The experience of long-term opiate maintenance treatment and reported barriers to recovery: A qualitative systematic review

Background/Aim: To inform understanding of the experience of long-term opiate maintenance and identify barriers to recovery. Methods: A qualitative systematic review. Results: 14 studies in 17 papers, mainly from the USA (65%), met inclusion criteria, involving 1,088 participants. Studies focused on methadone prescribing. Participants reported stability; however, many disliked methadone. Barriers to full recovery were primarily ‘inward focused'. Conclusion: This is the first review of qualitative literature on long-term maintenance, finding that universal service improvements could be made to address reported barriers to recovery, including involving ex-users as positive role models, and increasing access to psychological support. Treatment policies combining harm minimisation and abstinence-orientated approaches may best support individualised recovery

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Epigenetics in Rheumatoid Arthritis

Epigenetics is a steadily growing research area. In many human diseases, especially in cancers, but also in autoimmune diseases, epigenetic aberrations have been found. Rheumatoid arthritis is an autoimmune disease characterized by chronic inflammation and destruction of synovial joints. Even though the etiology is not yet fully understood, rheumatoid arthritis is generally considered to be caused by a combination of genetic predisposition, deregulated immunomodulation, and environmental influences. To gain a better understanding of this disease, researchers have become interested in studying epigenetic changes in rheumatoid arthritis. Here, we want to review the current knowledge on epigenetics in rheumatoid arthritis