Detection of Methicillin Resistant Staphylococcus aureus and Determination of Minimum Inhibitory Concentration of Vancomycin for Staphylococcus aureus Isolated from Pus/Wound Swab Samples of the Patients Attending a Tertiary Care Hospital in Kathmandu, Ne

The present study was conducted to evaluate the performance of cefoxitin disc diffusion method and oxacillin broth microdilution method for detection of methicillin resistant S. aureus (MRSA), taking presence of mecA gene as reference. In addition, inducible clindamycin resistance and beta-lactamase production were studied and minimum inhibitory concentration (MIC) of vancomycin for S. aureus isolates was determined. A total of 711 nonrepeated pus/wound swab samples from different anatomic locations were included in the study. The Staphylococcus aureus was identified on the basis of colony morphology, Gram's stain, and biochemical tests. A total of 110 (15.47%) S. aureus isolates were recovered, of which 39 (35.50%) isolates were identified as MRSA by cefoxitin disc diffusion method. By oxacillin broth microdilution method, 31.82% of the Staphylococcus aureus isolates were found to be MRSA. However, mecA gene was present in only 29.1% of the isolates. Further, beta-lactamase production was observed in 71.82% of the isolates, while inducible clindamycin resistance was found in 10% of S. aureus isolates. The MIC value of vancomycin for S. aureus ranged from 0.016 g/mL to 1 g/mL. On the basis of the absolute sensitivity (100%), both phenotypic methods could be employed for routine diagnosis of MRSA in clinical microbiology laboratory; however cefoxitin disc diffusion could be preferred over MIC method considering time and labour factor

Prognosis of West Nile virus associated acute flaccid paralysis: a case series

Introduction: Little is known about the long-term health related quality of life outcomes in patients with West Nile virus associated acute flaccid paralysis. We describe the quality of life scores of seven patients with acute flaccid paralysis who presented to hospital between 2003 and 2006, and were followed for up to two years. Case presentations: Between 2003 and 2006, 157 symptomatic patients with West Nile virus were enrolled in a longitudinal cohort study of West Nile virus in Canada. Seven patients (4%) had acute flaccid paralysis. The firs

Predictors of inhospital mortality and re-hospitalization in older adults with community-acquired pneumonia: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>A better understanding of potentially modifiable predictors of in-hospital mortality and re-admission to the hospital following discharge may help to improve management of community-acquired pneumonia in older adults. We aimed to assess the associations of potentially modifiable factors with mortality and re-hospitalization in older adults hospitalized with community-acquired pneumonia.</p> <p>Methods</p> <p>A prospective cohort study was conducted from July 2003 to April 2005 in two Canadian cities. Patients aged 65 years or older hospitalized for community-acquired pneumonia were followed up for up to 30 days from initial hospitalization for mortality and these patients who were discharged alive within 30 days of initial hospitalization were followed up to 90 days of initial hospitalization for re-hospitalization. Separate logistic regression analyses were performed identify the predictors of mortality and re-hospitalization.</p> <p>Results</p> <p>Of 717 enrolled patients hospitalized for community-acquired pneumonia, 49 (6.8%) died within 30 days of hospital admission. Among these patients, 526 were discharged alive within 30 days of hospitalization of whom 58 (11.2%) were re-hospitalized within 90 days of initial hospitalization. History of hip fracture (odds ratio (OR) = 4.00, 95% confidence interval (CI) = (1.46, 10.96), P = .007), chronic obstructive pulmonary disease (OR = 2.31, 95% CI = (1.18, 4.50), P = .014), cerebrovascular disease (OR = 2.11, 95% CI = (1.03, 4.31), P = .040) were associated with mortality. Male sex (OR = 2.35, 95% CI = (1.13, 4.85), P = .022) was associated with re-hospitalization while vitamin E supplementation was protective (OR = 0.37 (0.16, 0.90), P = .028). Lower socioeconomic status, prior influenza and pneumococcal vaccinations, appropriate antibiotic prescription upon admission, and lower nutrition risk were not significantly associated with mortality or re-hospitalization.</p> <p>Conclusion</p> <p>Chronic comorbidities appear to be the most important predictors of death and re-hospitalization in older adults hospitalized with community-acquired pneumonia while vitamin E supplementation was protective.</p

Integrated Life Cycle Sustainability Assessment of Forest Based Drop-In Biofuel

Biofuel production requires careful sustainability analysis across its life cycle that compares the tradeoffs between the environmental, economic and social costs and benefits. This dissertation focuses on assessing the sustainability of ThermoDeOxygenated (TDO) drop-in biofuel technology, which is compatible with the existing transportation infrastructure, developed at the University of Maine. The assessment was carried out in an integrated framework by incorporating social, environmental and economic variables in R software platform. A life cycle assessment model was developed to assess the energy and greenhouse gas emissions of drop-in biofuel. It was found that this fuel has remarkably low greenhouse gas emissions and fossil fuel energy requirement compared to other similar technologies and conventional diesel. The level of emission reductions depend on how the co-products are treated. For example, when there is excess char and that displaces coal, an energy intense non-renewable fuel, the benefits are remarkably high. These benefits are relatively low when less energy intense products such as biomass and electricity consumption mix are displaced. The Renewable Fuel Standard (RSF2) compliant feedstock (i.e. forest biomass) availability was estimated taking into consideration both economic and ecological factors. The new estimates found that 3.9 million dry tons of biomass can be harvested from Maine’s forest annually. The study found that the Environmenal Protection Agency (EPA)’s definition of renewable biomass is unclear, especially in the case of naturally regenerated forest biomass in Maine, which significantly affects the amount of RFS compliant biomass from Maine’s forest. A key outcome of this dissertation is that there is a need for integrated sustainability assessment models to better inform decision makers. This dissertation has developed an integrated framework based on multi-criteria decision analysis to evaluate the sustainability of drop-in biofuel

Incorporating Biodiversity Impact into Environmental Life Cycle Assessment of Woodchips for Bioethanol Production

A clean and sustainable source of energy is important for the development of a nation. The replacement of fossil fuels with renewable energy is an important strategy promoted by oil consuming countries, notably the U.S. Among the explored sources of alternative energy, ethanol from biomass has garnered much support owing to its renewal and domestic label. Nevertheless, the environmental benefits from bioethanol production depend on the production and processing operations of feedstocks, and fuel and other material inputs during feedstock growth. Similarly, forest biomass harvesting has issues on land use and biodiversity impacts. As a result, environmental and economic viabilities along with potential impacts on land use and biodiversity are important factors for consideration before embarking on a large scale commercial production of bioethanol. One of the tools to assess the environmental sustainability of a product system is life cycle assessment (LCA), which accounts resources consumption and environmental emissions across all life stages of a product. The major life cycle impact categories considered in current LCA practice include climate change potential, acidification potential, eutrophication potential, photochemical oxidants, fossil fuel depletion, fresh water toxicity, etc. Conventional LCA methodology, however, does not account for biodiversity impacts and land use implications in its current methodological framework. This research first applies the conventional LCA approach to analyze the environmental impacts of woodchips production in view of the impending large scale bioethanol production in the U.S. Two types of forests are considered: naturally and artificially regenerated forests. The results show that the dominant environmental contributors in both scenarios for woodchips production are climate change potential and human toxicity potential. Most of the impacts are due to the combustion of fossil fuels (i.e. diesel) used for operating machinery and transportation. The other component of this study is the development of a framework to incorporate biodiversity impact category into the existing LCA method. It applies this framework in the context of woodchips production used in biorefinery for ethanol production. In the revised framework, the Landscape Management System (LMS) tool is used to model forest management activities for a set of harvesting systems under consideration. Forest stands level information, such as habitat type and stand development stages, are used to prepare a habitat-species relationship matrix. This matrix information was incorporated into LCA inventory modeling to prepare a set of characterization factors. Habitat type classifications and corresponding area calculations are spatially presented using Geographical Information Systems (GIS). Different biodiversity impact indicators (e.g. species richness and habitat naturalness, etc.) are used to model the biodiversity impact. The results show that biodiversity impact increases linearly as the ethanol blending proportion to gasoline is increased. The potential biodiversity impact is found to be higher in the first decade of the considered time frame, and it shows a leveling trend towards the next decades. From this analysis the general conclusion is drawn that when a whole rotation length of forest is considered, the potential biodiversity impact is relatively unchanged after the first few decades

Integrated Life Cycle Sustainability Assessment of Forest Based Drop-In Biofuel

Biofuel production requires careful sustainability analysis across its life cycle that compares the tradeoffs between the environmental, economic and social costs and benefits. This dissertation focuses on assessing the sustainability of ThermoDeOxygenated (TDO) drop-in biofuel technology, which is compatible with the existing transportation infrastructure, developed at the University of Maine. The assessment was carried out in an integrated framework by incorporating social, environmental and economic variables in R software platform. A life cycle assessment model was developed to assess the energy and greenhouse gas emissions of drop-in biofuel. It was found that this fuel has remarkably low greenhouse gas emissions and fossil fuel energy requirement compared to other similar technologies and conventional diesel. The level of emission reductions depend on how the co-products are treated. For example, when there is excess char and that displaces coal, an energy intense non-renewable fuel, the benefits are remarkably high. These benefits are relatively low when less energy intense products such as biomass and electricity consumption mix are displaced. The Renewable Fuel Standard (RSF2) compliant feedstock (i.e. forest biomass) availability was estimated taking into consideration both economic and ecological factors. The new estimates found that 3.9 million dry tons of biomass can be harvested from Maine’s forest annually. The study found that the Environmenal Protection Agency (EPA)’s definition of renewable biomass is unclear, especially in the case of naturally regenerated forest biomass in Maine, which significantly affects the amount of RFS compliant biomass from Maine’s forest. A key outcome of this dissertation is that there is a need for integrated sustainability assessment models to better inform decision makers. This dissertation has developed an integrated framework based on multi-criteria decision analysis to evaluate the sustainability of drop-in biofuel

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance