Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUÇÃO: A malaria é uma doença endêmica na região da Amazônia Brasileira, e a detecção de possíveis fatores de risco pode ser de grande interesse às autoridades em saúde pública. O objetivo deste artigo é investigar a associação entre variáveis ambientais e os registros anuais de malária na região amazônica usando métodos bayesianos espaço-temporais. MÉTODOS: Utilizaram-se modelos de regressão espaço-temporais de Poisson para analisar os dados anuais de contagem de casos de malária entre os anos de 1999 a 2008, considerando a presença de alguns fatores como a taxa de desflorestamento. em uma abordagem bayesiana, as inferências foram obtidas por métodos Monte Carlo em cadeias de Markov (MCMC) que simularam amostras para a distribuição conjunta a posteriori de interesse. A discriminação de diferentes modelos também foi discutida. RESULTADOS: O modelo aqui proposto sugeriu que a taxa de desflorestamento, o número de habitants por km² e o índice de desenvolvimento humano (IDH) são importantes para a predição de casos de malária. CONCLUSÕES: É possível concluir que o desenvolvimento humano, o crescimento populacional, o desflorestamento e as alterações ecológicas associadas a estes fatores estão associados ao aumento do risco de malária. Pode-se ainda concluir que o uso de modelos de regressão de Poisson que capturam o efeito temporal e espacial em um enfoque bayesiano é uma boa estratégia para modelar dados de contagem de malária.

Genetic Parameters For the Somatic Cells Count In the Milk of Buffaloes Using Ordinary Test Day Models

The buffaloes dairy milk production (BDMP) has increased in the last 20 years, mainly for the manufacturing of mozzarella cheese, which is recognized by its high nutritional quality. However, this quality can be affected by several factors i. e. high somatic cells count (SCC) provokes changes in the milk's constituents. As in bovine dairy milk, the SCC is used as diagnostic tool for milk quality; because it enables the diagnosis of sub-clinic mastitis and also allows the selection of individuals genetically resistant to that disease. Based on it, we collected information about SCC and BDMP along the lactation in Murrah breed buffaloes, during the period between 1997 and 2005. Curves were designed to estimate genetic parameters. These parameters were estimated by ordinary test-day models. There were observed variations in the estimated heritability for both characteristics the lowest score for somatic cells count (SSCC) was seen at first month (0.01) and the highest at sixth months (0.29 the genetic correlation between these traits varied from -1 at the 1 and 9(th) months to 0.31 and 0.30 in the2 and 4(th) month of lactation. Phenotypic correlations were all negative (-0.07 in the second month and up to -0.35 in the eighth month of lactation). These results showed that environmental factors are more important than genetics in explain SCC, for this reason, selection for genetic resistance to mastitis in buffalos based in SCC should not be done. In the other hand, negative phenotypic correlations demonstrated that as the SCC increased, the milk production decreased.

Multivariate models for correlated count data

In this study, we deal with the problem of overdispersion beyond extra zeros for a collection of counts that can be correlated. Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial distributions have been considered. First, we propose a multivariate count model in which all counts follow the same distribution and are correlated. Then we extend this model in a sense that correlated counts may follow different distributions. To accommodate correlation among counts, we have considered correlated random effects for each individual in the mean structure, thus inducing dependency among common observations to an individual. The method is applied to real data to investigate variation in food resources use in a species of marsupial in a locality of the Brazilian Cerrado biome. © 2013 Copyright Taylor and Francis Group, LLC.

Animal models for clinical and gestational diabetes: maternal and fetal outcomes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Anti-inflammatory mechanisms of the annexin A1 protein and its mimetic peptide Ac2-26 in models of ocular inflammation in vivo and in vitro

Annexin A1 (AnxA1) is a protein that displays potent anti-inflammatory properties, but its expression in eye tissue and its role in ocular inflammatory diseases have not been well studied. We investigated the mechanism of action and potential uses of AnxA1 and its mimetic peptide (Ac2-26) in the endotoxin-induced uveitis (EIU) rodent model and in human ARPE-19 cells activated by LPS. In rats, analysis of untreated EIU after 24 and 48 h or EIU treated with topical applications or with a single s.c. injection of Ac2-26 revealed the anti-inflammatory actions of Ac2-26 on leukocyte infiltration and on the release of inflammatory mediators; the systemic administration of Boc2, a formylated peptide receptor (fpr) antagonist, abrogated the peptide's protective effects. Moreover, AnxA1-/- mice exhibited exacerbated EIU compared with wild-type animals. Immunohistochemical studies of ocular tissue showed a specific AnxA1 posttranslational modification in EIU and indicated that the fpr2 receptor mediated the anti-inflammatory actions of AnxA1. In vitro studies confirmed the roles of AnxA1 and fpr2 and the protective effects of Ac2-26 on the release of chemical mediators in ARPE-19 cells. Molecular analysis of NF-κB translocation and IL-6, IL-8, and cyclooxygenase-2 gene expression indicated that the protective effects of AnxA1 occur independently of the NF-κB signaling pathway and possibly in a posttranscriptional manner. Together, our data highlight the role of AnxA1 in ocular inflammation, especially uveitis, and suggest the use of AnxA1 or its mimetic peptide Ac2-26 as a therapeutic approach. Copyright © 2013 by The American Association of Immunologists, Inc.