1,721,455 research outputs found
Genetic variants in taste-related genes and risk of pancreatic cancer
No full textPancreatic ductal adenocarcinoma is an aggressive and relatively rare cancer with a dismal 5-year survival rate and a clear genetic background. Genetic variants in taste receptors and taste-related genes have been associated with a variety of human traits and phenotypes among which several cancer types and pancreatic cancer risk factors. In this study, we analysed 2854 single-nucleotide polymorphisms in 50 taste-related genes, including 37 taste receptors. To cover all the genetic variability of the selected genes and to include also regulatory elements, we added 5000 nucleotides to both ends of each gene. We used a two-phase approach, with the PanScan data set (3314 cases and 3431 controls) as the discovery phase and PanC4 (3893 cases and 3632 controls) as validation phase, for a total of 7207 cases and 7063 controls. The datasets were downloaded from the NCBI database of genotypes and phenotypes (dbGaP). We observed that the taste 1 receptor member 2 (TAS1R2)-rs11261087 variant was associated with pancreatic cancer risk in both phases independently, with a consistent association of the T allele with decreased risk of developing the disease [phase 1 odds ratio (OR) = 0.89, 95% confidence interval (CI) 0.80-0.98; phase 2 OR = 0.91, 95% CI 0.83-0.99; all subjects together OR = 0.90, 95% CI 0.84-0.96, P = 0.002]. However, neither the association observed in the validation phase nor those observed in the joint analysis were statistically significant considering multiple testing. Functional studies are warranted to better understand the impact of the genetic variability of TAS1R2 on PDAC risk
Genetic polymorphisms in inflammatory genes and pancreatic cancer risk: a two-phase study on more than 14 000 individuals
No full textThere is overwhelming evidence that inflammation plays a key role in the pathogenesis of cancer and its progression. Inflammation is regulated through a complex network of genes and polymorphic variants in these genes have been found to be associated to risk of various human cancers, alone or in combination with environmental variables. Despite this, not much is known on the genetic variability of genes that regulate inflammation and risk of pancreatic ductal adenocarcinoma (PDAC). We performed a two-phase association study considering the genetic variability of 76 genes that are key players in inflammatory response. We analysed tagging single nucleotide polymorphisms (SNPs) and regulatory SNPs on 7207 PDAC cases and 7063 controls and observed several associations with PDAC risk. The most significant association was between the carriers of the A allele of the CCL4-rs1719217 polymorphism, which was reported to be also associated with the expression level of the CCL4 gene, and increased risk of developing PDAC (odds ratio = 1.12, 95% confidence interval = 1.06-1.18, P = 3.34 × 10-5). This association was significant also after correction for multiple testing, highlighting the importance of using potentially functional SNPs in order to discover more genetic variants associated with PDAC risk
A database of single-nucleotide polymorphisms and a genotyping microarray for genetic epidemiology of lung cancer
No full textThe authors set up a database of 105 genes potentially related to lung cancer susceptibility and 464 of their polymorphisms. The database is based on extensive literature searches, and includes genes likely to influence the internal dose of genotoxic compounds that reach the lungs, following exposure to environmental insults, such as tobacco smoke. The authors selected a subset of 250 single-nucleotide polymorphisms with appreciable frequency in at least one major ethnic group and/or a clear functional role, which represent the best candidates as lung cancer risk factors. They developed a microarray for genotyping these polymorphisms, based on arrayed primer extension (APEX)
Regression and machine learning approaches identify potential risk factors for glioblastoma multiforme
Full text linkGlioblastoma multiforme is a lethal disease, with a 5-year survival rate of <10%. The identification of risk factors for glioblastoma multiforme is essential for the understanding of this disease and could facilitate more effective stratification of high-risk individuals. However, our current knowledge of glioblastoma multiforme risk factors is limited. Given the complexity and heterogeneity of the disease, traditional epidemiological approaches may be insufficient to study risk factors for glioblastoma multiforme. The combination of traditional approaches with machine learning models could prove effective in identifying relevant factors for glioblastoma multiforme risk. In this study, we developed glioblastoma multiformerisk models in the UK Biobank cohort using 576 glioblastoma multiforme cases and 302 602 controls. First, 369 exposures were tested with traditional regression models in a case–control study and significant associations were identified. Subsequently, significant features were filtered based on their completion rate and correlation. The selected exposures were then used to develop two machine learning models: a support vector machine and a Multi-Layer Perceptron. To address the imbalance within the subpopulation, two controls per case with full data were selected, resulting in 442 glioblastoma multiforme cases and 884 controls being analysed with the machine learning models. Relevant factors for glioblastoma multiforme risk were identified by explaining the results of the two models with Shapley Additive explanations. Traditional regression methods identified 38 significant associations between environmental exposures and glioblastoma multiforme risk under the Bonferroni threshold (P < 1.35 × 10−4). Subsequent filtration results in the selection of 12 exposures, which were then analysed with age, sex and a polygenic score using the two machine learning models. Support vector machine and the multi-layer perceptron demonstrated a good sensitivity (0.91 and 0.82, respectively). In addition to age and genetics, Shapley Additive explanations demonstrated significant contributions of insulin-like growth factor 1 blood levels and the right-hand grip strength on the predictions made by the models, with the latter effect potentially being confounded by endogenous testosterone levels. The integration of machine learning with traditional models has the potential to enhance the identification of risk factors for glioblastoma multiforme
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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