Nemacol is a small molecule inhibitor of C. elegans vesicular acetylcholine transporter with anthelmintic potential.

Nematode parasites of humans and livestock pose a significant burden to human health, economic development, and food security. Anthelmintic drug resistance is widespread among parasites of livestock and many nematode parasites of humans lack effective treatments. Here, we present a nitrophenyl-piperazine scaffold that induces motor defects rapidly in the model nematode Caenorhabditis elegans. We call this scaffold Nemacol and show that it inhibits the vesicular acetylcholine transporter (VAChT), a target recognized by commercial animal and crop health groups as a viable anthelmintic target. We demonstrate that it is possible to create Nemacol analogs that maintain potent in vivo activity whilst lowering their affinity to the mammalian VAChT 10-fold. We also show that Nemacol enhances the ability of the anthelmintic Ivermectin to paralyze C. elegans and the ruminant nematode parasite Haemonchus contortus. Hence, Nemacol represents a promising new anthelmintic scaffold that acts through a validated anthelmintic target.

Properties of Markov Chain Monte Carlo Performance across Many Empirical Alignments.

Nearly all current Bayesian phylogenetic applications rely on Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution for trees and other parameters of the model. These approximations are only reliable if Markov chains adequately converge and sample from the joint posterior distribution. Although several studies of phylogenetic MCMC convergence exist, these have focused on simulated data sets or select empirical examples. Therefore, much that is considered common knowledge about MCMC in empirical systems derives from a relatively small family of analyses under ideal conditions. To address this, we present an overview of commonly applied phylogenetic MCMC diagnostics and an assessment of patterns of these diagnostics across more than 18,000 empirical analyses. Many analyses appeared to perform well and failures in convergence were most likely to be detected using the average standard deviation of split frequencies, a diagnostic that compares topologies among independent chains. Different diagnostics yielded different information about failed convergence, demonstrating that multiple diagnostics must be employed to reliably detect problems. The number of taxa and average branch lengths in analyses have clear impacts on MCMC performance, with more taxa and shorter branches leading to more difficult convergence. We show that the usage of models that include both Γ-distributed among-site rate variation and a proportion of invariable sites is not broadly problematic for MCMC convergence but is also unnecessary. Changes to heating and the usage of model-averaged substitution models can both offer improved convergence in some cases, but neither are a panacea.

Developing, delivering and evaluating primary mental health care: the co-production of a new complex intervention.

BACKGROUND: Health services face the challenges created by complex problems, and so need complex intervention solutions. However they also experience ongoing difficulties in translating findings from research in this area in to quality improvement changes on the ground. BounceBack was a service development innovation project which sought to examine this issue through the implementation and evaluation in a primary care setting of a novel complex intervention. METHODS: The project was a collaboration between a local mental health charity, an academic unit, and GP practices. The aim was to translate the charity's model of care into practice-based evidence describing delivery and impact. Normalisation Process Theory (NPT) was used to support the implementation of the new model of primary mental health care into six GP practices. An integrated process evaluation evaluated the process and impact of care. RESULTS: Implementation quickly stalled as we identified problems with the described model of care when applied in a changing and variable primary care context. The team therefore switched to using the NPT framework to support the systematic identification and modification of the components of the complex intervention: including the core components that made it distinct (the consultation approach) and the variable components (organisational issues) that made it work in practice. The extra work significantly reduced the time available for outcome evaluation. However findings demonstrated moderately successful implementation of the model and a suggestion of hypothesised changes in outcomes. CONCLUSIONS: The BounceBack project demonstrates the development of a complex intervention from practice. It highlights the use of Normalisation Process Theory to support development, and not just implementation, of a complex intervention; and describes the use of the research process in the generation of practice-based evidence. Implications for future translational complex intervention research supporting practice change through scholarship are discussed.