1992
DOI: 10.1007/bf02399808
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The effect of genetic variability (degree of homozygosity) on serum levels of the anterior pituitary hormones prolactin, corticotropin, and growth hormone in rats

Abstract: Male and female wild Norway rats (Rattus norvegicus Erxleben) and males and female albino outbred rats (Ipf:RIZ) were crossbred. The resulting animals (F1 hybrids) were the control, noninbred group (0% inbred). By systematic full-sib mating, two experimental groups (50 and 91% of inbred) were produced. Half of each group (both males and females) was exposed to physical stress (3 days of starvation and 3 hr of swimming). The other half of each group was anesthetized using ether to collect blood. The anterior pi… Show more

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“…Consequently, oCRF DOSE was also included as a candidate predictor in the model. Since the kinetics of HPA axis activity are poorly understood, and because numerous studies indicate that various aspects of HPA axis function may best fit quadratic or higher-order polynomial functions (Hanada et al, 1985;Kemppainen et al, 1986;Dellwo and Beauchene, 1990;Coppinger et al, 1991;Kosowska, 1992;Apple et al, 1993;Kling et al, 1993), we included squared and cubed polynomials of each candidate predictor in addition to product interaction terms between each of the primary predictors in the model. To eliminate the inherent tendency for multicollinearity in polynomial models, each of the candidate predictor variables was converted to an orthogonal polynomial.…”
Section: Discussionmentioning
confidence: 99%
“…Consequently, oCRF DOSE was also included as a candidate predictor in the model. Since the kinetics of HPA axis activity are poorly understood, and because numerous studies indicate that various aspects of HPA axis function may best fit quadratic or higher-order polynomial functions (Hanada et al, 1985;Kemppainen et al, 1986;Dellwo and Beauchene, 1990;Coppinger et al, 1991;Kosowska, 1992;Apple et al, 1993;Kling et al, 1993), we included squared and cubed polynomials of each candidate predictor in addition to product interaction terms between each of the primary predictors in the model. To eliminate the inherent tendency for multicollinearity in polynomial models, each of the candidate predictor variables was converted to an orthogonal polynomial.…”
Section: Discussionmentioning
confidence: 99%