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P S Horn

Publications and source records attributed to P S Horn.

4 recordsLinked to original sources
PubMed2001

Effect of maternal hypercholesterolemia on fetal sterol metabolism in the Golden Syrian hamster.

The fetus obtains a significant amount of cholesterol from de novo synthesis. Studies have suggested that maternal cholesterol may also contribute to the cholesterol accrued in the fetus. Thus, the present studies were completed to determine whether diet-induced maternal hypercholesterolemia would affect fetal sterol metabolism. To accomplish this, maternal plasma cholesterol concentrations were increased sequentially by feeding hamsters 0.0%, 0.12%, 0.5%, and 2.0% cholesterol. At 11 days into a gestational period of 15.5 days, cholesterol concentrations and sterol synthesis rates were measured in the three fetal tissues: the placenta, yolk sac, and fetus. In the placenta and yolk sac, the cholesterol concentration increased significantly when dams were fed as little as 0.12% cholesterol (P < 0.0167), and sterol synthesis rates decreased in dams fed at least 0.5% or 2% cholesterol, respectively (P < 0.0167). In the fetus, changes in fetal cholesterol concentration and sterol synthesis rates occurred only when dams were fed at least 0.5% cholesterol, which corresponded to a greater than 2-fold increase in maternal plasma cholesterol concentrations. When the cholesterol concentration in the fetal tissues in each animal was plotted as a function of maternal plasma cholesterol concentration, a linear relationship was found (P < 0.001). These studies demonstrate that sterol homeostasis in fetal tissues, including the fetus, is affected by maternal plasma cholesterol concentration in a gradient fashion and that sterol metabolism in the fetus is dependent on sterol homeostasis in the yolk sac and/or placenta.

Animal Feed↗
PubMed2001

Effect of outliers and nonhealthy individuals on reference interval estimation.

BACKGROUND: Improvement in reference interval estimation using a new outlier detection technique, even with a physician-determined healthy sample, is examined. The effect of including physician-determined nonhealthy individuals in the sample is evaluated. METHODS: Traditional data transformation coupled with robust and exploratory outlier detection methodology were used in conjunction with various reference interval determination techniques. A simulation study was used to examine the effects of outliers on known reference intervals. Physician-defined healthy groups with and without nonhealthy individuals were compared on real data. RESULTS: With 5% outliers in simulated samples, the described outlier detection techniques had narrower reference intervals. Application of the technique to real data provided reference intervals that were, on average, 10% narrower than those obtained when outlier detection was not used. Only 1.6% of the samples were identified as outliers and removed from reference interval determination in both the healthy and combined samples. CONCLUSIONS: Even in healthy samples, outliers may exist. Combining traditional and robust statistical techniques provide a good method of identifying outliers in a reference interval setting. Laboratories in general do not have a well-defined healthy group from which to compute reference intervals. The effect of nonhealthy individuals in the computation increases reference interval width by approximately 10%. However, there is a large deviation among analytes.

Disease↗
PubMed1998

A robust approach to reference interval estimation and evaluation.

We propose a new methodology for the estimation of reference intervals for data sets with small numbers of observations or for those with substantial numbers of outliers. We propose a prediction interval that uses robust estimates of location and scale. The SAS software can be readily modified to do these calculations. We compared four reference interval procedures (nonparametric, transformed, robust with a nonparametric lower limit, and transformed robust) for sample sizes of 20, 40, 60, 80, 100, and 120 from chi 2 distributions of 1, 4, 7, and 10 df. chi 2 distributions were chosen because they simulate the skewness of distributions often found in clinical chemistry populations. We used the root mean square error as the measure of performance and used computer simulation to calculate this measure. The robust estimator showed the best performance for small sample sizes. As the sample size increased, the performance values converged. The robust method for calculating upper reference interval values yields reasonable results. In two examples using real data for haptoglobin and glucose, the robust estimator provides slightly smaller upper reference limits than the other procedures. Lastly, the robust estimator was compared with the other procedures in a population where 5% of the values were multiplied by a factor of 5. The reference intervals were calculated with and without outlier detection. In this case, the robust approach consistently yielded upper reference interval values that were closer to those of the true underlying distributions. We propose that robust statistical analysis can be of great use for determinations of reference intervals from limited or possibly unreliable data.

Aged↗