DIRECT EFFECT OF MELATONIN UPON THE RELEASE OF GONADOTROPINS FROM THE SUPERFUSED PITUITARY GLAND OF THE MALE GOLDEN HAMSTER

The pineal gland is known to be light sensitive and to be involved in the seasonal reproduction of male golden hamster Mesocricetus auratus. In general, the pineal gland has been demonstrated to be inhibitory to the reproductive system of the male golden hamster. Melatonin is a pineal hormone which can mimic the action of the pineal gland upon the reproductive system. However, the actual site(s) of melatonin action in the hamster has not been demonstrated. In this study a direct effect of melatonin on the release of FSH and LH from superfused hamster pituitary glands was investigated.^ The superfused pituitary glands showed a stable in vitro basal release of FSH and LH for up to 10 hours. The superfused pituitaries demonstrated reproducible responses to repeated pulses of 10('-8) M LHRH, and a dose-dependent response to stimulation with different concentrations of LHRH.^ Melatonin inhibited the basal release of FSH and LH from superfused hamster pituitary glands. This effect of melatonin was specific and not a general indolamine or catecholamine effect.^ The superfused pituitaries had a diurnal differential responsiveness to physiological concentrations of melatonin with respect to FSH and LH release which were related to the light cycle used to maintain the experimental animals. A LD 14:10 photoperiod cycle was used with light on from 5 a.m. till 7 p.m.. With pituitary glands obtained at 8:30 a.m., the basal release of FSH exhibited an initial inhibition, a gradual rebound at approximately two hours after the beginning of melatonin superfusion, and a significant overshoot of FSH release after the cessation of infusion with melatonin (Morning Response). If the pituitary glands were obtained from hamsters which were sacrificed at 3:30 p.m., the release rate of FSH exhibited an inhibition during the entire period of melatonin infusion with a rebound effect appearing only after melatonin infusion was discontinued (Afternoon Response). There was no significant difference in the responsiveness of the pituitary gland to infusion with melatonin at either 8:30 a.m. or 3:30 p.m. with respect to LH release. Also, melatonin could not inhibit the gonadotropins response to continuous superfusion with 10('-9) M LHRH in pituitaries obtained at either 8:30 a.m. or 3:30 p.m., nor inhibit the stimulatory effect of pulsatile 10('-9) M LHRH. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of author.) UMI^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^