Current epidemiological trends for Chagas disease in Latin America and future challenges in epidemiology, surveillance and health policy

Chagas disease, named after Carlos Chagas, who first described it in 1909, exists only on the American Continent. It is caused by a parasite, Trypanosoma cruzi, which is transmitted to humans by blood-sucking triatomine bugs and via blood transfusion. Chagas disease has two successive phases: acute and chronic. The acute phase lasts six-eight weeks. Several years after entering the chronic phase, 20-35% of infected individuals, depending on the geographical area, will develop irreversible lesions of the autonomous nervous system in the heart, oesophagus and colon, and of the peripheral nervous system. Data on the prevalence and distribution of Chagas disease improved in quality during the 1980s as a result of the demographically representative cross-sectional studies in countries where accurate information was not previously available. A group of experts met in Brasilia in 1979 and devised standard protocols to carry out countrywide prevalence studies on human T. cruzi infection and triatomine house infestation. Thanks to a coordinated multi-country programme in the Southern Cone countries, the transmission of Chagas disease by vectors and via blood transfusion was interrupted in Uruguay in 1997, in Chile in 1999 and in Brazil in 2006; thus, the incidence of new infections by T. cruzi across the South American continent has decreased by 70%. Similar multi-country initiatives have been launched in the Andean countries and in Central America and rapid progress has been reported towards the goal of interrupting the transmission of Chagas disease, as requested by a 1998 Resolution of the World Health Assembly. The cost-benefit analysis of investment in the vector control programme in Brazil indicates that there are savings of US$17 in medical care and disabilities for each dollar spent on prevention, showing that the programme is a health investment with very high return. Many well-known research institutions in Latin America were key elements of a worldwide network of laboratories that carried out basic and applied research supporting the planning and evaluation of national Chagas disease control programmes. The present article reviews the current epidemiological trends for Chagas disease in Latin America and the future challenges in terms of epidemiology, surveillance and health policy.

Sentinel network for monitoring in vitro susceptibility of Plasmodium falciparum to antimalarial drugs in Colombia: a proof of concept

Drug resistance is one of the principal obstacles blocking worldwide malaria control. In Colombia, malaria remains a major public health concern and drug-resistant parasites have been reported. In vitro drug susceptibility assays are a useful tool for monitoring the emergence and spread of drug-resistant Plasmodium falciparum. The present study was conducted as a proof of concept for an antimalarial drug resistance surveillance network based on in vitro susceptibility testing in Colombia. Sentinel laboratories were set up in three malaria endemic areas. The enzyme linked immunosorbent assay-histidine rich protein 2 and schizont maturation methods were used to assess the susceptibility of fresh P. falciparum isolates to six antimalarial drugs. This study demonstrates that an antimalarial drug resistance surveillance network based on in vitro methods is feasible in the field with the participation of a research institute, local health institutions and universities. It could also serve as a model for a regional surveillance network. Preliminary susceptibility results showed widespread chloroquine resistance, which was consistent with previous reports for the Pacific region. However, high susceptibility to dihydroartemisinin and lumefantrine compounds, currently used for treatment in the country, was also reported. The implementation process identified critical points and opportunities for the improvement of network sustainability strategies.

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.


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.





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.

Hormonal protocols for in vitro production of Zebu and taurine embryos

The objective of this work was to evaluate the effects of hormonal synchronization protocols, associated or not with follicular development stimulation, on the recovery of oocytes and on in vitro production of Bos indicus and B. taurus embryos, in different seasons. Ultrasound-guided follicular aspirations (n=237) were performed without pre-treatment (G1, control group) and after follicular wave synchronization (G2), or after follicular wave synchronization and follicle growth induction (G3). Bos indicus produced more oocytes and embryos than B. taurus (18.7±0.9 vs. 11.9±0.6 oocytes and 4.8±0.3 vs. 2.1±0.2 embryos). On average, oocyte and embryo yields were higher in G3 than in G2, and both were greater than in G1, which lead to a higher conversion of oocytes to embryos in these treatments. The hot or the cold season did not affect the B. indicus outcomes, whereas, in B. taurus, both oocyte recovery and embryo production were higher in the cold season. Follicular wave synchronization improves ovum pick-up and in vitro production of embryos in both cattle subspecies evaluated.

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.

Readjustment of abdominal computed tomography protocols in a university hospital: impact on radiation dose

AbstractObjective:To assess the reduction of estimated radiation dose in abdominal computed tomography following the implementation of new scan protocols on the basis of clinical suspicion and of adjusted images acquisition parameters.Materials and Methods:Retrospective and prospective review of reports on radiation dose from abdominal CT scans performed three months before (group A – 551 studies) and three months after (group B – 788 studies) implementation of new scan protocols proposed as a function of clinical indications. Also, the images acquisition parameters were adjusted to reduce the radiation dose at each scan phase. The groups were compared for mean number of acquisition phases, mean CTDIvol per phase, mean DLP per phase, and mean DLP per scan.Results:A significant reduction was observed for group B as regards all the analyzed aspects, as follows: 33.9%, 25.0%, 27.0% and 52.5%, respectively for number of acquisition phases, CTDIvol per phase, DLP per phase and DLP per scan (p < 0.001).Conclusion:The rational use of abdominal computed tomography scan phases based on the clinical suspicion in conjunction with the adjusted images acquisition parameters allows for a 50% reduction in the radiation dose from abdominal computed tomography scans.

Harvesting and wood transport planning with SNAP III program (Scheduling and Network Analysis Program) in a pine plantation in Southeast Brazil

The objective of this study was to verify the potential of SNAP III (Scheduling and Network Analysis Program) as a support tool for harvesting and wood transport planning in Brazil harvesting subsystem definition and establishment of a compatible route were assessed. Initially, machine operational and production costs were determined in seven subsystems for the study area, and quality indexes, construction and maintenance costs of forest roads were obtained and used as SNAP III program input data. The results showed, that three categories of forest road occurrence were observed in the study area: main, secondary and tertiary which, based on quality index, allowed a medium vehicle speed of about 41, 30 and 24 km/hours and a construction cost of about US$ 5,084.30, US$ 2,275.28 and US$ 1,650.00/km, respectively. The SNAP III program used as a support tool for the planning, was found to have a high potential tool in the harvesting and wood transport planning. The program was capable of defining efficiently, the harvesting subsystem on technical and economical basis, the best wood transport route and the forest road to be used in each period of the horizon planning.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Experimental evaluation of the performance of a wireless sensor network in agricultural environments

The aim of this study was to perform an experimental study to evaluate the proper operation distance between the nodes of a wireless sensor network available on the market for different agricultural crops (maize, physic nut, eucalyptus). The experimental data of the network performance offers to farmers and researchers information that might be useful to the sizing and project of the wireless sensor networks in similar situations to those studied. The evaluation showed that the separation of the nodes depends on the type of culture and it is a critical factor to ensure the feasibility of using WSN. In the configuration used, sending packets every 2 seconds, the battery life was about four days. Therefore, the autonomy may be increased with a longer interval of time between sending packets.