3D noise power spectrum applied on clinical mdct scanners: effects of reconstruction algorithms and reconstruction filters

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters.A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed.In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements.The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

Childhood socioeconomic position and objectively measured physical capability levels in adulthood: a systematic review and meta-analysis.

BACKGROUND: Grip strength, walking speed, chair rising and standing balance time are objective measures of physical capability that characterise current health and predict survival in older populations. Socioeconomic position (SEP) in childhood may influence the peak level of physical capability achieved in early adulthood, thereby affecting levels in later adulthood. We have undertaken a systematic review with meta-analyses to test the hypothesis that adverse childhood SEP is associated with lower levels of objectively measured physical capability in adulthood. METHODS AND FINDINGS: Relevant studies published by May 2010 were identified through literature searches using EMBASE and MEDLINE. Unpublished results were obtained from study investigators. Results were provided by all study investigators in a standard format and pooled using random-effects meta-analyses. 19 studies were included in the review. Total sample sizes in meta-analyses ranged from N = 17,215 for chair rise time to N = 1,061,855 for grip strength. Although heterogeneity was detected, there was consistent evidence in age adjusted models that lower childhood SEP was associated with modest reductions in physical capability levels in adulthood: comparing the lowest with the highest childhood SEP there was a reduction in grip strength of 0.13 standard deviations (95% CI: 0.06, 0.21), a reduction in mean walking speed of 0.07 m/s (0.05, 0.10), an increase in mean chair rise time of 6% (4%, 8%) and an odds ratio of an inability to balance for 5s of 1.26 (1.02, 1.55). Adjustment for the potential mediating factors, adult SEP and body size attenuated associations greatly. However, despite this attenuation, for walking speed and chair rise time, there was still evidence of moderate associations. CONCLUSIONS: Policies targeting socioeconomic inequalities in childhood may have additional benefits in promoting the maintenance of independence in later life.

A protocol for a systematic review on the impact of unpublished studies and studies published in the gray literature in meta-analyses.

BACKGROUND: Meta-analyses are particularly vulnerable to the effects of publication bias. Despite methodologists' best efforts to locate all evidence for a given topic the most comprehensive searches are likely to miss unpublished studies and studies that are published in the gray literature only. If the results of the missing studies differ systematically from the published ones, a meta-analysis will be biased with an inaccurate assessment of the intervention's effects.As part of the OPEN project (http://www.open-project.eu) we will conduct a systematic review with the following objectives:â-ª To assess the impact of studies that are not published or published in the gray literature on pooled effect estimates in meta-analyses (quantitative measure).â-ª To assess whether the inclusion of unpublished studies or studies published in the gray literature leads to different conclusions in meta-analyses (qualitative measure). METHODS/DESIGN: Inclusion criteria: Methodological research projects of a cohort of meta-analyses which compare the effect of the inclusion or exclusion of unpublished studies or studies published in the gray literature.Literature search: To identify relevant research projects we will conduct electronic searches in Medline, Embase and The Cochrane Library; check reference lists; and contact experts.Outcomes: 1) The extent to which the effect estimate in a meta-analyses changes with the inclusion or exclusion of studies that were not published or published in the gray literature; and 2) the extent to which the inclusion of unpublished studies impacts the meta-analyses' conclusions.Data collection: Information will be collected on the area of health care; the number of meta-analyses included in the methodological research project; the number of studies included in the meta-analyses; the number of study participants; the number and type of unpublished studies; studies published in the gray literature and published studies; the sources used to retrieve studies that are unpublished, published in the gray literature, or commercially published; and the validity of the methodological research project.Data synthesis: Data synthesis will involve descriptive and statistical summaries of the findings of the included methodological research projects. DISCUSSION: Results are expected to be publicly available in the middle of 2013.

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Subclinical thyroid dysfunction and the risk for fractures: a systematic review and meta-analysis.

BACKGROUND: Data on the association between subclinical thyroid dysfunction and fractures conflict. PURPOSE: To assess the risk for hip and nonspine fractures associated with subclinical thyroid dysfunction among prospective cohorts. DATA SOURCES: Search of MEDLINE and EMBASE (1946 to 16 March 2014) and reference lists of retrieved articles without language restriction. STUDY SELECTION: Two physicians screened and identified prospective cohorts that measured thyroid function and followed participants to assess fracture outcomes. DATA EXTRACTION: One reviewer extracted data using a standardized protocol, and another verified data. Both reviewers independently assessed methodological quality of the studies. DATA SYNTHESIS: The 7 population-based cohorts of heterogeneous quality included 50,245 participants with 1966 hip and 3281 nonspine fractures. In random-effects models that included the 5 higher-quality studies, the pooled adjusted hazard ratios (HRs) of participants with subclinical hyperthyroidism versus euthyrodism were 1.38 (95% CI, 0.92 to 2.07) for hip fractures and 1.20 (CI, 0.83 to 1.72) for nonspine fractures without statistical heterogeneity (P = 0.82 and 0.52, respectively; I2= 0%). Pooled estimates for the 7 cohorts were 1.26 (CI, 0.96 to 1.65) for hip fractures and 1.16 (CI, 0.95 to 1.42) for nonspine fractures. When thyroxine recipients were excluded, the HRs for participants with subclinical hyperthyroidism were 2.16 (CI, 0.87 to 5.37) for hip fractures and 1.43 (CI, 0.73 to 2.78) for nonspine fractures. For participants with subclinical hypothyroidism, HRs from higher-quality studies were 1.12 (CI, 0.83 to 1.51) for hip fractures and 1.04 (CI, 0.76 to 1.42) for nonspine fractures (P for heterogeneity = 0.69 and 0.88, respectively; I2 = 0%). LIMITATIONS: Selective reporting cannot be excluded. Adjustment for potential common confounders varied and was not adequately done across all studies. CONCLUSION: Subclinical hyperthyroidism might be associated with an increased risk for hip and nonspine fractures, but additional large, high-quality studies are needed. PRIMARY FUNDING SOURCE: Swiss National Science Foundation.

Subclinical hypothyroidism and the risk of coronary heart disease: a meta-analysis.

PURPOSE: Subclinical hypothyroidism has been associated with elevated cholesterol and increased risk for atherosclerosis, but data on the risk of coronary heart disease (CHD) are conflicting. We performed a systematic review to determine whether subclinical hypothyroidism is associated with CHD in adults. METHODS: We searched MEDLINE from 1966 to April 2005, and the bibliographies of key articles to identify studies that provided risk estimates for CHD or cardiovascular mortality associated with subclinical hypothyroidism. Two authors independently reviewed each potential study for eligibility, assessed methodologic quality, and extracted the data. RESULTS: We identified 14 observational studies that met eligibility criteria. Subclinical hypothyroidism increased the risk of CHD (summary odds ratio [OR]: 1.65, 95% confidence interval [CI], 1.28-2.12). The summary OR for CHD was 1.81 (CI, 1.38-2.39) in 9 studies adjusted or matched for demographic characteristics, and 2.38 (CI, 1.53-3.69) after pooling the studies that adjusted for most cardiovascular risk factors. Sensitivity analyses including only population-based studies and those with formal outcome adjudication procedures yielded similar results. Subgroup analyses by type of study design showed a similar trend, but lower risk, in the 5 prospective cohort studies (OR 1.42, CI, 0.91-2.21), compared with the case-control and cross-sectional studies (OR 1.72, CI, 1.25-2.38). CONCLUSION: Our systematic review indicates that subclinical hypothyroidism is associated with an increased risk of CHD. Clinical trials are needed to assess whether thyroxine replacement reduces the risk of CHD in subjects with subclinical hypothyroidism.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Association between the PNPLA3 (rs738409 C>G) variant and hepatocellular carcinoma: Evidence from a meta-analysis of individual participant data.

The incidence of hepatocellular carcinoma (HCC) is increasing in Western countries. Although several clinical factors have been identified, many individuals never develop HCC, suggesting a genetic susceptibility. However, to date, only a few single-nucleotide polymorphisms have been reproducibly shown to be linked to HCC onset. A variant (rs738409 C>G, encoding for p.I148M) in the PNPLA3 gene is associated with liver damage in chronic liver diseases. Interestingly, several studies have reported that the minor rs738409[G] allele is more represented in HCC cases in chronic hepatitis C (CHC) and alcoholic liver disease (ALD). However, a significant association with HCC related to CHC has not been consistently observed, and the strength of the association between rs738409 and HCC remains unclear. We performed a meta-analysis of individual participant data including 2,503 European patients with cirrhosis to assess the association between rs738409 and HCC, particularly in ALD and CHC. We found that rs738409 was strongly associated with overall HCC (odds ratio [OR] per G allele, additive model=1.77; 95% confidence interval [CI]: 1.42-2.19; P=2.78 × 10(-7) ). This association was more pronounced in ALD (OR=2.20; 95% CI: 1.80-2.67; P=4.71 × 10(-15) ) than in CHC patients (OR=1.55; 95% CI: 1.03-2.34; P=3.52 × 10(-2) ). After adjustment for age, sex, and body mass index, the variant remained strongly associated with HCC. Conclusion: Overall, these results suggest that rs738409 exerts a marked influence on hepatocarcinogenesis in patients with cirrhosis of European descent and provide a strong argument for performing further mechanistic studies to better understand the role of PNPLA3 in HCC development.

Efficacity of in-hospital multidimensional interventions of secondary prevention after acute coronary syndrome : a systematic review and meta-analysis

RAPPORT DE SYNTHÈSE : Contexte Les programmes de prévention cardiovasculaire secondaire après un événement coronarien aigu ont pu démontrer leur efficacité dans le contexte des soins ambulatoires. L'hospitalisation pour une maladie aiguë peut être considérée comme un «instant charnière», particulièrement adapté à un changement de comportement de santé et où des interventions de prévention secondaire, telle l'éducation du patient, pourraient être particulièrement efficaces. De plus, la prescription de médicaments de prévention cardiovasculaire durant l'hospitalisation semble augmenter la proportion des patients traités selon les recommandations sur le long terme. Récemment, plusieurs études ont évalué l'efficacité de programmes de prévention ayant pour but l'éducation des patients et/ou une augmentation du taux de prescription de médicaments prouvés efficaces par les médecins en charge. L'article faisant l'objet du travail de thèse synthétise la littérature existante concernant l'efficacité en termes de mortalité des interventions multidimensionnelles de prévention cardiovasculaire après un syndrome coronarien aigu, débutées à l'hôpital, centrées sur le patient et ciblant plusieurs facteurs de risque cardiovasculaire. MÉTHODE ET RÉSULTATS : En utilisant une stratégie de recherche définie à l'avance, nous avons inclus des essais cliniques avec groupe contrôle et des études avant-après, débutées à l'hôpital et qui incluaient des résultats cliniques de suivi en terme de mortalité, de taux de réadmission et/ou de récidive de syndrome coronarien aigu. Nous avons catégorisé les études selon qu'elles ciblaient les patients (par exemple une intervention d'éducation aux patients par des infirmières), les soignants (par exemple des cours destinés aux médecins-assistants pour leur enseigner comment prodiguer des interventions éducatives) ou le système de soins (par exemple la mise en place d'itinéraires cliniques au niveau de l'institution). Globalement, les interventions rapportées dans les 14 études répondant aux critères montraient une réduction du risque relatif (RR) de mortalité après un an (RR= 0.79; 95% intervalle de confiance (IC), 0.69-0.92; n=37'585). Cependant, le bénéfice semblait dépendre du type d'étude et du niveau d'intervention. Les études avant-après suggéraient une réduction du risque de mortalité (RR, 0.77; 95% IC, 0.66-0.90; n=3680 décès), tandis que le RR était de 0.96 (95% IC, 0.64-1.44; n=99 décès) pour les études cliniques contrôlées. Seules les études avant-après et les études ciblant les soignants et le système, en plus de cibler les patients, semblaient montrer un bénéfice en termes de mortalité à une année. CONCLUSIONS ET PERSPECTIVES : Les preuves d'efficacité des interventions de prévention secondaires débutées à l'hôpital, ciblant le patient, sont prometteuses, mais pas définitives. En effet, seules les études avant-après montrent un bénéfice en termes de mortalité. Les recherches futures dans ce domaine devraient tester formellement quels éléments des interventions amènent le plus de bénéfices pour les patients.

Meta-analysis: subclinical thyroid dysfunction and the risk for coronary heart disease and mortality

BACKGROUND: Data on the association between subclinical thyroid dysfunction and coronary heart disease (CHD) and mortality are conflicting. PURPOSE: To summarize prospective evidence about the relationship between subclinical thyroid dysfunction and CHD and mortality. DATA SOURCES: MEDLINE (1950 to January 2008) without language restrictions and reference lists of retrieved articles were searched. STUDY SELECTION: Two reviewers screened and selected cohort studies that measured thyroid function and then followed persons prospectively to assess CHD or mortality. DATA EXTRACTION: By using a standardized protocol and forms, 2 reviewers independently abstracted and assessed studies. DATA SYNTHESIS: Ten of 12 identified studies involved population-based cohorts that included 14 449 participants. All 10 population-based cohort studies examined risks associated with subclinical hypothyroidism (2134 CHD events and 2822 deaths), whereas only 5 examined risks associated with subclinical hyperthyroidism (1392 CHD events and 1993 deaths). In a random-effects model, the relative risk (RR) for subclinical hypothyroidism for CHD was 1.20 (95% CI, 0.97 to 1.49; P for heterogeneity = 0.14; I(2 )= 33.4%). Risk estimates were lower when higher-quality studies were pooled (RR, 1.02 to 1.08) and were higher among participants younger than 65 years (RR, 1.51 [CI, 1.09 to 2.09] for studies with mean participant age <65 years and 1.05 [CI, 0.90 to 1.22] for studies with mean participant age > or =65 years). The RR was 1.18 (CI, 0.98 to 1.42) for cardiovascular mortality and 1.12 (CI, 0.99 to 1.26) for total mortality. For subclinical hyperthyroidism, the RR was 1.21 (CI, 0.88 to 1.68) for CHD, 1.19 (CI, 0.81 to 1.76) for cardiovascular mortality, and 1.12 (CI, 0.89 to 1.42) for total mortality (P for heterogeneity >0.50; I(2 )= 0% for all studies). LIMITATIONS: Individual studies adjusted for different potential confounders, and 1 study provided only unadjusted data. Publication bias or selective reporting of outcomes could not be excluded. CONCLUSION: Subclinical hypothyroidism and hyperthyroidism may be associated with a modest increased risk for CHD and mortality, with lower risk estimates when pooling higher-quality studies and larger CIs for subclinical hyperthyroidism

Clinical effectiveness of direct class II restorations: a meta-analysis.

Purpose: More than five hundred million direct dental restorations are placed each year worldwide. In about 55% of the cases, resin composites or compomers are used, and in 45% amalgam. The longevity of posterior resin restorations is well documented. However, data on resin composites that are placed without enamel/dentin conditioning and resin composites placed with self-etching adhesive systems are missing. Material and Methods: The database SCOPUS was searched for clinical trials on posterior resin composites without restricting the search to the year of publication. The inclusion criteria were: (1) prospective clinical trial with at least 2 years of observation; (2) minimum number of restorations at last recall = 20; (3) report on dropout rate; (4) report of operative technique and materials used; (5) utilization of Ryge or modified Ryge evaluation criteria. For amalgam, only those studies were included that directly compared composite resin restorations with amalgam. For the statistical analysis, a linear mixed model was used with random effects to account for the heterogeneity between the studies. P-values under 0.05 were considered significant. Results: Of the 373 clinical trials, 59 studies met the inclusion criteria. In 70% of the studies, Class II and Class I restorations had been placed. The overall success rate of composite resin restorations was about 90% after 10 years, which was not different from that of amalgam. Restorations with compomers had a significantly lower longevity. The main reason for replacement were bulk fractures and caries adjacent to restorations. Both of these incidents were infrequent in most studies and accounted only for about 6% of all replaced restorations after 10 years. Restorations with macrofilled composites and compomer suffered significantly more loss of anatomical form than restorations with other types of material. Restorations that were placed without enamel acid etching and a dentin bonding agent showed significantly more marginal staining and detectable margins compared to those restorations placed using the enamel-etch or etch-and-rinse technique; restorations with self-etching systems were between the other groups. Restorations with compomer suffered significantly more chippings (repairable fracture) than restorations with other materials, which did not statistically differ among each other. Restorations that were placed with a rubber-dam showed significantly fewer material fractures that needed replacement, and this also had a significant effect on the overall longevity. Conclusion: Restorations with hybrid and microfilled composites that were placed with the enamel-etching technique and rubber-dam showed the best overall performance; the longevity of these restorations was similar to amalgam restorations. Compomer restorations, restorations placed with macrofilled composites, and resin restorations with no-etching or self-etching adhesives demonstrated significant shortcomings and shorter longevity.

Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations.

Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2x10(-201)), ABCG2 (p = 3.1x10(-26)), SLC17A1 (p = 3.0x10(-14)), SLC22A11 (p = 6.7x10(-14)), SLC22A12 (p = 2.0x10(-9)), SLC16A9 (p = 1.1x10(-8)), GCKR (p = 1.4x10(-9)), LRRC16A (p = 8.5x10(-9)), and near PDZK1 (p = 2.7x10(-9)). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0x10(-26)) and propionyl-L-carnitine (p = 5.0x10(-8)) concentrations, which in turn were associated with serum UA levels (p = 1.4x10(-57) and p = 8.1x10(-54), respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.

Meta-analysis : subclinical thyroid dysfunction and the risk for coronary heart disease and mortality

Rapport de synthèse : Description : ce travail de thèse évalue de façon systématique les études sur l'association entre les dysfonctions thyroïdiennes infracliniques d'une part, et la maladie coronarienne et la mortalité d'autre part. Les hypothyroïdies infracliniques affectent environ 4-5% de la population adulte alors que la prévalence de l'hyperthyroïdie infraclinique est inférieure (environ 1%). L'éventuelle association entre elles pourrait justifier un dépistage systématique des dysfonctions thyroïdiennes infracliniques. Les précédentes études sur l'association entre l'hypothyroïdie infraclinique et la maladie coronarienne ont donné des résultats conflictuels. La parution de nouveaux articles récents basés sur de grandes cohortes prospectives nous a permis d'effectuer une méta-analyse basée uniquement sur des études de cohorte prospectives, augmentant ainsi la validité des résultats. Résultats: 10 des 12 études identifiées pour notre revue systématique sont basées sur des cohortes issues de la population générale («population-based »), regroupant en tout 14 449 participants. Ces 10 études examinent toutes le risque associé à l'hypothyroïdie infraclinique (avec 2134 événements coronariens et 2822 décès), alors que 5 étudient également le risque associé à l'hyperthyroïdie infraclinique (avec 1392 événements coronariens et 1993 décès). En utilisant un modèle statistique de type random-effect model, le risque relatif [RR] lié à l'hypothyroïdie infraclinique pour la maladie coronarienne est de 1.20 (intervalle de confiance [IC] de 95%, 0.97 à 1.49). Le risque diminue lorsque l'on regroupe uniquement les études de meilleure qualité (RR compris entre 1.02 et 1.08). Il est plus élevé parmi les participants de moins de 65 ans (RR, 1.51 [IC, 1.09 à 2.09] et 1.05 [IC, 0.90 à 1.22] pour les études dont l'âge moyen des participants est >_ 65 ans). Le RR de la mortalité cardiovasculaire est de 1.18 (IC, 0.98 à 1.42) et de 1.12 (IC, 0.99 à 1.26) pour la mortalité totale. En cas d'hyperthyroïdie infraclinique, les RR de la maladie coronarienne sont de 1.21 (IC, 0.88 à 1.68), de 1.19 (IC, 0.81 à 1.76) pour la mortalité cardiovasculaire, et de 1.12 (IC, 0.89 à 1.42) pour la mortalité totale. Conclusions et perspectives : nos résultats montrent que les dysfonctions thyroïdiennes infracliniques (hypothyroïdie et hyperthyroïdie infracliniques) représentent un facteur de risque modifiable, bien que modéré, de la maladie coronarienne et de la mortalité. L'efficacité du traitement de ces dysfonctions thyroïdiennes infracliniques doit encore être prouvée du point de vue cardiovasculaire et de la mortalité. Il est nécessaire d'effectuer des études contrôlées contre placebo avec le risque cardiovasculaire et la mortalité comme critères d'efficacité, avant de pouvoir proposer des recommandations sur le dépistage des ces dysfonctions thyroïdiennes dans la population adulte.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.