2 rows where funder_names contains "FP7 Ideas: European Research Council"
✎ View and edit SQL
Suggested facets: created (date), title (array), ISSN (array), orcids (array), names (array), award_numbers (array)
funder_names (array) ✖
|1||1||["Human gut microbiome: hopes, threats and promises"]||10.1136/gutjnl-2018-316723||http://dx.doi.org/10.1136/gutjnl-2018-316723||2018-06-22T16:20:25Z||["Gastroenterology"]||132||230||["0017-5749", "1468-3288"]||Gut||<jats:p>The microbiome has received increasing attention over the last 15 years. Although gut microbes have been explored for several decades, investigations of the role of microorganisms that reside in the human gut has attracted much attention beyond classical infectious diseases. For example, numerous studies have reported changes in the gut microbiota during not only obesity, diabetes, and liver diseases but also cancer and even neurodegenerative diseases. The human gut microbiota is viewed as a potential source of novel therapeutics. Between 2013 and 2017, the number of publications focusing on the gut microbiota was, remarkably, 12 900, which represents four-fifths of the total number of publications over the last 40 years that investigated this topic. This review discusses recent evidence of the impact of the gut microbiota on metabolic disorders and focus on selected key mechanisms. This review also aims to provide a critical analysis of the current knowledge in this field, identify putative key issues or problems and discuss misinterpretations. The abundance of metagenomic data generated on comparing diseased and healthy subjects can lead to the erroneous claim that a bacterium is causally linked with the protection or the onset of a disease. In fact, environmental factors such as dietary habits, drug treatments, intestinal motility and stool frequency and consistency are all factors that influence the composition of the microbiota and should be considered. The cases of the bacteria <jats:italic>Prevotella copri</jats:italic> and <jats:italic>Akkermansia muciniphila</jats:italic> will be discussed as key examples.</jats:p>||1||["http://orcid.org/0000-0003-2040-2448"]||["Patrice D Cani"]||||["FP7 Ideas: European Research Council", "Fonds Baillet Latour", "Fonds De La Recherche Scientifique - FNRS", "WELBIO"]||["10.13039/100011199", "10.13039/501100010563", "10.13039/501100002661", [""]]|
|33||33||["Adherence and persistence to direct oral anticoagulants in atrial fibrillation: a population-based study"]||10.1136/heartjnl-2019-315307||http://dx.doi.org/10.1136/heartjnl-2019-315307||2019-10-10T21:25:20Z||["Cardiology and Cardiovascular Medicine"]||30||2||["1355-6037", "1468-201X"]||Heart||<jats:sec><jats:title>Background</jats:title><jats:p>Despite simpler regimens than vitamin K antagonists (VKAs) for stroke prevention in atrial fibrillation (AF), adherence (taking drugs as prescribed) and persistence (continuation of drugs) to direct oral anticoagulants are suboptimal, yet understudied in electronic health records (EHRs).</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>We investigated (1) time trends at individual and system levels, and (2) the risk factors for and associations between adherence and persistence.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In UK primary care EHR (The Health Information Network 2011–2016), we investigated adherence and persistence at 1 year for oral anticoagulants (OACs) in adults with incident AF. Baseline characteristics were analysed by OAC and adherence/persistence status. Risk factors for non-adherence and non-persistence were assessed using Cox and logistic regression. Patterns of adherence and persistence were analysed.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Among 36 652 individuals with incident AF, cardiovascular comorbidities (median CHA<jats:sub>2</jats:sub>DS<jats:sub>2</jats:sub>VASc[Congestive heart failure, Hypertension, Age≥75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category] 3) and polypharmacy (median number of drugs 6) were common. Adherence was 55.2% (95% CI 54.6 to 55.7), 51.2% (95% CI 50.6 to 51.8), 66.5% (95% CI 63.7 to 69.2), 63.1% (95% CI 61.8 to 64.4) and 64.7% (95% CI 63.2 to 66.1) for all OACs, VKA, dabigatran, rivaroxaban and apixaban. One-year persistence was 65.9% (95% CI 65.4 to 66.5), 63.4% (95% CI 62.8 to 64.0), 61.4% (95% CI 58.3 to 64.2), 72.3% (95% CI 70.9 to 73.7) and 78.7% (95% CI 77.1 to 80.1) for all OACs, VKA, dabigatran, rivaroxaban and apixaban. Risk of non-adherence and non-persistence increased over time at individual and system levels. Increasing comorbidity was associated with reduced risk of non-adherence a…||11||["http://orcid.org/0000-0001-8741-3411"]||["Amitava Banerjee", "Valerio Benedetto", "Philip Gichuru", "Jane Burnell", "Sotiris Antoniou", "Richard J Schilling", "William David Strain", "Ronan Ryan", "Caroline Watkins", "Tom Marshall", "Chris J Sutton"]||["(FP/2007-2013)/ERC Grant Agreement no. 339239."]||["FP7 Ideas: European Research Council"]||["10.13039/100011199"]|