Hypo- and hyperthyroidism affect NEI concentration in discrete brain areas of adult male rats

To date, there has been only one in vitro study of the relationship between neuropeptide EI (NEI) and the hypothalamic-pituitary-thyroid (HPT) axis. To investigate the possible relationship between NEI and the HPT axis, we developed a rat model of hypothyroidism and hyperthyroidism that allows us to determine whether NEI content is altered in selected brain areas after treatment, as well as whether such alterations are related to the time of day. Hypothyroidism and hyperthyroidism, induced in male rats, with 6-propyl-1-thiouracil and L-thyroxine, respectively, were confirmed by determination of triiodothyronine, total thyroxine, and thyrotropin levels. All groups were studied at the morning and the afternoon. In rats with hypothyroidism, NEI concentration, evaluated on postinduction days 7 and 24, was unchanged or slightly elevated on day 7 but was decreased on day 24. In rats with hyperthyroidism, NEI content, which was evaluated after 4 days of L-thyroxine administration, was slightly elevated, principally in the preoptic area in the morning and in the median eminence-arcuate nucleus and pineal gland in the afternoon, the morning and afternoon NEI contents being similar in the controls. These results provide the bases to pursue the study of the interaction between NEI and the HPT axis. (C) 2011 Elsevier Inc. All rights reserved.

Performance of balanced two-stage empirical predictors of realized cluster latent values from finite populations: A simulation study

Predictors of random effects are usually based on the popular mixed effects (ME) model developed under the assumption that the sample is obtained from a conceptual infinite population; such predictors are employed even when the actual population is finite. Two alternatives that incorporate the finite nature of the population are obtained from the superpopulation model proposed by Scott and Smith (1969. Estimation in multi-stage surveys. J. Amer. Statist. Assoc. 64, 830-840) or from the finite population mixed model recently proposed by Stanek and Singer (2004. Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 1119-1130). Predictors derived under the latter model with the additional assumptions that all variance components are known and that within-cluster variances are equal have smaller mean squared error (MSE) than the competitors based on either the ME or Scott and Smith`s models. As population variances are rarely known, we propose method of moment estimators to obtain empirical predictors and conduct a simulation study to evaluate their performance. The results suggest that the finite population mixed model empirical predictor is more stable than its competitors since, in terms of MSE, it is either the best or the second best and when second best, its performance lies within acceptable limits. When both cluster and unit intra-class correlation coefficients are very high (e.g., 0.95 or more), the performance of the empirical predictors derived under the three models is similar. (c) 2007 Elsevier B.V. All rights reserved.