University of Aberdeen

About: University of Aberdeen is a education organization based out in Aberdeen, United Kingdom. It is known for research contribution in the topics: Population & Health care. The organization has 21174 authors who have published 49962 publications receiving 2105479 citations. The organization is also known as: Aberdeen University.

Prevalence of chronic pain in the UK: a systematic review and meta-analysis of population studies

Abstract: Objectives There is little consensus regarding the burden of pain in the UK. The purpose of this review was to synthesise existing data on the prevalence of various chronic pain phenotypes in order to produce accurate and contemporary national estimates. Design Major electronic databases were searched for articles published after 1990, reporting population-based prevalence estimates of chronic pain (pain lasting >3 months), chronic widespread pain, fibromyalgia and chronic neuropathic pain. Pooled prevalence estimates were calculated for chronic pain and chronic widespread pain. Results Of the 1737 articles generated through our searches, 19 studies matched our inclusion criteria, presenting data from 139 933 adult residents of the UK. The prevalence of chronic pain, derived from 7 studies, ranged from 35.0% to 51.3% (pooled estimate 43.5%, 95% CIs 38.4% to 48.6%). The prevalence of moderate-severely disabling chronic pain (Von Korff grades III/IV), based on 4 studies, ranged from 10.4% to 14.3%. 12 studies stratified chronic pain prevalence by age group, demonstrating a trend towards increasing prevalence with increasing age from 14.3% in 18–25 years old, to 62% in the over 75 age group, although the prevalence of chronic pain in young people (18–39 years old) may be as high as 30%. Reported prevalence estimates were summarised for chronic widespread pain (pooled estimate 14.2%, 95% CI 12.3% to 16.1%; 5 studies), chronic neuropathic pain (8.2% to 8.9%; 2 studies) and fibromyalgia (5.4%; 1 study). Chronic pain was more common in female than male participants, across all measured phenotypes. Conclusions Chronic pain affects between one-third and one-half of the population of the UK, corresponding to just under 28 million adults, based on data from the best available published studies. This figure is likely to increase further in line with an ageing population.

A translocation signal for delivery of oomycete effector proteins into host plant cells

Abstract: Bacterial, oomycete and fungal plant pathogens establish disease by translocation of effector proteins into host cells, where they may directly manipulate host innate immunity. In bacteria, translocation is through the type III secretion system, but analogous processes for effector delivery are uncharacterized in fungi and oomycetes. Here we report functional analyses of two motifs, RXLR and EER, present in translocated oomycete effectors. We use the Phytophthora infestans RXLR-EER-containing protein Avr3a as a reporter for translocation because it triggers RXLR-EER-independent hypersensitive cell death following recognition within plant cells that contain the R3a resistance protein. We show that Avr3a, with or without RXLR-EER motifs, is secreted from P. infestans biotrophic structures called haustoria, demonstrating that these motifs are not required for targeting to haustoria or for secretion. However, following replacement of Avr3a RXLR-EER motifs with alanine residues, singly or in combination, or with residues KMIK-DDK--representing a change that conserves physicochemical properties of the protein--P. infestans fails to deliver Avr3a or an Avr3a-GUS fusion protein into plant cells, demonstrating that these motifs are required for translocation. We show that RXLR-EER-encoding genes are transcriptionally upregulated during infection. Bioinformatic analysis identifies 425 potential genes encoding secreted RXLR-EER class proteins in the P. infestans genome. Identification of this class of proteins provides unparalleled opportunities to determine how oomycetes manipulate hosts to establish infection.

Defining Feasibility and Pilot Studies in Preparation for Randomised Controlled Trials: Development of a Conceptual Framework.

Abstract: We describe a framework for defining pilot and feasibility studies focusing on studies conducted in preparation for a randomised controlled trial. To develop the framework, we undertook a Delphi survey; ran an open meeting at a trial methodology conference; conducted a review of definitions outside the health research context; consulted experts at an international consensus meeting; and reviewed 27 empirical pilot or feasibility studies. We initially adopted mutually exclusive definitions of pilot and feasibility studies. However, some Delphi survey respondents and the majority of open meeting attendees disagreed with the idea of mutually exclusive definitions. Their viewpoint was supported by definitions outside the health research context, the use of the terms 'pilot' and 'feasibility' in the literature, and participants at the international consensus meeting. In our framework, pilot studies are a subset of feasibility studies, rather than the two being mutually exclusive. A feasibility study asks whether something can be done, should we proceed with it, and if so, how. A pilot study asks the same questions but also has a specific design feature: in a pilot study a future study, or part of a future study, is conducted on a smaller scale. We suggest that to facilitate their identification, these studies should be clearly identified using the terms 'feasibility' or 'pilot' as appropriate. This should include feasibility studies that are largely qualitative; we found these difficult to identify in electronic searches because researchers rarely used the term 'feasibility' in the title or abstract of such studies. Investigators should also report appropriate objectives and methods related to feasibility; and give clear confirmation that their study is in preparation for a future randomised controlled trial designed to assess the effect of an intervention.

Improving the forecast for biodiversity under climate change

Abstract: BACKGROUND As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change. Most current predictive models, however, exclude important biological mechanisms such as demography, dispersal, evolution, and species interactions. These biological mechanisms have been shown to be important in mediating past and present responses to climate change. Thus, current modeling efforts do not provide sufficiently accurate predictions. Despite the many complexities involved, biologists are rapidly developing tools that include the key biological processes needed to improve predictive accuracy. The biggest obstacle to applying these more realistic models is that the data needed to inform them are almost always missing. We suggest ways to fill this growing gap between model sophistication and information to predict and prevent the most damaging aspects of climate change for life on Earth. ADVANCES On the basis of empirical and theoretical evidence, we identify six biological mechanisms that commonly shape responses to climate change yet are too often missing from current predictive models: physiology; demography, life history, and phenology; species interactions; evolutionary potential and population differentiation; dispersal, colonization, and range dynamics; and responses to environmental variation. We prioritize the types of information needed to inform each of these mechanisms and suggest proxies for data that are missing or difficult to collect. We show that even for well-studied species, we often lack critical information that would be necessary to apply more realistic, mechanistic models. Consequently, data limitations likely override the potential gains in accuracy of more realistic models. Given the enormous challenge of collecting this detailed information on millions of species around the world, we highlight practical methods that promote the greatest gains in predictive accuracy. Trait-based approaches leverage sparse data to make more general inferences about unstudied species. Targeting species with high climate sensitivity and disproportionate ecological impact can yield important insights about future ecosystem change. Adaptive modeling schemes provide a means to target the most important data while simultaneously improving predictive accuracy. OUTLOOK Strategic collections of essential biological information will allow us to build generalizable insights that inform our broader ability to anticipate species’ responses to climate change and other human-caused disturbances. By increasing accuracy and making uncertainties explicit, scientists can deliver improved projections for biodiversity under climate change together with characterizations of uncertainty to support more informed decisions by policymakers and land managers. Toward this end, a globally coordinated effort to fill data gaps in advance of the growing climate-fueled biodiversity crisis offers substantial advantages in efficiency, coverage, and accuracy. Biologists can take advantage of the lessons learned from the Intergovernmental Panel on Climate Change’s development, coordination, and integration of climate change projections. Climate and weather projections were greatly improved by incorporating important mechanisms and testing predictions against global weather station data. Biology can do the same. We need to adopt this meteorological approach to predicting biological responses to climate change to enhance our ability to mitigate future changes to global biodiversity and the services it provides to humans.

Resistance of Candida species to antifungal agents: molecular mechanisms and clinical consequences

Abstract: Candida albicans and related species pathogenic for man become resistant to antifungal agents, in particular triazole compounds, by expression of efflux pumps that reduce drug accumulation, alteration of the structure or concentration of antifungal target proteins, and alteration of membrane sterol composition. The clinical consequences of antifungal resistance can be seen in treatment failures in patients and in changes in the prevalences of Candida species causing disease. These effects were seen unequivocally in HIV-infected patients with oropharyngeal candida infections, but their incidence has decreased dramatically with the introduction of highly active antiretroviral therapy. The evidence for similar emergence of antifungal-resistant yeast strains and species in other types of candida infections is confounded by non-standardised susceptibility testing methods and definitions of a resistant fungal isolate. Recent large-scale surveys of yeasts isolated from blood cultures, based on standardised methodology and resistance definitions, do not support the view that antifungal resistance in pathogenic yeasts constitutes a significant or growing therapeutic problem.