The influence of pulmonary surfactant on nanoparticulate drug delivery systems

The pulmonary route is very attractive for drug delivery by inhalation. In this regard, nanoparticulate drug delivery systems, designed as multifunctional engineered nanoparticles, are very promising since they combine several opportunities like a rather uniform distribution of drug dose among all ventilated alveoli allowing for uniform cellular drug internalization. However, although the field of nanomedicine offers multiple opportunities, it still is in its infancy and the research has to proceed in order to obtain a specific targeting of the drug combined with minimum side effects. If inhaled nanoparticulate drug delivery systems are deposited on the pulmonary surfactant, they come into contact with phospholipids and surfactant proteins. It is highly likely that the interaction of nanoparticulate drug delivery systems with surfactant phospholipids and proteins will be able to mediate/modulate the further fate of this specific drug delivery system. In the present comment, we discuss the potential interactions of nanoparticulate drug delivery systems with pulmonary surfactant as well as the potential consequences of this interaction.

Change-Enabled Software Systems

Few real software systems are built completely from scratch nowadays. Instead, systems are built iteratively and incrementally, while integrating and interacting with components from many other systems. Adaptation, reconfiguration and evolution are normal, ongoing processes throughout the lifecycle of a software system. Nevertheless the platforms, tools and environments we use to develop software are still largely based on an outmoded model that presupposes that software systems are closed and will not significantly evolve after deployment. We claim that in order to enable effective and graceful evolution of modern software systems, we must make these systems more amenable to change by (i) providing explicit, first-class models of software artifacts, change, and history at the level of the platform, (ii) continuously analysing static and dynamic evolution to track emergent properties, and (iii) closing the gap between the domain model and the developers' view of the evolving system. We outline our vision of dynamic, evolving software systems and identify the research challenges to realizing this vision.

Comparative Analysis of Evolving Software Systems Using the Gini Coefficient

Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics --- for example, in terms of "average" values --- can be highly misleading. Many metrics, it turns out, are distributed like wealth --- with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.