Surveillance, health promotion and control of Chagas disease in the Amazon Region - Medical attention in the Brazilian Amazon Region: a proposal

We refer to Oswaldo Cruz’s reports dating from 1913 about the necessities of a healthcare system for the Brazilian Amazon Region and about the journey of Carlos Chagas to 27 locations in this region and the measures that would need to be adopted. We discuss the risks of endemicity of Chagas disease in the Amazon Region. We recommend that epidemiological surveillance of Chagas disease in the Brazilian Amazon Region and Pan-Amazon region should be implemented through continuous monitoring of the human population that lives in the area, their housing, the environment and the presence of triatomines. The monitoring should be performed with periodic seroepidemiological surveys, semi-annual visits to homes by health agents and the training of malaria microscopists and healthcare technicians to identify Trypanosoma cruzi from patients’ samples and T. cruzi infection rates among the triatomines caught. We recommend health promotion and control of Chagas disease through public health policies, especially through sanitary education regarding the risk factors for Chagas disease. Finally, we propose a healthcare system through base hospitals, intermediate-level units in the areas of the Brazilian Amazon Region and air transportation, considering the distances to be covered for medical care.

The network social support experience of people involved in home care

Objective: To build a theoretical model to configure the network social support experience of people involved in home care. Method: A quantitative approach research, utilizing the Grounded Theory method. The simultaneous data collection and analysis allowed the interpretation of the phenomenon meaning The network social support of people involved in home care. Results: The population passive posture in building their well-being was highlighted. The need of a shared responsibility between the involved parts, population and State is recognized. Conclusion: It is suggested for nurses to be stimulated to amplify home care to attend the demands of caregivers; and to elaborate new studies with different populations, to validate or complement the proposed theoretical model.


Governmental surveillance system of healthcare-associated infection in Brazil


Objective: This study aimed to describe the structure of governmental surveillance systems for Healthcare Associated Infection (HAI) in the Brazilian Southeastern and Southern States. Method: A cross-sectional, descriptive and exploratory study, with data collection by means of two-phases: characterization of the healthcare structure and of the HAI surveillance system. Results: The governmental teams for prevention and control of HAI in each State ranged from one to six members, having at least one nurse. All States implemented their own surveillance system. The information systems were classified into chain (n=2), circle (n=4) or wheel (n=1). Conclusion: Were identified differences in the structure and information flow from governmental surveillance systems, possibly limiting a nationwide standardization. The present study points to the need for establishing minimum requirements in public policies, in order to guide the development of HAI surveillance systems.


Social network of family caregivers of disabled and dependent patients

Cross-sectional study that used the Social Network Index and the genogram to assess the social network of 110 family caregivers of dependent patients attended by a Home Care Service in São Paulo, Brazil. Data were analyzed using the test U of Mann-Whitney, Kruskal-Wallis and Spearman correlation. Results were considered statistically significant when p<0,05. Few caregivers participated in activities outside the home and the average number of people they had a bond was 4,4 relatives and 3,6 friends. Caregivers who reported pain and those who had a partner had higher average number of relatives who to trust. The average number of friends was higher in the group that reported use of medication for depression. Total and per capita incomes correlated with the social network. It was found that family members are the primary caregiver’s social network.





Evaluation of oviposition traps as an entomological surveillance method for Aedes aegypti (Diptera, Culicidae)

This study aimed to describe the behavior of oviposition traps for Aedes aegypti over time, to compare it with the larval survey and to investigate the association with climatic variables. It was conducted in São José do Rio Preto city, São Paulo. Daily climatic data and fortnightly measurements for oviposition traps and larval infestation were collected from October 2003 to September 2004. Three different periods were identified in the behavior of oviposition traps' positivity and mean number of eggs: increase, plateau and decrease in values. These measurements followed the variation of climatic data from the first and third periods. High correlation was obtained between the positivity and the mean number of eggs. The oviposition traps showed higher capacity to detect the vector than did larval survey. It was observed that the first (October to December) and third (May to September) periods were considered to be the most suitable to use oviposition traps than larval surveys.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.