Elective Open Suprarenal Aneurysm Repair in England from 2000 to 2010 an Observational Study of Hospital Episode Statistics

Background: Open surgery is widely used as a benchmark for the results of fenestrated endovascular repair of complex abdominal aortic aneurysms (AAA). However, the existing evidence stems from single-centre experiences, and may not be reproducible in wider practice. National outcomes provide valuable information regarding the safety of suprarenal aneurysm repair. Methods: Demographic and clinical data were extracted from English Hospital Episodes Statistics for patients undergoing elective suprarenal aneurysm repair from 1 April 2000 to 31 March 2010. Thirty-day mortality and five-year survival were analysed by logistic regression and Cox proportional hazards modeling. Results: 793 patients underwent surgery with 14% overall 30-day mortality, which did not improve over the study period. Independent predictors of 30-day mortality included age, renal disease and previous myocardial infarction. 5-year survival was independently reduced by age, renal disease, liver disease, chronic pulmonary disease, and known metastatic solid tumour. There was significant regional variation in both 30-day mortality and 5-year survival after risk-adjustment. Regional differences in outcome were eliminated in a sensitivity analysis for perioperative outcome, conducted by restricting analysis to survivors of the first 30 days after surgery. Conclusions: Elective suprarenal aneurysm repair was associated with considerable mortality and significant regional variation across England. These data provide a benchmark to assess the efficacy of complex endovascular repair of supra-renal aneurysms, though cautious interpretation is required due to the lack of information regarding aneurysm morphology. More detailed study is required, ideally through the mandatory submission of data to a national registry of suprarenal aneurysm repair

MR and CT techniques

Magnetic resonance imaging (MRI) and computed tomography (CT) are routinely used in female pelvis imaging. MRI is primarily useful for locoregional characterization of benign and malignant diseases. CT is less accurate in locoregional evaluation, but remains useful in the follow-up of treated gynecological malignancies, as well as in the setting of emergency and in the guidance of biopsies. Although transabdominal and transvaginal ultrasonography (US) is not under the scope of this chapter, it remains the first-line imaging method for most gynecological conditions.info:eu-repo/semantics/publishedVersio

Highly Accurate Diagnosis of Pleural Tuberculosis by Immunological Analysis of the Pleural Effusion

Pleural TB is notoriously difficult to diagnose due to its paucibacillary nature yet it is the most common cause of pleural effusions in TB endemic countries such as The Gambia. We identified both cellular and soluble biomarkers in the pleural fluid that allowed highly accurate diagnosis of pleural TB compared to peripheral blood markers. Multi-plex cytokine analysis on unstimulated pleural fluid showed that IP-10 resulted in a positive likelihood ratio (LR) of 9.6 versus 2.8 for IFN-γ; a combination of IP-10, IL-6 and IL-10 resulted in an AUC of 0.96 and positive LR of 10. A striking finding was the significantly higher proportion of PPD-specific IFN-γ+TNF-α+ cell population (PPD-IGTA) in the pleural fluid compared to peripheral blood of TB subjects. Presence of this pleural PPD-IGTA population resulted in 95% correct classification of pleural TB disease with a sensitivity of 95% and specificity of 100%. These data suggest that analysis of the site of infection provides superior diagnostic accuracy compared to peripheral blood for pleural TB, likely due to the sequestration of effector cells at this acute stage of disease

Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle

<p>Abstract</p> <p>Background</p> <p>'Selection signatures' delimit regions of the genome that are, or have been, functionally important and have therefore been under either natural or artificial selection. In this study, two different and complementary methods--integrated Haplotype Homozygosity Score (|iHS|) and population differentiation index (F_ST)--were applied to identify traces of decades of intensive artificial selection for traits of economic importance in modern cattle.</p> <p>Results</p> <p>We scanned the genome of a diverse set of dairy and beef breeds from Germany, Canada and Australia genotyped with a 50 K SNP panel. Across breeds, a total of 109 extreme |iHS| values exceeded the empirical threshold level of 5% with 19, 27, 9, 10 and 17 outliers in Holstein, Brown Swiss, Australian Angus, Hereford and Simmental, respectively. Annotating the regions harboring clustered |iHS| signals revealed a panel of interesting candidate genes like SPATA17, MGAT1, PGRMC2 and ACTC1, COL23A1, MATN2, respectively, in the context of reproduction and muscle formation. In a further step, a new Bayesian F_ST-based approach was applied with a set of geographically separated populations including Holstein, Brown Swiss, Simmental, North American Angus and Piedmontese for detecting differentiated loci. In total, 127 regions exceeding the 2.5 per cent threshold of the empirical posterior distribution were identified as extremely differentiated. In a substantial number (56 out of 127 cases) the extreme F_STvalues were found to be positioned in poor gene content regions which deviated significantly (p < 0.05) from the expectation assuming a random distribution. However, significant F_STvalues were found in regions of some relevant genes such as SMCP and FGF1.</p> <p>Conclusions</p> <p>Overall, 236 regions putatively subject to recent positive selection in the cattle genome were detected. Both |iHS| and F_STsuggested selection in the vicinity of the Sialic acid binding Ig-like lectin 5 gene on BTA18. This region was recently reported to be a major QTL with strong effects on productive life and fertility traits in Holstein cattle. We conclude that high-resolution genome scans of selection signatures can be used to identify genomic regions contributing to within- and inter-breed phenotypic variation.</p

Problematic online behaviors among adolescents and emerging adults: associations between cyberbullying perpetration, problematic social media use, and psychosocial factors

Over the past two decades, young people's engagement in online activities has grown markedly. The aim of the present study was to examine the relationship between two specific online behaviors (i.e., cyberbullying perpetration, problematic social media use) and their relationships with social connectedness, belongingness, depression, and self-esteem among high school and university students. Data were collected from two different study groups via two questionnaires that included the Cyberbullying Offending Scale, Social Media Use Questionnaire, Social Connectedness Scale, General Belongingness Scale, Short Depression-Happiness Scale, and Single Item Self-Esteem Scale. Study 1 comprised 804 high school students (48% female; mean age 16.20 years). Study 2 comprised 760 university students (60% female; mean age 21.48 years). Results indicated that problematic social media use and cyberbullying perpetration (which was stronger among high school students) were directly associated with each other. Belongingness (directly) and social connectedness (indirectly) were both associated with cyberbullying perpetration and problematic social media use. Path analysis demonstrated that while age was a significant direct predictor of problematic social media use and cyberbullying perpetration among university students, it was not significant among high school students. In both samples, depression was a direct predictor of problematic social media use and an indirect predictor of cyberbullying perpetration. However, majority of these associations were relatively weak. The present study significantly adds to the emerging body of literature concerning the associations between problematic social media use and cyberbullying perpetration

MET and AKT Genetic Influence on Facial Emotion Perception

Background: Facial emotion perception is a major social skill, but its molecular signal pathway remains unclear. The MET/ AKT cascade affects neurodevelopment in general populations and face recognition in patients with autism. This study explores the possible role of MET/AKT cascade in facial emotion perception. Methods: One hundred and eighty two unrelated healthy volunteers (82 men and 100 women) were recruited. Four single nucleotide polymorphisms (SNP) of MET (rs2237717, rs41735, rs42336, and rs1858830) and AKT rs1130233 were genotyped and tested for their effects on facial emotion perception. Facial emotion perception was assessed by the face task of Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Thorough neurocognitive functions were also assessed. Results: Regarding MET rs2237717, individuals with the CT genotype performed better in facial emotion perception than those with TT (p = 0.016 by ANOVA, 0.018 by general linear regression model [GLM] to control for age, gender, and education duration), and showed no difference with those with CC. Carriers with the most common MET CGA haplotype (frequency = 50.5%) performed better than non-carriers of CGA in facial emotion perception (p = 0.018, df = 1, F = 5.69, p = 0.009 by GLM). In MET rs2237717/AKT rs1130233 interaction, the C carrier/G carrier group showed better facial emotion perception than those with the TT/AA genotype (p = 0.035 by ANOVA, 0.015 by GLM), even when neurocognitive functions were controlled (p = 0.046 by GLM)

Global Pyrogeography: the Current and Future Distribution of Wildfire

Climate change is expected to alter the geographic distribution of wildfire, a complex abiotic process that responds to a variety of spatial and environmental gradients. How future climate change may alter global wildfire activity, however, is still largely unknown. As a first step to quantifying potential change in global wildfire, we present a multivariate quantification of environmental drivers for the observed, current distribution of vegetation fires using statistical models of the relationship between fire activity and resources to burn, climate conditions, human influence, and lightning flash rates at a coarse spatiotemporal resolution (100 km, over one decade). We then demonstrate how these statistical models can be used to project future changes in global fire patterns, highlighting regional hotspots of change in fire probabilities under future climate conditions as simulated by a global climate model. Based on current conditions, our results illustrate how the availability of resources to burn and climate conditions conducive to combustion jointly determine why some parts of the world are fire-prone and others are fire-free. In contrast to any expectation that global warming should necessarily result in more fire, we find that regional increases in fire probabilities may be counter-balanced by decreases at other locations, due to the interplay of temperature and precipitation variables. Despite this net balance, our models predict substantial invasion and retreat of fire across large portions of the globe. These changes could have important effects on terrestrial ecosystems since alteration in fire activity may occur quite rapidly, generating ever more complex environmental challenges for species dispersing and adjusting to new climate conditions. Our findings highlight the potential for widespread impacts of climate change on wildfire, suggesting severely altered fire regimes and the need for more explicit inclusion of fire in research on global vegetation-climate change dynamics and conservation planning

Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other “omics” disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials

Indicators of "Healthy Aging" in older women (65-69 years of age). A data-mining approach based on prediction of long-term survival

<p>Abstract</p> <p>Background</p> <p>Prediction of long-term survival in healthy adults requires recognition of features that serve as early indicators of successful aging. The aims of this study were to identify predictors of long-term survival in older women and to develop a multivariable model based upon longitudinal data from the Study of Osteoporotic Fractures (SOF).</p> <p>Methods</p> <p>We considered only the youngest subjects (<it>n </it>= 4,097) enrolled in the SOF cohort (65 to 69 years of age) and excluded older SOF subjects more likely to exhibit a "frail" phenotype. A total of 377 phenotypic measures were screened to determine which were of most value for prediction of long-term (19-year) survival. Prognostic capacity of individual predictors, and combinations of predictors, was evaluated using a cross-validation criterion with prediction accuracy assessed according to time-specific AUC statistics.</p> <p>Results</p> <p>Visual contrast sensitivity score was among the top 5 individual predictors relative to all 377 variables evaluated (mean AUC = 0.570). A 13-variable model with strong predictive performance was generated using a forward search strategy (mean AUC = 0.673). Variables within this model included a measure of physical function, smoking and diabetes status, self-reported health, contrast sensitivity, and functional status indices reflecting cumulative number of daily living impairments (HR ≥ 0.879 or RH ≤ 1.131; P < 0.001). We evaluated this model and show that it predicts long-term survival among subjects assigned differing causes of death (e.g., cancer, cardiovascular disease; P < 0.01). For an average follow-up time of 20 years, output from the model was associated with multiple outcomes among survivors, such as tests of cognitive function, geriatric depression, number of daily living impairments and grip strength (P < 0.03).</p> <p>Conclusions</p> <p>The multivariate model we developed characterizes a "healthy aging" phenotype based upon an integration of measures that together reflect multiple dimensions of an aging adult (65-69 years of age). Age-sensitive components of this model may be of value as biomarkers in human studies that evaluate anti-aging interventions. Our methodology could be applied to data from other longitudinal cohorts to generalize these findings, identify additional predictors of long-term survival, and to further develop the "healthy aging" concept.</p