Cross-sectional survey of users of internet depression communities

Background: Internet-based depression communities provide a forum for individuals to communicate and share information and ideas. There has been little research into the health status and other characteristics of users of these communities. Methods: Online cross-sectional survey of Internet depression communities to identify depressive morbidity among users of Internet depression communities in six European countries; to investigate whether users were in contact with health services and receiving treatment; and to identify user perceived effects of the communities. Results: Major depression was highly prevalent among respondents (varying by country from 40% to 64%). Forty-nine percent of users meeting criteria for major depression were not receiving treatment, and 35% had no consultation with health services in the previous year. Thirty-six percent of repeat community users who had consulted a health professional in the previous year felt that the Internet community had been an important factor in deciding to seek professional help. Conclusions: There are high levels of untreated and undiagnosed depression in users of Internet depression communities. This group represents a target for intervention. Internet communities can provide information and support for stigmatizing conditions that inhibit more traditional modes of information seeking

Reaching back: the relative strength of the retroactive emotional attentional blink

Visual stimuli with emotional content appearing in close temporal proximity either before or after a target a stimulus can hinder conscious perceptual processing of the target via an emotional attentional blink (EAB). This occurs for targets that appear after the emotional stimulus (forward EAB) and for those appearing before the emotional stimulus (retroactive EAB). Additionally, the traditional attentional blink (AB) occurs because detection of any target hinders detection of a subsequent target. The present study investigated the relations between these different attentional processes. Rapid sequences of landscape images were presented to thirty-one male participants with occasional landscape targets (rotated images). For the forward EAB, emotional or neutral distractor images of people were presented before the target; for the retroactive EAB, such images were also targets and presented after the landscape target. In the latter case, this design allowed investigation of the AB as well. Erotic and gory images caused more EABs than neutral images, but there were no differential effects on the AB. This pattern is striking because while using different target categories (rotated landscapes, people) appears to have eliminated the AB, the retroactive EAB still occurred, offering additional evidence for the power of emotional stimuli over conscious attention

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data

<p>Abstract</p> <p>Background</p> <p>Neuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.</p> <p>Findings</p> <p>In this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.</p> <p>Conclusions</p> <p>The BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.</p

Independent prognostic value of angiogenesis and the level of plasminogen activator inhibitor type 1 in breast cancer patients

Tumour angiogenesis and the levels of plasminogen activator inhibitor type I (PAI-I) are both informative prognostic markers in breast cancer. In cell cultures and in animal model systems, PAI-I has a proangiogenic effect. To evaluate the interrelationship of angiogenesis and the PAI-I level in breast cancer, we have evaluated the prognostic value of those factors in a total of 228 patients with primary, unilateral, invasive breast cancer, evaluated at a median follow-up time of 12 years. Microvessels were immunohistochemically stained by antibodies against CD34 and quantitated by the Chalkley counting technique. The levels of PAI-I and its target proteinase uPA in tumour extracts were analysed by ELISA. The Chalkley count was not correlated with the levels of uPA or PAI-I. High values of uPA, PAI-I, and Chalkley count were all significantly correlated with a shorter recurrence-free survival and overall survival. In the multivariate analysis, the uPA level did not show independent prognostic impact for any of the analysed end points. In contrast, the risk of recurrence was independently and significantly predicted by both the PAI-I level and the Chalkley count, with a hazard ratio (95% CI) of 1.6 (1.01-2.69) and 1.4 (1.02-1.81), respectively. For overall survival, the Chalkley count, but not PAI-I, was of significant independent prognostic value. The risk of death was 1.7 (1,30-2.15) for Chalkley counts in the upper tertile compared to the lower one. We conclude that the PAI-I level and the Chalkley count are independent prognostic markers for recurrence-free survival in patients with primary breast cancer, suggesting that the prognostic impact of PAI-I is not only based on its involvement in angiogenesis. (C) 2003 Cancer Research UK

Decision Models and Technology Can Help Psychiatry Develop Biomarkers

Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care

Interpolative multidimensional scaling techniques for the identification of clusters in very large sequence sets

<p>Abstract</p> <p>Background</p> <p>Modern pyrosequencing techniques make it possible to study complex bacterial populations, such as <it>16S rRNA</it>, directly from environmental or clinical samples without the need for laboratory purification. Alignment of sequences across the resultant large data sets (100,000+ sequences) is of particular interest for the purpose of identifying potential gene clusters and families, but such analysis represents a daunting computational task. The aim of this work is the development of an efficient pipeline for the clustering of large sequence read sets.</p> <p>Methods</p> <p>Pairwise alignment techniques are used here to calculate genetic distances between sequence pairs. These methods are pleasingly parallel and have been shown to more accurately reflect accurate genetic distances in highly variable regions of <it>rRNA </it>genes than do traditional multiple sequence alignment (MSA) approaches. By utilizing Needleman-Wunsch (NW) pairwise alignment in conjunction with novel implementations of interpolative multidimensional scaling (MDS), we have developed an effective method for visualizing massive biosequence data sets and quickly identifying potential gene clusters.</p> <p>Results</p> <p>This study demonstrates the use of interpolative MDS to obtain clustering results that are qualitatively similar to those obtained through full MDS, but with substantial cost savings. In particular, the wall clock time required to cluster a set of 100,000 sequences has been reduced from seven hours to less than one hour through the use of interpolative MDS.</p> <p>Conclusions</p> <p>Although work remains to be done in selecting the optimal training set size for interpolative MDS, substantial computational cost savings will allow us to cluster much larger sequence sets in the future.</p

Could Public Restrooms Be an Environment for Bacterial Resistomes?

PMCID: PMC3547874This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited