Evaluation of dengue NS1 antigen detection for diagnosis in public health laboratories, São Paulo State, 2009

The present work evaluated the diagnostic accuracy of detection of Dengue NS1 antigen employing two NS1 assays, an immunochromatographic assay and ELISA, in the diagnostic routine of Public Health laboratories. The results obtained with NS1 assay were compared with virus isolation and, in a subpopulation of cases, they were compared with the IgM-ELISA results obtained with convalescent samples. A total of 2,321 sera samples were analyzed by one of two NS1 techniques from March to October 2009. The samples were divided into five groups: groups I, II and III included samples tested by NS1 and virus isolation, and groups IV and V included patients with a first sample tested by NS1 and a second sample tested by IgM-ELISA. Sensitivity, specificity, positive and negative predictive values, Kappa Index and Kappa Concordance were calculated. The results showed that NS1 testing in groups I, II and III had high sensitivity (98.0%, 99.5% and 99.3%), and predictive values and Kappa index between 0.9 - 1.0. Groups IV and V only had Kappa Concordance calculated, since the samples were analyzed according to the presence of NS1 antigen or IgM antibody. Concordance of 92.1% was observed when comparing the results of NS1-negative samples with IgM-ELISA. Based on the findings, it is possible to suggest that the tests for NS1 detection may be important tools for monitoring the introduction and spread of Dengue serotypes.

Co-infection of Dengue virus by serotypes 3 and 4 in patients from Amazonas, Brazil

The natural co-infection with dengue virus can occur in highly endemic areas where different serotypes have been observed for many years. We report here four cases of DENV-3/DENV-4 co-infection detected by serological and molecular tests among 674 patients with acute undifferentiated fever from the tropical medicine reference center of Manaus City, Brazil, between 2005 and 2010. Analysis of the sequences obtained indicated the presence of genotype 3 and 1 for DENV-3 and DENV-4 respectively.

First isolation of dengue 4 in the state of São Paulo, Brazil, 2011

We report the first isolation of Dengue virus 4 (DENV-4) in the state of São Paulo, from two patients - one living in São José do Rio Preto and the other one in Paulo de Faria, both cities located in the Northwest region of the state. The virus isolations were accomplished in the clone C6/36 Aedes albopictus cell line, followed by indirect immunofluorescence assays, performed with type-specific monoclonal antibodies that showed positive reactions for DENV-4. The results were confirmed by Nested RT-PCR and Real-Time RT-PCR assays. The introduction of DENV-4 in a country that already has to deal with the transmission of three other serotypes increases the possibility of the occurrence of more severe cases of the disease. The importance of early detection of dengue cases, before the virus spreads and major outbreaks occur, should be emphasized.

Modeling assembly program with constraints. A contribution to WCET problem

Dissertação para obtenção do Grau de Mestre em Lógica Computacional

Spatial distribution of dengue in the city of Cruzeiro, São Paulo State, Brazil: use of geoprocessing tools

The aim of this article is to identify patterns in spatial distribution of cases of dengue fever that occurred in the municipality of Cruzeiro, State of São Paulo, in 2006. This is an ecological and exploratory study using the tools of spatial analysis in the preparation of thematic maps with data from Sinan-Net. An analysis was made by area, taking as unit the IBGE census, the analysis included four months in 2006 which show the occurrence of the disease in the city. The thematic maps were constructed by TerraView 3.3.1 software, the same software provided the values of the indicators of Global Moran (I M) every month and the Kernel estimation. In the year 2006, 691 cases of dengue were georeferenced (with a rate of 864.2 cases/100,000 inhabitants); the indicators of Moran and p-values obtained were I M = 0.080 (March) p = 0.11; I M = 0.285 (April) p = 0.01; I M = 0.201 (May) p = 0.01 and I M = 0.002 (June) p = 0.57. The first cases were identified in the Northeast and Central areas of Cruzeiro and the recent cases, in the North, Northeast and Central. It was possible to identify census tracts where the epidemic began and how it occurred temporally and spatially in the city.

Designing a vaccination strategy against dengue

In this work we propose a mathematical approach to estimate the dengue force of infection, the average age of dengue first infection, the optimum age to vaccinate children against dengue in a routine fashion and the optimum age interval to introduce the dengue vaccine in a mass vaccination campaign. The model is based on previously published models for vaccination against other childhood infections, which resulted in actual vaccination programmes in Brazil. The model was applied for three areas of distinct levels of endemicity of the city of Recife in Northeastern State of Pernambuco, Brazil. Our results point to an optimal age to introduce the dengue vaccine in the routine immunization programme at two years of age and an age interval to introduce a mass vaccination between three and 14 years of age.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.