Sincronização dos ritmos de atividade motora e temperatura central à disponibilidade de glicose em ratos

Food is essential for the survival of all animals. Its temporal availability is an important enviromental cue for the behavioral and physiological organization throughout the 24 hours of day in different species. Rats and mice, for example, show increased locomotion in the hours before food availability when it is presented in a recurrent manner, a behavior named foodanticipatory activity. Several lines of evidence indicate that this anticipation is mediated by a circadian oscillator. In this work, based on the hypothesis that pre- or post-ingestive humoral signals are involved in the entrainment process, we tested whether the daily intake of glucose is sufficient to induce anticipatory activity in rats. The rhythms of motor activity and central temperature were recorded in animals undergoing 10 days of temporal glucose (solution at 50%) or chow restriction in light-dark (LD) and constant darkness (DD). Animals under temporal glucose restriction increase motor activity and and central temperature in the hours preceding glucose availability and such aticipation is extremely similar to that observed in animals under temporal chow restriction. Glucose ingestion is, therefore, a sufficient temporal cue to induce anticipation in rats. It is possible that the increase in plasma glucose after food ingestion constitutes one of the signals involved in the behavioral entrainment process to food availability

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma