Modelling meso-scale diffusion processes in stochastic fluid bio-membranes

The space–time dynamics of rigid inhomogeneities (inclusions) free to move in a randomly fluctuating fluid bio-membrane is derived and numerically simulated as a function of the membrane shape changes. Both vertically placed (embedded) inclusions and horizontally placed (surface) inclusions are considered. The energetics of the membrane, as a two-dimensional (2D) meso-scale continuum sheet, is described by the Canham–Helfrich Hamiltonian, with the membrane height function treated as a stochastic process. The diffusion parameter of this process acts as the link coupling the membrane shape fluctuations to the kinematics of the inclusions. The latter is described via Ito stochastic differential equation. In addition to stochastic forces, the inclusions also experience membrane-induced deterministic forces. Our aim is to simulate the diffusion-driven aggregation of inclusions and show how the external inclusions arrive at the sites of the embedded inclusions. The model has potential use in such emerging fields as designing a targeted drug delivery system.

The impact of the nursing workforce on patient outcomes in intensive care

Introduction: Evidence from studies conducted mainly in the US and mainland Europe suggests that characteristics of the workforce, such as nurse patient ratios and workload (measured in a number of different ways) may be linked to variations in patient outcomes across health care settings (Carmel and Rowan 2001). Few studies have tested this relationship in the UK thus questions remain about whether we are justified in extrapolating evidence from studies conducted in very different health care systems. Objectives: To investigate whether characteristics of the nursing workforce affect patient mortality UK Intensive Care Units. Data: Patient data came from the case mix programme, Intensive Care National Audit and Research Centre (ICNARC), while information about the units came from a survey of all ICUs in England (Audit Comission 1998). The merged data set contained information on 43,859 patients in 69 units across England. ICNARC also supplied a risk adjustment variable to control for patient characteristics that are often the most important determinants of survival. Methods: Multivariate multilevel logistic regression. Findings: Higher numbers of direct care nurses and lower scores on measures of workload(proportion of occupied beds at the time the patient was admitted and mean daily transfers into the unit) were associated with lower mortality rates. Furthermore, the effect of the number of direct care nurses was greatest on the life chances of the patients who were most at risk of dying. Implications: This study has wide implications for workforce policy and planning because it shows that the size of the nursing workforce is associated with mortality (West et al 2006). Few studies have demonstrated this relationship in the UK. This study has a number of strengths and weaknesses and further research is required to determine whether this relationship between the nursing workforce and patient outcomes is causal.