. One crucial step in eliminating hospital-acquired influenza is to require influenza vaccination of all health care workers. This article challenges nursing leadership to seize opportunities to lead health care initiatives and encourage courageous innovative actions that depart from old paradigms; these actions must be based on scientific evidence, reduce costs, and promote patient safety and quality care and outcomes. Copyright © 2013 American Association of Occupational Health Nurses, Inc.

Hart S.N.,Mayo Medical School | Therneau T.M.,Mayo Medical School | Zhang Y.,Mayo Medical School | Poland G.A.,Mayo Vaccine Research Group | And 2 more authors.
Journal of Computational Biology | Year: 2013

Background: Given the high technical reproducibility and orders of magnitude greater resolution than microarrays, next-generation sequencing of mRNA (RNA-Seq) is quickly becoming the de facto standard for measuring levels of gene expression in biological experiments. Two important questions must be taken into consideration when designing a particular experiment, namely, 1) how deep does one need to sequence? and, 2) how many biological replicates are necessary to observe a significant change in expression? Results: Based on the gene expression distributions from 127 RNA-Seq experiments, we find evidence that 91% ± 4% of all annotated genes are sequenced at a frequency of 0.1 times per million bases mapped, regardless of sample source. Based on this observation, and combining this information with other parameters such as biological variation and technical variation that we empirically estimate from our large datasets, we developed a model to estimate the statistical power needed to identify differentially expressed genes from RNA-Seq experiments. Conclusions: Our results provide a needed reference for ensuring RNA-Seq gene expression studies are conducted with the optimally sample size, power, and sequencing depth. We also make available both R code and an Excel worksheet for investigators to calculate for their own experiments. © Mary Ann Liebert, Inc.

Wicker S.,Goethe University Frankfurt | Marckmann G.,University of Tubingen | Poland G.A.,Mayo Vaccine Research Group | Rabenau H.F.,Goethe University Frankfurt
Infection Control and Hospital Epidemiology | Year: 2010

Despite decades of effort to encourage healthcare workers (HCWs) to be immunized, vaccination rates remain insufficient. Among German HCWs, 831 (68.4%) of 1,215 respondents supported mandatory vaccinations for HCWs in general. However, acceptance of mandatory vaccination varied significantly between physicians and nurses and also depended on the targeted disease. © 2010 by The Society for Healthcare Epidemiology of America. All rights reserved.

Oberg A.L.,Mayo Medical School | Oberg A.L.,Mayo Vaccine Research Group | Bot B.M.,Mayo Medical School | Bot B.M.,Seattle Genetics | And 5 more authors.
BMC Genomics | Year: 2012

Background: mRNA expression data from next generation sequencing platforms is obtained in the form of counts per gene or exon. Counts have classically been assumed to follow a Poisson distribution in which the variance is equal to the mean. The Negative Binomial distribution which allows for over-dispersion, i.e., for the variance to be greater than the mean, is commonly used to model count data as well.Results: In mRNA-Seq data from 25 subjects, we found technical variation to generally follow a Poisson distribution as has been reported previously and biological variability was over-dispersed relative to the Poisson model. The mean-variance relationship across all genes was quadratic, in keeping with a Negative Binomial (NB) distribution. Over-dispersed Poisson and NB distributional assumptions demonstrated marked improvements in goodness-of-fit (GOF) over the standard Poisson model assumptions, but with evidence of over-fitting in some genes. Modeling of experimental effects improved GOF for high variance genes but increased the over-fitting problem.Conclusions: These conclusions will guide development of analytical strategies for accurate modeling of variance structure in these data and sample size determination which in turn will aid in the identification of true biological signals that inform our understanding of biological systems. © 2012 Oberg et al.; licensee BioMed Central Ltd.

Sullivan S.J.,Mayo Vaccine Research Group | Jacobson R.M.,Mayo Vaccine Research Group | Jacobson R.M.,Mayo Medical School | Dowdle W.R.,Task Force for Global Health | And 2 more authors.
Mayo Clinic Proceedings | Year: 2010

Within 2 months of its discovery last spring, a novel influenza A (H1N1) virus, currently referred to as 2009 H1N1, caused the first influenza pandemic in decades. The virus has caused disproportionate disease among young people with early reports of virulence similar to that of seasonal influenza. This clinical review provides an update encompassing the virology, epidemiology, clinical manifestations, diagnosis, treatment, and prevention of the 2009 H1N1 virus. Because information about this virus, its prevention, and treatment are rapidly evolving, readers are advised to seek additional information. We performed a literature search of PubMed using the following keywords: H1N1, influenza, vaccine, pregnancy, children, treatment, epidemiology, and review. Studies were selected for inclusion in this review on the basis of their relevance. Recent studies and articles were preferred. © 2010 Mayo Foundation for Medical Education and Research.

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