Characterization of human pancreatic orthotopic tumor xenografts suitable for drug screening

Background Efforts to identify novel therapeutic options for human pancreatic ductal adenocarcinoma (PDAC) have failed to result in a clear improvement in patient survival to date. Pancreatic cancer requires efficient therapies that must be designed and assayed in preclinical models with improved predictor ability. Among the available preclinical models, the orthotopic approach fits with this expectation, but its use is still occasional. Methods An in vivo platform of 11 orthotopic tumor xenografts has been generated by direct implantation of fresh surgical material. In addition, a frozen tumorgraft bank has been created, ensuring future model recovery and tumor tissue availability. Results Tissue microarray studies allow showing a high degree of original histology preservation and maintenance of protein expression patterns through passages. The models display stable growth kinetics and characteristic metastatic behavior. Moreover, the molecular diversity may facilitate the identification of tumor subtypes and comparison of drug responses that complement or confirm information obtained with other preclinical models. Conclusions This panel represents a useful preclinical tool for testing new agents and treatment protocols and for further exploration of the biological basis of drug responses.

Characterization of human pancreatic orthotopic tumor xenografts suitable for drug screening

Background Efforts to identify novel therapeutic options for human pancreatic ductal adenocarcinoma (PDAC) have failed to result in a clear improvement in patient survival to date. Pancreatic cancer requires efficient therapies that must be designed and assayed in preclinical models with improved predictor ability. Among the available preclinical models, the orthotopic approach fits with this expectation, but its use is still occasional. Methods An in vivo platform of 11 orthotopic tumor xenografts has been generated by direct implantation of fresh surgical material. In addition, a frozen tumorgraft bank has been created, ensuring future model recovery and tumor tissue availability. Results Tissue microarray studies allow showing a high degree of original histology preservation and maintenance of protein expression patterns through passages. The models display stable growth kinetics and characteristic metastatic behavior. Moreover, the molecular diversity may facilitate the identification of tumor subtypes and comparison of drug responses that complement or confirm information obtained with other preclinical models. Conclusions This panel represents a useful preclinical tool for testing new agents and treatment protocols and for further exploration of the biological basis of drug responses.

Screening of the inhibitory effect of xenobiotics on alcohol metabolism using S9 rat liver fractions.

The purpose of this work was to develop and optimize a simple and suitable method to detect the potential inhibitory effect of drugs and medicines on alcohol dehydrogenase (ADH) activity in order to evaluate the possible interactions between medicines and alcohol metabolism. Commonly used medicines that are often involved in court litigations related with driving under the influence of alcohol were selected. Alprazolam, flunitrazepam and tramadol were tested as drugs with no known effect on ADH activity. Cimetidine, reported previously as having inhibitory effect on ADH, and 4-methylpyrazole (4-MP), a well known ADH inhibitor, were tested as positive controls. Apart from 4-MP, tramadol was identified as having the higher inhibitory effect with an IC50 of 44.7×10(-3)mM, followed by cimetidine (IC50 of 122.9×10(-3)mM). Alprazolam and flunitrazepam also reduced liver ADH activity but to a smaller extent (inhibition of 11.8±5.0% for alprazolam 1.0mM and 34.5±7.1% for flunitrazepam 0.04mM). Apart from cimetidine, this is the first report describing the inhibitory effect of these drugs on ethanol metabolism. The results also show the suitability of the method to screen for inhibitory effect of drugs on ethanol metabolism helping to identify drugs for which further study is justified.

Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology.

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.

Genomic islands: tools of bacterial horizontal gene transfer and evolution.

Abstract Bacterial genomes evolve through mutations, rearrangements or horizontal gene transfer. Besides the core genes encoding essential metabolic functions, bacterial genomes also harbour a number of accessory genes acquired by horizontal gene transfer that might be beneficial under certain environmental conditions. The horizontal gene transfer contributes to the diversification and adaptation of microorganisms, thus having an impact on the genome plasticity. A significant part of the horizontal gene transfer is or has been facilitated by genomic islands (GEIs). GEIs are discrete DNA segments, some of which are mobile and others which are not, or are no longer mobile, which differ among closely related strains. A number of GEIs are capable of integration into the chromosome of the host, excision, and transfer to a new host by transformation, conjugation or transduction. GEIs play a crucial role in the evolution of a broad spectrum of bacteria as they are involved in the dissemination of variable genes, including antibiotic resistance and virulence genes leading to generation of hospital 'superbugs', as well as catabolic genes leading to formation of new metabolic pathways. Depending on the composition of gene modules, the same type of GEIs can promote survival of pathogenic as well as environmental bacteria.

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.

Biomarkers of angiogenesis for the development of antiangiogenic therapies in oncology: tools or decorations?

Since 2004, four antiangiogenic drugs have been approved for clinical use in patients with advanced solid cancers, on the basis of their capacity to improve survival in phase III clinical studies. These achievements validated the concept introduced by Judah Folkman that the inhibition of tumor angiogenesis could control tumor growth. It has been suggested that biomarkers of angiogenesis would greatly facilitate the clinical development of antiangiogenic therapies. For these four drugs, the pharmacodynamic effects observed in early clinical studies were important to corroborate activities, but were not essential for the continuation of clinical development and approval. Furthermore, no validated biomarkers of angiogenesis or antiangiogenesis are available for routine clinical use. Thus, the quest for biomarkers of angiogenesis and their successful use in the development of antiangiogenic therapies are challenges in clinical oncology and translational cancer research. We review critical points resulting from the successful clinical trials, review current biomarkers, and discuss their potential impact on improving the clinical use of available antiangiogenic drugs and the development of new ones.

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.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Callings in career: A typological approach to essential and optional components

A sense of calling in career is supposed to have positive implications for individuals and organizations but current theoretical development is plagued with incongruent conceptualizations of what does or does not constitute a calling. The present study used cluster analysis to identify essential and optional components of a presence of calling among 407 German undergraduate students from different majors. Three types of calling merged: "negative career self-centered", "pro-social religious", and "positive varied work orientation". All types could be described as vocational identity achieved (high commitment/high self-exploration), high in career confidence and career engagement. Not defining characteristics were centrality of work or religion, endorsement of specific work values, or positivity of core self-evaluations. The results suggest that callings entail intense self-exploration and might be beneficial because they correspond with identity achievement and promote career confidence and engagement while not necessarily having pro-social orientations. Suggestions for future research, theory and practice are suggested.

The lower quality of preventive care among forced migrants in a country with universal healthcare coverage.

OBJECTIVE: To assess the association between socio-demographic factors and the quality of preventive care and chronic care of cardiovascular (CV) risk factors in a country with universal health care coverage. METHODS: Our retrospective cohort assessed a random sample of 966 patients aged 50-80years followed over 2years (2005-2006) in 4 Swiss university primary care settings (Basel/Geneva/Lausanne/Zürich). We used RAND's Quality Assessment Tools indicators and examined recommended preventive care among different socio-demographic subgroups. RESULTS: Overall patients received 69.6% of recommended preventive care. Preventive care indicators were more likely to be met among men (72.8% vs. 65.4%; p<0.001), younger patients (from 71.0% at 50-59years to 66.7% at 70-80years, p for trend=0.03) and Swiss patients (71.1% vs. 62.7% in forced migrants; p=0.001). This latter difference remained in multivariate analysis adjusted for gender, age, civil status and occupation (OR 0.68; 95% CI 0.54-0.86). Forced migrants had lower scores for physical examination and breast and colon cancer screening (all p≤0.02). No major differences were seen for chronic care of CV risk factors. CONCLUSION: Despite universal healthcare coverage, forced migrants receive less preventive care than Swiss patients in university primary care settings. Greater attention should be paid to forced migrants for preventive care.

Screening of Brazilian soybean genotypes with high potential for somatic embryogenesis and plant regeneration

The aim of this work was to identify Brazilian soybean (Glycine max) genotypes with potential to respond to in vitro culture stimuli for primary somatic embryo induction, secondary embryo proliferation and plant regeneration. Differences among eight tested cultivars were observed at each stage. Two cultivars, IAS-5 and BRSMG 68 Vencedora, were selected for the evaluation of the capacity for embryo differentiation and plant regeneration. These cultivars had high embryo induction frequencies, repetitive embryogenic proliferation, and low precocious embryo germination in the initial experiment. The effect of abscisic acid (ABA) and charcoal addition on plant regeneration was investigated. The addition of ABA to proliferation medium and of ABA and activated charcoal to maturation medium increased embryo differentiation rates, which resulted in a higher number of regenerated plants. The BRSMG 68 Vencedora cultivar was found to have a high potential for embryo induction, embryo proliferation and plant regeneration. The potential of this cultivar for somatic embryogenesis was similar to that observed for cultivar IAS-5, which is currently used for soybean transformation in Brazil. BRSMG 68 Vencedora may be a good alternative genotype for soybean genetic engineering via somatic embryogenesis protocols.