Uso de benzodiazepinas y riesgo de demencia: una revisión sistemática de la literatura

Introducción: La demencia se ha convertido en una enfermedad de alta prevalencia en el adulto mayor. Hoy en día el uso de benzodiazepinas en esta población es alto, siendo reportado previamente cambios cognitivos por estos medicamentos, sin ser claro si existe mayor riesgo de demencia asociada a su consumo. El objetivo de este estudio es determinar si existe riesgo en el uso de benzodiazepinas para el posterior desarrollo de demencia. Metodología. Revisión de la literatura de estudios de cohortes y casos y controles que midan el riesgo de demencia asociada al uso de benzodiazepinas para su posterior análisis estadístico. Resultados: De los 638 artículos encontrados en la búsqueda, 4 cumplieron con los criterios de selección. El cálculo estadístico arroja un OR de modelo de efectos aleatorios de 1,8683 (IC 95 %, 0,7258-4,8098) y un OR de efectos fijos 4,2549 (IC 95 %, 3,9389-4,5963). Discusión: Los hallazgos sugieren un mayor riesgo de demencia asociado al uso de benzodiazepinas, los hallazgos deben ser manejados con cautela por la heterogeneidad obtenida. Conclusiones: Puede existir un riesgo de demencia asociada al uso de benzodiazepinas en mayores de 45 años, se considera que el uso en el adulto mayor debe ser más prudente.

Impacto de las cirugías múltiples, en un solo tiempo quirúrgico, sobre el patrón de marcha en niños con parálisis cerebral

Introducción: La Parálisis Cerebral (PC) es la enfermedad neurológica más incapacitante en niños, su historia natural tiende al deterioro motor y funcional. Con este estudio se busca establecer sí las cirugías múltiples de miembros inferiores, en un tiempo quirúrgico, mantienen el nivel motor y funcional. Material y Método: Estudio analítico de cohortes. Se compara un grupo de pacientes sometidos a cirugías múltiples contra un grupo de pacientes no operados, en el Instituto de Ortopedia Infantil Roosevelt. Se evaluaron los pacientes con dos Laboratorios para el Análisis del Movimiento (LAM) y se midieron los desenlaces mediante el cambio en la puntuación del perfil de marcha (GPS) y el nivel funcional motor grueso (GMFCS). Resultados: 109 pacientes cumplieron con los criterios de selección, 67 pacientes fueron sometidos a cirugía y 42 pacientes no. Los pacientes operados mejoraron el GPS promedio (diferencia -1,94; p=0,002) comparado con los pacientes no operados (diferencia 1,74; p=0,001), indicando una mejoría significativa de la cinemática de la marcha. En un modelo de regresión logística predictivo, el paciente que es operado tiene una probabilidad del 78% de mantener su patrón de marcha, mientras que sí no se opera su probabilidad disminuye al 37%. El nivel funcional motor GMFCS no mostró cambios significativos entre los grupos. Discusión: Las cirugías múltiples de miembros inferiores mantienen de manera significativa el patrón de marcha en pacientes con PC. Se destaca el seguimiento de los pacientes mediante el LAM y se sugiere el uso del GPS para valorar resultados en este tipo de pacientes.

Eficacia y seguridad de la melatonina para el manejo de los trastornos del sueño en el autismo: meta-análisis

INTRODUCCIÓN: El 80% de los niños y adolescentes con trastornos del espectro autista (TEA) presenta algún trastorno del sueño, en cuya génesis al parecer intervienen alteraciones en la regulación de la melatonina. El objetivo de este metaanálisis fue determinar la eficacia y seguridad de la melatonina para el manejo de ciertos trastornos del sueño en niños con TEA. MÉTODOS: Tres revisores extrajeron los datos relevantes de los ensayos clínicos aleatorizados doble ciego de alta calidad publicados en bases de datos primarias, de ensayos clínicos, de revisiones sistemáticas y de literatura gris; además se realizó búsqueda en bola de nieve. Se analizaron los datos con RevMan 5.3. Se realizó un análisis del inverso de la varianza por un modelo de efectos aleatorios para las diferencias de medias de los desenlaces propuestos: duración del tiempo total, latencia de sueño y número de despertares nocturnos. Se evaluó la heterogeneidad interestudios con el parámetro I2 RESULTADOS: La búsqueda inicial arrojó 355 resultados, de los cuales tres cumplieron los criterios de selección. La melatonina resultó ser un medicamento seguro y eficaz para aumentar la duración total del sueño y disminuir la latencia de sueño en niños y adolescentes con TEA; hasta el momento la evidencia sobre el número de despertares nocturnos no es estadísticamente significativa. DISCUSIÓN: A la luz de la evidencia disponible, la melatonina es una elección segura y eficaz para el manejo de ciertos problemas del sueño en niños y adolescentes con TEA. Es necesario realizar estudios con mayores tamaños muestrales y comparados con otros medicamentos disponibles en el mercado.

Bayesian estimation and selection of nonlinear vector error correction models: The case of the sugar-ethanol-oil nexus in Brazil

Nonlinear adjustment toward long-run price equilibrium relationships in the sugar-ethanol-oil nexus in Brazil is examined. We develop generalized bivariate error correction models that allow for cointegration between sugar, ethanol, and oil prices, where dynamic adjustments are potentially nonlinear functions of the disequilibrium errors. A range of models are estimated using Bayesian Monte Carlo Markov Chain algorithms and compared using Bayesian model selection methods. The results suggest that the long-run drivers of Brazilian sugar prices are oil prices and that there are nonlinearities in the adjustment processes of sugar and ethanol prices to oil price but linear adjustment between ethanol and sugar prices.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

Improving sequence-based fold recognition by using 3D model quality assessment

Motivation: The ability of a simple method (MODCHECK) to determine the sequence–structure compatibility of a set of structural models generated by fold recognition is tested in a thorough benchmark analysis. Four Model Quality Assessment Programs (MQAPs) were tested on 188 targets from the latest LiveBench-9 automated structure evaluation experiment. We systematically test and evaluate whether the MQAP methods can successfully detect native-likemodels. Results: We show that compared with the other three methods tested MODCHECK is the most reliable method for consistently performing the best top model selection and for ranking the models. In addition, we show that the choice of model similarity score used to assess a model's similarity to the experimental structure can influence the overall performance of these tools. Although these MQAP methods fail to improve the model selection performance for methods that already incorporate protein three dimension (3D) structural information, an improvement is observed for methods that are purely sequence-based, including the best profile–profile methods. This suggests that even the best sequence-based fold recognition methods can still be improved by taking into account the 3D structural information.

Construction of neurofuzzy models for imbalanced data classification

We propose a new class of neurofuzzy construction algorithms with the aim of maximizing generalization capability specifically for imbalanced data classification problems based on leave-one-out (LOO) cross validation. The algorithms are in two stages, first an initial rule base is constructed based on estimating the Gaussian mixture model with analysis of variance decomposition from input data; the second stage carries out the joint weighted least squares parameter estimation and rule selection using orthogonal forward subspace selection (OFSS)procedure. We show how different LOO based rule selection criteria can be incorporated with OFSS, and advocate either maximizing the leave-one-out area under curve of the receiver operating characteristics, or maximizing the leave-one-out Fmeasure if the data sets exhibit imbalanced class distribution. Extensive comparative simulations illustrate the effectiveness of the proposed algorithms.

Partitions selection strategy for set of clustering solutions

Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK Multi-Objective Clustering with automatic K-determination and MOCLE-Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number of partitions, becoming difficult for an expert to manually analyze all of them. In order to deal with this problem, we present two selection strategies, which are based on the corrected Rand, to choose a subset of solutions. To test them, they are applied to the set of solutions produced by MOCK and MOCLE in the context of several datasets. The study was also extended to select a reduced set of partitions from the initial population of MOCLE. These analysis show that both versions of selection strategy proposed are very effective. They can significantly reduce the number of solutions and, at the same time, keep the quality and the diversity of the partitions in the original set of solutions. (C) 2010 Elsevier B.V. All rights reserved.

A flexible model for survival data with a cure rate: a Bayesian approach

In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real dataset.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude.

Estimates of genetic parameters for visual scores and their correlation with production and reproductive traits in Brahman cattle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparação antropométrica, composição corporal, somatotipo e saltos verticais de atletas de voleibol de praia masculino

The kinanthropometric characteristics are used by the sports science as selection criteria and detection of talents. Hence, this study aimed at comparing the anthropometrical profile, the body composition, the somatotype and the vertical jumps of the beach volleyball players. This study consists of 79 male beach volleyball players, being forty nine (n=49) Brazilian participants of the National Circuit and thirty (n=30) of 15 countries participating in the XV Pan American Games. In order to analyze the vertical jumps of the Brazilian the participants were allocated into two groups (G1 and G2) in agreement with the national ranking of their teams. The vertical jump protocol developed by Smith and collaborators was used to evaluate the vertical jumps of spike and block. The Heath-Carter anthropometrical technique (1990) was used for calculating the somatotype. The Student s t test with the Bonferroni adjustment was used to calculate the differences among the investigated variables. The multiple regression analysis was used to identify the contributions of the anthropometrical variables in the performance of the vertical jumps and the multivariance analysis was used to calculate the differences among the components of the somatotype. The Brazilian athletes of G1 were better than G2 in the spike jump (p <0.01), block jump (p <0.01) and in the block difference (p <0.01). The prediction model of the spike jump of G2 included the body mass and standing spike reach (adjusted R2 = 0.77), the body mass and the standing block reach were also included in the model of the block jump (adjusted R2 = 0.73). The regression model of G1 was not statistically significant. As for the somatotype, statistically significant differences were found between the Brazilians and the Pan Americans (Wilks' lambda = 0.498; p <0.05). The Brazilian somatotype was classified as balanced mesomorph (2.7-4.3-3.0) and the Pan American somatotype as endomorphic mesomorph (3.5-4.6-2.4). As to the specific position of the block game (2.8-4.3-2.9) and the defense game (2.6-4.4-3.0), the Brazilian somatotype was classified as balanced mesomorph and the Pan American somatotype, the block (3.7-4.4-2.4) and the defense (3.4-4.9-2.3), was classified as endomorphic mesomorph. In conclusion, the vertical jump height (spike and block) influences the male Brazilian beach volleyball players performance. The physical type of the Brazilian blockers and defenders was similar with relationship to the somatotype. The Brazilian and Pan American beach volleyball players differ in terms of kinanthropometric characteristics. This work had a multidisciplinary feature with the participation of several departments and laboratories, like the Physiotherapy Department, the Nutrition Department, the Physical Education Laboratory, thus corroborating the multidisciplinary research feature

Fatores genéticos e ambientais sobre a probabilidade de prenhez precoce em bovinos Caracu

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)