Neurodevelopment outcome of newborns with cerebral subependymal pseudocysts at 18 and 46 months: a prospective study.

OBJECTIVES: Subependymal pseudocysts (SEPC) are cerebral periventricular cysts located on the floor of the lateral ventricle and result from regression of the germinal matrix. They are increasingly diagnosed on neonatal cranial ultrasound. While associated pathologies are reported, information about long-term prognosis is missing, and we aimed to investigate long-term follow-up of these patients. STUDY DESIGN: Newborns diagnosed with SEPC were enrolled for follow-up. Neurodevelopment outcome was assessed at 6, 18 and 46 months of age. RESULTS: 74 newborns were recruited: we found a high rate of antenatal events (63%), premature infants (66% <37 weeks, 31% <32 weeks) and twins (30%). MRI was performed in 31 patients, and cystic periventricular leukomalacia (c-PVL) was primarily falsely diagnosed in 9 of them. Underlying disease was diagnosed in 17 patients, 8 with congenital cytomegalovirus (CMV) infection, 5 with genetic and 4 with metabolic disease. Neurological examination (NE) at birth was normal for patients with SEPCs and no underlying disease, except one. Mean Developmental Quotient and IQ of these patients was 98.2 (±9.6SD; range 77-121), 94.6 (±14.2SD; 71-120) and 99.6 (±12.3SD; 76-120) at 6, 18 and 46 months of age, respectively, with no differences between the subtypes of SEPC. A subset analysis showed no outcome differences between preterm infants with or without SEPC, or between preterm of <32 GA and ≥32 GA. CONCLUSIONS: Neurodevelopment of newborns with SEPC was normal when no underlying disease was present. This study suggests that if NE is normal at birth and congenital CMV infection can be excluded, then no further investigations are needed. Moreover, it is crucial to differentiate SEPC from c-PVL which carries a poor prognosis.

Uric acid and cardio-metabolic risk factors : an epidemiological approach

The role of serum uric acid (SUA) in cardio-metabolic conditions has long been contentious. It is still unclear if SUA is an independent risk factor or marker of cardio-metabolic conditions and most observed associations are not necessarily causal. This study aimed to further understand and explore the causal role of SUA in cardio-metabolic conditions using genetic and non-genetic epidemiological methods in population-based data. In the first part of this study, we found moderate to high heritability estimates for SUA and fractional excretion of urate (FEUA) suggesting the role of genetic factors in the etiology of hyperuricemia. With regards to the role of SUA on inflammatory markers (IMs), a strong positive association of SUA with C-reactive protein (CRP) and a weaker positive association with tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) was observed, which was in part mediated by body mass index (BMI). These findings suggest that SUA may have a role in sterile inflammation. In view of the inconsistency surrounding the causal nature and direction of the relation between SUA and adiposity, we applied a bidirectional Mendelian randomization approach using genetic variants to decipher the association. The finding that elevated SUA is a consequence rather than a cause of adiposity was not totally unexpected and is compatible with the hypothesis that hyperinsulinemia, accompanying obesity, enhances renal proximal tubular reabsorption of uric acid. The fourth part of this study examined the relationship between SUA and blood pressure (BP) in young adults. The association between SUA and BP, significant only in females, was strongly attenuated upon adjustment for BMI. The possibility that BMI lies in the causal pathway may explain the attenuation observed in the associations of SUA with BP and IMs. Finally, a significant hockey-stick shaped association of SUA with social phobia in our data suggests a protective effect of SUA only up to a certain concentration. Although our study findings have shed some light on the uncertainty underlying the pathophysiology of SUA, more compelling evidence using longitudinal designs, randomized controlled trials and the use of robust genetic tools is warranted to increase our understanding of the clinical significance of SUA.

Testing the effects of genetic crossing distance on embryo survival within a metapopulation of brown trout (Salmo trutta)

Predicting progeny performance from parental genetic divergence can potentially enhance the efficiency of supportive breeding programmes and facilitate risk assessment. Yet, experimental testing of the effects of breeding distance on offspring performance remains rare, especially in wild populations of vertebrates. Recent studies have demonstrated that embryos of salmonid fish are sensitive indicators of additive genetic variance for viability traits. We therefore used gametes of wild brown trout (Salmo trutta) from five genetically distinct populations of a river catchment in Switzerland, and used a full factorial design to produce over 2,000 embryos in 100 different crosses with varying genetic distances (FST range 0.005-0.035). Customized egg capsules allowed recording the survival of individual embryos until hatching under natural field conditions. Our breeding design enabled us to evaluate the role of the environment, of genetic and nongenetic parental contributions, and of interactions between these factors, on embryo viability. We found that embryo survival was strongly affected by maternal environmental (i.e. non-genetic) effects and by the microenvironment, i.e. by the location within the gravel. However, embryo survival was not predicted by population divergence, parental allelic dissimilarity, or heterozygosity, neither in the field nor under laboratory conditions. Our findings suggest that the genetic effects of inter-population hybridization within a genetically differentiated meta-population can be minor in comparison to environmental effects.

Second neoplasms after invasive and borderline ovarian cancer.

Excess risk of subsequent cancers has been documented in women diagnosed with ovarian cancer. We updated to 2006 data on second cancers in women diagnosed with invasive and borderline ovarian cancer in the Swiss canton of Vaud. Between 1974 and 2006, 304 borderline and 1530 invasive first ovarian tumours were abstracted from the Vaud Cancer Registry database and followed up till the end of 2006. Calculation of expected numbers of tumours in the cohorts was based on site-specific, age-specific and calendar-year-specific incidence rates. We computed the standardized incidence ratios (SIRs) of second cancers, and the corresponding 95% confidence intervals (CI). There was no change in the incidence of malignant cancers, but that of borderline tumours increased over more recent years. Overall, 110 second neoplasms were observed versus 49.7 expected after invasive ovarian cancer (SIR 2.21; 95% CI: 1.82-2.67). Significant excess risks were observed for cancers of the breast, corpus uteri and leukaemias. When synchronous cancers were excluded, the overall SIR for all sites declined to 1.05. Thirty-one second neoplasms were observed after borderline tumours compared with 21.1 expected (SIR=1.47; 95% CI: 1.00-2.09). SIRs were above unity for ovary, colorectum and uterus. After exclusion of synchronous neoplasms, SIR for all neoplasms declined to 1.09, and remained significant only for second ovarian cancers (SIR=4.93). The present record linkage cohort study shows an excess risk for selected synchronous neoplasms in women diagnosed with both borderline and invasive ovarian cancer, likely because of shared genetic and perhaps environmental factors.

No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels.

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

Hyperuricaemia and gout: state of the art and future perspectives.

Major progress has been made in the past decade in understanding the pathogenesis and treatment of gout. These advances include identification of the genetic and environmental risk factors for gout, recognition that gout is an important risk factor for cardiovascular disease, elucidation of the pathways regulating the acute gout attack and the development of novel therapeutic agents to treat both the acute and chronic phases of the disease. This review summarises these advances and highlights the research agenda for the next decade.

Early glutathione deficit impairs parvalbumin expression in gaba interneurons and kainate-induced gamma oscillations

An increased oxidative stress and alteration of the antioxidant systems have been observed in schizophrenia. Glutathione (GSH), a major redox regulator, is decreased in patients' cerebrospinal fluid, prefrontal cortex in vivo and striatum post-mortem tissue. Most importantly, there is genetic and functional evidence for the implication of the gene of the glutamate cysteine ligase (GCL) catalytic subunit, the key GSH-synthesizing enzyme. We have developed animal models for a GSH deficit to study the consequences of such deficit on the brain development. A GSH deficit combined with elevated dopamine (DA) during development leads to reduced parvalbumin (PV) expression in a subclass of GABA interneurons in rat anterior cingulate cortex (ACC). Similar changes are observed in postmortem brain tissue of schizophrenic patients. GSH dysregulation increases vulnerability to oxidative stress, that in turn could lead to cortical circuit anomalies in the schizophrenic brain. In the present study, we use a GCL modulatory subunit (GCLM) knock-out (KO) mouse model that presents up to 80% decreased brain GSH levels. During postnatal development, a subgroup of animals from each genotype is exposed to elevated oxidative stress induced by treatment with the DA reuptake inhibitor GBR12909. Results reveal a significant genotype-specific delay International Congress on Schizophrenia Research 136 10. 10. Neuroanatomy, Animal Downloaded from http://schizophreniabulletin.oxfordjournals.org at Bibliotheque Cantonale et Universitaire on June 18, 2010 in cortical PV expression at postnatal day P10 in GCLM-KO mice, as compared to wild-type. This effect seems to be further exaggerated in animals treated with GBR12909 from P5 to P10. At P20, PV expression is no longer significantly reduced in GCLM-KO ACC without GBR but is reduced if GBR is applied from P10 to P20. However, our result show that GCLM-KO mice exhibit increased oxidative stress, cortical altered myelin development as shown by MBP marker, and more specifically impairment of the peri-neuronal net known to modulate PV connectivity. In addition, we also observe a reduced PV expression in the ventro-temporal hippocampus of adult GCLM-KO mice, suggesting that anomalies of the PV interneurons prevail at least in some brain regions throughout the adulthood. Interestingly, the power of kainate-induced gamma oscillations, known to be dependent on proper activation of PV interneuron's, is also lower in hippocampal slices of adult GCLM KO mice. These results suggest that the PV positive GABA interneurons is particularly vulnerable to increased oxidative stress

Depistage mutationnel des genes de la peripherine/RDS, rhodopsine et ROM-1 dans 69 cas index de retinite pigmentaire et autres dystrophies retiniennes [Mutational screening of peripherin/RDS genes, rhodopsin and ROM-1 in 69 index cases with retinitis pigmentosa and other retinal dystrophies]

PURPOSE: Phenotypic, genetic and molecular characterization of 69 index patients with retinitis pigmentosa (RP) and various inherited retinal diseases. PATIENTS AND METHOD: patients went through complete ocular examination and blood samples were drawn for mutational screening of three candidate genes: rhodopsin (RHO), peripherin/RDS, and ROM-1. RESULTS: the most frequent type of RP among our population was the autosomal dominant (43.6%). Three RHO mutations were found among the RP patients. A RDS mutation was detected in three unrelated families segregating dominant macular dystrophy. DISCUSSION AND CONCLUSIONS: 18% of the autosomal dominant RP patients presented a RHO mutation; RDS R172W mutation was present in 25% of the dominant macular dystrophies.

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.

Redox dysregulation affects the ventral but not dorsal hippocampus: impairment of parvalbumin neurons, gamma oscillations, and related behaviors.

Elevated oxidative stress and alteration in antioxidant systems, including glutathione (GSH) decrease, are observed in schizophrenia. Genetic and functional data indicate that impaired GSH synthesis represents a susceptibility factor for the disorder. Here, we show that a genetically compromised GSH synthesis affects the morphological and functional integrity of hippocampal parvalbumin-immunoreactive (PV-IR) interneurons, known to be affected in schizophrenia. A GSH deficit causes a selective decrease of PV-IR interneurons in CA3 and dendate gyrus (DG) of the ventral but not dorsal hippocampus and a concomitant reduction of beta/gamma oscillations. Impairment of PV-IR interneurons emerges at the end of adolescence/early adulthood as oxidative stress increases or cumulates selectively in CA3 and DG of the ventral hippocampus. Such redox dysregulation alters stress and emotion-related behaviors but leaves spatial abilities intact, indicating functional disruption of the ventral but not dorsal hippocampus. Thus, a GSH deficit affects PV-IR interneuron's integrity and neuronal synchrony in a region- and time-specific manner, leading to behavioral phenotypes related to psychiatric disorders.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Comparative genome analysis of Pseudomonas knackmussii B13, the first bacterium known to degrade chloroaromatic compounds.

Pseudomonas knackmussii B13 was the first strain to be isolated in 1974 that could degrade chlorinated aromatic hydrocarbons. This discovery was the prologue for subsequent characterization of numerous bacterial metabolic pathways, for genetic and biochemical studies, and which spurred ideas for pollutant bioremediation. In this study, we determined the complete genome sequence of B13 using next generation sequencing technologies and optical mapping. Genome annotation indicated that B13 has a variety of metabolic pathways for degrading monoaromatic hydrocarbons including chlorobenzoate, aminophenol, anthranilate and hydroxyquinol, but not polyaromatic compounds. Comparative genome analysis revealed that B13 is closest to Pseudomonas denitrificans and Pseudomonas aeruginosa. The B13 genome contains at least eight genomic islands [prophages and integrative conjugative elements (ICEs)], which were absent in closely related pseudomonads. We confirm that two ICEs are identical copies of the 103 kb self-transmissible element ICEclc that carries the genes for chlorocatechol metabolism. Comparison of ICEclc showed that it is composed of a variable and a 'core' region, which is very conserved among proteobacterial genomes, suggesting a widely distributed family of so far uncharacterized ICE. Resequencing of two spontaneous B13 mutants revealed a number of single nucleotide substitutions, as well as excision of a large 220 kb region and a prophage that drastically change the host metabolic capacity and survivability.

Profiling of steroid metabolites after transdermal and oral administration of testosterone by ultra-high pressure liquid chromatography coupled to quadrupole time-of-flight mass spectrometry.

The screening of testosterone (T) misuse for doping control is based on the urinary steroid profile, including T, its precursors and metabolites. Modifications of individual levels and ratio between those metabolites are indicators of T misuse. In the context of screening analysis, the most discriminant criterion known to date is based on the T glucuronide (TG) to epitestosterone glucuronide (EG) ratio (TG/EG). Following the World Anti-Doping Agency (WADA) recommendations, there is suspicion of T misuse when the ratio reaches 4 or beyond. While this marker remains very sensitive and specific, it suffers from large inter-individual variability, with important influence of enzyme polymorphisms. Moreover, use of low dose or topical administration forms makes the screening of endogenous steroids difficult while the detection window no longer suits the doping habit. As reference limits are estimated on the basis of population studies, which encompass inter-individual and inter-ethnic variability, new strategies including individual threshold monitoring and alternative biomarkers were proposed to detect T misuse. The purpose of this study was to evaluate the potential of ultra-high pressure liquid chromatography (UHPLC) coupled with a new generation high resolution quadrupole time-of-flight mass spectrometer (QTOF-MS) to investigate the steroid metabolism after transdermal and oral T administration. An approach was developed to quantify 12 targeted urinary steroids as direct glucuro- and sulfo-conjugated metabolites, allowing the conservation of the phase II metabolism information, reflecting genetic and environmental influences. The UHPLC-QTOF-MS(E) platform was applied to clinical study samples from 19 healthy male volunteers, having different genotypes for the UGT2B17 enzyme responsible for the glucuroconjugation of T. Based on reference population ranges, none of the traditional markers of T misuse could detect doping after topical administration of T, while the detection window was short after oral TU ingestion. The detection ability of the 12 targeted steroids was thus evaluated by using individual thresholds following both transdermal and oral administration. Other relevant biomarkers and minor metabolites were studied for complementary information to the steroid profile, including sulfoconjugated analytes and hydroxy forms of glucuroconjugated metabolites. While sulfoconjugated steroids may provide helpful screening information for individuals with homozygotous UGT2B17 deletion, hydroxy-glucuroconjugated analytes could enhance the detection window of oral T undecanoate (TU) doping.

Genetic structure of the Lesser Gymnure (Genus Hylomys) in SE-Asia: evidence for two species

The Lesser Gymnure is a small galericine Insectivore living in the mainland forests of Southeast Asia, including the major islands of Sumatra, Java and Borneo. It is the only representative of the supposed monospecific genus Hylomys which is morphologically rather constant over its geographic range. Only marginal subspecific differentiation is currently recognized based in slight pelage colour variations. We report here the results of 35 gene loci revealed by protein electrophoresis on 23 specimens sampled over most of Southeast Asia. They were compared with outgroup, Erinaceaus europeaus, member of a distinct subfamily. The surveyed populations of Lesser Gymnures clearly group themselves into distinct taxa, one of which seems restricted to Sumatra, while the other occupies the whole geographic range. The genetic distance between these groups is two times greater than the divergence observed within groups: it is of the same order of magnitude as what is usually reported for congeneric mammal species, which supports their specific distinction. The lack of gene flow is also demonstrated by several diagnostic loci defining unambiguously each species. Both are only distantly related to the outgroup, a result which is consistant with their actual classification into two distinct subfamilies (Erinaceae and Galericinae). Concordant genetic and geographic subdivision of the widespread species further suggest that eustatic sea level changes during the Pleistocene produced predictable patterns in species differentiation.

ADC mapping of chronic cerebral hypoperfusion induced by carotid artery stenosis.

BACKGROUND: Carotid artery stenosis is associated with the occurrence of acute and chronic ischemic lesions that increase with age in the elderly population. Diffusion Imaging and ADC mapping may be an appropriate method to investigate patients with chronic hypoperfusion consecutive to carotid stenosis. This non-invasive technique allows to investigate brain integrity and structure, in particular hypoperfusion induced by carotid stenosis diseases. The aim of this study was to evaluate the impact of a carotid stenosis on the parenchyma using ADC mapping. METHODS: Fifty-nine patients with symptomatic (33) and asymptomatic (26) carotid stenosis were recruited from our multidisciplinary consultation. Both groups demonstrated a similar degree of stenosis. All patients underwent MRI of the brain including diffusion-weighted MR imaging with ADC mapping. Regions of interest were defined in the anterior and posterior paraventricular regions both ipsilateral and contralateral to the stenosis (anterior circulation). The same analysis was performed for the thalamic and occipital regions (posterior circulation). RESULTS: ADC values of the affected vascular territory were significantly higher on the side of the stenosis in the periventricular anterior (P<0.001) and posterior (P<0.01) area. There was no difference between ipsilateral and contralateral ADC values in the thalamic and occipital regions. CONCLUSIONS: We have shown that carotid stenosis is associated with significantly higher ADC values in the anterior circulation, probably reflecting an impact of chronic hypoperfusion on the brain parenchyma in symptomatic and asymptomatic patients. This is consistent with previous data in the literature.