Exploring the potential impact of hospital ward-based pharmacy interns on drug safety.

Clinical pharmacists play an important role in improving drug safety on hospital wards. However, little is known about the impact of pharmacy interns. The objective of our study was, therefore, to investigate the impact of hospital ward-based pharmacy interns on drug safety. This study was conducted as part of the project "P-STAT 2: Pharmacy interns on the ward" on 14 surgical wards in seven hospitals in Germany and a total of 27 pharmacy interns participated. All patients admitted to the participating wards from 1st June 2008 until 31st October 2008 and from 1st December 2008 till 30th April 2009 were included. The pharmacy interns were involved in medication reconciliation, and identifying, resolving, and preventing drug-related problems (DRPs) using the classification system APS-Doc. A total of 6,551 patients were included. Patients received on average (+/- SD) 4.4 +/- 3.9 drugs. The pharmacy interns detected a total of 4,085 DRPs and on average 0.6 +/- 1.2 DRPs per patient. Most frequently detected DRPs were potential drug-drug interactions (n = 591, 14%), missing drug strength, when different strengths were available (n = 373, 9%), and incomplete medication record (n = 296, 7%). The pharmacy interns conducted an intervention for 98% (n = 4,011) of all DRPs. According to their documentation, 74% of the DRPs (n = 3,038) were solved. Drugs which were most often related with DRPs were simvastatin, diclofenac, and ibuprofen. This is the very first study exploring the potential impact of pharmacy interns on drug safety on surgical wards in Europe. Pharmacy interns can play an important role to improve drug safety on hospital wards.

Functional clustering in hippocampal cultures: relating network structure and dynamics.

In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures.

Functional clustering algorithm for the analysis of dynamic network data.

We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

Internetwork and intranetwork communications during bursting dynamics: applications to seizure prediction.

We use a simple dynamical model of two interacting networks of integrate-and-fire neurons to explain a seemingly paradoxical result observed in epileptic patients indicating that the level of phase synchrony declines below normal levels during the state preceding seizures (preictal state). We model the transition from the seizure free interval (interictal state) to the seizure (ictal state) as a slow increase in the mean depolarization of neurons in a network corresponding to the epileptic focus. We show that the transition from the interictal to preictal and then to the ictal state may be divided into separate dynamical regimes: the formation of slow oscillatory activity due to resonance between the two interacting networks observed during the interictal period, structureless activity during the preictal period when the two networks have different properties, and bursting dynamics driven by the network corresponding to the epileptic focus. Based on this result, we hypothesize that the beginning of the preictal period marks the beginning of the transition of the epileptic network from normal activity toward seizing.

Inelastic gravitational billiards.

We present an experimental investigation of gravitational billiards where the particle undergoes inelastic collisions with its boundary. The motion is mapped for an inelastic particle contained within parabolic, wedge, and hyperbolic boundaries. For the parabola, stable orbits are found and the wedge demonstrates a characteristic instability for its vertex angle. In the instance of the hyperbola, there are several features of the dynamics similar to the parabola at low driving and the wedge for higher driving. However, the low driving case for a hyperbola can only be completely understood by considering inelasticity effects predicted by a numerical simulation and the observation that the velocity dependent inelasticity allows the particle to sample several nearby trajectories for fixed driving.