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ObjectivesTo estimate the frequency of all-cause and ambulatory care sensitive condition (ACSCs)-related hospitalisations among individuals with dementia. In addition, to investigate differences by stage of dementia based on recorded cognitive function.SettingData from a large London dementia care clinical case register, linked to a national hospitalisation database.ParticipantsIndividuals aged ≥65 years with a confirmed dementia diagnosis with recorded cognitive function.Outcome measuresAcute general hospital admissions were evaluated within 6 months of a randomly selected cognitive function score in patients with a clinical diagnosis of dementia. To evaluate associations between ACSC-related hospital admissions (overall and individual ACSCs) and stage of dementia, an ordinal regression was performed, modelling stage of dementia as the dependant variable (to facilitate efficient model selection, with no implication concerning the direction of causality).ResultsOf the 5294 people with dementia, 2993 (56.5%) had at least one hospitalisation during a 12-month period of evaluation, and 1192 (22.5%) had an ACSC-related admission. Proportions with an all-cause or ACSC-related hospitalisation were greater in the groups with more advanced dementia (all-cause 53.9%, 57.1% and 60.9%, p 0.002; ACSC-related 19.5%, 24.0% and 25.3%, p<0.0001 in the mild, moderate and severe groups, respectively). An ACSC-related admission was associated with 1.3-fold (95% CI 1.1 to 1.5) increased odds of more severe dementia after adjusting for demographic factors. Concerning admissions for individual ACSCs, the most common ACSC was urinary tract infection /pyelonephritis (9.8% of hospitalised patients) followed by pneumonia (7.1%); in an adjusted model, these were each associated with 1.4-fold increased odds of more severe dementia (95% CI 1.2 to 1.7 and 1.1 to 1.7, respectively).ConclusionsPotentially avoidable hospitalisations were common in people with dementia, particularly in those with greater cognitive impairment. Our results call for greater attention to the extent of cognitive status impairment, and not just dementia diagnosis, when evaluating measures to reduce the risk of potentially avoidable hospitalisations.

ObjectivesTo evaluate the risk and common causes of hospitalisation in patients with newly diagnosed dementia and variation by severity of cognitive impairment.SettingWe used data from a large London mental healthcare case register linked to a national hospitalisation database.ParticipantsIndividuals aged ≥65 years with newly diagnosed dementia with recorded cognitive function and the catchment population within the same geography.Outcome measuresWe evaluated the risk and duration of hospitalisation in the year following a dementia diagnosis. In addition we identified the most common causes of hospitalisation and calculated age-standardised and gender-standardised admission ratios by dementia severity (mild/moderate/severe) relative to the catchment population.ResultsOf the 5218 patients with dementia, 2596 (49.8%) were hospitalised in the year following diagnosis. The proportion of individuals with mild, moderate and severe dementia who had a hospital admission was 47.9%, 50.8% and 51.7%, respectively (p= 0.097). Duration of hospital stay increased with dementia severity (median 2 days in mild to 4 days in severe dementia, p 0.0001). After excluding readmissions for the same cause, the most common primary hospitalisation discharge diagnoses among patients with dementia were urinary system disorders, pneumonia and fracture of femur, accounting for 15%, 10% and 6% of admissions, respectively. Overall, patients with dementia were hospitalised 30% more than the catchment population, and this trend was observed for most of the discharge diagnoses evaluated. Standardised admission ratios for urinary and respiratory disorders were higher in those with more severe dementia at diagnosis.ConclusionsIndividuals with a dementia diagnosis were more likely to be hospitalised than individuals in the catchment population. The length of hospital stay increased with dementia severity. Most of the common causes of hospitalisation were more common than expected relative to the catchment population, but standardised admission ratios only varied by dementia stage for certain groups of conditions.

RORγt is an isoform of RORC, preferentially expressed in Th17 cells, that functions as a critical regulator of type 3 immunity. As murine Th17-driven inflammatory disease models were greatly diminished in RORC knock-out mice, this receptor was prioritised as an attractive therapeutic target for the treatment of several autoimmune diseases. Human genetic studies indicate a significant contributory role for RORC in several human disease conditions. Furthermore, genome-wide association studies (GWAS) report a significant association between inflammatory bowel disease (IBD) and the RORC regulatory variant rs4845604. To investigate if the rs4845604 variant may affect CD4 + T cell differentiation events, naïve CD4 + T cells were isolated from eighteen healthy subjects homozygous for the rs4845604 minor (A) or major (G) allele). Isolated cells from each subject were differentiated into distinct T cell lineages by culturing in either T cell maintenance medium or Th17 driving medium conditions for six days in the presence of an RORC inverse agonist (to prevent constitutive receptor activity) or an inactive diastereomer (control). Our proof of concept study indicated that genotype had no significant effect on the mean number of naïve CD4 T cells isolated, nor the frequency of Th1-like and Th17-like cells following six days of culture in any of the four culture conditions. Analysis of the derived RNA-seq count data identified genotype-driven transcriptional effects in each of the four culture conditions. Subsequent pathway enrichment analysis of these profiles reported perturbation of metabolic signalling networks, with the potential to affect the cellular detoxification response. This investigation reveals that rs4845604 genotype is associated with transcriptional effects in CD4 + T cells that may perturb immune and metabolic pathways. Most significantly, the rs4845604 GG, IBD risk associated, genotype may be associated with a differential detoxification response. This observation justifies further investigation in a larger cohort of both healthy and IBD-affected individuals.

The complement system is an ancient and critical part of innate immunity. Recent studies have highlighted novel roles of complement beyond lysis of invading pathogens with implications in regulating the innate immune response, as well as contributing to metabolic reprogramming of T-cells, synoviocytes as well as cells in the CNS. These findings hint that complement can be an immunometabolic regulator, but whether this is also the case for the terminal step of the complement pathway, the membrane attack complex (MAC) is not clear. In this study we focused on determining whether MAC is an immunometabolic regulator of the innate immune response in human monocyte-derived macrophages. Here, we uncover previously uncharacterized metabolic changes and mitochondrial dysfunction occurring downstream of MAC deposition. These alterations in glycolytic flux and mitochondrial morphology and function mediate NLRP3 inflammasome activation, pro-inflammatory cytokine release and gasdermin D formation. Together, these data elucidate a novel signalling cascade, with metabolic alterations at its center, in MAC-stimulated human macrophages that drives an inflammatory consequence in an immunologically relevant cell type.

Neuroticism is a moderately heritable personality trait considered to be a risk factor for developing major depression, anxiety disorders and dementia. We performed a genome-wide association study in 2,235 participants drawn from a population-based study of neuroticism, making this the largest association study for neuroticism to date. Neuroticism was measured by the Eysenck Personality Questionnaire. After Quality Control, we analysed 430,000 autosomal SNPs together with an additional 1.2 million SNPs imputed with high quality from the Hap Map CEU samples. We found a very small effect of population stratification, corrected using one principal component, and some cryptic kinship that required no correction. NKAIN2 showed suggestive evidence of association with neuroticism as a main effect (p < 10(-6)) and GPC6 showed suggestive evidence for interaction with age (p approximately = 10(-7)). We found support for one previously-reported association (PDE4D), but failed to replicate other recent reports. These results suggest common SNP variation does not strongly influence neuroticism. Our study was powered to detect almost all SNPs explaining at least 2% of heritability, and so our results effectively exclude the existence of loci having a major effect on neuroticism.

Bias in epidemiological studies can adversely affect the validity of study findings. Sensitivity analyses, known as quantitative bias analyses, are available to quantify potential residual bias arising from measurement error, confounding, and selection into the study. Effective application of these methods benefits from the input of multiple parties including clinicians, epidemiologists, and statisticians. This article provides an overview of a few common methods to facilitate both the use of these methods and critical interpretation of applications in the published literature. Examples are given to describe and illustrate methods of quantitative bias analysis. This article also outlines considerations to be made when choosing between methods and discusses the limitations of quantitative bias analysis.

RORγt is an isoform of RORC, preferentially expressed in Th17 cells, that functions as a critical regulator of type 3 immunity. As murine Th17-driven inflammatory disease models were greatly diminished in RORC knock-out mice, this receptor was prioritised as an attractive therapeutic target for the treatment of several autoimmune diseases. Human genetic studies indicate a significant contributory role for RORC in several human disease conditions. Furthermore, genome-wide association studies (GWAS) report a significant association between inflammatory bowel disease (IBD) and the RORC regulatory variant rs4845604. To investigate if the rs4845604 variant may affect CD4+ T cell differentiation events, naïve CD4+ T cells were isolated from eighteen healthy subjects homozygous for the rs4845604 minor (A) or major (G) allele). Isolated cells from each subject were differentiated into distinct T cell lineages by culturing in either T cell maintenance medium or Th17 driving medium conditions for six days in the presence of an RORC inverse agonist (to prevent constitutive receptor activity) or an inactive diastereomer (control). Our proof of concept study indicated that genotype had no significant effect on the mean number of naïve CD4 T cells isolated, nor the frequency of Th1-like and Th17-like cells following six days of culture in any of the four culture conditions. Analysis of the derived RNA-seq count data identified genotype-driven transcriptional effects in each of the four culture conditions. Subsequent pathway enrichment analysis of these profiles reported perturbation of metabolic signalling networks, with the potential to affect the cellular detoxification response. This investigation reveals that rs4845604 genotype is associated with transcriptional effects in CD4+ T cells that may perturb immune and metabolic pathways. Most significantly, the rs4845604 GG, IBD risk associated, genotype may be associated with a differential detoxification response. This observation justifies further investigation in a larger cohort of both healthy and IBD-affected individuals.

We describe a method for applying geographical information systems (GIS) to exploring biodiversity in the wild relatives of crop species and illustrate its application to the wild common bean (Phaseolus vulgaris L.). We use the latitude, longitude and altitude of the location of origin of each accession in a germplasm collection of wild P. vulgaris, along with long-term monthly mean values of rainfall, temperature and diurnal temperature range for about 10000 stations throughout Latin America to produce maps indicating areas with 'bean-favouring' climates. In a test case, these identify a new suitable area in Colombia where wild P. vulgaris has been reported in the literature, and two more areas which are strong candidates on other grounds. Dividing the 'bean-favouring' climates into clusters identifies areas that have similar climates but are geographically remote, where we can expect to find wild beans with similar ecological adaptation. We discuss the implications of these results for conserving and improving the common bean, and the application of these methods to other species.[PUBLICATION ABSTRACT]

Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained. Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts. Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs).   It also introduces several important new topics, including the following: * Use of the software SAS, in addition to GenStat and R. * Meta-analysis and the multiple testing problem. * The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography. Praise for the first edition: \"One of the main strengths of the text is the bridge it provides between traditional analysis of variance and regression models and the more recently developed class of mixed models...Each chapter is well-motivated by at least one carefully chosen example...demonstrating the broad applicability of mixed models in many different disciplines...most readers will likely learn something new, and those previously unfamiliar with mixed models will obtain a solid foundation on this topic.\"—Kerrie Nelson University of South Carolina, in American Statistician, 2007