1,721,262 research outputs found
Dietary patterns and gastric cancer risk : a sistematic review and meta-analysis
No full textTo assess the role of diet and the risk of gastric cancer (GC), we conducted a systemic review of the literature through Medline and Embase databases, and a meta-analysis of epidemiological studies evaluating the association between dietary patterns and gastric cancer risk. Of the 16 papers identified, 9 derived dietary patterns through an a posteriori method, 5 through a priori scores, and 2 used both approaches. Among the studies based on a posteriori dietary patterns, the RRs for overall GC ranged from 0.4 to 0.6 for the favorable ones, and from 1.7 to 3.0 for the unfavorable ones; among the studies based on a priori dietary patterns, the RRs for overall GC ranged from 0.2 to 0.7 for favorable ones, and from 1.8 to 6.9, plus an outlier RR of 41.2, for the unfavorable ones, for the highest versus the lowest category. Of these studies, 8 were included in our meta-analysis, as they provide a similar method to derive dietary patterns. A favourable role on GC emerged for the “Prudent/healthy”, with an odds ratio (OR) of 0.75 (95% confidence intervals, CI: 0.63-0.90), for the highest versus the lowest category. Similar results emerged by anatomical subtype. An unfavourable role on GC emerged for the “Western/unhealthy” dietary pattern, with an OR=1.51 (95% CI: 1.21-1.89). This association was weaker for the distal/NOS (not otherwise specified) category (OR=1.36) compared to the cardia GC (OR=2.05
PARTIAL LEAST SQUARE PATH MODELING APPROACH IN BIOMEDICAL RESEARCH
Full text linkThe Partial Least Squares Path Modeling (PLS-PM) is a method meant to estimate a network of causal relationships defined according to a theoretical model. The complexity of the theoretical construct is studied by taking into account the relationships among non measurable indicators (latent variables), represented by a set of observed variables (manifest variables). PLS-PM aims to estimate, through a system of interdependent equations based on simple and multiple regressions, the network of relations among the manifest variables and their own latent variable, and among the latent variables inside the model. The causal relationships among variables are represented through a Path Diagram, in which the latent variables are enclosed in circles and the manifest variables are enclosed in boxes. PLS-PM involves three sets of relations: 1) structural or inner model, 2) measurement or outer model, 3) the weight relations upon which latent variable scores can be calculated. The first model takes into account the relations among the latent variables and the second takes into account the relations between manifest variables and the corresponding latent variable. In the structural model each endogenous (dependent) latent variable is linked to the others by a multiple regression model. The structural design only assumes recursive models, i.e. the path diagram takes the form of a causal chain with no loops. Different types of measurement models exists, depending the kind of relationship: 1) reflective model (observed variables are considered being caused by the latent variable (i.e., indicators reflect the construct; the latent variable is considered as the cause of the manifest variables and each manifest variable is an effect of the unique corresponding latent variable); 2) formative model (the latent variables are considered as being caused by its manifest variables); and 3) MIMIC model (multiple effect indicators for multiple causes, it represents a mixture of both the reflective and the formative models within the same block of manifest variables). Independently from the type of measurement model, the standardized latent variable scores are computed as a linear combination of its manifest variables and outer weights (the so-called weight relation).
Once the theoretical model is specified, the next phase in PLS-PM is the estimation of the model parameters. The PLS algorithm consists of three stages. The first stage is an iterative procedure of ordinary least squares regressions taking into account the relationships of the structural and measurement model, in order to calculate weights required to give final estimates for each latent variable. This first stage is the “core” stage in the PLS algorithm. Subsequently, the second and third stage involve the non-iterative estimation of the coefficients of the structural and measurement model, respectively. The structural model coefficients (path coefficients) are calculated by ordinary least squares regressions between latent variables. The measurement model coefficients (loading coefficients) are also estimated by regressions but taking into account the kind of mode to be used (reflective or formative).
PLS-PM has been widely used in economical (the customer satisfaction is a typical example) and psychological settings. In biomedical context, the published articles are scanty and generally published in open access journals.
The aim of this study was to apply the PLS-PM in a different field, since it has been widely used in economical (the customer satisfaction is a typical example) and psychological setting. In biomedical context, the published articles are scanty and generally published in open access journals.
I used the PLS-PM method in order to analyze the adherence of the procedures provided for diagnosis, treatment (surgical and medical), and follow-up of breast cancer through a set of indicators. Indeed, the used approaches in this field since oversimplify the complex problem since they do not consider simultaneously multiple aspects of the diagnostic, therapeutic and follow-up pathways. This method has several strengths, as PLS-PM allows the reduction of dimensionality of several health indicators into a smaller number of latent variables (and more interpretable), and then allows to study causal relationships between these latent variables, representing the different aspects of the diagnostic, therapeutic and follow-up pathways. This method also requires no distributional assumptions with respect to the variables included in the model. The limit of this method is the bias deriving from the a priori selection of the relationships among latent variables and of the indicators used to characterize the latent variable. Although the limited sample size makes the analyses explorative-orientated only, the present study represents an unique example of PLS-PM application in the biomedical research, in particular in the evaluation of the adherence of the diagnostic and treatment procedures for breast cancer
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Direct simulations of globular clusters with N=128K stars and central black holes
No full textThe dynamics of a large stellar cluster containing N = 128,000 stars has been simulated by a direct summation (O(N2)) method by using a hyper-systolic algorithm on a heterogeneous platform. Preliminary simulations have been carried out on model systems with and without the presence, in their center, of a black hole whose mass has been varied from 0.02 to 0.1 times the total mass of the cluster. These simulations followed the evolution of the globular cluster in order to describe its dynamics over an interval of time up to 20 crossing times. The platform heterogeneity, allowing a very efficient use of the computational resources, can be considered a key feature for sustaining large computational loads. Our results show that the massive object in the center of the cluster plays an important role in altering quickly the surrounding star distribution, causing a sort of ``violent relaxation'' while the following evolution occurs via two-body collisional relaxation
Reply to letter to the editor : adenosine di-phosphate receptor antagonist discontinuation management prior to coronary artery surgery
No full textVariations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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