Estudio de las prescripciones farmacológicas en niños a nivel de atención primaria: evaluación de los usos off-label o fuera de ficha técnica.

OBJECTIVE to evaluate the prescription profile and to assess the off-label and unlicensed uses of medicines among non-hospitalised pediatric patients. DESIGN cross-sectional study. SETTING pediatric units in two urban health centers and general emergency room (Hospital Materno-Infantil, Málaga). MAIN MEASUREMENTS sociodemographics variables, reasons for consultation and information about therapeutic medications. The classification of prescriptions was established according to information requirements contained in the Summary of Products Characteristics (SPC). RESULTS A total of 388 children were included (a subsample of 105 treated in the emergency room). Four hundred sixty-two prescriptions (involving 74 different active ingredients) were evaluated. Each infant received and average of 1,7 drugs (95% CI: 1,6-1,9). The most prescribed medicines were ibuprofen, paracetamol, amoxicillin-clavulanate and budesonide. The therapeutic group with the greatest variety of drugs was the respiratory group. 27,4% (95% CI: 23,5-31) of prescriptions were off-label and the main cause was different age (60%; 95% CI: 54,1-63), followed by different dose (21,5%; 95% CI: 18-25), different indication (12%; 95% CI: 9,2-15) and different route of administration (7%; 95% CI: 5,4-10). CONCLUSIONS The rate of off-label uses presents intermediate figures. Around one third of the paediatric outpatients in our sample are exposed to at least one off-label or unlicensed prescription. We should, however, point out that such usage is based on scant official, quality information, although it is not necessarily incorrect. Evidence-based medicine should be encouraged to improve drug therapy in children, as well as following the rules on drugs in special situations.

Clustering of risk behaviors with cigarette consumption: a population-based survey.

OBJECTIVE: This study assessed clustering of multiple risk behaviors (i.e., low leisure-time physical activity, low fruits/vegetables intake, and high alcohol consumption) with level of cigarette consumption. METHODS: Data from the 2002 Swiss Health Survey, a population-based cross-sectional telephone survey assessing health and self-reported risk behaviors, were used. 18,005 subjects (8052 men and 9953 women) aged 25 years old or more participated. RESULTS: Smokers more frequently had low leisure time physical activity, low fruits/vegetables intake, and high alcohol consumption than non- and ex-smokers. Frequency of each risk behavior increased steadily with cigarette consumption. Clustering of risk behaviors increased with cigarette consumption in both men and women. For men, the odds ratios of multiple (> or =2) risk behaviors other than smoking, adjusted for age, nationality, and educational level, were 1.14 (95% confidence interval: 0.97, 1.33) for ex-smokers, 1.24 (0.93, 1.64) for light smokers (1-9 cigarettes/day), 1.72 (1.36, 2.17) for moderate smokers (10-19 cigarettes/day), and 3.07 (2.59, 3.64) for heavy smokers (> or =20 cigarettes/day) versus non-smokers. Similar odds ratios were found for women for corresponding groups, i.e., 1.01 (0.86, 1.19), 1.26 (1.00, 1.58), 1.62 (1.33, 1.98), and 2.75 (2.30, 3.29). CONCLUSIONS: Counseling and intervention with smokers should take into account the strong clustering of risk behaviors with level of cigarette consumption.

Asymmetric tunnels in P2MP LSPs as a label space reduction method

The objective of traffic engineering is to optimize network resource utilization. Although several works have been published about minimizing network resource utilization, few works have focused on LSR (label switched router) label space. This paper proposes an algorithm that takes advantage of the MPLS label stack features in order to reduce the number of labels used in LSPs. Some tunnelling methods and their MPLS implementation drawbacks are also discussed. The described algorithm sets up NHLFE (next hop label forwarding entry) tables in each LSR, creating asymmetric tunnels when possible. Experimental results show that the described algorithm achieves a great reduction factor in the label space. The presented works apply for both types of connections: P2MP (point-to-multipoint) and P2P (point-to-point)

A Label space reduction algorithm for P2MP LSPs using asymmetric tunnels

The aim of traffic engineering is to optimise network resource utilization. Although several works on minimizing network resource utilization have been published, few works have focused on LSR label space. This paper proposes an algorithm that uses MPLS label stack features in order to reduce the number of labels used in LSPs forwarding. Some tunnelling methods and their MPLS implementation drawbacks are also discussed. The algorithm described sets up the NHLFE tables in each LSR, creating asymmetric tunnels when possible. Experimental results show that the algorithm achieves a large reduction factor in the label space. The work presented here applies for both types of connections: P2MP and P2P

First-line gefitinib in Caucasian EGFR mutation-positive NSCLC patients: a phase-IV, open-label, single-arm study.

BACKGROUND: Phase-IV, open-label, single-arm study (NCT01203917) to assess efficacy and safety/tolerability of first-line gefitinib in Caucasian patients with stage IIIA/B/IV, epidermal growth factor receptor (EGFR) mutation-positive non-small-cell lung cancer (NSCLC). METHODS: TREATMENT: gefitinib 250 mg day(-1) until progression. Primary endpoint: objective response rate (ORR). Secondary endpoints: disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and safety/tolerability. Pre-planned exploratory objective: EGFR mutation analysis in matched tumour and plasma samples. RESULTS: Of 1060 screened patients with NSCLC (859 known mutation status; 118 positive, mutation frequency 14%), 106 with EGFR sensitising mutations were enrolled (female 70.8%; adenocarcinoma 97.2%; never-smoker 64.2%). At data cutoff: ORR 69.8% (95% confidence interval (CI) 60.5-77.7), DCR 90.6% (95% CI 83.5-94.8), median PFS 9.7 months (95% CI 8.5-11.0), median OS 19.2 months (95% CI 17.0-NC; 27% maturity). Most common adverse events (AEs; any grade): rash (44.9%), diarrhoea (30.8%); CTC (Common Toxicity Criteria) grade 3/4 AEs: 15%; SAEs: 19%. Baseline plasma 1 samples were available in 803 patients (784 known mutation status; 82 positive; mutation frequency 10%). Plasma 1 EGFR mutation test sensitivity: 65.7% (95% CI 55.8-74.7). CONCLUSION: First-line gefitinib was effective and well tolerated in Caucasian patients with EGFR mutation-positive NSCLC. Plasma samples could be considered for mutation analysis if tumour tissue is unavailable.

Overcoming the rare species modelling paradox: test of a novel hierarchical framework with an Iberian endemic plant

Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models aren't overfitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.

Impact of tamoxifen dose on tamoxifen and its active metabolites exposure in breast cancer patients: preliminary results from a prospective, open-label trial

Background: CYP2D6 is the key enzyme responsible for tamoxifen bioactivation mainly into endoxifen. This gene is highly polymorphic and breast cancer patients classified as CYP2D6 poor metabolizers (PM) or intermediate metabolizers (IM) appear to show low concentrations of endoxifen and to achieve less benefit from tamoxifen treatment. Purpose: This prospective, open-label trial aimed to assess how the increase of tamoxifen dose influences the level of endoxifen in the different genotype groups (poor-, intermediate-, and extensive-metabolizers (EM)). We examined the impact of doubling tamoxifen dose to 20mg twice daily on endoxifen plasma concentrations across these genotype groups. Patients and methods: Patients were assayed for CYP2D6 genotype and phenotype using dextromethorphan test. Tamoxifen, N-desmethyltamoxifen, 4-hydroxytamoxifen and endoxifen plasma levels were determined on 2 occasions at baseline (20mg/day of tamoxifen) and at day 30, 90 and 120 after dose increase (20 mg twice daily) using liquid chromatography-tandem-mass spectrometry. Endoxifen plasma levels were measured 6 to 24 hours after last drug intake to evaluate its accumulation before and after doubling tamoxifen dosage. ANOVA was used to evaluate endoxifen levels increase and difference between genotype groups. Results: 63 patients are available for analysis to date. Tamoxifen, N-desmethyltamoxifen, 4-hydroxytamoxifen and endoxifen plasma reached steady state at 30 day after tamoxifen dose escalation, with a significant increase compared to baseline by 1.6 to 1.8 fold : geometric mean plasma concentrations (CV %) were 140 ng/mL (45%) at baseline vs 255 (47%) at day 30 for tamoxifen (P < 0.0001); 256 (49%) vs 408 (64%) for N-desmethyltamoxifen (P < 0.0001); 2.4 (46%) vs 3.9 (51%) for 4-OH-tamoxifen (P < 0.0001); and 20 (91%) vs 33 (91%) for endoxifen (P < 0.02). On baseline, endoxifen levels tended to be lower in PM: 7 ng/mL (36%), than IM: 16 ng/mL (70%), P=0.08, and EM: 24 ng/mL (71%), P<0.001. After doubling tamoxifen dosage, endoxifen concentrations rose similarly in PM, IM and EM with respectively, 1.5 (18%), 1.5 (28%) and 1.7 (30%) fold increase from baseline, P=0.18. Conclusion: Endoxifen exposure varies widely under standard tamoxifen dosage, with CYP2D6 genotype explaining only a minor part of this variability. It increases consistently on doubling tamoxifen dose, similarly across genotypes. This would enable exposure optimization based on concentration monitoring.

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Hierarchical analyses of genetic differentiation in a hybrid zone of Sorex araneus (Insectivora : Soricidae)

Microsatellites are used to unravel the fine-scale genetic structure of a hybrid zone between chromosome races Valais and Cordon of the common shrew (Sorex araneus) located in the French Alps. A total of 269 individuals collected between 1992 and 1995 was typed for seven microsatellite loci. A modified version of the classical multiple correspondence analysis is carried out. This analysis clearly shows the dichotomy between the two races. Several approaches are used to study genetic structuring. Gene flow is clearly reduced between these chromosome races and is estimated at one migrant every two generations using X-statistics and one migrant per generation using F-statistics. Hierarchical F- and R-statistics are compared and their efficiency to detect inter- and intraracial patterns of divergence is discussed. Within-race genetic structuring is significant, but remains weak. F-ST displays similar values on both sides of the hybrid zone, although no environmental barriers are found on the Cordon side, whereas the Valais side is divided by several mountain rivers. We introduce the exact G-test to microsatellite data which proved to be a powerful test to detect genetic differentiation within as well as among races. The genetic background of karyotypic hybrids was compared with the genetic background of pure parental forms using a CRT-MCA. Our results indicate that, without knowledge of the karyotypes, we would not have been able to distinguish these hybrids from karyotypically pure samples.

A hierarchical approach to model parameter optimization for developmental systems.

In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.

Proteomic analysis of mononuclear cells of patients with minimal-change nephrotic syndrome of childhood.

Background/Aims. Recently, peripheral blood mononuclear cell transcriptome analysis has identified genes that are upregulated in relapsing minimal-change nephrotic syndrome (MCNS). In order to investigate protein expression in peripheral blood mononuclear cells (PBMC) from relapsing MCNS patients, we performed proteomic comparisons of PBMC from patients with MCNS in relapse and controls. METHODS: PBMC from a total of 20 patients were analysed. PBMC were taken from five patients with relapsing MCNS, four in remission, five patients with other glomerular diseases and six controls. Two dimensional electrophoresis was performed and proteome patterns were compared. RESULTS: Automatic heuristic clustering analysis allowed us to pool correctly the gels from the MCNS patients in the relapse and in the control groups. Using hierarchical population matching, nine spots were found to be increased in PBMC from MCNS patients in relapse. Four spots were identified by mass spectrometry. Three of the four proteins identified (L-plastin, alpha-tropomyosin and annexin III) were cytoskeletal-associated proteins. Using western blot and immunochemistry, L-plastin and alpha-tropomyosin 3 concentrations were found to be enhanced in PBMC from MCNS patients in relapse. Conclusions. These data indicate that a specific proteomic profile characterizes PBMC from MCNS patients in relapse. Proteins involved in PBMC cytoskeletal rearrangement are increased in relapsing MCNS. We hypothesize that T-cell cytoskeletal rearrangement may play a role in the pathogenesis of MCNS by altering the expression of cell surface receptors and by modifying the interaction of these cells with glomerular cells.

Hierarchical categorisation of tags for delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.

Discourse Type Clustering using POS n-gram Profiles and High-Dimensional Embeddings

Abstract: To cluster textual sequence types (discourse types/modes) in French texts, K-means algorithm with high-dimensional embeddings and fuzzy clustering algorithm were applied on clauses whose POS (part-ofspeech) n-gram profiles were previously extracted. Uni-, bi- and trigrams were used on four 19th century French short stories by Maupassant. For high-dimensional embeddings, power transformations on the chi-squared distances between clauses were explored. Preliminary results show that highdimensional embeddings improve the quality of clustering, contrasting the use of bi and trigrams whose performance is disappointing, possibly because of feature space sparsity.