56 resultados para automated thematic analysis of textual data


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Background & Aims: Steatosis is a frequent histologic finding in chronic hepatitis C (CHC), but it is unclear whether steatosis is an independent predictor for liver fibrosis. We evaluated the association between steatosis and fibrosis and their common correlates in persons with CHC and in subgroup analyses according to hepatitis C virus (HCV) genotype and body mass index. Methods: We conducted a meta-analysis on individual data from 3068 patients with histologically confirmed CHC recruited from 10 clinical centers in Italy, Switzerland, France, Australia, and the United States. Results: Steatosis was present in 1561 patients (50.9%) and fibrosis in 2688 (87.6%). HCV genotype was 1 in :1694 cases (55.2%), 2 in 563 (18.4%), 3 in 669 (21.8%), and 4 in :142 (4.6%). By stepwise logistic regression, steatosis was associated independently with genotype 3, the presence of fibrosis, diabetes, hepatic inflammation, ongoing alcohol abuse, higher body mass index, and older age. Fibrosis was associated independently with inflammatory activity, steatosis, male sex, and older age, whereas HCV genotype 2 was associated with reduced fibrosis. In the subgroup analyses, the association between steatosis and fibrosis invariably was dependent on a simultaneous association between steatosis and hepatic inflammation. Conclusions: In this large and geographically different group of CHC patients, steatosis is confirmed as significantly and independently associated with fibrosis in CHC. Hepatic inflammation may mediate fibrogenesis in patients with liver steatosis. Control of metabolic factors (such as overweight, via lifestyle adjustments) appears important in the management of CHC.

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Background: Automated measurement of LV function could extend the clinical utility of echo by less expert readers. We sought to define normal ranges of global 2D strain (2DS) and strain-rate (SR) in an international, multicenter study of healthy subjects, and to assess the determinants of variation. Methods: SR and 2DS were measured in 18 myocardial segts in both apical and short axis views of 227 normal subjects (38% men, 48±14y) with no cardiac history, risk factors or drug therapy. The association of age and resting hemodynamics with global strain indices was sought using multiple regression. Differences in variance were expressed as F values. Results: Baseline SBP was 127±18 mmHg, pulse was 76±13/min and ejection fraction 50±20%. Although global longitudinal strain was influenced by endsystolic volume (F=4.2, p

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This article applies methods of latent class analysis (LCA) to data on lifetime illicit drug use in order to determine whether qualitatively distinct classes of illicit drug users can be identified. Self-report data on lifetime illicit drug use (cannabis, stimulants, hallucinogens, sedatives, inhalants, cocaine, opioids and solvents) collected from a sample of 6265 Australian twins (average age 30 years) were analyzed using LCA. Rates of childhood sexual and physical abuse, lifetime alcohol and tobacco dependence, symptoms of illicit drug abuse/dependence and psychiatric comorbidity were compared across classes using multinomial logistic regression. LCA identified a 5-class model: Class 1 (68.5%) had low risks of the use of all drugs except cannabis; Class 2 (17.8%) had moderate risks of the use of all drugs; Class 3 (6.6%) had high rates of cocaine, other stimulant and hallucinogen use but lower risks for the use of sedatives or opioids. Conversely, Class 4 (3.0%) had relatively low risks of cocaine, other stimulant or hallucinogen use but high rates of sedative and opioid use. Finally, Class 5 (4.2%) had uniformly high probabilities for the use of all drugs. Rates of psychiatric comorbidity were highest in the polydrug class although the sedative/opioid class had elevated rates of depression/suicidal behaviors and exposure to childhood abuse. Aggregation of population-level data may obscure important subgroup differences in patterns of illicit drug use and psychiatric comorbidity. Further exploration of a 'self-medicating' subgroup is needed.

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This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).

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This paper describes the application of a new technique, rough clustering, to the problem of market segmentation. Rough clustering produces different solutions to k-means analysis because of the possibility of multiple cluster membership of objects. Traditional clustering methods generate extensional descriptions of groups, that show which objects are members of each cluster. Clustering techniques based on rough sets theory generate intensional descriptions, which outline the main characteristics of each cluster. In this study, a rough cluster analysis was conducted on a sample of 437 responses from a larger study of the relationship between shopping orientation (the general predisposition of consumers toward the act of shopping) and intention to purchase products via the Internet. The cluster analysis was based on five measures of shopping orientation: enjoyment, personalization, convenience, loyalty, and price. The rough clusters obtained provide interpretations of different shopping orientations present in the data without the restriction of attempting to fit each object into only one segment. Such descriptions can be an aid to marketers attempting to identify potential segments of consumers.

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This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

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A hydraulic jump is characterized by strong energy dissipation and mixing, large-scale turbulence, air entrainment, waves and spray. Despite recent pertinent studies, the interaction between air bubbles diffusion and momentum transfer is not completely understood. The objective of this paper is to present experimental results from new measurements performed in rectangular horizontal flume with partially-developed inflow conditions. The vertical distributions of void fraction and air bubbles count rate were recorded for inflow Froude number Fr1 in the range from 5.2 to 14.3. Rapid detrainment process was observed near the jump toe, whereas the structure of the air diffusion layer was clearly observed over longer distances. These new data were compared with previous data generally collected at lower Froude numbers. The comparison demonstrated that, at a fixed distance from the jump toe, the maximum void fraction Cmax increases with the increasing Fr1. The vertical locations of the maximum void fraction and bubble count rate were consistent with previous studies. Finally, an empirical correlation between the upper boundary of the air diffusion layer and the distance from the impingement point was provided.

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There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

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The writers measured velocity, pressure and energy distributions, wavelengths, and wave amplitudes along undular jumps in a smooth rectangular channel 0.25 m wide. In each case the upstream flow was a fully developed shear flow. Analysis of the data shows that the jump has strong three-dimensional features and that the aspect ratio of the channel is an important parameter. Energy dissipation on the centerline is far from negligible and is largely constrained to the reach between the start of the lateral shock waves and the first wave crest of the jump, in which the boundary layer develops under a strong adverse pressure gradient. A Boussinesq-type solution of the free-surface profile, velocity, and energy and pressure distributions is developed and compared with the data. Limitations of the two-dimensional analysis are discussed.

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Formaldehyde-derived oxazolidine derivatives 4-7 of the beta-adrenoreceptor antagonists metoprolol 1, atenolol 2 and timolol 3 have been synthesised. Conformational analysis of 1-3 and the oxazolidine derivatives 4-7 has been performed using H-1 NMR spectroscopy and computational methods. The H-1 NMR studies show that for the aryloxypropanolamine beta-adrenoreceptor antagonists there is a predominance of the conformer in which the amine group is approximately antiperiplanar or trans to the aryloxymethylene group. Both H-1 NMR data and theoretical studies indicate that the oxazolidine derivatives 4-7 and the aryloxypropanolamine beta-adrenoreceptor antagonists 1-3 adopt similar conformations around the beta-amino alcohol moiety. Thus, oxazolidine ring formation does not dramatically alter the preferred conformation adopted by the beta-amino alcohol moiety of 1-3. Oxazolidine derivatives of aryloxypropanolamine beta-adrenoreceptor antagonists may therefore be appropriate as prodrugs, or semi-rigid analogues, when greater lipophilicity is required for drug delivery.