Induced Defects in Carbonaceous Materials for Hydrogen Storage

The induced defects in carbonaceous materials for hydrogen storage were studied. The effect of exfoliation was studied and the graphite nanofibers (GNF) diameter before and after exfoliation was quantified. Thermal decomposition of the GNF before and after sulfuric/nitric acid exfoliation indicated a clear loss of thermal stability. GNF exfoliation enhanced the hydrogen uptake by a factor of five compared to the untreated GNF. The amorphous carbon was reactive than GNF, and decomposed before the GNF. The higher pretreatment temperature was intended to preferentially remove amorphous carbon leaving a higher purity of exfoliated GNF

Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration

Revisiting patient expectations and experiences of antibiotics in an era of antimicrobial resistance: Qualitative study.

OBJECTIVE: To investigate contemporary patient expectations and experiences of antibiotic prescribing in England. BACKGROUND: Primary care providers' compliance with patient influences has been identified as a motivation for antibiotic-prescribing behaviour. Since 2013, there have been concerted efforts to publicize and address the growing threat of antimicrobial resistance. A fresh qualitative insight into patient expectations and experiences is needed. DESIGN: Qualitative study using semi-structured interviews. SETTING AND PARTICIPANTS: Two English regions, one an urban metropolitan area and the other a town in rural England. Patients who recently consulted for infections were recruited. The information power approach was used to determine the number of participants, yielding a sample of 31 participants. MAIN MEASURES: Thematic analysis was carried out to analyse the interview data. RESULTS: Five themes were identified: beliefs, expectations, experiences of taking antibiotic, experience of antimicrobial resistance and side-effects, and experiences of consultations. The accounts reflected improved public knowledge: antibiotics were perceived to be much-needed medicines that should be prescribed when appropriate. The data showed that patients formed expectations of expectations, trying to read the prescribers' intentions and reflect on the dependency between what prescribers and patients wanted. Patient experiences featured as nuanced and detailed with knowledge of AMR and side-effects of antibiotics in the context of positive consultation experiences. CONCLUSIONS: The study highlighted complex interplays between adherence to antibiotics and consuming antibiotics in reflexive, informed ways. Ensuring that present and future patients are informed about potential benefits and harms of antibiotic use will contribute to future antimicrobial stewardship

Best (but oft-forgotten) practices:the design, analysis, and interpretation of Mendelian randomization studies

Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel’s first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genome-wide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy