Urotensin-II System in Genetic Control of Blood Pressure and Renal Function.

Urotensin-II controls ion/water homeostasis in fish and vascular tone in rodents. We hypothesised that common genetic variants in urotensin-II pathway genes are associated with human blood pressure or renal function. We performed family-based analysis of association between blood pressure, glomerular filtration and genes of the urotensin-II pathway (urotensin-II, urotensin-II related peptide, urotensin-II receptor) saturated with 28 tagging single nucleotide polymorphisms in 2024 individuals from 520 families; followed by an independent replication in 420 families and 7545 unrelated subjects. The expression studies of the urotensin-II pathway were carried out in 97 human kidneys. Phylogenetic evolutionary analysis was conducted in 17 vertebrate species. One single nucleotide polymorphism (rs531485 in urotensin-II gene) was associated with adjusted estimated glomerular filtration rate in the discovery cohort (p = 0.0005). It showed no association with estimated glomerular filtration rate in the combined replication resource of 8724 subjects from 6 populations. Expression of urotensin-II and its receptor showed strong linear correlation (r = 0.86, p<0.0001). There was no difference in renal expression of urotensin-II system between hypertensive and normotensive subjects. Evolutionary analysis revealed accumulation of mutations in urotensin-II since the divergence of primates and weaker conservation of urotensin-II receptor in primates than in lower vertebrates. Our data suggest that urotensin-II system genes are unlikely to play a major role in genetic control of human blood pressure or renal function. The signatures of evolutionary forces acting on urotensin-II system indicate that it may have evolved towards loss of function since the divergence of primates.

The persistence of lifestyles: rates and correlates of homicide in Western Europe from 1960 to 2010

This article analyses rates and correlates of homicide in 15 West European countries from 1960 to 2010. The results show that the levels of homicide in 2010 and the trends in homicide from 1960 to 2010 are not related to any of the traditional demographic and socioeconomic predictors of crime. Homicide victimization rates show an increase from the mid-1960s until the early 1990s, and a decrease since then. Victims of both genders and all group ages follow the same trend, except in the case of infanticide, which decreased during the whole period. These results do not support the hypothesis of a homicide trend driven by the evolution of victimization of young men in public space. The authors propose an explanation based on a lifestyle approach.

Historical reconstruction of polychlorinated biphenyl (PCB) exposures for workers in a capacitor manufacturing plant

We developed a semiquantitative job exposure matrix (JEM) for workers exposed to polychlorinated biphenyls (PCBs) at a capacitor manufacturing plant from 1946 to 1977. In a recently updated mortality study, mortality of prostate and stomach cancer increased with increasing levels of cumulative exposure estimated with this JEM (trend p values = 0.003 and 0.04, respectively). Capacitor manufacturing began with winding bales of foil and paper film, which were placed in a metal capacitor box (pre-assembly), and placed in a vacuum chamber for flood-filling (impregnation) with dielectric fluid (PCBs). Capacitors dripping with PCB residues were then transported to sealing stations where ports were soldered shut before degreasing, leak testing, and painting. Using a systematic approach, all 509 unique jobs identified in the work histories were rated by predetermined process- and plant-specific exposure determinants; then categorized based on the jobs' similarities (combination of exposure determinants) into 35 job exposure categories. The job exposure categories were ranked followed by a qualitative PCB exposure rating (baseline, low, medium, and high) for inhalation and dermal intensity. Category differences in other chemical exposures (solvents, etc.) prevented further combining of categories. The mean of all available PCB concentrations (1975 and 1977) for jobs within each intensity rating was regarded as a representative value for that intensity level. Inhalation (in microgram per cubic milligram) and dermal (unitless) exposures were regarded as equally important. Intensity was frequency adjusted for jobs with continuous or intermittent PCB exposures. Era-modifying factors were applied to the earlier time periods (1946-1974) because exposures were considered to have been greater than in later eras (1975-1977). Such interpolations, extrapolations, and modifying factors may introduce non-differential misclassification; however, we do believe our rigorous method minimized misclassification, as shown by the significant exposure-response trends in the epidemiologic analysis.

Statistically significant changes of antimüllerian hormone and inhibin levels during the physiologic menstrual cycle in reproductive age women.

OBJECTIVE: To define the dynamics of antimüllerian hormone (AMH) and inhibins during the physiologic menstrual cycle. DESIGN: Longitudinal study. SETTING: University hospital. PATIENT(S): 36 young, healthy, normal weight Caucasian women without medication. INTERVENTION(S): Normal ovulatory menstrual cycles were evaluated by regular blood sampling taken every other day and periovulatory every day. MAIN OUTCOME MEASURE(S): Serum concentrations of AMH, inhibin A and B, follicle-stimulating hormone (FSH), luteinizing hormone (LH), prolactin, estradiol, progesterone, and free testosterone were measured in all blood samples. RESULT(S): Median AMH levels are statistically significantly higher in the late follicular compared with ovulation or the early luteal phase. There are statistically significant correlations between both AMH and FSH, and AMH and free testosterone in all cycle phases. Inhibin A increases strongly in the late follicular phase and peaks at day LH + 4. Inhibin B shows a broad midfollicular and a sharp early luteal peak, the difference being statistically significant between day LH + 4 and the earlier time points and between day LH + 2 and day LH. Although there is a negative association between inhibin A or B and the body mass index (BMI), there is no correlation between AMH and the BMI. CONCLUSION(S): Levels of AMH show a statistically significant change during the menstrual cycle and may influence the circulating gonadotropin and steroid hormone levels.

Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster.

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

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.

Clinicopathological features of eyelid skin tumors. A retrospective study of 5504 cases and review of literature.

Eyelid tumors are the most common neoplasm in daily ophthalmology practice and encompass a wide variety of benign and malignant tumors. In this retrospective study, we report the clinical and histological features of 5504 eyelid skin tumors diagnosed at the Laboratory of Ophthalmopathology of the Hôpital Ophtalmique Jules Gonin, Lausanne, Switzerland, between January 1989 and December 2007. Benign tumors largely predominated over malignant ones, representing 84% of cases in this series, and the 5 most frequent subtypes were squamous cell papilloma (26%), seborrheic keratosis (21%), melanocytic nevus (20%), hidrocystoma (8%), and xanthoma/xanthelasma (6%). Basal cell carcinoma was the most frequent malignant tumor (86%), followed by squamous cell carcinoma (7%) and sebaceous carcinoma (3%). For several tumor subtypes, there was a poor correlation between clinical and histological diagnosis, stressing the numerous pitfalls in the diagnosis of eyelid tumors. We further discuss our results with reference to previously published series.

Extensor carpi ulnaris accessory tendinous slips (ECUATS): MRI features at 3.0T and correlation with tendinosis : 49

Purpose: To assess the visibility and the features of ECUATS on 3.0-T MRI studies, and evaluate their correlation with tendinosis. Methods and materials: Our retrospective study was approved by IRB, with waiver of informed consent. Fifty wrist MRI and 48 MR arthrographies from 98 patients (55 males, 43 females, mean age 42.3 years) performed between January and November 2009 on 3.0-T units were reviewed. Images (transverse T1, T2, FS Gd T1 and VIBE) were independently analyzed by two radiologists, and a consensus reached with a third reader in case of disagreement. The visibility of ECUATS was assessed on each available transverse sequence. When present, ECUATS' origins, diameters and insertions were noted. ECU tendinosis was also evaluated. Inter-rater agreement was assessed using Cohen's Kappa coefficient. Results: ECUATS observed prevalence was 23.5% (23/98). ECUATS were more frequently noted on the VIBE sequence, with a good inter-rater agreement (Kappa = 0.72). Origins were noted in 95.7% of cases: 3 were at the level of, and 20 distal to ECU subsheath. Insertions were seen in 43.5%: 2 were on 5th metacarpal bone, 8 on extensor apparatus of 5th finger. ECUATS mean shortest and longest diameters were 0.54 and 0.85 mm respectively. ECU tendinosis was statistically more frequently noted in patients with ECUATS (p <0.05). Conclusion: ECUATS are readily visible on 3.0-T MRI studies, especially on transverse GRE VIBE images. ECU tendinosis is more frequently noted in patients bearing ECUATS.

Effect of spatial processes on the evolution of ecological niches and dispersal in metacommunity systems

RésuméLa coexistence de nombreuses espèces différentes a de tout temps intrigué les biologistes. La diversité et la composition des communautés sont influencées par les perturbations et l'hétérogénéité des conditions environnementales. Bien que dans la nature la distribution spatiale des conditions environnementales soit généralement autocorrélée, cet aspect est rarement pris en compte dans les modèles étudiant la coexistence des espèces. Dans ce travail, nous avons donc abordé, à l'aide de simulations numériques, la coexistence des espèces ainsi que leurs caractéristiques au sein d'un environnement autocorrélé.Afin de prendre en compte cet élément spatial, nous avons développé un modèle de métacommunauté (un ensemble de communautés reliées par la dispersion des espèces) spatialement explicite. Dans ce modèle, les espèces sont en compétition les unes avec les autres pour s'établir dans un nombre de places limité, dans un environnement hétérogène. Les espèces sont caractérisées par six traits: optimum de niche, largeur de niche, capacité de dispersion, compétitivité, investissement dans la reproduction et taux de survie. Nous nous sommes particulièrement intéressés à l'influence de l'autocorrélation spatiale et des perturbations sur la diversité des espèces et sur les traits favorisés dans la métacommunauté. Nous avons montré que l'autocorrélation spatiale peut avoir des effets antagonistes sur la diversité, en fonction du taux de perturbations considéré. L'influence de l'autocorrélation spatiale sur la capacité de dispersion moyenne dans la métacommunauté dépend également des taux de perturbations et survie. Nos résultats ont aussi révélé que de nombreuses espèces avec différents degrés de spécialisation (i.e. différentes largeurs de niche) peuvent coexister. Toutefois, les espèces spécialistes sont favorisées en absence de perturbations et quand la dispersion est illimitée. A l'opposé, un taux élevé de perturbations sélectionne des espèces plus généralistes, associées avec une faible compétitivité.L'autocorrélation spatiale de l'environnement, en interaction avec l'intensité des perturbations, influence donc de manière considérable la coexistence ainsi que les caractéristiques des espèces. Ces caractéristiques sont à leur tour souvent impliquées dans d'importants processus, comme le fonctionnement des écosystèmes, la capacité des espèces à réagir aux invasions, à la fragmentation de l'habitat ou aux changements climatiques. Ce travail a permis une meilleure compréhension des mécanismes responsables de la coexistence et des caractéristiques des espèces, ce qui est crucial afin de prédire le devenir des communautés naturelles dans un environnement changeant.AbstractUnderstanding how so many different species can coexist in nature is a fundamental and long-standing question in ecology. Community diversity and composition are known to be influenced by heterogeneity in environmental conditions and disturbance. Though in nature the spatial distribution of environmental conditions is frequently autocorrelated, this aspect is seldom considered in models investigating species coexistence. In this work, we thus addressed several questions pertaining to species coexistence and composition in spatially autocorrelated environments, with a numerical simulations approach.To take into account this spatial aspect, we developed a spatially explicit model of metacommunity (a set of communities linked by dispersal of species). In this model, species are trophically equivalent, and compete for space in a heterogeneous environment. Species are characterized by six life-history traits: niche optimum, niche breadth, dispersal, competitiveness, reproductive investment and survival rate. We were particularly interested in the influence of environmental spatial autocorrelation and disturbance on species diversity and on the traits of the species favoured in the metacommunity. We showed that spatial autocorrelation can have antagonistic effects on diversity depending on disturbance rate. Similarly, spatial autocorrelation interacted with disturbance rate and survival rate to shape the mean dispersal ability observed in the metacommunity. Our results also revealed that many species with various degrees of specialization (i.e. different niche breadths) can coexist together. However specialist species were favoured in the absence of disturbance, and when dispersal was unlimited. In contrast, high disturbance rate selected for more generalist species, associated with low competitive ability.The spatial structure of the environment, together with disturbance and species traits, thus strongly impacts species diversity and, more importantly, species composition. Species composition is known to affect several important metacommunity properties such as ecosystem functioning, resistance and reaction to invasion, to habitat fragmentation and to climate changes. This work allowed a better understanding of the mechanisms responsible for species composition, which is of crucial importance to predict the fate of natural metacommunities in changing environments

Temporal dynamics of auditory what and where processing in humans

Recent evidence suggests the human auditory system is organized,like the visual system, into a ventral 'what' pathway, devoted toidentifying objects and a dorsal 'where' pathway devoted to thelocalization of objects in space w1x. Several brain regions have beenidentified in these two different pathways, but until now little isknown about the temporal dynamics of these regions. We investigatedthis issue using 128-channel auditory evoked potentials(AEPs).Stimuli were stationary sounds created by varying interaural timedifferences and environmental real recorded sounds. Stimuli ofeach condition (localization, recognition) were presented throughearphones in a blocked design, while subjects determined theirposition or meaning, respectively.AEPs were analyzed in terms of their topographical scalp potentialdistributions (segmentation maps) and underlying neuronalgenerators (source estimation) w2x.Fourteen scalp potential distributions (maps) best explained theentire data set.Ten maps were nonspecific (associated with auditory stimulationin general), two were specific for sound localization and two werespecific for sound recognition (P-values ranging from 0.02 to0.045).Condition-specific maps appeared at two distinct time periods:;200 ms and ;375-550 ms post-stimulus.The brain sources associated with the maps specific for soundlocalization were mainly situated in the inferior frontal cortices,confirming previous findings w3x. The sources associated withsound recognition were predominantly located in the temporal cortices,with a weaker activation in the frontal cortex.The data show that sound localization and sound recognitionengage different brain networks that are apparent at two distincttime periods.References1. Maeder et al. Neuroimage 2001.2. Michel et al. Brain Research Review 2001.3. Ducommun et al. Neuroimage 2002.

Hemispheric language asymmetry in first episode psychosis and schizotypy : the role of cannabis consumption and cognitive disorganization

Cannabis use has been related to an elevated psychosis risk and attenuated cognitive functioning. Cannabis-related cognitive impairments are also observed in populations along the psychosis dimension. We here investigated whether a potential behavioural marker of the psychosis dimension (attenuated functional hemispheric asymmetry) is even further attenuated in individuals using cannabis (CU) versus those not using cannabis (nCU). We tested 29 patients with first episode psychosis (FEP; 11 CU) and 90 healthy controls (38 CU) on lateralized lexical decisions assessing left hemisphere language dominance. In patients, psychotic symptoms were assessed (PANSS). In controls, self-reported schizotypy was assessed (O-LIFE questionnaire). Results indicated that nCU FEP patients had a relative reduced hemispheric asymmetry, as did controls with increasing cognitive disorganisation scores, in particular when belonging to the group of nCU controls. Positive, disorganised and negative PANSS scores in patients and negative and positive schizotypy in controls were unrelated to hemispheric asymmetry. These findings suggest that cannabis use balances rather than exacerbates uncommon hemispheric laterality patterns. Moreover, in healthy populations, the potential stabilisation of typical hemispheric asymmetry in CU might be most relevant to individuals with elevated cognitive disorganisation. We discuss the potential beneficial and harmful effects of cannabis use along the psychosis dimension together with propositions for future studies that should account for the mediating role of additional substances (e.g. nicotine), cannabis composition (e.g. cannabidiol content), and individual differences (e.g. physical health, or absence of significant polysubstance use).

The micro-architecture estimation by TBS discriminate women with and without osteoporotic fracture independently of age, BMI and BMD: The Osteo-Mobile Vaud cohort study.

Introduction: « Osteo-Mobile Vaud » is a mobile osteoporosis (OP) screening program. The women > 60 years living in the region Vaud will be offered OP screening with new equipment installed in a bus. The main goal is to evaluate the fracture risk with the combination of clinical risk factors (CRF) and informations extracted by a single DXA: bone mineral density (BMD), vertebral fracture assessment (VFA), and micro-architecture (MA) evaluation. MA is yet evaluable in daily practice by the Trabecular Bone Score (TBS) measure. TBS is a novel grey-level texture measurement reflecting bone MA based on the use of experimental variograms of 2D projection images. TBS is very simple to obtain, by reanalyzing a lumbar DXA-scan. TBS has proven to have diagnosis and prognosis value, partially independent of CRF and BMD. A 55-years follow- up is planned. Method: The Osteo-Mobile Vaud cohort (1500 women, > 60 years, living in the region Vaud) started in July 2010. CRF for OP, lumbar spine and hip BMD, VFA by DXA and MA evaluation by TBS are recorded. Preliminary results are reported. Results: In July 31th, we evaluated 510 women: mean age 67 years, BMI 26 kg/m². 72 women had one or more fragility fractures, 39 had vertebral fracture (VFx) grade 2/3. TBS decreases with age (-0.005 / year, p<0.001), and with BMI (-0.011 per kg/m², p<0.001). Correlation between BMD and site matched TBS is low (r=0.4, p<0.001). For the lowest T-score BMD, odds ratio (OR, 95% CI) for VFx grade 2/3 and clinical OP Fx are 1.8 (1.1-2.9) and 2.3 (1.5-3.4). For TBS, age-, BMI- and BMD adjusted ORs (per SD decrease) for VFx grade 2/3 and clinical OP Fx are 1.9 (1.2-3.0) and 1.8 (1.2-2.7). The TBS added value was independent of lumbar spine BMD or the lowest T-score (femoral neck, total hip or lumbar spine). Conclusion: As in the already published studies, these preliminary results confirm the partial independence between BMD and TBS. More importantly, a combination of TBS and BMD may increase significantly the identification of women with prevalent OP Fx. For the first time we are able to have complementary information about fracture (VFA), density (BMD), and micro-architecture (TBS) from a simple, low ionizing radiation and cheap device: DXA. The value of such informations in a screening program will be evaluated.

Study of the incorporation and the anti-tumour efficacy of radio-iododeoxyuridine according to the cellular cycle and in presence of synthesis inhibitor of thymidine

Résumé La iododeoxyuridine (IdUrd), une fois marqué au 123I ou au 125I, est un agent potentiel pour des thérapies par rayonnements Auger. Cependant, des limitations restreignent son incorporation dans l'ADN. Afin d'augmenter celle-ci, différents groupes ont étudié la fluorodeoxyuridine (FdUrd), qui favorise l'incorporation d'analogue de la thymidine, sans toutefois parvenir à une toxicité associé plus importante. Dans notre approche, 3 lignées cellulaires de glioblastomes humains et une lignée de cancer ovarien ont été utilisées. Nous avons observé, 16 à 24 h après un court pré-traitement à la FdUrd, un fort pourcentage de cellules s'accumulant en phase S. Plus qu'une accumulation, c'était une synchronisation des cellules, celles-ci restant capables d'incorporer la radio-IdIrd et repartant dans le cycle cellulaire. De plus, ces cellules accumulées après un pré-traitement à la FdUrd étaient plus radio-sensibles. Après le même intervalle de 16 à 24 h suivant la FdUrd, les 4 lignées cellulaires ont incorporé des taux plus élevés de radio-IdUrd que sans ce prétraitement. Une corrélation temporelle entre l'accumulation des cellules en phase S et la forte incorporation de radio-IdUrd a ainsi été révélée 16 à 24 h après pré-traitement à la FdUrd. Les expériences de traitement par rayonnements Auger sur les cellules accumulées en phase S ont montré une augmentation significative de l'efficacité thérapeutique de 125I-IdUrd comparé aux cellules non prétraitées à la FdUrd. Une première estimation a permis de déterminer que 100 désintégrations de 125I par cellules étant nécessaires afin d'atteindre l'efficacité thérapeutique. De plus, p53 semble jouer un rôle dans l'induction directe de mort cellulaire après des traitements par rayonnements Auger, comme indiqué par les mesures par FACS d'apoptose et de nécrose 24 et 48 h après le traitement. Concernant les expériences in vivo, nous avons observé une incorporation marquée de la radio-IdUrd dans l'ADN après un pré-traitement à la FdUrd dans un model de carcinomatose ovarienne péritonéale. Une augmentation encore plus importante a été observée après injection intra-tumorale dans des transplants sous-cutanés de glioblastomes sur des souris nues. Ces modèles pourraient être utilisés pour de plus amples études de diffusion de radio-IdUrd et de thérapie par rayonnement Auger. En conclusion, ce travail montre une première application réussie de la FdUrd afin d'accroître l'efficacité de la radio-IdUrd par traitements aux rayonnements Auger. La synchronisation des cellules en phase S combinée avec la forte incorporation de radio-IdUrd dans l'ADN différées après un pré-traitement à la FdUrd ont montré le gain thérapeutique attendu in vitro. De plus, des études in vivo sont tout indiquées après les observations encourageantes d'incorporation de radio-IdUrd dans les models de transplants sous-cutanés de glioblastomes et de tumeurs péritonéales ovariennes. Summary Iododeoxyuridine (IdUrd), labelled with 123I or 125I, could be a potential Auger radiation therapy agent. However, limitations restrict its DNA incorporation in proliferating cells. Therefore, fluorodeoxyuridine (FdUrd), which favours incorporation of thymidine analogues, has been studied by different groups in order to increase radio-IdUrd DNA incorporation, however therapeutic efficacy increase could not be reached. In our approach, 3 human glioblastoma cell lines with different p53 expression and one ovarian cancer line were pre-treated with various FdUrd conditions. We observed a high percentage of cells accumulating in early S phase 16 to 24 h after a short and non-toxic FdUrd pre-treatment. More than an accumulation, this was a synchronization, cells remaining able to incorporate radio-IdUrd and re-entering the cell cycle. Furthermore, the S phase accumulated cells post FdUrd pre-treatment were more radiosensitive. After the same delay of 16 to 24 h post FdUrd pre-treatment, the 4 cell lines were incorporating higher rates of radio-IdUrd compared with untreated cells. A time correlation between S phase accumulation and high radio-IdUrd incorporation was therefore revealed 16 to 24 h post FdUrd pre-treatment. Auger radiation treatment experiments performed on S phase enriched cells showed a significant increase of killing efficacy of 125I-IdUrd compared with cells not pre-treated with FdUrd. A first estimation indicates further that about 100 125I decays were required to reach killing in the targeted cells. Moreover, p53 might play a role on the direct induction of cell death pathways after Auger radiation treatments, as indicated by differential apoptosis and necrosis induction measured by FACS 24 and 48 h after treatment initiation. Concerning in vivo results, we observed a marked DNA incorporation increase of radio-IdUrd after FdUrd pre-treatment in peritoneal carcinomatosis in SCID mice. Even higher incorporation increase was observed after intra-tumoural injection of radio-IdUrd in subcutaneous glioblastoma transplants in nude mice. These tumour models might be further useful for diffusion of radio-IdUrd and Auger radiation therapy studies. In conclusion, these data show a first successful application of thymidine synthesis inhibition able to increase the efficacy of radio-IdUrd Auger radiation treatment. The S phase synchronization combined with a high percentage DNA incorporation of radio-IdUrd delayed post FdUrd pre-treatment provided the expected therapeutic gain in vitro. Further in vivo studies are indicated after the observations of encouraging radio-IdUrd uptake experiments in glioblastoma subcutaneous xenografts and in an ovarian peritoneal carcinomatosis model.

Correlations of glycogen synthase and phosphorylase activities with glycogen concentration in human muscle biopsies. Evidence for a double-feedback mechanism regulating glycogen synthesis and breakdown.

The purpose of this study was to verify in man the relationships of muscle glycogen synthase and phosphorylase activities with glycogen concentration that were reported in animal studies. The upper level of glycogen concentration in muscle is known to be tightly controlled, and glycogen concentration was reported to have an inhibitory effect on synthase activity and a stimulatory effect on phosphorylase activity. Glycogen synthase and phosphorylase activity and glycogen concentration were measured in muscle biopsies in a group of nine normal subjects after stimulating an increase of their muscle glycogen concentration through either an intravenous glucose-insulin infusion to stimulate glycogen synthesis, or an Intralipid (Vitrum, Stockholm, Sweden) infusion in the basal state to inhibit glycogen mobilization by favoring lipid oxidation at the expense of glucose oxidation. Phosphorylase activity increased from 71.3 +/- 21.0 to 152.8 +/- 20.0 nmol/min/mg protein (P < .005) after the glucose-insulin infusion. Phosphorylase activity was positively correlated with glycogen concentration (P = .005 and P = .0001) after the glucose-insulin and Intralipid infusions, respectively. Insulin-stimulated glycogen synthase activity was significantly negatively correlated with glycogen concentration at the end of the Intralipid infusion (P < .005). In conclusion, by demonstrating a negative correlation of glycogen concentration with glycogen synthase and a positive correlation with phosphorylase, this study might confirm in man the double-feedback mechanism by which changes in glycogen concentration regulate glycogen synthase and phosphorylase activities. It suggests that this mechanism might play an important role in the regulation of glucose storage.