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

Direct activation of NADPH oxidase 2 by 2-deoxyribose-1-phosphate triggers nuclear factor kappa B-dependent angiogenesis.

AbstractAims: Deoxyribose-1-phosphate (dRP) is a proangiogenic paracrine stimulus released by cancer cells, platelets, and macrophages and acting on endothelial cells. The objective of this study was to clarify how dRP stimulates angiogenic responses in human endothelial cells.Results: Live cell imaging, electron paramagnetic resonance, pull-down of dRP-interacting proteins, followed by immunoblotting, gene silencing of different NADPH oxidases (NOXs), and their regulatory cosubunits by small interfering RNA (siRNA) transfection, and experiments with inhibitors of the sugar transporter glucose transporter 1 (GLUT1) were utilized to demonstrate that dRP acts intracellularly by directly activating the endothelial NOX2 complex, but not NOX4. Increased reactive oxygen species generation in response to NOX2 activity leads to redox-dependent activation of the transcription factor nuclear factor kappa B (NF-κB), which, in turn, induces vascular endothelial growth factor receptor 2 (VEGFR2) upregulation. Using endothelial tube formation assays, gene silencing by siRNA, and antibody-based receptor inhibition, we demonstrate that the activation of NF-κB and VEGFR2 is necessary for the angiogenic responses elicited by dRP. The upregulation of VEGFR2 and NOX2-dependent stimulation of angiogenesis by dRP were confirmed in excisional wound and Matrigel plug vascularization assays in vivo using NOX2−/− mice.Innovation: For the first time, we demonstrate that dRP acts intracellularly and stimulates superoxide anion generation by direct binding and activation of the NOX2 enzymatic complex.Conclusions: This study describes a novel molecular mechanism underlying the proangiogenic activity of dRP, which involves the sequential activation of NOX2 and NF-κB and upregulation of VEGFR2. Antioxid. Redox Signal. 28, 110–130

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

Funder: UK Medical Research Council (MC UU 00002/7) and Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z)Abstract: 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