Optimizing the use of systemic corticosteroids in severe asthma (ROSA II project): a national Delphi consensus study

Although the prevalence of severe asthma is not high (5–10% of patients), it is responsible for a large part of the overall disease burden and costs (50–60% of total costs), especially if the condition remains uncontrolled (which occurs in around 40% of cases). Currently, for patients without disease control or presenting frequent exacerbations despite optimal therapy, add-on treatments, traditionally long-acting anticholinergics, oral corticosteroids (OCS), or biologic agents (monoclonal antibodies) are recommended. Nonetheless, the long-term use of oral/systemic corticosteroids (CS) is significantly associated with adverse effects, acute and chronic complications that may decrease health-related quality of life and worsen prognosis, thus requiring additional monitoring and management. Conversely, target therapies (i.e., omalizumab, mepolizumab, reslizumab, benralizumab, and more recently, dupilumab) have been developed grounded on the different phenotypes and endotypes of severe asthma, and are gradually reducing the reliance on OCS (i.e., greater specificity for achieving disease control by reducing the risk of exacerbations and requirements for rescue medication and OCS, with limited adverse events).This work was supported by AstraZeneca.info:eu-repo/semantics/publishedVersio

Markedly Divergent Tree Assemblage Responses to Tropical Forest Loss and Fragmentation across a Strong Seasonality Gradient

We examine the effects of forest fragmentation on the structure and composition of tree assemblages within three seasonal and aseasonal forest types of southern Brazil, including evergreen, Araucaria, and deciduous forests. We sampled three southernmost Atlantic Forest landscapes, including the largest continuous forest protected areas within each forest type. Tree assemblages in each forest type were sampled within 10 plots of 0.1 ha in both continuous forests and 10 adjacent forest fragments. All trees within each plot were assigned to trait categories describing their regeneration strategy, vertical stratification, seed-dispersal mode, seed size, and wood density. We detected differences among both forest types and landscape contexts in terms of overall tree species richness, and the density and species richness of different functional groups in terms of regeneration strategy, seed dispersal mode and woody density. Overall, evergreen forest fragments exhibited the largest deviations from continuous forest plots in assemblage structure. Evergreen, Araucaria and deciduous forests diverge in the functional composition of tree floras, particularly in relation to regeneration strategy and stress tolerance. By supporting a more diversified light-demanding and stress-tolerant flora with reduced richness and abundance of shade-tolerant, old-growth species, both deciduous and Araucaria forest tree assemblages are more intrinsically resilient to contemporary human-disturbances, including fragmentation-induced edge effects, in terms of species erosion and functional shifts. We suggest that these intrinsic differences in the direction and magnitude of responses to changes in landscape structure between forest types should guide a wide range of conservation strategies in restoring fragmented tropical forest landscapes worldwide

Predicting consumer biomass, size-structure, production, catch potential, responses to fishing and associated uncertainties in the world's marine ecosystems

Existing estimates of fish and consumer biomass in the world’s oceans are disparate. This creates uncertainty about the roles of fish and other consumers in biogeochemical cycles and ecosystem processes, the extent of human and environmental impacts and fishery potential. We develop and use a size-based macroecological model to assess the effects of parameter uncertainty on predicted consumer biomass, production and distribution. Resulting uncertainty is large (e.g. median global biomass 4.9 billion tonnes for consumers weighing 1 g to 1000 kg; 50% uncertainty intervals of 2 to 10.4 billion tonnes; 90% uncertainty intervals of 0.3 to 26.1 billion tonnes) and driven primarily by uncertainty in trophic transfer efficiency and its relationship with predator-prey body mass ratios. Even the upper uncertainty intervals for global predictions of consumer biomass demonstrate the remarkable scarcity of marine consumers, with less than one part in 30 million by volume of the global oceans comprising tissue of macroscopic animals. Thus the apparently high densities of marine life seen in surface and coastal waters and frequently visited abundance hotspots will likely give many in society a false impression of the abundance of marine animals. Unexploited baseline biomass predictions from the simple macroecological model were used to calibrate a more complex size- and trait-based model to estimate fisheries yield and impacts. Yields are highly dependent on baseline biomass and fisheries selectivity. Predicted global sustainable fisheries yield increases ≈4 fold when smaller individuals (< 20 cm from species of maximum mass < 1kg) are targeted in all oceans, but the predicted yields would rarely be accessible in practice and this fishing strategy leads to the collapse of larger species if fishing mortality rates on different size classes cannot be decoupled. Our analyses show that models with minimal parameter demands that are based on a few established ecological principles can support equitable analysis and comparison of diverse ecosystems. The analyses provide insights into the effects of parameter uncertainty on global biomass and production estimates, which have yet to be achieved with complex models, and will therefore help to highlight priorities for future research and data collection. However, the focus on simple model structures and global processes means that non-phytoplankton primary production and several groups, structures and processes of ecological and conservation interest are not represented. Consequently, our simple models become increasingly less useful than more complex alternatives when addressing questions about food web structure and function, biodiversity, resilience and human impacts at smaller scales and for areas closer to coasts

Heterogeneity of Multifunctional IL-17A Producing S. Typhi-Specific CD8+ T Cells in Volunteers following Ty21a Typhoid Immunization

Salmonella enterica serovar Typhi (S. Typhi), the causative agent of typhoid fever, continues to cause significant morbidity and mortality world-wide. CD8+ T cells are an important component of the cell mediated immune (CMI) response against S. Typhi. Recently, interleukin (IL)-17A has been shown to contribute to mucosal immunity and protection against intracellular pathogens. To investigate multifunctional IL-17A responses against S. Typhi antigens in T memory subsets, we developed multiparametric flow cytometry methods to detect up to 6 cytokines/chemokines (IL-10, IL-17A, IL-2, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) and macrophage inflammatory protein-1β (MIP-1β)) simultaneously. Five volunteers were immunized with a 4 dose regimen of live-attenuated S. Typhi vaccine (Ty21a), peripheral blood mononuclear cells (PBMC) were isolated before and at 11 time points after immunization, and CMI responses were evaluated. Of the 5 immunized volunteers studied, 3 produced detectable CD8+ T cell responses following stimulation with S. Typhi-infected autologous B lymphoblastoid cell lines (B-LCL). Additionally, 2 volunteers had detectable levels of intracellular cytokines in response to stimulation with S. Typhi-infected HLA-E restricted cells. Although the kinetics of the responses differed among volunteers, all of the responses were bi- or tri-phasic and included multifunctional CD8+ T cells. Virtually all of the IL-17A detected was derived from multifunctional CD8+ T cells. The presence of these multifunctional IL-17A+ CD8+ T cells was confirmed using an unsupervised analysis program, flow cytometry clustering without K (FLOCK). This is the first report of IL-17A production in response to S. Typhi in humans, indicating the presence of a Tc17 response which may be important in protection. The presence of IL-17A in multifunctional cells co-producing Tc1 cytokines (IL-2, IFN-γ and TNF-α) may also indicate that the distinction between Tc17 and Tc1 responses in humans is not as clearly delineated as suggested by in vitro experiments and animal models

Brain energy metabolism: A roadmap for future research

Although we have learned much about how the brain fuels its functions over the last decades, there remains much still to discover in an organ that is so complex. This article lays out major gaps in our knowledge of interrelationships between brain metabolism and brain function, including biochemical, cellular, and subcellular aspects of functional metabolism and its imaging in adult brain, as well as during development, aging, and disease. The focus is on unknowns in metabolism of major brain substrates and associated transporters, the roles of insulin and of lipid droplets, the emerging role of metabolism in microglia, mysteries about the major brain cofactor and signaling molecule NAD+, as well as unsolved problems underlying brain metabolism in pathologies such as traumatic brain injury, epilepsy, and metabolic downregulation during hibernation. It describes our current level of understanding of these facets of brain energy metabolism as well as a roadmap for future research

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

<p>Abstract</p> <p>Background</p> <p>The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.</p> <p>Results</p> <p>In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification.</p> <p>Conclusion</p> <p>High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.</p