Microbial diversities (16S and 18S rRNA gene pyrosequencing) and environmental pathogens within drinking water biofilms grown on the common premise plumbing materials unplasticized polyvinylchloride and copper

Drinking water (DW) biofilm communities influence the survival of opportunistic pathogens, yet knowledge about the microbial composition of DW biofilms developed on common in-premise plumbing material is limited. Utilizing 16S and 18S rRNA gene pyrosequencing, this study characterized the microbial community structure within DW biofilms established on unplasticized polyvinyl chloride (uPVC) and copper (Cu) surfaces and the impact of introducing Legionella pneumophila (Lp) and Acanthamoeba polyphaga. Mature (\u3e 1 year old) biofilms were developed before inoculation with sterilized DW (control, Con), Lp, or Lp and A. polyphaga (LpAp). Comparison of uPVC and Cu biofilms indicated significant differences between bacterial (P = 0.001) and eukaryotic (P \u3c 0.01) members attributable to the unique presence of several family taxa: Burkholderiaceae, Characeae, Epistylidae, Goniomonadaceae, Paramoebidae, Plasmodiophoridae, Plectidae, Sphenomonadidae, and Toxariaceae within uPVC biofilms; and Enterobacteriaceae, Erythrobacteraceae, Methylophilaceae, Acanthamoebidae, and Chlamydomonadaceae within Cu biofilms. Introduction of Lp alone or with A. polyphaga had no effect on bacterial community profiles (P \u3e 0.05) but did affect eukaryotic members (uPVC, P \u3c 0.01; Cu, P = 0.001). Thus, established DW biofilms host complex communities that may vary based on substratum matrix and maintain consistent bacterial communities despite introduction of Lp, an environmental pathogen

Microbial diversities (16S and 18S rRNA gene pyrosequencing) and environmental pathogens within drinking water biofilms grown on the common premise plumbing materials unplasticized polyvinylchloride and copper

Drinking water (DW) biofilm communities influence the survival of opportunistic pathogens, yet knowledge about the microbial composition of DW biofilms developed on common in-premise plumbing material is limited. Utilizing 16S and 18S rRNA gene pyrosequencing, this study characterized the microbial community structure within DW biofilms established on unplasticized polyvinyl chloride (uPVC) and copper (Cu) surfaces and the impact of introducing Legionella pneumophila (Lp) and Acanthamoeba polyphaga. Mature (\u3e 1 year old) biofilms were developed before inoculation with sterilized DW (control, Con), Lp, or Lp and A. polyphaga (LpAp). Comparison of uPVC and Cu biofilms indicated significant differences between bacterial (P = 0.001) and eukaryotic (P \u3c 0.01) members attributable to the unique presence of several family taxa: Burkholderiaceae, Characeae, Epistylidae, Goniomonadaceae, Paramoebidae, Plasmodiophoridae, Plectidae, Sphenomonadidae, and Toxariaceae within uPVC biofilms; and Enterobacteriaceae, Erythrobacteraceae, Methylophilaceae, Acanthamoebidae, and Chlamydomonadaceae within Cu biofilms. Introduction of Lp alone or with A. polyphaga had no effect on bacterial community profiles (P \u3e 0.05) but did affect eukaryotic members (uPVC, P \u3c 0.01; Cu, P = 0.001). Thus, established DW biofilms host complex communities that may vary based on substratum matrix and maintain consistent bacterial communities despite introduction of Lp, an environmental pathogen

Pathways, Practices and Architectures: Containing Anti-Microbial Resistance (AMR) in the Cystic Fibrosis Clinic

Antimicrobial resistance (AMR) and the adaptation of microbial life to antibiotics is recognised as a major healthcare challenge. Whereas most social science engagement with AMR has focussed on aspects of ‘behaviour’ (prescribing, antibiotic usage, patient ‘compliance’, etc), this article instead explores AMR in the context of building design and healthcare architecture, focussing on the layout, design and ritual practices of three cystic fibrosis (CF) outpatient clinics. CF is a life-threatening multi-system genetic condition, often characterised by frequent respiratory infections and antibiotic treatment. Preventing AMR and cross-infection in CF increasingly depends on the spatiotemporal isolation of both people and pathogens. Our research aims to bring to the fore the role of the built environment exploring how containment and segregation are varyingly performed in interaction with material design, focussing on three core themes. These include, first, aspects of flow, movement and the spatiotemporal choreography of CF care. Second, the management of waiting and the materiality of the waiting room is a recurrent concern in our fieldwork. Finally, we take up the question of air, the intangibility of air-borne risks and their material mitigation in the CF clinic

The degree of retinopathy is equally predictive for renal and macrovascular outcomes in the ACCORD Trial

AIMS: Diabetic retinopathy (DR) is associated with a higher risk of renal and cardiovascular events. We sought to compare the risk for renal versus cardiovascular (CV) outcomes, stratified by retinopathy severity. METHODS: ACCORD was a randomized trial of people with type 2 diabetes, at high-risk for CV disease. A subgroup (n=3,369 from 71 clinics) had stereoscopic fundus photographs graded centrally. Participants were stratified at baseline to moderate/severe DR or no/mild DR and were monitored for renal and CV outcomes at follow-up visits over 4 years. The composite renal outcome was composed of serum creatinine doubling, macroalbuminuria, or end-stage renal disease. The composite CV outcome was the ACCORD trial primary outcome. Competing risk techniques were used to estimate the relative risk (RR) of renal versus CV composite outcomes within each DR stratum. RESULTS: The hazards ratio for doubling of serum creatinine and incident CV event in the moderate/severe DR versus no/mild DR strata were: 2.31 (95% CI: 1.25-4.26) and 1.98 (95% CI: 1.49-2.62), respectively. The RR of the two composite outcomes was highly similar in the no/mild DR stratum (adjusted RR at 4 years for CV versus renal events=0.96, 95% CI: 0.72-1.28) and the moderate/severe DR stratum (adjusted RR=0.92, 95% CI: 0.64-1.31). CONCLUSIONS: Thus, in people with type 2 diabetes at high risk for cardiovascular disease, incident CV versus renal events was similar, irrespective of the severity of the DR. Further evaluation of the specificity of DR for microvascular versus macrovascular events in other populations is warranted

A Bioinformatics Classifier and Database for Heme-Copper Oxygen Reductases

Background: Heme-copper oxygen reductases (HCOs) are the last enzymatic complexes of most aerobic respiratory chains, reducing dioxygen to water and translocating up to four protons across the inner mitochondrial membrane (eukaryotes) or cytoplasmatic membrane (prokaryotes). The number of completely sequenced genomes is expanding exponentially, and concomitantly, the number and taxonomic distribution of HCO sequences. These enzymes were initially classified into three different types being this classification recently challenged. Methodology:We reanalyzed the classification scheme and developed a new bioinformatics classifier for the HCO and Nitric oxide reductases (NOR), which we benchmark against a manually derived gold standard sequence set. It is able to classify any given sequence of subunit I from HCO and NOR with a global recall and precision both of 99.8%. We use this tool to classify this protein family in 552 completely sequenced genomes. Conclusions: We concluded that the new and broader data set supports three functional and evolutionary groups of HCOs. Homology between NORs and HCOs is shown and NORs closest relationship with C Type HCOs demonstrated. We established and made available a classification web tool and an integrated Heme-Copper Oxygen reductase and NOR protein database (www.evocell.org/hco)

Describing the impact of health research: a Research Impact Framework

BACKGROUND: Researchers are increasingly required to describe the impact of their work, e.g. in grant proposals, project reports, press releases and research assessment exercises. Specialised impact assessment studies can be difficult to replicate and may require resources and skills not available to individual researchers. Researchers are often hard-pressed to identify and describe research impacts and ad hoc accounts do not facilitate comparison across time or projects. METHODS: The Research Impact Framework was developed by identifying potential areas of health research impact from the research impact assessment literature and based on research assessment criteria, for example, as set out by the UK Research Assessment Exercise panels. A prototype of the framework was used to guide an analysis of the impact of selected research projects at the London School of Hygiene and Tropical Medicine. Additional areas of impact were identified in the process and researchers also provided feedback on which descriptive categories they thought were useful and valid vis-à-vis the nature and impact of their work. RESULTS: We identified four broad areas of impact: I. Research-related impacts; II. Policy impacts; III. Service impacts: health and intersectoral and IV. Societal impacts. Within each of these areas, further descriptive categories were identified. For example, the nature of research impact on policy can be described using the following categorisation, put forward by Weiss: Instrumental use where research findings drive policy-making; Mobilisation of support where research provides support for policy proposals; Conceptual use where research influences the concepts and language of policy deliberations and Redefining/wider influence where research leads to rethinking and changing established practices and beliefs. CONCLUSION: Researchers, while initially sceptical, found that the Research Impact Framework provided prompts and descriptive categories that helped them systematically identify a range of specific and verifiable impacts related to their work (compared to ad hoc approaches they had previously used). The framework could also help researchers think through implementation strategies and identify unintended or harmful effects. The standardised structure of the framework facilitates comparison of research impacts across projects and time, which is useful from analytical, management and assessment perspectives

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages