Development of new local anesthetic agents has been focused on the potency of their nerve-blocking effects, duration of action and safety and has resulted in a substantial number of agents in clinical use. It is well established and well documented that the nerve blocking effects of local anesthetics are secondary to their interaction with the Na+ channels thereby blocking nerve membrane excitability and the generation of action potentials. Accumulating data suggest however that local anesthetics also possess a wide range of anti-inflammatory actions through their effects on cells of the immune system, as well as on other cells, e.g. microorganisms, thrombocytes and erythrocytes. The potent anti-inflammatory properties of local anesthetics, superior in several aspects to traditional anti-inflammatory agents of the NSAID and steroid groups and with fewer side-effects, has prompted clinicians to introduce them in the treatment of various inflammation-related conditions and diseases. They have proved successful in the treatment of burn injuries, interstitial cystitis, ulcerative proctitis, arthritis and herpes simplex infections. The detailed mechanisms of action are not fully understood but seem to involve a reversible interaction with membrane proteins and lipids thus regulating cell metabolic activity, migration, exocytosis and phagocytosis.

This study examined the influence of source credibility and message framing on promoting physical exercise in university students. Participants were randomly assigned to reading a positively or negatively framed communication that was attributed to either a credible or a noncredible source. Exercise intentions and attitudes were measured immediately following the delivery of the communication and following a 2‐week delay. Exercise behavior was also measured following the delay. There were Source Frame interactions for the exercise intentions, exercise behaviors, and cognitive response/elaboration measures such that participants receiving a positively framed communication from a credible source elaborated more and reported more positive exercise intentions and behaviors than participants in the other conditions. The results of the present investigation indicate that it might be beneficial for health professionals to provide exercise‐related information stressing the benefits of participating in exercise, rather than the traditional fear appeals, to motivate clients to engage in regular physical exercise. Implications for future research are discussed.

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Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand–protein binding free energy estimation. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico ’.

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