Attitudes towards morphine use among Swiss nurses and physicians

Background: There is little information regarding risk perception and attitudes on morphine use in Switzerland. Objectives: We aimed at assessing such attitudes in a sample of health professionals in the French-speaking part of Switzerland. Study design: Cross-sectional study. Setting: five non-university hospitals of the French-speaking canton of Valais, Switzerland. Methods: 431 nurses and 40 physicians (age range: 20-63). Risk perception and attitudes towards morphine use were assessed using a validated questionnaire. Results: Over half of participants showed a negative attitude regarding most adverse events related to morphine, while less than one third showed a similar attitude regarding other statements. On bivariate analyses, participants working in geriatrics showed a more negative attitude towards use of morphine than participants working in medicine and surgery. Non-Swiss participants also showed a more negative attitude than Swiss regarding use of morphine. Conversely, no differences were found between genders, profession (nurses or physicians), years of experience (<=14 and >14) and religion (catholic vs. others/no religion). These findings were further confirmed by multivariate adjustment. Limitations: possible selection bias due to responders only. Results limited to French speaking participants. Conclusion: Attitudes regarding morphine uses are mainly driven by its potential adverse effects and vary according to specialty and nationality. Educational measures directed at health professionals working in geriatrics or coming from abroad might reduce the high morphinophobia levels observed in these groups.

Cyberhealth serving to support individual intake of medication.

The Internet and new communication technologies are deeply affecting healthcare systems and the provision of care. The purpose of this article is to evaluate the possibility that cyberhealth, via the development of widespread easy access to wireless personal computers, tablets and smartphones, can effectively influence intake of medication and long-term medication adherence, which is a complex, difficult and dynamic behaviour to adopt and to sustain over time. Because of its novelty, the impact of cyberhealth on drug intake has not yet been well explored. Initial results have provided some evidence, but more research is needed to determine the impact of cyberhealth resources on long-term adherence and health outcomes, its user-friendliness and its adequacy in meeting e-patient needs. The purpose of such Internet-based interventions, which provide different levels of customisation, is not to take over the roles of healthcare providers; on the contrary, cyberhealth platforms should reinforce the alliance between healthcare providers and patients by filling time-gaps between visits and allowing patients to upload and/or share feedback material to be used during the visits. This shift, however, is not easily endorsed by healthcare providers, who must master new eHealth skills, but healthcare systems have a unique opportunity to invest in the Internet and to use this powerful tool to design the future of integrated care. Before this can occur, however, important issues must be addressed and resolved, for example ethical considerations, the scientific quality of programmes, reimbursement of activity, data security and the ownership of uploaded data.

Modular support for developing mobile ad hoc applications

Abstract This PhD thesis addresses the issue of alleviating the burden of developing ad hoc applications. Such applications have the particularity of running on mobile devices, communicating in a peer-to-peer manner and implement some proximity-based semantics. A typical example of such application can be a radar application where users see their avatar as well as the avatars of their friends on a map on their mobile phone. Such application become increasingly popular with the advent of the latest generation of mobile smart phones with their impressive computational power, their peer-to-peer communication capabilities and their location detection technology. Unfortunately, the existing programming support for such applications is limited, hence the need to address this issue in order to alleviate their development burden. This thesis specifically tackles this problem by providing several tools for application development support. First, it provides the location-based publish/subscribe service (LPSS), a communication abstraction, which elegantly captures recurrent communication issues and thus allows to dramatically reduce the code complexity. LPSS is implemented in a modular manner in order to be able to target two different network architectures. One pragmatic implementation is aimed at mainstream infrastructure-based mobile networks, where mobile devices can communicate through fixed antennas. The other fully decentralized implementation targets emerging mobile ad hoc networks (MANETs), where no fixed infrastructure is available and communication can only occur in a peer-to-peer fashion. For each of these architectures, various implementation strategies tailored for different application scenarios that can be parametrized at deployment time. Second, this thesis provides two location-based message diffusion protocols, namely 6Shot broadcast and 6Shot multicast, specifically aimed at MANETs and fine tuned to be used as building blocks for LPSS. Finally this thesis proposes Phomo, a phone motion testing tool that allows to test proximity semantics of ad hoc applications without having to move around with mobile devices. These different developing support tools have been packaged in a coherent middleware framework called Pervaho.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

Expression of E-Selectin and Vascular Cell Adhesion Molecule-1 in So-Called 'Negative' Appendices: First Results to Support the Pathological Diagnosis in Borderline Cases.

Background: Since the rate of histologically 'negative' appendices still ranges between 15 and 20%, appendicitis in 'borderline' cases remains a challenging disease. As previously described, cell adhesion molecule expression correlates with different stages of appendicitis. Therefore, it was of interest to determine whether the 'negative' appendix correlated with the absence of E-selectin or vascular cell adhesion molecule-1 (VCAM-1). Methods: Nineteen grossly normal appendices from a series of 120 appendectomy specimens from patients with suspected appendicitis were analysed in frozen sections for the expression of E-selectin and VCAM-1. As control, 5 normal appendices were stained. Results: This study showed a coexpression of E-selectin and VCAM-1 in endothelial cells in early and recurrent appendicitis. In patients with symptoms for less than 6 h, only E-selectin was detected. Cases with fibrosis and luminal obliteration were only positive for VCAM-1. In cases of early appendicitis with symptoms of less than 6 h duration, a discordance between histological and immunohistochemical results was found. Conclusions: This report indicates that E-selectin and VCAM-1 expression could be useful parameters in the diagnosis of appendicitis in borderline cases.

TACI, unlike BAFF-R, is solely activated by oligomeric BAFF and APRIL to support survival of activated B cells and plasmablasts.

The cytokine BAFF binds to the receptors TACI, BCMA, and BAFF-R on B cells, whereas APRIL binds to TACI and BCMA only. The signaling properties of soluble trimeric BAFF (BAFF 3-mer) were compared with those of higher-order BAFF oligomers. All forms of BAFF bound BAFF-R and TACI, and elicited BAFF-R-dependent signals in primary B cells. In contrast, signaling through TACI in mature B cells or plasmablasts was only achieved by higher-order BAFF and APRIL oligomers, all of which were also po-tent activators of a multimerization-dependent reporter signaling pathway. These results indicate that, although BAFF-R and TACI can provide B cells with similar signals, only BAFF-R, but not TACI, can respond to soluble BAFF 3-mer, which is the main form of BAFF found in circulation. BAFF 60-mer, an efficient TACI agonist, was also detected in plasma of BAFF transgenic and nontransgenic mice and was more than 100-fold more active than BAFF 3-mer for the activation of multimerization-dependent signals. TACI supported survival of activated B cells and plasmablasts in vitro, providing a rational basis to explain the immunoglobulin deficiency reported in TACI-deficient persons.

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Le Soutien psychiatrique Intensif dans le Milieu (SIM) à Lausanne: un projet pilote [Intensive Psychiatric Support at the Setting in Lausanne: a pilot project]

While the hospital remains an important element of the psychiatric health-care system, the fact that it is always the best place to treat acute psychotic episodes is still debated. After a brief review of the literature relative to the main existing community care models, the authors describe the development in the Department Universitaire de Psychiatrie Adulte (DUPA), of an alternative to hospitalisation for patient going through a severe acute psychiatric episode. They present three clinical situations and the aims of the research project, which will follow this pilot phase.

System-level Support for Mobile Ad hoc Communications: an Algorithmic & Practical Approach

A mobile ad hoc network (MANET) is a decentralized and infrastructure-less network. This thesis aims to provide support at the system-level for developers of applications or protocols in such networks. To do this, we propose contributions in both the algorithmic realm and in the practical realm. In the algorithmic realm, we contribute to the field by proposing different context-aware broadcast and multicast algorithms in MANETs, namely six-shot broadcast, six-shot multicast, PLAN-B and ageneric algorithmic approach to optimize the power consumption of existing algorithms. For each algorithm we propose, we compare it to existing algorithms that are either probabilistic or context-aware, and then we evaluate their performance based on simulations. We demonstrate that in some cases, context-aware information, such as location or signal-strength, can improve the effciency. In the practical realm, we propose a testbed framework, namely ManetLab, to implement and to deploy MANET-specific protocols, and to evaluate their performance. This testbed framework aims to increase the accuracy of performance evaluation compared to simulations, while keeping the ease of use offered by the simulators to reproduce a performance evaluation. By evaluating the performance of different probabilistic algorithms with ManetLab, we observe that both simulations and testbeds should be used in a complementary way. In addition to the above original contributions, we also provide two surveys about system-level support for ad hoc communications in order to establish a state of the art. The first is about existing broadcast algorithms and the second is about existing middleware solutions and the way they deal with privacy and especially with location privacy. - Un réseau mobile ad hoc (MANET) est un réseau avec une architecture décentralisée et sans infrastructure. Cette thèse vise à fournir un support adéquat, au niveau système, aux développeurs d'applications ou de protocoles dans de tels réseaux. Dans ce but, nous proposons des contributions à la fois dans le domaine de l'algorithmique et dans celui de la pratique. Nous contribuons au domaine algorithmique en proposant différents algorithmes de diffusion dans les MANETs, algorithmes qui sont sensibles au contexte, à savoir six-shot broadcast,six-shot multicast, PLAN-B ainsi qu'une approche générique permettant d'optimiser la consommation d'énergie de ces algorithmes. Pour chaque algorithme que nous proposons, nous le comparons à des algorithmes existants qui sont soit probabilistes, soit sensibles au contexte, puis nous évaluons leurs performances sur la base de simulations. Nous montrons que, dans certains cas, des informations liées au contexte, telles que la localisation ou l'intensité du signal, peuvent améliorer l'efficience de ces algorithmes. Sur le plan pratique, nous proposons une plateforme logicielle pour la création de bancs d'essai, intitulé ManetLab, permettant d'implémenter, et de déployer des protocoles spécifiques aux MANETs, de sorte à évaluer leur performance. Cet outil logiciel vise à accroître la précision desévaluations de performance comparativement à celles fournies par des simulations, tout en conservant la facilité d'utilisation offerte par les simulateurs pour reproduire uneévaluation de performance. En évaluant les performances de différents algorithmes probabilistes avec ManetLab, nous observons que simulateurs et bancs d'essai doivent être utilisés de manière complémentaire. En plus de ces contributions principales, nous fournissons également deux états de l'art au sujet du support nécessaire pour les communications ad hoc. Le premier porte sur les algorithmes de diffusion existants et le second sur les solutions de type middleware existantes et la façon dont elles traitent de la confidentialité, en particulier celle de la localisation.