Avaliação do estado nutricional precedente ao uso de nutrição enteral

CONTEXTO: O adequado diagnóstico do estado nutricional é de vital importância para a prescrição da terapia nutricional enteral no ambiente hospitalar. OBJETIVO: Avaliar indicadores do estado nutricional em pacientes ingressantes na terapia nutricional enteral em uma unidade hospitalar. MÉTODOS: Estudo transversal com 100 pacientes adultos, sendo analisado o estado nutricional de ingresso à terapia nutricional enteral, por meio do índice de massa corporal obtido do peso e estatura estimados a partir de fórmulas de predição, e de indicadores laboratoriais do estado metabólico e nutricional. RESULTADOS: do total, 29% dos pacientes foram classificados como desnutridos pelo índice de massa corporal, enquanto 80% dos mesmos apresentaram albumina abaixo do valor de referência (<3,2 g/dL). Não houve diferença na distribuição das causas de base da internação entre os grupos classificados quanto ao estado nutricional pelo índice de massa corporal, prevalecendo as doenças cardiovasculares e pulmonares entre as principais causas. As concentrações abaixo dos valores de referência de albumina não foram diferentes entre os grupos classificados pelo índice de massa corporal e pelo diagnóstico de internação. CONCLUSÃO: O índice de massa corporal estimado foi indicador específico do estado nutricional, porém pouco sensível, enquanto a albumina mostrou-se mais sensível, o que reafirma a necessidade da combinação de vários indicadores para obtenção de um adequado diagnóstico nutricional.

Predictive formulas for food base excess and urine pH estimations of cats

Food base excess (BE, mEq/kg) can be calculated from the diet macroelements, together with either the sulfur amino acids methionine and cysteine (BEaa) or total sulfur (BEs) concentrations. The present study compared the use of sulfur or methionine and cysteine for calculating the food BE (experiment 1) and investigated the influence of food BE on blood gas analysis and the urine pH of cats, and proposes a prediction equation to estimate the urine pH of cats fed kibble diets based on the calculated food BE (experiments 2 and 3). In experiment 1, nine healthy, adult cats were used in a change-over design and fed with nine commercial dry cat foods. The cats were housed in metabolism cages over seven days for adaptation and three days for total urine collection. All of the urine produced over 24h was pooled by cat and diet. The cats' acid-base status was assessed through blood gas analysis after 10 days of diet consumption. A mean difference of -115mEq/kg between BEs and BEaa was observed, which could be explained by a greater concentration of sulfur in the whole diet than in methionine and cysteine. Urine pH presented a stronger correlation with food BEs (R2=0.95; P<0.001) than with food BEaa (R2=0.86; P<0.001). Experiment 2 included 30 kibble diets, and each diet was tested in six cats. The food BEs varied between -180 and +307mEq/kg, and the urine pH of the cats varied between 5.60 and 7.74. A significant correlation was found between the measured urine pH and the food BEs (urinary pH=6.269+[0.0036×BEs]+[0.000003×BEs2]; R2=0.91; P<0.001). In experiment 3, eight kibble diets were tested (food BEs between -187mEq/kg and +381mEq/kg) to validate the equation proposed in experiment 2 and to compare the obtained results with previously published formulae. The results of the proposed formula presented a high concordance correlation coefficient (0.942) and high accuracy (0.979) with the measured values, and the estimates of urine pH did not differ from the values obtained in cats (P>0.05). The cats' venous blood pH, bicarbonate, and blood BE were correlated with food BEs (P<0.001); the consumption of diets with low food BEs induced a reduction in these parameters. In conclusion, food macroelement composition has a strong influence on cats' acid-base equilibrium and food BEs calculation is a useful tool to formulate and balance kibble diets for felines. © 2013 Elsevier B.V.

Leaf area prediction models for Zinnia elegans Jacq., Zinnia haageana regel and 'profusion cherry'

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Latent heat loss of Holstein cows in a tropical environment: a prediction model

Nove vacas Holandesas lactantes com 526 ± 5 kg de peso corporal (cinco predominantemente pretas e quatro predominantemente brancas), criadas em região tropical e manejadas em pastagens, foram observadas com os objetivos de determinar simultaneamente as taxas de evaporação cutânea e respiratória em ambiente tropical e desenvolver modelos de predição. Para a medição da perda de calor latente pela superfície corporal, utilizou-se uma cápsula ventilada e, para a perda por respiração, utilizou-se uma máscara facial. Os resultados mostraram que as vacas que tinham maior peso corporal (classe 2 e 3) apresentaram maiores taxas evaporativas. Quando a temperatura do ar aumentou de 10 para 36ºC e a umidade relativa do ar caiu de 90 para 30%, a eliminação de calor por evaporação respiratória aumentou de aproximadamente 5 para 57 W m-2 e a evaporação na superfície corporal passou de 30 para 350 W m-2. Esses resultados confirmam que a eliminação de calor latente é o principal mecanismo de perda de energia térmica sob altas temperaturas (>30ºC); a evaporação cutânea é a maior via e corresponde a aproximadamente 85% da perda total de calor, enquanto o restante é eliminado pelo sistema respiratório. O modelo para predizer o fluxo de perda de calor latente baseado em variáveis fisiológicas e ambientais pode ser utilizado para estimar a contribuição da evaporação na termorregulação, enquanto o modelo baseado somente na temperatura do ar deve ser usado apenas para a simples caracterização do processo evaporativo.

Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection

Genomewide marker information can improve the reliability of breeding value predictions for young selection candidates in genomic selection. However, the cost of genotyping limits its use to elite animals, and how such selective genotyping affects predictive ability of genomic selection models is an open question. We performed a simulation study to evaluate the quality of breeding value predictions for selection candidates based on different selective genotyping strategies in a population undergoing selection. The genome consisted of 10 chromosomes of 100 cM each. After 5,000 generations of random mating with a population size of 100 (50 males and 50 females), generation G(0) (reference population) was produced via a full factorial mating between the 50 males and 50 females from generation 5,000. Different levels of selection intensities (animals with the largest yield deviation value) in G(0) or random sampling (no selection) were used to produce offspring of G(0) generation (G(1)). Five genotyping strategies were used to choose 500 animals in G(0) to be genotyped: 1) Random: randomly selected animals, 2) Top: animals with largest yield deviation values, 3) Bottom: animals with lowest yield deviations values, 4) Extreme: animals with the 250 largest and the 250 lowest yield deviations values, and 5) Less Related: less genetically related animals. The number of individuals in G(0) and G(1) was fixed at 2,500 each, and different levels of heritability were considered (0.10, 0.25, and 0.50). Additionally, all 5 selective genotyping strategies (Random, Top, Bottom, Extreme, and Less Related) were applied to an indicator trait in generation G(0), and the results were evaluated for the target trait in generation G(1), with the genetic correlation between the 2 traits set to 0.50. The 5 genotyping strategies applied to individuals in G(0) (reference population) were compared in terms of their ability to predict the genetic values of the animals in G(1) (selection candidates). Lower correlations between genomic-based estimates of breeding values (GEBV) and true breeding values (TBV) were obtained when using the Bottom strategy. For Random, Extreme, and Less Related strategies, the correlation between GEBV and TBV became slightly larger as selection intensity decreased and was largest when no selection occurred. These 3 strategies were better than the Top approach. In addition, the Extreme, Random, and Less Related strategies had smaller predictive mean squared errors (PMSE) followed by the Top and Bottom methods. Overall, the Extreme genotyping strategy led to the best predictive ability of breeding values, indicating that animals with extreme yield deviations values in a reference population are the most informative when training genomic selection models.

Prediction equations to estimate the demand of energy and crude protein for maintenance, gain and egg production for laying Japanese quails

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Use of ridge regression for the prediction of early growth performance in crossbred calves

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Prediction of intake and average daily gain by different feeding systems for goats

A main purpose of a mathematical nutrition model (a.k.a., feeding systems) is to provide a mathematical approach for determining the amount and composition of the diet necessary for a certain level of animal productive performance. Therefore, feeding systems should be able to predict voluntary feed intake and to partition nutrients into different productive functions and performances. In the last decades, several feeding systems for goats have been developed. The objective of this paper is to compare and evaluate the main goat feeding systems (AFRC, CSIRO, NRC, and SRNS), using data of individual growing goat kids from seven studies conducted in Brazil. The feeding systems were evaluated by regressing the residuals (observed minus predicted) on the predicted values centered on their means. The comparisons showed that these systems differ in their approach for estimating dry matter intake (DMI) and energy requirements for growing goats. The AFRC system was the most accurate for predicting DMI (mean bias = 91 g/d, P < 0.001; linear bias 0.874). The average ADG accounted for a large part of the bias in the prediction of DMI by CSIRO, NRC, and, mainly, AFRC systems. The CSIRO model gave the most accurate predictions of ADG when observed DMI was used as input in the models (mean bias 12 g/d, P < 0.001; linear bias -0.229). while the AFRC was the most accurate when predicted DMI was used (mean bias 8g/d. P > 0.1; linear bias -0.347). (C) 2011 Elsevier B.V. All rights reserved.

Fine root isotropy in Eucalyptus grandis plantations. Towards the prediction of root length densities from root counts on trench walls

The objectives of the study were to assess changes in fine root anisotropy and specific root lengths throughout the development of Eucalyptus grandis ( W. Hill ex Maiden) plantations and to establish a predictive model of root length density (RLD) from root intercept counts on trench walls. Fine root densities (<1 mm in diameter) were studied in 6-, 12-, 22-, 28-, 54-, 68- and 72-month-old E. grandis plantations established on deep Ferralsols in southern Brazil. Fine root intercepts were counted on 3 faces of 90-198 soil cubes (1 dm(3) in volume) in each stand and fine root lengths (L) were measured inside 576 soil cubes, sampled between the depths of 10 cm and 290 cm. The number of fine root intercepts was counted on one vertical face perpendicular to the planting row (N(t)), one vertical face parallel to the planting row (N(l)) and one horizontal face (N(h)), for each soil cube sampled. An overall isotropy of fine roots was shown by paired Student's t-tests between the numbers of fine roots intersecting each face of soil cubes at most stand ages and soil depths. Specific root lengths decreased with stand age in the upper soil layers and tended to increase in deep soil layers at the end of the rotation. A linear regression established between N(t) and L for all the soil cubes sampled accounted for 36% of the variability of L. Such a regression computed for mean Nt and L values at each sampling depth and stand age explained only 55% of the variability, as a result of large differences in the relationship between L and Nt depending on stand productivity. The equation RLD=1.89*LAI*N(t), where LAI was the stand leaf area index (m(2) m(-2)) and Nt was expressed as the number of root intercepts per cm(2), made it possible to predict accurately (R(2)=0.84) and without bias the mean RLDs (cm cm(-3)) per depth in each stand, for the whole data set of 576 soil cubes sampled between 2 years of age and the end of the rotation.

The Accuracy of National Body Fat Cutoff Levels in the Prediction of Elevated Blood Pressure among Brazilian Male Adolescents

Objective: To assess viability of the development of percentage body fat cutoffs based on blood pressure values in Brazilian adolescents.Methods: A cross-sectional study was conducted with a sample of 358 male subjects from 8 to 18 years old. Blood pressure was measured by the oscilometric method, and body composition was measured by dual-energy X-ray absorptiometry (DXA).Results: For the identification of elevated blood pressure, these nationally developed body fat cutoffs presented relative accuracy. The cutoffs were significantly associated with elevated blood pressure [odds ratio = 5.91 (95% confidence interval: 3.54-9.86)].Conclusions: Development of national body fat cutoffs is viable, because presence of high accuracy is an indication of elevated blood pressure.

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in plants is extremely important since it involves economic aspects and environmental impacts, Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.

Prediction of Soil Properties from the Concepts of Energy Transfer in Dynamic Penetration Tests

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Spacecraft's attitude prediction: solar radiation torque and the Earth's shadow

A semi-analytical approach is proposed to study the rotational motion of an artificial satellite, under the influence of torque due to the solar radiation pressure, and taking into account the influence of Earth's shadow. Using Andoyer variables the equations for the rotational motion are presented in extended Hamiltonian form. In order to get a solution for the state variables close to an actual motion, the considered model for the shadow function takes into account physical and geometric factors and three specific regions: shadow, penumbra and full light. A mapping for the shadow function is proposed and a semi-analytical process is applied. When the satellite is totally illuminated or it is inside the penumbra, a known analytical solution is used to compute the satellite's attitude. A numerical simulation shows, when the penumbra region is included, the attenuation of the rotational motion during the transition from the shadow to the illuminate region and vice versa. (c) 2005 Published by Elsevier Ltd on behalf of COSPAR.

Analytical attitude prediction of spin stabilized spacecrafts perturbed by magnetic residual torque

An analytical approach for spin-stabilized spacecraft attitude prediction is presented for the influence of the residual magnetic torques. Assuming an inclined dipole model for the Earth's magnetic field, an analytical averaging method is applied to obtain the mean residual torque every orbital period. The orbit mean anomaly is utilized to compute the average components of residual torque in the spacecraft body frame reference system. The theory is developed for time variations in the orbital elements, and non-circular orbits, giving rise to many curvature integrals. It is observed that the residual magnetic torque does not have component along the spin axis. The inclusion of this torque on the rotational motion differential equations of a spin stabilized spacecraft yields conditions to derive an analytical solution. The solution shows that residual torque does not affect the spin velocity magnitude, contributing only for the precession and the drift of the spin axis of the spacecraft. (c) 2005 COSPAR. Published by Elsevier Ltd. All rights reserved.

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened Steel turning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)