Suivi de l'adhésion thérapeutique des patients hypertendus ou dyslipi- démiques par les cercles de qualité médecins-pharmaciens [Monitoring drug adherence for hypertensive and dyslipidemic patients in networks of community-based pharmacists and physicians].

Patient adherence is often poor for hypertension and dyslipidaemia. A monitoring of drug adherence might improve these risk factors control, but little is known in ambulatory care. We conducted a randomised controlled study in networks of community-based pharmacists and physicians in the canton of Fribourg to examine whether monitoring drug adherence with an electronic monitor (MEMS) would improve risk factor control among treated, but uncontrolled hypertensive and dyslipidemic patients. The results indicate that MEMS achieve a better blood pressure control and lipid profile, although its implementation requires considerable resources. The study also shows the value of collaboration between physicians and pharmacists in the field of patient adherence to improve ambulatory care of patients with cardiovascular risk factors.

Postmortem computed tomography angiography vs. conventional autopsy: advantages and inconveniences of each method.

PURPOSE: Postmortem computed tomography angiography (PMCTA) was introduced into forensic investigations a few years ago. It provides reliable images that can be consulted at any time. Conventional autopsy remains the reference standard for defining the cause of death, but provides only limited possibility of a second examination. This study compares these two procedures and discusses findings that can be detected exclusively using each method. MATERIALS AND METHODS: This retrospective study compared radiological reports from PMCTA to reports from conventional autopsy for 50 forensic autopsy cases. Reported findings from autopsy and PMCTA were extracted and compared to each other. PMCTA was performed using a modified heart-lung machine and the oily contrast agent Angiofil® (Fumedica AG, Muri, Switzerland). RESULTS: PMCTA and conventional autopsy would have drawn similar conclusions regarding causes of death. Nearly 60 % of all findings were visualized with both techniques. PMCTA demonstrates a higher sensitivity for identifying skeletal and vascular lesions. However, vascular occlusions due to postmortem blood clots could be falsely assumed to be vascular lesions. In contrast, conventional autopsy does not detect all bone fractures or the exact source of bleeding. Conventional autopsy provides important information about organ morphology and remains the only way to diagnose a vital vascular occlusion with certitude. CONCLUSION: Overall, PMCTA and conventional autopsy provide comparable findings. However, each technique presents advantages and disadvantages for detecting specific findings. To correctly interpret findings and clearly define the indications for PMCTA, these differences must be understood.

Unconditioned responses and functional fear networks in human classical conditioning.

Human imaging studies examining fear conditioning have mainly focused on the neural responses to conditioned cues. In contrast, the neural basis of the unconditioned response and the mechanisms by which fear modulates inter-regional functional coupling have received limited attention. We examined the neural responses to an unconditioned stimulus using a partial-reinforcement fear conditioning paradigm and functional MRI. The analysis focused on: (1) the effects of an unconditioned stimulus (an electric shock) that was either expected and actually delivered, or expected but not delivered, and (2) on how related brain activity changed across conditioning trials, and (3) how shock expectation influenced inter-regional coupling within the fear network. We found that: (1) the delivery of the shock engaged the red nucleus, amygdale, dorsal striatum, insula, somatosensory and cingulate cortices, (2) when the shock was expected but not delivered, only the red nucleus, the anterior insular and dorsal anterior cingulate cortices showed activity increases that were sustained across trials, and (3) psycho-physiological interaction analysis demonstrated that fear led to increased red nucleus coupling to insula but decreased hippocampus coupling to the red nucleus, thalamus and cerebellum. The hippocampus and the anterior insula may serve as hubs facilitating the switch between engagement of a defensive immediate fear network and a resting network.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Structural Analysis of Networks

Network analysis naturally relies on graph theory and, more particularly, on the use of node and edge metrics to identify the salient properties in graphs. When building visual maps of networks, these metrics are turned into useful visual cues or are used interactively to filter out parts of a graph while querying it, for instance. Over the years, analysts from different application domains have designed metrics to serve specific needs. Network science is an inherently cross-disciplinary field, which leads to the publication of metrics with similar goals; different names and descriptions of their analytics often mask the similarity between two metrics that originated in different fields. Here, we study a set of graph metrics and compare their relative values and behaviors in an effort to survey their potential contributions to the spatial analysis of networks.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.