Investimento materno e história reprodutiva de mães residentes em contextos com diferentes graus de urbanização

A presente pesquisa tem como objetivo caracterizar o investimento materno e a história reprodutiva de mães que vivem em diferentes contextos. Para tal, 150 mães que residiam em três contextos com diferentes graus de urbanização foram entrevistadas. Por meio da análise estatística dos dados constatou-se que houve diferenças significativas entre os contextos, além de correlações positivas entre os núcleos reprodutivos, como idade da primeira relação sexual e idade da mãe no nascimento do primeiro filho. Conclui-se que as estratégias reprodutivas são influenciadas pela história de vida das mães, das condições sociodemográficas atuais e do contexto onde vivem.

Avaliação da atenção básica em saúde: uma nova proposta

Este artigo apresenta uma nova proposta de avaliação da Atenção Básica em Saúde (ABS), utilizando a abordagem sistêmica de Donabedian, com a modificação de que a avaliação desse serviço deveria iniciar pelo componente Processo, visando identificar a adequação da oferta e das relações entre os distintos procedimentos desse nível de atenção. A partir dessa análise deverá ser realizada a busca do resultado preditivo, permitindo a relação entre esses componentes. Com base nessa análise, a avaliação de estrutura ganha significado para o processo de decisão. Faz parte da abordagem a avaliação da rede de atenção, tendo como foco e imagem-objetivo a atenção básica como porta de entrada da rede. Apresenta uma concepção de rede mais dinâmica e flexível e propõe a utilização na avaliação do método misto, englobando a abordagem quantitativa baseada nos bancos de dados existentes no sistema Datasus e complementada pela abordagem qualitativa, permitindo maior compreensão do significado das relações encontradas nos serviços e na rede. Nesse desenho metodológico, será possível a identificação das variáveis de funcionamento e a organização da ABS e da rede de serviços, possibilitando direcionar a tomada de decisão para a melhoria de qualidade da atenção básica de saúde.

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

Searching for molecular markers in head and neck squamous cell carcinomas (HNSCC) by statistical and bioinformatic analysis of larynx-derived SAGE libraries

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in humans. The average 5-year survival rate is one of the lowest among aggressive cancers, showing no significant improvement in recent years. When detected early, HNSCC has a good prognosis, but most patients present metastatic disease at the time of diagnosis, which significantly reduces survival rate. Despite extensive research, no molecular markers are currently available for diagnostic or prognostic purposes. Methods: Aiming to identify differentially-expressed genes involved in laryngeal squamous cell carcinoma (LSCC) development and progression, we generated individual Serial Analysis of Gene Expression (SAGE) libraries from a metastatic and non-metastatic larynx carcinoma, as well as from a normal larynx mucosa sample. Approximately 54,000 unique tags were sequenced in three libraries. Results: Statistical data analysis identified a subset of 1,216 differentially expressed tags between tumor and normal libraries, and 894 differentially expressed tags between metastatic and non-metastatic carcinomas. Three genes displaying differential regulation, one down-regulated (KRT31) and two up-regulated (BST2, MFAP2), as well as one with a non-significant differential expression pattern (GNA15) in our SAGE data were selected for real-time polymerase chain reaction (PCR) in a set of HNSCC samples. Consistent with our statistical analysis, quantitative PCR confirmed the upregulation of BST2 and MFAP2 and the downregulation of KRT31 when samples of HNSCC were compared to tumor-free surgical margins. As expected, GNA15 presented a non-significant differential expression pattern when tumor samples were compared to normal tissues. Conclusion: To the best of our knowledge, this is the first study reporting SAGE data in head and neck squamous cell tumors. Statistical analysis was effective in identifying differentially expressed genes reportedly involved in cancer development. The differential expression of a subset of genes was confirmed in additional larynx carcinoma samples and in carcinomas from a distinct head and neck subsite. This result suggests the existence of potential common biomarkers for prognosis and targeted-therapy development in this heterogeneous type of tumor.