35 resultados para multivariate binary data


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To assess the completeness and reliability of the Information System on Live Births (Sinasc) data. A cross-sectional analysis of the reliability and completeness of Sinasc's data was performed using a sample of Live Birth Certificate (LBC) from 2009, related to births from Campinas, Southeast Brazil. For data analysis, hospitals were grouped according to category of service (Unified National Health System, private or both), 600 LBCs were randomly selected and the data were collected in LBC-copies through mothers and newborns' hospital records and by telephone interviews. The completeness of LBCs was evaluated, calculating the percentage of blank fields, and the LBCs agreement comparing the originals with the copies was evaluated by Kappa and intraclass correlation coefficients. The percentage of completeness of LBCs ranged from 99.8%-100%. For the most items, the agreement was excellent. However, the agreement was acceptable for marital status, maternal education and newborn infants' race/color, low for prenatal visits and presence of birth defects, and very low for the number of deceased children. The results showed that the municipality Sinasc is reliable for most of the studied variables. Investments in training of the professionals are suggested in an attempt to improve system capacity to support planning and implementation of health activities for the benefit of maternal and child population.

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Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.

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to analyze the factors associated with the underreporting on the part of nurses within Primary Health Care of abuse against children and adolescents. cross-sectional study with 616 nurses. A questionnaire addressed socio-demographic data, profession, instrumentation and knowledge on the topic, identification and reporting of abuse cases. Bivariate and multivariate logistic regression was used. female nurses, aged between 21 and 32 years old, not married, with five or more years since graduation, with graduate studies, and working for five or more years in PHC predominated. The final regression model showed that factors such as working for five or more years, having a reporting form within the PHC unit, and believing that reporting within Primary Health Care is an advantage, facilitate reporting. the study's results may, in addition to sensitizing nurses, support management professionals in establishing strategies intended to produce compliance with reporting as a legal device that ensures the rights of children and adolescents.

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Patients with obstructive sleep apnea syndrome usually present with changes in upper airway morphology and/or body fat distribution, which may occur throughout life and increase the severity of obstructive sleep apnea syndrome with age. To correlate cephalometric and anthropometric measures with obstructive sleep apnea syndrome severity in different age groups. A retrospective study of cephalometric and anthropometric measures of 102 patients with obstructive sleep apnea syndrome was analyzed. Patients were divided into three age groups (≥20 and <40 years, ≥40 and <60 years, and ≥60 years). Pearson's correlation was performed for these measures with the apnea-hypopnea index in the full sample, and subsequently by age group. The cephalometric measures MP-H (distance between the mandibular plane and the hyoid bone) and PNS-P (distance between the posterior nasal spine and the tip of the soft palate) and the neck and waist circumferences showed a statistically significant correlation with apnea-hypopnea index in both the full sample and in the ≥40 and <60 years age group. These variables did not show any significant correlation with the other two age groups (<40 and ≥60 years). Cephalometric measurements MP-H and PNS-P and cervical and waist circumferences correlated with obstructive sleep apnea syndrome severity in patients in the ≥40 and <60 age group.

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Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.

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The syndrome of resistance to thyroid hormone (RTH β) is an inherited disorder characterized by variable tissue hyposensitivity to 3,5,30-l-triiodothyronine (T3), with persistent elevation of free-circulating T3 (FT3) and free thyroxine (FT4) levels in association with nonsuppressed serum thyrotropin (TSH). Clinical presentation is variable and the molecular analysis of THRB gene provides a short cut diagnosis. Here, we describe 2 cases in which RTH β was suspected on the basis of laboratory findings. The diagnosis was confirmed by direct THRB sequencing that revealed 2 novel mutations: the heterozygous p.Ala317Ser in subject 1 and the heterozygous p.Arg438Pro in subject 2. Both mutations were shown to be deleterious by SIFT, PolyPhen, and Align GV-GD predictive methods.

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To identify the prevalence and the severity of malocclusions and to analyze factors associated with the need for orthodontic treatment of Brazilian adolescents. This exploratory, cross-sectional study was carried out based on secondary data from the national epidemiological survey on oral health in Brazil (2002-2003). Socio-demographic conditions, self-perception, and the existence and degree of malocclusion, using the Dental Aesthetic Index, were evaluated in 16,833 adolescent Brazilians selected by probabilistic sample by conglomerates. The dependent variable - need orthodontic treatment - was estimated from the severity of malocclusion. The magnitude and direction of the association in bivariate and multivariate analyzes from a Robust Poisson regression was estimated RESULTS: The majority of the adolescents needed orthodontic treatment (53.2%). In the multivariate analysis, the prevalence of the need for orthodontic treatment was larger among females, non-whites, those that perceived a need for treatment, and those that perceived their appearance as normal, bad, or very bad. The need for orthodontic treatment was smaller among those that lived in the Northeast and Central West macro-regions compared to those living in Southeast Brazil and it was also smaller among those that perceived their chewing to be normal or their oral health to be bad or very bad. There was a high prevalence of orthodontic treatment need among adolescents in Brazil and this need was associated with demographic and subjective issues. The high prevalence of orthodontic needs in adolescents is a challenge to the goals of Brazil's universal public health system.

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In this work, we discuss the use of multi-way principal component analysis combined with comprehensive two-dimensional gas chromatography to study the volatile metabolites of the saprophytic fungus Memnoniella sp. isolated in vivo by headspace solid-phase microextraction. This fungus has been identified as having the ability to induce plant resistance against pathogens, possibly through its volatile metabolites. Adequate culture media was inoculated, and its headspace was then sampled with a solid-phase microextraction fiber and chromatographed every 24 h over seven days. The raw chromatogram processing using multi-way principal component analysis allowed the determination of the inoculation period, during which the concentration of volatile metabolites was maximized, as well as the discrimination of the appropriate peaks from the complex culture media background. Several volatile metabolites not previously described in the literature on biocontrol fungi were observed, as well as sesquiterpenes and aliphatic alcohols. These results stress that, due to the complexity of multidimensional chromatographic data, multivariate tools might be mandatory even for apparently trivial tasks, such as the determination of the temporal profile of metabolite production and extinction. However, when compared with conventional gas chromatography, the complex data processing yields a considerable improvement in the information obtained from the samples. This article is protected by copyright. All rights reserved.

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Purpose. To understand the role of polymorphisms in the LEP (rs7799039 and rs2167270) and LEPR (rs1137101 and rs1137100) genes in DTC susceptibility and their effect on leptin levels. Methods. We studied 153 patients with DTC and 234 controls through TaqMan SNP Genotyping and ELISA, comparing these data to the clinicopathological data of patients with DTC. Results. Patients with AA genotype of rs7799039 had higher levels of serum leptin (9.22 ± 0.98 ng/mL) than those with AG genotype (10.07 ± 0.60 ng/mL; P = 0.005). Individuals with AG genotype of rs2167270 also produced higher serum leptin levels (10.05 ± 0.59 ng/mL) than the subjects with GG genotype (9.52 ± 0.79 ng/mL; P < 0.05). A multivariate logistic regression adjusted for gender, age, and BMI showed that the AG genotype of rs7799039 was an independent risk for DTC (OR, 11.689; P = 0.0183; 95% CI, 1.516-90.119). Similarly, AG and GG genotypes of rs1137101 increased the susceptibility to DTC (OR, 3.747; P = 0.027; 95% CI, 1.161-12.092 and OR, 5.437; P = 0.013; 95% CI, 1.426-20.729). Conclusions. We demonstrated that rs7799039 and rs2167270 polymorphisms modify the serum leptin concentrations in patients with DTC. Furthermore, polymorphisms rs7799039 and rs1137101 increase the risk of DTC development, although they do not correlate with tumor aggressiveness.

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Perineural invasion (PNI) and lymphovascular invasion (LVI) have been associated with the risk of local recurrences and lymph node metastasis. The aim of this study was to evaluate the prognostic impact of PNI and LVI in patients with advanced stage squamous cell carcinoma of the tongue and floor of the mouth. One hundred and forty-two patients without previous treatment were selected. These patients underwent radical surgery with neck dissection and adjuvant treatment. Clinicopathological data were retrieved from the medical charts, including histopathology and surgery reports. Univariate analysis was performed to assess the impact of studied variables on survival. Overall survival was negatively influenced by six tumour-related factors: increasing T stage (P = 0.003), more than two clinically positive nodes (P = 0.002), extracapsular spread of lymph node metastasis (P < 0.001), tumour thickness (P = 0.04), PNI (P < 0.001), and LVI (P = 0.012). Disease-free survival was influenced by PNI (P = 0.04), extracapsular spread of lymph node metastasis (P = 0.008), and N stage (P = 0.006). Multivariate analysis showed PNI to be an independent predictor for overall survival (P = 0.01) and disease-free survival (P = 0.03). Thus the presence of PNI in oral carcinoma surgical specimens has a significant impact on survival outcomes in patients with advanced stage tumours submitted to radical surgery and adjuvant radiotherapy/radiochemotherapy.

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Since the last decade, the combined use of chemometrics and molecular spectroscopic techniques has become a new alternative for direct drug determination, without the need of physical separation. Among the new methodologies developed, the application of PARAFAC in the decomposition of spectrofluorimetric data should be highlighted. The first objective of this article is to describe the theoretical basis of PARAFAC. For this purpose, a discussion about the order of chemometric methods used in multivariate calibration and the development of multi-dimensional methods is presented first. The other objective of this article is to divulge for the Brazilian chemical community the potential of the combination PARAFAC/spectrofluorimetry for the determination of drugs in complex biological matrices. For this purpose, two applications aiming at determining, respectively, doxorrubicine and salicylate in human plasma are presented.

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Chemometric activities in Brazil are described according to three phases: before the existence of microcomputers in the 1970s, through the initial stages of microcomputer use in the 1980s and during the years of extensive microcomputer applications of the ´90s and into this century. Pioneering activities in both the university and industry are emphasized. Active research areas in chemometrics are cited including experimental design, pattern recognition and classification, curve resolution for complex systems and multivariate calibration. New trends in chemometrics, especially higher order methods for treating data, are emphasized.

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In this work, the artificial neural networks (ANN) and partial least squares (PLS) regression were applied to UV spectral data for quantitative determination of thiamin hydrochloride (VB1), riboflavin phosphate (VB2), pyridoxine hydrochloride (VB6) and nicotinamide (VPP) in pharmaceutical samples. For calibration purposes, commercial samples in 0.2 mol L-1 acetate buffer (pH 4.0) were employed as standards. The concentration ranges used in the calibration step were: 0.1 - 7.5 mg L-1 for VB1, 0.1 - 3.0 mg L-1 for VB2, 0.1 - 3.0 mg L-1 for VB6 and 0.4 - 30.0 mg L-1 for VPP. From the results it is possible to verify that both methods can be successfully applied for these determinations. The similar error values were obtained by using neural network or PLS methods. The proposed methodology is simple, rapid and can be easily used in quality control laboratories.

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The caffeine solubility in supercritical CO2 was studied by assessing the effects of pressure and temperature on the extraction of green coffee oil (GCO). The Peng-Robinson¹ equation of state was used to correlate the solubility of caffeine with a thermodynamic model and two mixing rules were evaluated: the classical mixing rule of van der Waals with two adjustable parameters (PR-VDW) and a density dependent one, proposed by Mohamed and Holder² with two (PR-MH, two parameters adjusted to the attractive term) and three (PR-MH3 two parameters adjusted to the attractive and one to the repulsive term) adjustable parameters. The best results were obtained with the mixing rule of Mohamed and Holder² with three parameters.

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The validation of an analytical procedure must be certified through the determination of parameters known as figures of merit. For first order data, the acuracy, precision, robustness and bias is similar to the methods of univariate calibration. Linearity, sensitivity, signal to noise ratio, adjustment, selectivity and confidence intervals need different approaches, specific for multivariate data. Selectivity and signal to noise ratio are more critical and they only can be estimated by means of the calculation of the net analyte signal. In second order calibration, some differentes approaches are necessary due to data structure.