1000 resultados para Índices fisiológicos


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BACKGROUND: The model for end-stage liver disease (MELD) was developed to predict short-term mortality in patients with cirrhosis. There are few reports studying the correlation between MELD and long-term posttransplantation survival. AIM: To assess the value of pretransplant MELD in the prediction of posttransplant survival. METHODS: The adult patients (age >18 years) who underwent liver transplantation were examined in a retrospective longitudinal cohort of patients, through the prospective data base. We excluded acute liver failure, retransplantation and reduced or split-livers. The liver donors were evaluated according to: age, sex, weight, creatinine, bilirubin, sodium, aspartate aminotransferase, personal antecedents, brain death cause, steatosis, expanded criteria donor number and index donor risk. The recipients' data were: sex, age, weight, chronic hepatic disease, Child-Turcotte-Pugh points, pretransplant and initial MELD score, pretransplant creatinine clearance, sodium, cold and warm ischemia times, hospital length of stay, blood requirements, and alanine aminotransferase (ALT >1,000 UI/L = liver dysfunction). The Kaplan-Meier method with the log-rank test was used for the univariable analyses of posttransplant patient survival. For the multivariable analyses the Cox proportional hazard regression method with the stepwise procedure was used with stratifying sodium and MELD as variables. ROC curve was used to define area under the curve for MELD and Child-Turcotte-Pugh. RESULTS: A total of 232 patients with 10 years follow up were available. The MELD cutoff was 20 and Child-Turcotte-Pugh cutoff was 11.5. For MELD score > 20, the risk factors for death were: red cell requirements, liver dysfunction and donor's sodium. For the patients with hyponatremia the risk factors were: negative delta-MELD score, red cell requirements, liver dysfunction and donor's sodium. The regression univariated analyses came up with the following risk factors for death: score MELD > 25, blood requirements, recipient creatinine clearance pretransplant and age donor >50. After stepwise analyses, only red cell requirement was predictive. Patients with MELD score < 25 had a 68.86%, 50,44% and 41,50% chance for 1, 5 and 10-year survival and > 25 were 39.13%, 29.81% and 22.36% respectively. Patients without hyponatremia were 65.16%, 50.28% and 41,98% and with hyponatremia 44.44%, 34.28% and 28.57% respectively. Patients with IDR > 1.7 showed 53.7%, 27.71% and 13.85% and index donor risk <1.7 was 63.62%, 51.4% and 44.08%, respectively. Age donor > 50 years showed 38.4%, 26.21% and 13.1% and age donor <50 years showed 65.58%, 26.21% and 13.1%. Association with delta-MELD score did not show any significant difference. Expanded criteria donors were associated with primary non-function and severe liver dysfunction. Predictive factors for death were blood requirements, hyponatremia, liver dysfunction and donor's sodium. CONCLUSION: In conclusion MELD over 25, recipient's hyponatremia, blood requirements, donor's sodium were associated with poor survival.

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This work reports an economic evaluation of the dried banana production from an agroindustry located in Guaraqueçaba - PR State, Brazil. The conventional and the organic banana processings were evaluated by comparing the economic viability pointers. The dried organic banana is exported to the Europe and the dried conventional banana is commercialized in the region of Curitiba - PR. Both processings presented positive economic viability, however the dried organic banana presented better indices (TIR 94%, VPL R$ 486,009.39 and benefit cost relation of 2.11) than the conventional dried banana (TIR 14%, VPL R$ 34,668.00 and benefit cost relation of 1.17). The dried organic banana presented a cost of production of R$ 3.64, being 50.1% relative to the expense with insumos and 27% with labour. The dried conventional banana presented a cost of R$ 3.21, being 45.3% for insumos and 31.2% for labour.

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In the last few years the sugar-cane mechanical harvested area has increased, especially in regions with appropriated slop. The use of this technology brings some inconveniences, such as, the increase in the percentage of extraneous matter, which causes the reduction of technological quality of the raw material, and losses in the field. Extraneous matter (trash) is composed of tops and leaves in major percentage, plus soil and roots, and eventually some metal parts. In the green cane harvest system the percentage of extraneous matter has a tendency to increase due to the great amount of vegetal matter to be processed. The increase in the blower fan speed to reduce the amount of extraneous matter can lead to an unacceptable economic level of raw material losses. The main objective of this work was, using a cane loss monitor, to evaluate and quantify the amount of visible losses of sugar cane through the primary extractor at two different fan speeds. Afterwards these losses were related to the harvester cleaning efficiency. The piezoelectric transducer shows a reasonable sensibility. The results show that the cleaning efficiency in the primary extractor (85% mean), the cane losses (between 5.68% and 2.15%) and fan speed are interrelated. The total losses and specially splinters (between 3.19% and 0.91%), showed a significant difference among the treatments.

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Livestock facilities, where animals carry their productive cycle, must have as main characteristic, the control of influence over climatic factors on animals. The environment variations can be controlled through the use of different ventilation systems. The objective of this research was to evaluate the influence of different environment conditioning systems on swine nursery. Three treatments have been tested: natural ventilation, cooled ventilation and forced ventilation. The climatic parameters evaluated were: air temperature, relative humidity and black globe temperature. The physiological parameters analyzed were: respiratory frequency and back fat thickness. Number of born alive piglets, average weight at weaning and number of weaned piglets were also evaluated parameters. The use of cooled ventilation systems were able to decreased animal's air temperature and respiratory frequency, and the black globe temperature and humidity index (WBGT) and the radiating thermal load (RTL).

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Base cutting and feeding into harvesters of plants lying close to the ground surface require an efficient sweeping action of the cutting mechanism. It is not the case of conventional sugarcane harvesters which have rigid blades mounted on discs capable to contaminate the cane with dirt as well as damage the ratoons. The objective of this work was to simulate the sweeping performance of a segmented base cutter. The model was developed using the laws of dynamic. Simulation included two rotational speeds (400 and 600 rpm), two cutting heights (0.12 and 0.13 m) and two disk tilting angles (-10º and -12º). The simulated sweeping angle varied between 56º and 193º, which are very promising as a mean to cutting and feeding cane sticks lying on the ground. Cutting height was the variable that affected sweeping action the most. This behavior indicates the need to have an automatic control of the cutting disk height in order to keep good sweeping performance as the harvester moves forward.

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Behavioral adjustments may occur fast and with less cost than the physiological adaptations. Considering the social behavior is suggestive that the frequency and the intensity of aggressive interactions, the total social cohesion and the extent of vicious attitudes may be used to evaluate welfare. This research presents an analysis of the interactions between the experimental factors such as temperature, genetic and time of the day in the behavior of female broiler breeders under controlled environment in a climatic chamber in order to enhance the different reaction of the birds facing distinct environmental conditions. The results showed significant differences between the behaviors expressed by the studied genetics presenting the need of monitoring them in real-time in order to predict their welfare in commercial housing, due to the complexity of the environmental variables that interfere in the well being process. The research also concluded that the welfare evaluation of female broiler breeders needs to consider the time of the day during the observation of the behaviors.

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The objective of this study was to analyze changes in the spectral behavior of the soybean crop through spectral profiles of the vegetation indexes NDVI and GVI, expressed by different physical values such as apparent bi-directional reflectance factor (BRF), surface BRF, and normalized BRF derived from images of the Landsat 5/TM. A soybean area located in Cascavel, Paraná, was monitored by using five images of Landsat 5/TM during the 2004/2005 harvesting season. The images were submitted to radiometric transformation, atmospheric correction and normalization, determining physical values of apparent BRF, surface BRF and normalized BRF. NDVI and GVI images were generated in order to distinguish the soybean biomass spectral response. The treatments showed different results for apparent, surface and normalized BRF. Through the profiles of average NDVI and GVI, it was possible to monitor the entire soybean cycle, characterizing its development. It was also observed that the data from normalized BRF negatively affected the spectral curve of soybean crop, mainly, during the phase of vegetative growth, in the 12-9-2004 image.

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Tomatoes are one of the most important vegetable crops grown in Brazil and are among the crops that have one of the highest post-harvest losses indexes in the country. The present work aimed at evaluating impact damage observed in packing lines of fresh tomatoes as well as to determine, under laboratory conditions, quality alterations of tomato fruits submitted to impact damage in different surface types. Critical points evaluation was accomplished using an instrumented sphere. Critical transference points found showed variations in acceleration levels from 30 to 129 G (m s-2). Tests carried out under laboratory conditions showed that padded surfaces reduced up to 31% impact damage. Incidence of severe internal physical damage was evaluated by a subjective scale and increased by 79% on hard surfaces for the highest fall drop. On the other hand, it was observed an effective reduction in physical damage on fruits when padded surfaces were used. When a 10-cm drop was performed, the maximum reduction measured was 10% for hard surfaces and 5% for previously padded surfaces. For quality parameters, it was observed for high drops on hard surfaces, highest values for weight loss, total acidity, lower values for vitamin C and Soluble Solids.

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The swine breeder rearing environment directly affects the animal's performance. This research had the objective of developing a thermal, aerial and acoustic environmental evaluation pattern for boar housing. The experiment was carried on a commercial swine farm in Salto County -SP, Brazil. Thermal, aerial and acoustic environment data of rearing conditions were registered. Data were statistically analyzed using as threshold the ideal housing environment that leads to animal welfare. Results showed that ambient temperature was around 70% beyond normal range, while air relative humidity, air speed and gases concentration were within threshold values. Noise level data besides being within normal range did not present large variation. In relation to the fuzzy logic analysis it was possible to build up a scenario which indicated that the best welfare indexes to male swine breeders happens when thermal comfort index are close to 80%, and noise level is lower than 40 dB. In the other hand the worst welfare index occur in the sector where the thermal comfort values are below 40% at the same time that the noise level is higher than 80 dB leading to inadequate conditions to the animal, and may directly interfere in the reproduction system performance.

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Remote sensing data are each time more available and can be used to monitor the vegetal development of main agricultural crops, such as the Arabic coffee in Brazil, since that the relationship between spectral and agronomical data be well known. Therefore, this work had the main objective to assess the use of Quickbird satellite images to estimate biophysical parameters of coffee crop. Test area was composed by 25 coffee fields located between the cities of Ribeirão Corrente, Franca and Cristais Paulista (SP), Brazil, and the biophysical parameters used were row and between plants spacing, plant height, LAI, canopy diameter, percentage of vegetation cover, roughness and biomass. Spectral data were the reflectance of four bands of QUICKBIRD and values of four vegetations indexes (NDVI, GVI, SAVI and RVI) based on the same satellite. All these data were analyzed using linear and nonlinear regression methods to generate estimation models of biophysical parameters. The use of regression models based on nonlinear equations was more appropriate to estimate parameters such as the LAI and the percentage of biomass, important to indicate the productivity of coffee crop.

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The main objective of this work was to evaluate the linear regression between spectral response and soybean yield in regional scale. In this study were monitored 36 municipalities from the west region of the states of Parana using five images of Landsat 5/TM during 2004/05 season. The spectral response was converted in physical values, apparent and surface reflectances, by radiometric transformation and atmospheric corrections and both used to calculate NDVI and GVI vegetation indices. Those ones were compared by multiple and simple regression with government official yield values (IBGE). Diagnostic processing method to identify influents values or collinearity was applied to the data too. The results showed that the mean surface reflectance value from all images was more correlated with yield than individual dates. Further, the multiple regressions using all dates and both vegetation indices gave better results than simple regression.

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The purpose of the present study was to verify the factorial validity of a learning strategy scale as well as to explore the concurrent validity of the instrument in regard to students´ academic achievement. The sample was composed of 815 basic education children from both public and private schools of São Paulo and Minas Gerais. The Learning Strategy Scale was collectively applied. Exploratory factorial analyses were conducted to achieve the purposes of the study. The alphas of Cronbach of the instrument and of its three subscales showed good reliability. Variance analyses showed significant differences between school achievement and punctuation in the scale. The data were discussed in terms of their possible implications for the psycho-educational evaluation area.

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The aim of this paper was to identify, according to gender, the indexes of Mental Health and the Psychosocial Risk Factors in workers at a state University. A sample of 400 was randomly selected, 253 female and 147 male. They were assessed by means of The Questionnaire SWS Survey (Self, Work and Social) (Ostermann & Gutiérrez, 1992), validated in Brazil by Guimarães and Macfadden (1999). Univariate, bivariate, multivariate statistics were assessed. Significant associations emerged from Mental Health and Gender, and Psychosocial Risk Factors and Gender. Women showed greater Psychosocial Risk Factors, Work and Social Stress, worst mental health than men (p < 0.05), which place them at greater risk for developing physical and/or mental illness.

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Thyroid nodules are frequent findings, especially when sensitive imaging methods are used. Although thyroid cancer is relatively rare, its incidence is increasing, particularly in terms of small tumors, which have an uncertain clinical relevance. Most patients with differentiated thyroid cancer exhibit satisfactory clinical outcomes when treatment is appropriate, and their mortality rate is similar to that of the overall population. However, relapse occurs in a considerable fraction of these patients, and some patients stop responding to conventional treatment and eventually die from their disease. Therefore, the challenge is how to identify the individuals who require more aggressive disease management while sparing the majority of patients from unnecessary treatments and procedures. We have updated the Brazilian Consensus that was published in 2007, emphasizing the diagnostic and therapeutic advances that the participants, representing several Brazilian university centers, consider most relevant in clinical practice. The formulation of the present guidelines was based on the participants' experience and a review of the relevant literature.

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PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.