‘It was all my fault’: negative interpretation bias in depressed adolescents

The extent to which cognitive models of development and maintenance of depression apply to adolescents is largely untested, despite the widespread application of Cognitive Behavior Therapy (CBT) for depressed adolescents. Cognitive models suggest that negative cognitions, including interpretation bias, play a role in etiology and maintenance of depression. Given that cognitive development is incomplete by the teenage years and that CBT is not superior to non-cognitive treatments in the treatment of adolescent depression, it is important to test the underlying model. The primary aim of this study was to test the hypothesis that interpretation biases are exhibited by depressed adolescents. Four groups of adolescents were recruited: clinically-referred depressed (n = 27), clinically-referred non-depressed (n = 24), community with elevated depression symptoms (n = 42) and healthy community (n = 150). Participants completed a 20 item ambiguous scenarios questionnaire. Clinically-referred depressed adolescents made significantly more negative interpretations and rated scenarios as less pleasant than all other groups. The results suggest that this element of the cognitive model of depression is applicable to adolescents. Other aspects of the model should be tested so that cognitive treatment can be modified or adapted if necessary

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes

Decrease in plasma miR-27a and miR-221 after concussion in Australian football players

Introduction: Sports-related concussion (SRC) is a common form of brain injury that lacks reliable methods to guide clinical decisions. MicroRNAs (miRNAs) can influence biological processes involved in SRC, and measurement of miRNAs in biological fluids may provide objective diagnostic and return to play/recovery biomarkers. Therefore, this prospective study investigated the temporal profile of circulating miRNA levels in concussed male and female athletes. Methods: Pre-season baseline blood samples were collected from amateur Australian rules football players (82 males, 45 females). Of these, 20 males and 8 females sustained an SRC during the subsequent season and underwent blood sampling at 2-, 6- and 13-days post-injury. A miRNA discovery Open Array was conducted on plasma to assess the expression of 754 known/validated miRNAs. miRNA target identified were further investigated with quantitative real-time PCR (qRT-PCR) in a validation study. Data pertaining to SRC symptoms, demographics, sporting history, education history and concussion history were also collected. Results: Discovery analysis identified 18 candidate miRNA. The consequent validation study found that plasma miR-221-3p levels were decreased at 6d and 13d, and that miR-27a-3p levels were decreased at 6d, when compared to baseline. Moreover, miR-27a and miR-221-3p levels were inversely correlated with SRC symptom severity. Conclusion: Circulating levels of miR-27a-3p and miR-221-3p were decreased in the sub-acute stages after SRC, and were inversely correlated with SRC symptom severity. Although further studies are required, these analyses have identified miRNA biomarker candidates of SRC severity and recovery that may one day assist in its clinical management

Lymphovascular Invasion at the Time of Radical Prostatectomy Adversely Impacts Oncological Outcomes.

Lymphovascular invasion, whereby tumour cells or cell clusters are identified in the lumen of lymphatic or blood vessels, is thought to be an essential step in disease dissemination. It has been established as an independent negative prognostic indicator in a range of cancers. We therefore aimed to assess the impact of lymphovascular invasion at the time of prostatectomy on oncological outcomes. We performed a multicentre, retrospective cohort study of 3495 men who underwent radical prostatectomy for localised prostate cancer. Only men with negative preoperative staging were included. We assessed the relationship between lymphovascular invasion and adverse pathological features using multivariable logistic regression models. Kaplan-Meier curves and Cox proportional hazard models were created to evaluate the impact of lymphovascular invasion on oncological outcomes. Lymphovascular invasion was identified in 19% (n = 653) of men undergoing prostatectomy. There was an increased incidence of lymphovascular invasion-positive disease in men with high International Society of Urological Pathology (ISUP) grade and non-organ-confined disease (p < 0.01). The presence of lymphovascular invasion significantly increased the likelihood of pathological node-positive disease on multivariable logistic regression analysis (OR 15, 95%CI 9.7-23.6). The presence of lymphovascular invasion at radical prostatectomy significantly increased the risk of biochemical recurrence (HR 2.0, 95%CI 1.6-2.4). Furthermore, lymphovascular invasion significantly increased the risk of metastasis in the whole cohort (HR 2.2, 95%CI 1.6-3.0). The same relationship was seen across D'Amico risk groups. The presence of lymphovascular invasion at the time of radical prostatectomy is associated with aggressive prostate cancer disease features and is an indicator of poor oncological prognosis

The ocean sampling day consortium

Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits

What is the Role of Community Capabilities for Maternal Health? An Exploration of Community Capabilities as Determinants to Institutional Deliveries in Bangladesh, India, and Uganda

Background: While community capabilities are recognized as important factors in developing resilient health systems and communities, appropriate metrics for these have not yet been developed. Furthermore, the role of community capabilities on access to maternal health services has been underexplored. In this paper, we summarize the development of a community capability score based on the Future Health System (FHS) project’s experience in Bangladesh, India, and Uganda, and, examine the role of community capabilities as determinants of institutional delivery in these three contexts. Methods: We developed a community capability score using a pooled dataset containing cross-sectional household survey data from Bangladesh, India, and Uganda. Our main outcome of interest was whether the woman delivered in an institution. Our predictor variables included the community capability score, as well as a series of previously identified determinants of maternal health. We calculate both population-averaged effects (using GEE logistic regression), as well as sub-national level effects (using a mixed effects model). Results: Our final sample for analysis included 2775 women, of which 1238 were from Bangladesh, 1199 from India, and 338 from Uganda. We found that individual-level determinants of institutional deliveries, such as maternal education, parity, and ante-natal care access were significant in our analysis and had a strong impact on a woman’s odds of delivering in an institution. We also found that, in addition to individual-level determinants, greater community capability was significantly associated with higher odds of institutional delivery. For every additional capability, the odds of institutional delivery would increase by up to almost 6 %. Conclusion: Individual-level characteristics are strong determinants of whether a woman delivered in an institution. However, we found that community capability also plays an important role, and should be taken into account when designing programs and interventions to support institutional deliveries. Consideration of individual factors and the capabilities of the communities in which people live would contribute to the vision of supporting people-centered approaches to health

Novel Bacterial Taxa in the Human Microbiome

The human gut harbors thousands of bacterial taxa. A profusion of metagenomic sequence data has been generated from human stool samples in the last few years, raising the question of whether more taxa remain to be identified. We assessed metagenomic data generated by the Human Microbiome Project Consortium to determine if novel taxa remain to be discovered in stool samples from healthy individuals. To do this, we established a rigorous bioinformatics pipeline that uses sequence data from multiple platforms (Illumina GAIIX and Roche 454 FLX Titanium) and approaches (whole-genome shotgun and 16S rDNA amplicons) to validate novel taxa. We applied this approach to stool samples from 11 healthy subjects collected as part of the Human Microbiome Project. We discovered several low-abundance, novel bacterial taxa, which span three major phyla in the bacterial tree of life. We determined that these taxa are present in a larger set of Human Microbiome Project subjects and are found in two sampling sites (Houston and St. Louis). We show that the number of false-positive novel sequences (primarily chimeric sequences) would have been two orders of magnitude higher than the true number of novel taxa without validation using multiple datasets, highlighting the importance of establishing rigorous standards for the identification of novel taxa in metagenomic data. The majority of novel sequences are related to the recently discovered genus Barnesiella, further encouraging efforts to characterize the members of this genus and to study their roles in the microbial communities of the gut. A better understanding of the effects of less-abundant bacteria is important as we seek to understand the complex gut microbiome in healthy individuals and link changes in the microbiome to disease

Application of two machine learning algorithms to genetic association studies in the presence of covariates

BACKGROUND: Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. METHODS AND RESULTS: In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. CONCLUSION: Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation