Transcutaneous electrical nerve stimulation for the management of tennis elbow: a pragmatic randomized controlled trial: the TATE trial (ISRCTN 87141084)

<p>Abstract</p> <p>Background</p> <p>Tennis elbow is a common and often extremely painful musculoskeletal condition, which has considerable impact on individuals as well as economic implications for healthcare utilization and absence from work. Many management strategies have been studied in clinical trials. Whilst corticosteroid injections offer short term pain relief, this treatment is unpleasant and is used with caution due to an associated high risk of pain recurrence in the long term. Systematic reviews conclude that there is no clear and effective treatment for symptoms of pain in the first 6 weeks of the condition. There is a clear need for an intervention that is acceptable to patients and provides them with effective short-term pain relief without increasing the risk of recurrence. Transcutaneous electrical nerve stimulation (TENS) is an inexpensive, non-invasive, non-pharmacological form of analgesia that is commonly used in the treatment of pain. TENS has very few contraindications and is simple to apply. It also benefits from being patient controlled, thereby promoting self-management. This study aims to assess the effectiveness, in terms of pain relief, and cost-effectiveness of a self-management package of treatment that includes TENS.</p> <p>Methods/Design</p> <p>The design of the study will be a two-group pragmatic randomized clinical trial. 240 participants aged 18 years and over with tennis elbow will be recruited from 20-30 GP practices in Staffordshire, UK. Participants are to be randomized on a 1:1 basis to receive either primary care management (standard GP consultation, medication, advice and education) or primary care management with the addition of TENS, over 6 weeks. Our primary outcome measure is average intensity of elbow pain in the past 24 hours (0-10 point numerical rating scale) at 6 weeks. Secondary outcomes include pain and limitation of function, global assessment of change, days of sick leave, illness perceptions, and overall health status. A cost-effectiveness analysis will also be performed. Patient adherence and satisfaction data will be collected at 6 weeks, 6 months and 12 months by postal questionnaire. A diary will also be completed for the first 2 weeks of treatment. Clinical effectiveness and cost-effectiveness analyses will be carried out using an intention-to-treat approach as the primary analysis.</p> <p>Discussion</p> <p>This paper presents detail on the rationale, design, methods and operational aspects of the trial.</p> <p>Trial registration</p> <p>Current Controlled Trials. ISRCTN87141084</p

An image classification approach to analyze the suppression of plant immunity by the human pathogen <it>Salmonella</it> Typhimurium

<p>Abstract</p> <p>Background</p> <p>The enteric pathogen <it>Salmonella</it> is the causative agent of the majority of food-borne bacterial poisonings. Resent research revealed that colonization of plants by <it>Salmonella</it> is an active infection process. <it>Salmonella</it> changes the metabolism and adjust the plant host by suppressing the defense mechanisms. In this report we developed an automatic algorithm to quantify the symptoms caused by <it>Salmonella</it> infection on <it>Arabidopsis</it>.</p> <p>Results</p> <p>The algorithm is designed to attribute image pixels into one of the two classes: healthy and unhealthy. The task is solved in three steps. First, we perform segmentation to divide the image into foreground and background. In the second step, a support vector machine (SVM) is applied to predict the class of each pixel belonging to the foreground. And finally, we do refinement by a neighborhood-check in order to omit all falsely classified pixels from the second step. The developed algorithm was tested on infection with the non-pathogenic <it>E. coli</it> and the plant pathogen <it>Pseudomonas syringae</it> and used to study the interaction between plants and <it>Salmonella</it> wild type and T3SS mutants. We proved that T3SS mutants of <it>Salmonella</it> are unable to suppress the plant defenses. Results obtained through the automatic analyses were further verified on biochemical and transcriptome levels.</p> <p>Conclusion</p> <p>This report presents an automatic pixel-based classification method for detecting “unhealthy” regions in leaf images. The proposed method was compared to existing method and showed a higher accuracy. We used this algorithm to study the impact of the human pathogenic bacterium <it>Salmonella</it> Typhimurium on plants immune system. The comparison between wild type bacteria and T3SS mutants showed similarity in the infection process in animals and in plants. Plant epidemiology is only one possible application of the proposed algorithm, it can be easily extended to other detection tasks, which also rely on color information, or even extended to other features.</p

Biomedical Discovery Acceleration, with Applications to Craniofacial Development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work

Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index

Marja-Liisa Lokki työryhmien Generation Scotland Consortium, LifeLines Cohort Study ja GIANT Consortium jäsenPeer reviewe