Péritonites infectieuses en dialyse péritonéale: Facteurs prédictifs et complications. Etude rétrospective au CHUV de 1995 à 2010

Contexte : La dialyse péritonéale (DP) est une méthode d'épuration extra-rénale qui utilise les propriétés physiologiques du péritoine comme membrane de dialyse. Cette technique requiert la présence d'un cathéter placé chirurgicalement dans le cul-de-sac de Douglas pour permettre l'instillation d'une solution de dialyse : le dialysat. Une des complications redoutée de cette technique est la survenue de péritonites infectieuses qui nécessitent l'administration rapide d'une antibiothérapie adéquate. Les péritonites peuvent parfois entrainer le retrait du cathéter de dialyse avec un échec définitif de la technique, ou plus rarement entrainer le décès du patient. Cette étude s'intéresse aux facteurs prédictifs de cette complication. Elle recense les germes impliqués et leur sensibilité aux différents antibiotiques. Cette étude analyse également les conséquences des péritonites, telles que la durée moyenne des hospitalisations, les échecs de la technique nécessitant un transfert définitif en hémodialyse et la survenue de décès. Méthode : Il s'agit d'une étude rétrospective monocentrique portant sur le dossier des patients inclus dans le programme de dialyse péritonéale du CHUV entre le 1er janvier 1995 et le 31 décembre 2010. Résultats : Cette étude inclus 108 patients, dont 65 hommes et 43 femmes. L'âge moyen est de 52.5 ans ± 17.84 (22-87). On répertorie 113 épisodes de péritonite pour une durée cumulative de 2932.24 mois x patients. L'incidence globale de péritonite s'élève à 1 épisode / 25.95 (mois x patient). La médiane de survie globale sans péritonite est de 23.56 mois. Une variabilité intergroupe statistiquement significative en matière de survie sans péritonite est démontrée entre les patients autonomes et non- autonomes [Log Rank (Mantel-Cox) :0.04], entre les patients diabétiques et non diabétiques [Log Rank (Mantel-Cox) : 0.002] et entre les patients cumulant un score de Charlson supérieur à 5 et ceux cumulant un score inférieur ou égal à 5 (Log Rank (Mantel-Cox) : 0.002). Une différence statistiquement significative en matière de survie de la technique a également pu être démontrée entre les patients autonomes et 2 non-autonome [Log Rank (Mantel-Cox) < 0.001], et entre les patients cumulant un score de Charlson supérieur ou inférieur ou égal à 5 [Log Rank (Mantel-Cox) : 0.047]. Le staphylococcus epidermidis est le pathogène le plus fréquemment isolé lors des péritonites (23.9%). Ce germe présente une sensibilité de 40.74% à l'oxacilline. Aucun cas de péritonite à MRSA n'a été enregistré dans ce collectif de patients. Une péritonite a causé la mort d'un patient (<1%). Conclusion : L'incidence de péritonite calculée satisfait les recommandations de la Société Internationale de Dialyse Péritonéale (ISPD). Une variabilité intergroupe statistiquement significative en terme de survie sans péritonite est mis en évidence pour : l'autonomie, le statut métabolique et le score de comorbidité de Charlson. Une variabilité intergroupe statistiquement significative en terme de survie de la technique est également démontrée pour : l'autonomie et le score de comorbidité de Charlson. Les statistiques de sensibilité mettent en évidence une excellente couverture antibiotique sur les germes isolés par le traitement empirique en vigueur (vancomycine + ceftazidime). La mortalité relative aux péritonites est extrêmement basse dans ce collectif de patients

The Effect of Melatonin on Behavioral, Molecular, and Histopathological Changes in Cuprizone Model of Demyelination

Multiple sclerosis (MS) is an autoimmune, demyelinating disease of the central nervous system. The protective effects of melatonin (MLT) on various neurodegenerative diseases, including MS, have been suggested. In the present study, we examined the effect of MLT on demyelination, apoptosis, inflammation, and behavioral dysfunctions in the cuprizone toxic model of demyelination. C57BL/6J mice were fed a chaw containing 0.2 % cuprizone for 5 weeks and received two doses of MLT (50 and 100 mg/kg) intraperitoneally for the last 7 days of cuprizone diet. Administration of MLT improved motor behavior deficits induced by cuprizone diet. MLT dose-dependently decreased the mean number of apoptotic cells via decreasing caspase-3 and Bax as well as increasing Bcl-2 levels. In addition, MLT significantly enhanced nuclear factor-κB activation and decreased heme oxygenase-1 level. However, MLT had no effect on interleukin-6 and myelin protein production. Our data revealed that MLT improved neurological deficits and enhanced cell survival but was not able to initiate myelin production in the cuprizone model of demyelination. These findings may be important for the design of potential MLT therapy in demyelinating disorders, such as MS. © 2015, Springer Science+Business Media New York

The Prevalence of Personality Disorders in Male Prisoners of Shahr-e-Kord Prison

Objectives: The purpose of the present study was to determine the prevalence of personality disorders in male prisoners in Shahr-e-Kord prison. Method: 203 men, 16 years or older were selected through a systemic random procedure as the subjects of the study. They were then assessed by a clinical interview checklist based on ICD-10 diagnostic criteria. Where there was a discrepancy on diagnosis, MMPI-2 was used as an aid. Findings: The prevalence of personality disorder was 55.2% amongst the subjects. The most prevalent

Calibration and Reduction of Large-Scale Dynamic Models - Application to Wind Turbine Blades

This thesis investigates the validity of structural dynamics models of wind turbine blades. An outlook on methods for model calibration to make models valid for their intended use is presented in the thesis. The intention is to make the models valid for robust predictions. The model validity is here assessed to be of hierarchical dual level. On one hand, a detailed structural dynamics model needs to be substantiated by good correlation between experimental results of wind turbine testing and theoretical simulation results using that model. On the other hand, after that detailed model has been validated, a model of significantly low order based on the detailed model has been validated by a good model-to-model correlation. With the connection between models, this implies that also the low order model is implicitly validated by testing. The development of a highly detailed structural dynamics model provides real physical insights to observation made during testing. This model is often developed using finite element analysis. A model verification and validation activity is done to create a three dimensional finite element model that is capable to predict the dynamics of wind turbine blade with sufficient accuracy. Integration of such large-scale models of wind turbine blades in aeroelastic simulations places an untenable demand on computational resources and, hence, means of speed-up become necessary. The common practice is to develop, calibrate and validate an industry-standard beam model against the simulated data obtained from the validated highly detailed rotor blade model. However, the validated beam model cannot well capture the coupling features of the highly detailed model because of its inherent limitations. Our scientific hypothesis is that it is possible to create low-order rotor blade models which preserve the vibrational pattern of the baseline model at its eigenfrequecies and also closely mimic its input-output behavior. Toward this end, a quasi optimal modal truncation algorithm is developed to yield reduced models which have the eigenmodes with highest contribution to the input-output map of the large-scale model. The predictive capability of the created reduced model is compared with that of the validated beam model

Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes

Approximate Bayesian Computation (ABC) methods have gained in popularity over the last decade because they expand the horizon of Bayesian parameter inference methods to the range of models for which an analytical formula for the likelihood function might be difficult, or even impossible, to establish. The majority of the ABC methods rely on the choice of a set of summary statistics to reduce the dimension of the data. However, as has been noted in the ABC literature, the lack of convergence guarantees induced by the absence of a vector of sufficient summary statistics that assures intermodel sufficiency over the set of competing models hinders the use of the usual ABC methods when applied to Bayesian model selection or assessment. In this paper, we present a novel ABC model selection procedure for dynamical systems based on a recently introduced multilevel Markov chain Monte Carlo method, self-regulating ABC-SubSim, and a hierarchical state-space formulation of dynamic models. We show that this formulation makes it possible to independently approximate the model evidence required for assessing the posterior probability of each of the competing models. We also show that ABC-SubSim not only provides an estimate of the model evidence as a simple by-product but also gives the posterior probability of each model as a function of the tolerance level, which allows the ABC model choices made in previous studies to be understood. We illustrate the performance of the proposed framework for ABC model updating and model class selection by applying it to two problems in Bayesian system identification: a single-degree-of-freedom bilinear hysteretic oscillator and a three-story shear building with Masing hysteresis, both of which are subject to a seismic excitation

Calibration and Reduction of Large-Scale Dynamic Models - Application to Wind Turbine Blades

This thesis investigates the validity of structural dynamics models of wind turbine blades. An outlook on methods for model calibration to make models valid for their intended use is presented in the thesis. The intention is to make the models valid for robust predictions. The model validity is here assessed to be of hierarchical dual level. On one hand, a detailed structural dynamics model needs to be substantiated by good correlation between experimental results of wind turbine testing and theoretical simulation results using that model. On the other hand, after that detailed model has been validated, a model of significantly low order based on the detailed model has been validated by a good model-to-model correlation. With the connection between models, this implies that also the low order model is implicitly validated by testing. The development of a highly detailed structural dynamics model provides real physical insights to observation made during testing. This model is often developed using finite element analysis. A model verification and validation activity is done to create a three dimensional finite element model that is capable to predict the dynamics of wind turbine blade with sufficient accuracy. Integration of such large-scale models of wind turbine blades in aeroelastic simulations places an untenable demand on computational resources and, hence, means of speed-up become necessary. The common practice is to develop, calibrate and validate an industry-standard beam model against the simulated data obtained from the validated highly detailed rotor blade model. However, the validated beam model cannot well capture the coupling features of the highly detailed model because of its inherent limitations. Our scientific hypothesis is that it is possible to create low-order rotor blade models which preserve the vibrational pattern of the baseline model at its eigenfrequecies and also closely mimic its input-output behavior. Toward this end, a quasi optimal modal truncation algorithm is developed to yield reduced models which have the eigenmodes with highest contribution to the input-output map of the large-scale model. The predictive capability of the created reduced model is compared with that of the validated beam model

Subsegmentation of the Kidney in Experimental MR Images Using Morphology-Based Regions-of-Interest or Multiple-Layer Concentric Objects.

Functional renal MRI promises access to a wide range of physiologically relevant parameters such as blood oxygenation, perfusion, tissue microstructure, pH, and sodium concentration. For quantitative comparison of results, representative values must be extracted from the parametric maps obtained with these different MRI techniques. To improve reproducibility of results this should be done based on regions-of-interest (ROIs) that are clearly and objectively defined.Semiautomated subsegmentation of the kidney in magnetic resonance images represents a simple but very valuable approach for the quantitative analysis of imaging parameters in multiple ROIs that are associated with specific anatomic locations. Thereby, it facilitates comparing MR parameters between different kidney regions, as well as tracking changes over time.Here we provide detailed step-by-step instructions for two recently developed subsegmentation techniques that are suitable for kidneys of small rodents: i) the placement of ROIs in cortex, outer and the inner medulla based on typical kidney morphology and ii) the division of the kidney into concentrically oriented layers.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers

Approximate Bayesian Computation by Subset Simulation for model selection in dynamical systems

Approximate Bayesian Computation (ABC) methods are originally conceived to expand the horizon of Bayesian inference methods to the range of models for which only forward simulation is available. However, there are well-known limitations of the ABC approach to the Bayesian model selection problem, mainly due to lack of a sufficient summary statistics that work across models. In this paper, we show that formulating the standard ABC posterior distribution as the exact posterior PDF for a hierarchical state-space model class allows us to independently estimate the evidence for each alternative candidate model. We also show that the model evidence is a simple by-product of the ABC-SubSim algorithm. The validity of the proposed approach to ABC model selection is illustrated using simulated data from a three-story shear building with Masing hysteresis