The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial

<p>Abstract</p> <p>Background</p> <p>Recruitment to clinical trials can be challenging. We identified anonymous potential participants to an existing pragmatic randomised controlled depression trial to assess the feasibility of using routinely collected data to identify potential trial participants. We discuss the strengths and limitations of this approach, assess its potential value, report challenges and ethical issues encountered.</p> <p>Methods</p> <p>Swansea University's Health Information Research Unit's Secure Anonymised Information Linkage (SAIL) database of routinely collected health records was interrogated, using Structured Query Language (SQL). Read codes were used to create an algorithm of inclusion/exclusion criteria with which to identify suitable anonymous participants. Two independent clinicians rated the eligibility of the potential participants' identified. Inter-rater reliability was assessed using the kappa statistic and inter-class correlation.</p> <p>Results</p> <p>The study population (N = 37263) comprised all adults registered at five general practices in Swansea UK. Using the algorithm 867 anonymous potential participants were identified. The sensitivity and specificity results > 0.9 suggested a high degree of accuracy from the algorithm. The inter-rater reliability results indicated strong agreement between the confirming raters. The Intra Class Correlation Coefficient (Cronbach's Alpha) > 0.9, suggested excellent agreement and Kappa coefficient > 0.8; almost perfect agreement.</p> <p>Conclusions</p> <p>This proof of concept study showed that routinely collected primary care data can be used to identify potential participants for a pragmatic randomised controlled trial of folate augmentation of antidepressant therapy for the treatment of depression. Further work will be needed to assess generalisability to other conditions and settings and the inclusion of this approach to support Electronic Enhanced Recruitment (EER).</p

Employing machine learning for reliable miRNA target identification in plants

<p>Abstract</p> <p>Background</p> <p>miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.</p> <p>Result</p> <p>In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like <it>Arabidopsis</it>, <it>Medicago</it>, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version.</p> <p>Conclusion</p> <p>A machine learning multivariate feature tool has been implemented in parallel and locally installable form, for plant miRNA target identification. The performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.</p

Effects of Aluminum Oxide Nanoparticles on the Growth, Development, and microRNA Expression of Tobacco (Nicotiana tabacum)

Nanoparticles are a class of newly emerging environmental pollutions. To date, few experiments have been conducted to investigate the effect nanoparticles may have on plant growth and development. It is important to study the effects nanoparticles have on plants because they are stationary organisms that cannot move away from environmental stresses like animals can, therefore they must overcome these stresses by molecular routes such as altering gene expression. microRNAs (miRNA) are a newly discovered, endogenous class of post-transcriptional gene regulators that function to alter gene expression by either targeting mRNAs for degradation or inhibiting mRNAs translating into proteins. miRNAs have been shown to mediate abiotic stress responses such as drought and salinity in plants by altering gene expression, however no study has been performed on the effect of nanoparticles on the miRNA expression profile; therefore our aim in this study was to classify if certain miRNAs play a role in plant response to Al2O3 nanoparticle stress. In this study, we exposed tobacco (Nicotiana tabacum) plants (an important cash crop as well as a model organism) to 0%, 0.1%, 0.5%, and 1% Al2O3 nanoparticles and found that as exposure to the nanoparticles increased, the average root length, the average biomass, and the leaf count of the seedlings significantly decreased. We also found that miR395, miR397, miR398, and miR399 showed an extreme increase in expression during exposure to 1% Al2O3 nanoparticles as compared to the other treatments and the control, therefore these miRNAs may play a key role in mediating plant stress responses to nanoparticle stress in the environment. The results of this study show that Al2O3 nanoparticles have a negative effect on the growth and development of tobacco seedlings and that miRNAs may play a role in the ability of plants to withstand stress to Al2O3 nanoparticles in the environment

Inferring MicroRNA Activities by Combining Gene Expression with MicroRNA Target Prediction

MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments

Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.</p> <p>Results</p> <p>A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods.</p> <p>Conclusions</p> <p>Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.</p

Protocol for a randomised controlled trial investigating the effectiveness of an online e-health application compared to attention placebo or sertraline in the treatment of generalised anxiety disorder

Background: Generalised anxiety disorder (GAD) is a high prevalence, chronic psychiatric disorder which commonly presents early in the lifespan. Internet e-health applications have been found to be successful in reducing symptoms of anxiety and stress for post traumatic stress disorder (PTSD), panic disorder, social phobia and depression. However, to date, there is little evidence for the effectiveness of e-health applications in adult GAD. There are no studies which have directly compared e-health applications with recognised evidence-based medication. This study aims to determine the effectiveness of a web-based program for treating GAD relative to sertraline and attention placebo.Methods/Design: 120 community-dwelling participants, aged 18-30 years with a clinical diagnosis of GAD will be recruited from the Australian Electoral Roll. They will be randomly allocated to one of three conditions: (i) an online treatment program for GAD, E-couch (ii) pharmacological treatment with a selective serotonin re-uptake inhibitor (SSRI), sertraline (a fixed-flexible dose of 25-100 mg/day) or (iii) an attention control placebo, HealthWatch. The treatment program will be completed over a 10 week period with a 12 month follow-up.Discussion: As of February 2010, there were no registered trials evaluating the effectiveness of an e-health application for GAD for young adults. Similarly to date, this will be the first trial to compare an e-health intervention with a pharmacological treatment.Trial Registration: Current Controlled Trials ISRCTN76298775

A Collection of Target Mimics for Comprehensive Analysis of MicroRNA Function in Arabidopsis thaliana

Many targets of plant microRNAs (miRNAs) are thought to play important roles in plant physiology and development. However, because plant miRNAs are typically encoded by medium-size gene families, it has often been difficult to assess their precise function. We report the generation of a large-scale collection of knockdowns for Arabidopsis thaliana miRNA families; this has been achieved using artificial miRNA target mimics, a recently developed technique fashioned on an endogenous mechanism of miRNA regulation. Morphological defects in the aerial part were observed for ∼20% of analyzed families, all of which are deeply conserved in land plants. In addition, we find that non-cleavable mimic sites can confer translational regulation in cis. Phenotypes of plants expressing target mimics directed against miRNAs involved in development were in several cases consistent with previous reports on plants expressing miRNA–resistant forms of individual target genes, indicating that a limited number of targets mediates most effects of these miRNAs. That less conserved miRNAs rarely had obvious effects on plant morphology suggests that most of them do not affect fundamental aspects of development. In addition to insight into modes of miRNA action, this study provides an important resource for the study of miRNA function in plants