Status Epilepticus Severity Score (STESS): a tool to orient early treatment strategy

BACKGROUND : Status epilepticus (SE) treatment ranges from small benzodiazepine doses to coma induction. For some SE subgroups, it is unclear how the risk of an aggressive therapeutic approach balances with outcome improvement. We recently developed a prognostic score (Status Epilepticus Severity Score, STESS), relying on four outcome predictors (age, history of seizures, seizure type and extent of consciousness impairment), determined before treatment institution. Our aim was to assess whether the score might have a role in the treatment strategy choice. METHODS : This cohort study involved adult patients in three centers. For each patient, the STESS was calculated before primary outcome assessment: survival vs. death at discharge. Its ability to predict survival was estimated through the negative predictive value for mortality (NPV). Stratified odds ratios (OR) for mortality were calculated considering coma induction as exposure; strata were defined by the STESS level. RESULTS : In the observed 154 patients, the STESS had an excellent negative predictive value (0.97). A favorable STESS was highly related to survival (P < 0.001), and to return to baseline clinical condition in survivors (P < 0.001). The combined Mantel-Haenszel OR for mortality in patients stratified after coma induction and their STESS was 1.5 (95 % CI: 0.59-3.83). CONCLUSION : The STESS reliably identifies SE patients who will survive. Early aggressive treatment could not be routinely warranted in patients with a favorable STESS, who will almost certainly survive their SE episode. A randomized trial using this score would be needed to confirm this hypothesis.

Detailed analysis and follow-up studies of a high-throughput screening for indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a key regulator of immune responses and therefore an important therapeutic target for the treatment of diseases that involve pathological immune escape, such as cancer. Here, we describe a robust and sensitive high-throughput screen (HTS) for IDO1 inhibitors using the Prestwick Chemical Library of 1200 FDA-approved drugs and the Maybridge HitFinder Collection of 14,000 small molecules. Of the 60 hits selected for follow-up studies, 14 displayed IC50 values below 20 μM under the secondary assay conditions, and 4 showed an activity in cellular tests. In view of the high attrition rate we used both experimental and computational techniques to identify and to characterize compounds inhibiting IDO1 through unspecific inhibition mechanisms such as chemical reactivity, redox cycling, or aggregation. One specific IDO1 inhibitor scaffold, the imidazole antifungal agents, was chosen for rational structure-based lead optimization, which led to more soluble and smaller compounds with micromolar activity.

Anàlisi del coneixement, de l'actitud i de l'ús que fan els cuidadors informals dels malalts d' Alzheimer en el domicili, de les contencions mecàniques i farmacològiques

L’objectiu d’aquest estudi consisteix en saber quins coneixements posseeixen, quines actituds i quin ús fan els cuidadors informals de persones diagnosticades d’Alzheimer en el domicili, de les contencions mecàniques i farmacològiques. Aquest projecte es tracta d’un disseny exploratori, descriptiu i transversal. Per a realitzar-lo es prendrà com a mostra a cuidadors principals informals, que tinguin al seu càrrec persones diagnosticades d’Alzheimer que siguin usuàries de l’atenció primària dels serveis sanitaris públics, de la província de Barcelona. L’estudi integrarà dues fases: la primera consistirà en realitzar una prova pilot a un nombre reduït de persones que servirà per avaluar i perfeccionar l’eina de la recollida de dades (els instruments a emprar seran una enquesta d’elaboració pròpia i entrevistes) i la segona consistirà en efectuar la recollida de dades a partir de l’enquesta modificada i validada en la fase anterior. En la fase pilot es realitzarà l’anàlisi de les dades a través de la tècnica coneguda com <> i en la fase següent, s’utilitzarà el paquet estadístic SPSS versió 21.0.0.

Differential protein labeling with thiol-reactive infrared DY-680 and DY-780 maleimides and analysis by two-dimensional gel electrophoresis.

Differential protein labeling with 2-DE separation is an effective method for distinguishing differences in the protein composition of two or more protein samples. Here, we report on a sensitive infrared-based labeling procedure, adding a novel tool to the many labeling possibilities. Defined amounts of newborn and adult mouse brain proteins and tubulin were exposed to maleimide-conjugated infrared dyes DY-680 and DY-780 followed by 1- and 2-DE. The procedure allows amounts of less than 5 microg of cysteine-labeled protein mixtures to be detected (together with unlabeled proteins) in a single 2-DE step with an LOD of individual proteins in the femtogram range; however, co-migration of unlabeled proteins and subsequent general protein stains are necessary for a precise comparison. Nevertheless, the most abundant thiol-labeled proteins, such as tubulin, were identified by MS, with cysteine-containing peptides influencing the accuracy of the identification score. Unfortunately, some infrared-labeled proteins were no longer detectable by Western blots. In conclusion, differential thiol labeling with infrared dyes provides an additional tool for detection of low-abundant cysteine-containing proteins and for rapid identification of differences in the protein composition of two sets of protein samples.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Traffic Safety Analysis for Local Agencies, 2012

Although many larger Iowa cities have staff traffic engineers who have a dedicated interest in safety, smaller jurisdictions do not. Rural agencies and small communities must rely on consultants, if available, or local staff to identify locations with a high number of crashes and to devise mitigating measures. However, smaller agencies in Iowa have other available options to receive assistance in obtaining and interpreting crash data. These options are addressed in this manual. Many proposed road improvements or alternatives can be evaluated using methods that do not require in-depth engineering analysis. The Iowa Department of Transportation (DOT) supported developing this manual to provide a tool that assists communities and rural agencies in identifying and analyzing local roadway-related traffic safety concerns. In the past, a limited number of traffic safety professionals had access to adequate tools and training to evaluate potential safety problems quickly and efficiently and select possible solutions. Present-day programs and information are much more conducive to the widespread dissemination of crash data, mapping, data comparison, and alternative selections and comparisons. Information is available and in formats that do not require specialized training to understand and use. This manual describes several methods for reviewing crash data at a given location, identifying possible contributing causes, selecting countermeasures, and conducting economic analyses for the proposed mitigation. The Federal Highway Administration (FHWA) has also developed other analysis tools, which are described in the manual. This manual can also serve as a reference for traffic engineers and other analysts.

Are myotonic dystrophy and peripheral neuropathy associated? : morphological, morphometric and electrophysiological analysis in DM1 transgenic mice

Abstract: Myotonic dystrophy (DM1), also known as Steinert disease, is an inherited autosomal dominant disease. It is characterized by myotonia, muscular weakness and atrophy, but DM1 may have manifestations in other organs such as eyes, heart, gonads, gastrointestinal and respiratory tracts, as well as brain. In 1992, it was demonstrated that this complex disease results from the expansion of CTG repeats in the 3' untranslated region of the DM protein kinase (DMPK) gene on chromosome 19. The size of the inherited expansion is critically linked to the severity of the disease and the age of onset. Although several electrophysiological and histological studies have been carried out to verify the possible involvement of peripheral nerve abnormality with DM1, the results have not been univocal. Therefore, at present the possible association between peripheral neuropatliy and DM1 remains debated. Recently, transgenic mice have been generated, that carry the human genomic DM1 region with 300 CTG repeats, and display the human DMl phenotype. The generation of these DM1 transgenic mice provides a useful tool to investigate the type and incidence of structural abnormalities in the peripheral nervous system associated with DM1 disease. By using the DM1 transgenic mice, we investigated the presence/absence of the three major peripheral neuropathies: axonal degeneration, axonal demyelination and neuronopathy. The morphological and morphometric analysis of sciatic, sural and phrenic nerves demonstrated the absence of axonal degeneration or demyelination. The morphometric analysis also ruled out any loss in the numbers of sensory or motor neurons in lumbar dorsal root ganglia and lumbar spinal cord enlargement respectively. Moreover, the éxamination of serial hind limb muscle sections from DMl mice showed a normal intramuscular axonal arborization as well as the absence of changes in the number and structure of endplates. Finally, the electrophysiological tests performed in DM1 transgenic mice showed that the compound muscle axon potentials (CMAPs) elicited in the hind limb digits in response to a stimulation of the sciatic nerve with anear-nerve electrode were similar to thosé obtained in wild type mice. On the basis of all our results, we hypothesized that 300 CTG repeats are not sufficient to induce disorder in the peripheral nervous system of this DM1 transgenic mouse model. Résumé La dystrophie myotonique (DM1), connue aussi sous le nom de maladie de Steinert, est une maladie héréditaire autosornale dominante. Elle est caractérisée par une myotonie, une faiblesse et une atrophie musculaires, mais peut aussi se manifester dans d'autres organes tels que les yeux, les voies digestive et respiratoire, ou le cerveau. En 1992, il a été montré que cette maladie complexe résultait de l'expansion d'une répétition de CTG dans une partie non traduite en 3' du gène codant pour la protéine kinase DM (DMPK), sur le chromosome 19. La taille de l'expansion héritée est étroitement liée à la sévérité et l'âge d'apparition de DM1. Bien que plusieurs études électrophysiologiques et histologiques aient été menées, pour juger d'une implication possible d'anomalies au niveau du système nerveux périphérique dans la DM1, les résultats n'ont jusqu'ici pas été univoques. Aujourd'hui, la question d'une neuropathie associée avec la DM1 reste donc controversée. Des souris transgéniques ont été élaborées, qui portent la séquence DM1 du génome humain avec 300 répétitions CTG et expriment le phénotype des patients DM1: Ces souris transgéniques DMl procurent un outil précieux pour l'étude du type et de l'incidence d'éventuelles anomalies du système nerveux périphérique dans la DM1. En utilisant ces souris transgéniques DM1, nous avons étudié la présence ou l'absence des trois principaux types de neuropathies périphériques: la dégénération axonale, la démyélinisation axonale et la neuronopathie. Les études morphologiques et morphométrique des nerfs sciatiques, suraux et phréniques ont montré l'absence de dégénération axonale ou de démyélinisation. L'analyse du nombre de cellules neuronales n'a pas dévoilé de diminution des nombres de neurones sensitifs dans les ganglions des racines dorsales lombaires ou de neurones moteurs dans la moëlle épinière lombaire des souris transgéniques DMl. De plus, l'examen de coupes sériées de muscle des membres postérieurs de souris DM1 a montré une arborisation axonale intramusculaire normale, de même que l'absence d'irrégularité dans le nombre ou la structure des plaques motrices. Enfin, les tests électrophysiologiques effectués sur les souris DMl ont montré que les potentiels d'action de la composante musculaire (CMAPs) évoqués dans les doigts des membres postérieurs, en réponse à une stimulation du nerf sciatique à l'aide d'une électrode paranerveuse, étaient identiques à ceux observées chez les souris sauvages. Sur la base de l'ensemble de ces résultats, nous avons émis l'hypothèse que 300 répétitions CTG ne sont pas suffisantes pour induire d'altérations dans le système nerveux périphérique du modèle de souris transgéniques DM 1.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Soluble MHC-peptide complexes: tools for the monitoring of T cell responses in clinical trials and basic research.

Soluble MHC-peptide complexes, commonly known as tetramers, allow the detection and isolation of antigen-specific T cells. Although other types of soluble MHC-peptide complexes have been introduced, the most commonly used MHC class I staining reagents are those originally described by Altman and Davis. As these reagents have become an essential tool for T cell analysis, it is important to have a large repertoire of such reagents to cover a broad range of applications in cancer research and clinical trials. Our tetramer collection currently comprises 228 human and 60 mouse tetramers and new reagents are continuously being added. For the MHC II tetramers, the list currently contains 21 human (HLA-DR, DQ and DP) and 5 mouse (I-A(b)) tetramers. Quantitative enumeration of antigen-specific T cells by tetramer staining, especially at low frequencies, critically depends on the quality of the tetramers and on the staining procedures. For conclusive longitudinal monitoring, standardized reagents and analysis protocols need to be used. This is especially true for the monitoring of antigen-specific CD4+ T cells, as there are large variations in the quality of MHC II tetramers and staining conditions. This commentary provides an overview of our tetramer collection and indications on how tetramers should be used to obtain optimal results.

Graphical probabilistic analysis of the combination of items of evidence

Unlike the evaluation of single items of scientific evidence, the formal study and analysis of the jointevaluation of several distinct items of forensic evidence has to date received some punctual, ratherthan systematic, attention. Questions about the (i) relationships among a set of (usually unobservable)propositions and a set of (observable) items of scientific evidence, (ii) the joint probative valueof a collection of distinct items of evidence as well as (iii) the contribution of each individual itemwithin a given group of pieces of evidence still represent fundamental areas of research. To somedegree, this is remarkable since both, forensic science theory and practice, yet many daily inferencetasks, require the consideration of multiple items if not masses of evidence. A recurrent and particularcomplication that arises in such settings is that the application of probability theory, i.e. the referencemethod for reasoning under uncertainty, becomes increasingly demanding. The present paper takesthis as a starting point and discusses graphical probability models, i.e. Bayesian networks, as frameworkwithin which the joint evaluation of scientific evidence can be approached in some viable way.Based on a review of existing main contributions in this area, the article here aims at presentinginstances of real case studies from the author's institution in order to point out the usefulness andcapacities of Bayesian networks for the probabilistic assessment of the probative value of multipleand interrelated items of evidence. A main emphasis is placed on underlying general patterns of inference,their representation as well as their graphical probabilistic analysis. Attention is also drawnto inferential interactions, such as redundancy, synergy and directional change. These distinguish thejoint evaluation of evidence from assessments of isolated items of evidence. Together, these topicspresent aspects of interest to both, domain experts and recipients of expert information, because theyhave bearing on how multiple items of evidence are meaningfully and appropriately set into context.

Using Digital Video Analysis to Monitor Driver Behavior at Intersections

Commercially available instruments for road-side data collection take highly limited measurements, require extensive manual input, or are too expensive for widespread use. However, inexpensive computer vision techniques for digital video analysis can be applied to automate the monitoring of driver, vehicle, and pedestrian behaviors. These techniques can measure safety-related variables that cannot be easily measured using existing sensors. The use of these techniques will lead to an improved understanding of the decisions made by drivers at intersections. These automated techniques allow the collection of large amounts of safety-related data in a relatively short amount of time. There is a need to develop an easily deployable system to utilize these new techniques. This project implemented and tested a digital video analysis system for use at intersections. A prototype video recording system was developed for field deployment. A computer interface was implemented and served to simplify and automate the data analysis and the data review process. Driver behavior was measured at urban and rural non-signalized intersections. Recorded digital video was analyzed and used to test the system.

Erythropoietin (EPO) immunoaffinity columns--a powerful tool for purifying EPO and its recombinant analogues.

The sample preparation method preceding the urinary erythropoietin (EPO) doping test is based on several concentration and ultrafiltration steps. In order to improve the quality of isoelectric focusing (IEF) gel results and therefore, the sensitivity of the EPO test, new sample preparation methods relying on affinity purification were recently proposed. This article focuses on the evaluation and validation of disposable immunoaffinity columns targeting both endogenous and recombinant EPO molecules in two World Anti-Doping Agency (WADA) accredited anti-doping laboratories. The use of the columns improved the resolution of the IEF profiles considerably when compared with the classical ultrafiltration method, and the columns' ability to ensure the isoform integrity of the endogenous and exogenous EPO molecules was confirmed. Immunoaffinity columns constitute therefore a potent and reliable tool for the preparation of urine samples and their use will significantly improve the sensitivity and specificity of the actual urinary EPO test.

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.

Analysis of cannabinoids in oral fluid by liquid chromatography-tandem mass spectrometry

A sensitive method was developed for quantifying a wide range of cannabinoids in oral fluid (OF) by liquid chromatography-tandem mass spectrometry (LC-MS/MS). These cannabinoids include a dagger(9)-tetrahydrocannabinol (THC), 11-hydroxy-a dagger(9)-tetrahydrocannabinol (11-OH-THC), 11-nor-9-carboxy-a dagger(9)-tetrahydrocannabinol (THCCOOH), cannabinol (CBN), cannabidiol (CBD), a dagger(9)-tetrahydrocannabinolic acid A (THC-A), 11-nor-9-carboxy-a dagger(9)-tetrahydrocannabinol glucuronide (THCCOOH-gluc), and a dagger(9)-tetrahydrocannabinol glucuronide (THC-gluc). Samples were collected using a Quantisal (TM) device. The advantages of performing a liquid-liquid extraction (LLE) of KCl-saturated OF using heptane/ethyl acetate versus a solid-phase extraction (SPE) using HLB copolymer columns were determined. Chromatographic separation was achieved in 11.5 min on a Kinetex (TM) column packed with 2.6-mu m core-shell particles. Both positive (THC, 11-OH-THC, CBN, and CBD) and negative (THCCOOH, THC-gluc, THCCOOH-gluc, and THC-A) electrospray ionization modes were employed with multiple reaction monitoring using a high-end AB Sciex API 5000 (TM) triple quadrupole LC-MS/MS system. Unlike SPE, LLE failed to extract THC-gluc and THCCOOH-gluc. However, the LLE method was more sensitive for the detection of THCCOOH than the SPE method, wherein the limit of detection (LOD) and limit of quantification (LOQ) decreased from 100 to 50 pg/ml and from 500 to 80 pg/ml, respectively. The two extraction methods were successfully applied to OF samples collected from volunteers before and after they smoked a homemade cannabis joint. High levels of THC were measured soon after smoking, in addition to significant amounts of THC-A. Other cannabinoids were found in low concentrations. Glucuronide conjugate levels were lower than the method's LOD for most samples. Incubation studies suggest that glucuronides could be enzymatically degraded by glucuronidase prior to OF collection

Effectiveness of pharmacist care in the improvement of adherence to antidepressants: a systematic review and meta-analysis

BACKGROUND: Pharmacists can play a decisive role in the management of ambulatory patients with depression who have poor adherence to antidepressant drugs. OBJECTIVE: To systematically evaluate the effectiveness of pharmacist care in improving adherence of depressed outpatients to antidepressants. METHODS: A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted. RCTs were identified through electronic databases (MEDLINE, Cochrane Central Register of Controlled Trials, Institute for Scientific Information Web of Knowledge, and Spanish National Research Council) from inception to April 2010, reference lists were checked, and experts were consulted. RCTs that evaluated the impact of pharmacist interventions on improving adherence to antidepressants in depressed patients in an outpatient setting (community pharmacy or pharmacy service) were included. Methodologic quality was assessed and methodologic details and outcomes were extracted in duplicate. RESULTS: Six RCTs were identified. A total of 887 patients with an established diagnosis of depression who were initiating or maintaining pharmacologic treatment with antidepressant drugs and who received pharmacist care (459 patients) or usual care (428 patients) were included in the review. The most commonly reported interventions were patient education and monitoring, monitoring and management of toxicity and adverse effects, adherence promotion, provision of written or visual information, and recommendation or implementation of changes or adjustments in medication. Overall, no statistical heterogeneity or publication bias was detected. The pooled odds ratio, using a random effects model, was 1.64 (95% CI 1.24 to 2.17). Subgroup analysis showed no statistically significant differences in results by type of pharmacist involved, adherence measure, diagnostic tool, or analysis strategy. CONCLUSIONS: These results suggest that pharmacist intervention is effective in the improvement of patient adherence to antidepressants. However, data are still limited and we would recommend more research in this area, specifically outside of the US.