1,721,059 research outputs found
Clinical and radiological course of intracerebral haemorrhage associated with the new non-vitamin K anticoagulants
No full textBackground Clinical outcome and mortality in intracerebral haemorrhage (ICH) associated with anticoagulant treatment is poor. Novel direct oral anticoagulant drugs (NOACs) are increasingly prescribed. Management of NOAC-associated ICH might be more challenging. The aim of this study was to compare the clinical and radiological course of ICH patients being treated with different forms of oral anticoagulant drugs. Method The study is a retrospective observational study. Haemorrhage in other intracranial compartments except the ventricular system were explicitly excluded. Four groups were categorised and compared with regard to their clinical and radiological course (NOACs, vitamin K antagonists [VKAs], platelet inhibitors and patients without anticoagulant/antiplatelet drugs). Clinical as well as radiological parameters were analysed. Results Overall, 182 patients were included (2011 to early 2016). Twenty-five patients with NOAC-associated ICH were included (47 with VKAs, 50 with platelet inhibitors and 60 patients without anticoagulant/antiplatelet drugs). The frequency of NOAC-associated ICH increased over the years. Diabetes was found significantly more often in the NOAC patients (p = 0.05). The clinical and radiological courses in the three different patient groups with impaired coagulation were similar. Mortality was significantly higher in patient groups with impaired coagulation (p = 0.04) compared to those without anticoagulant/antiplatelet drugs. Multivariate analysis revealed the Glasgow Coma Scale (GCS) score as a strong predictor for worse outcome and mortality. Conclusions The frequency of NOAC-associated ICH increased in the last 5 years. Diabetes might be a risk factor for ICH when receiving NOACs. Clinical outcome in NOAC-associated ICH is poor and mortality is as high as in patients with other oral anticoagulant/antiplatelet drugs
The Validity and Statistical Power of the Case-Only Study Design for Interaction Analysis: Gene-Gene Interaction and the Role of Genotype Imputation in Gene-Environment Interaction
Full text linkIn search of the origin of complex human diseases such as inflammatory bowel disease (IBD) and Parkinson disease (PD), not only are genetic and environmental factors thought to play a role, but gene-gene (G×G) and gene-environment (G×E) interactions may also contribute to the disease etiology. However, examining these interactions is challenging, as high statistical power is needed in order to detect them, especially when the effects are small and single nucleotide polymorphisms (SNPs) with low minor allele frequencies (MAFs) are examined. In epidemiological studies, the traditional case-control (CC) design is often employed, however, it often does not achieve the necessary statistical power for interaction analysis. The case-only (CO) study design proves to be of great use in these circumstances, as it not only obviates the need for controls, but given the same number of cases, it is statistically more powerful than the CC study design. However, two key assumptions must be fulfilled in order for the CO study design to be valid: (i) the disease of interest must be sufficiently rare, and (ii) the two risk factors (gene and environment in case of G×E interaction, both genetic in G×G interaction) must be independent in the general population. Nevertheless, the practical implementation of the CO study design in the context of G×G interaction analysis remained unexplored. Another aspect that increases the statistical power to detect interaction effects is the number of observations available for the analysis. Thus, combined data from the largest consortia comprising of numerous centers and thousands of cases gives the highest possible chance of detecting interactions to date. Depending on the center, a different genotyping chip is often used resulting in different genotyped SNPs. Genotype imputation uses a reference database to impute missing data and thus allows to gain information on numerous SNPs and make the analysis of data from different centers possible. It is a standard procedure in genome-wide-association-studies which analyse genetic main effects (MEs). The reference base used for genotype imputation is population based and assumed to consist of healthy individuals, therefore, their linkage disequilibrium (LD) structure may differ from diseased cases, particularly in areas with MEs. Thus, whether genotype imputation has an impact on the validity and statistical power of statistical tests for G×E interactions in CO studies would be a useful asset in the analysis, yet was unknown. This thesis examined two aspects of interaction analysis in the CO study design. First, whether imputing data from a reference base consisting of healthy individuals into diseased cases has consequences for the downstream G×E interaction analysis. The results showed, that imputation does not work well in areas with MEs and low minor allele frequencies of SNPs. The lower the LD to neighbouring SNPs was, the more the MAF resembled the reference base controls than the cases from the used dataset. This imputation bias further led to a loss of statistical power in the G×E interaction analysis. The second aspect of this thesis is the practical implementation of G×G interaction analysis in which SNPs were considered as proxies for genes. The (ii) assumption of independence of both factors is problematic in G×G interactions due to LD. Moreover, computational issues arise due to the large number of possible genome-wide interaction pairs that, given more than one center, need to be calculated separately for each. Thus, a method was proposed that practically implements G×G interaction analysis. The method includes, among others aspects, analysing SNPs on different chromosomes or chromosome arms to fulfil the (ii) assumption and focusing on SNPs with known MEs in order to reduce the computational burden. The largest available datasets for IBD and PD to date were used for the analysis of G×G interactions for these complex diseases. While the G×G interaction analysis for IBD found G×G interactions to be scarce, it yielded 10 unique significant G×G interactions for PD after multiple test correction. The findings of this thesis will add to an improved understanding of G×E and G×G interaction analysis in the CO study design. It points out areas of caution when examining G×E interaction using imputed data. Furthermore, this work shows how G×G interaction can be implemented in a statistically sound and computationally efficient manner. This could lead to further G×G interaction analyses, opening doors to more in-depth knowledge on the etiology of complex human diseases
Omics Analyses in Inflammatory Bowel Diseases and Pemphigus Vulgaris
Full text linkOmics-based analyses have greatly enhanced our understanding of complex human diseases such as inflammatory bowel disease (IBD) and pemphigus vulgaris (PV). Multiple genetic and environmental factors are thought to contribute to the pathogenesis of these complex diseases. In this thesis, omics analysis of the two aforementioned diseases has been performed to investigate: a) the link between the host genome and the gut microbiome in IBD, and b) the metabolomic and lipidomic profiles of patients in PV. Microbial dysbiosis is a typical feature observed in IBD patients. In addition, the composition of the microbiome and IBD risk have been linked to genetic variation. In this work, a family-based approach was adopted in the specific IBD scenario, using genotype, phenotype and microbiome data from a prospective study in Germany that comprises IBD patients and their families to examine possible associations between host genetics and gut microbiome in IBD. Our analyses resulted in the identification of novel chromosomal regions significantly linked to microbiome traits. This thesis also examined two other omics fields, namely metabolomics and lipidomics in relation PV. Here, to gain deeper insights into disease aetiology targeted metabolomic and lipidomic analyses were performed. One of the main findings was that the metabolite profiles of patients differed before and after therapy and displayed distinct clustering. Further, the cluster of treated patients shifted towards that of healthy controls relative to their untreated stage. In summary, the findings of this thesis will a) contribute to enhancing our understanding of how the genome and the microbiome join forces to shape IBD phenotype, b) inspire future large-scale omics-based studies on PV to better understand its pathogenesis, and c) help researchers to apply some of these methods to investigate other complex diseases
Network-based and statistical analysis of the human gut microbiome in the context of inflammatory bowel disease
Full text linkIn recent years, scientific interest in the human gut microbiota has surged, with publications containing the term "human gut microbiota" doubling in the past five years to exceed 80,000 hits on Google Scholar. This growth reflects the increasing recognition of the microbiome’s profound impact on its host, shaping processes that influence both health and disease. However, it is still uncertain whether microbiome changes drive disease or if disease alters the microbiome. Insights gained from microbiome data have highlighted the critical importance of maintaining microbial balance in the gut. Disruptions to this balance, termed dysbiosis, are strongly associated with various diseases, including inflammatory bowel disease (IBD). IBD is a chronic inflammatory condition affecting over 7 million people worldwide, and its prevalence continues to rise. Together, IBD and the gut microbiome represent a complex system that is challenging to analyze and describe due to their intricate interdependencies. Network-based approaches have emerged as powerful tools to address these challenges, offering a unique perspective to explore the relationships between microbial taxa and their interactions. This dissertation employs network-based and statistical methods to characterize the human gut microbiome, with a particular focus on co-occurrence network properties and their differences between healthy individuals and those with IBD. Through three interrelated studies, the research investigates how lifestyle factors and microbiome composition are associated with disease status and age-at-disease-onset, how network-based approaches can uncover dysbiosis patterns and characterize important taxa in IBD, and how variability in microbiome abundance data impact network properties, influencing the robustness and interpretation of microbiome studies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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