Mice fed either (1) a pelleted rodent diet, (2) evaporated milk, or (3) a synthetic diet (high protein, low fat) exhibited different rates of whole body mercury elimination and fecal mercury excretion after exposure (per os) to methylmercuric chloride. The percentage of the total mercury body burden present as mercuric mercury was highest (35.3%) in mice fed the synthetic diet (which had the highest rate of mercury elimination) and lowest (6.6%) in the animals having the lowest mercury elimination rate (milk-fed mice). Mice fed the synthetic diet had lower mercury concentrations and had a higher proportion of mercuric mercury in their tissues than the mice from the other dietary groups. Treatment of the mice with antibiotics throughout the experimental period to suppress the gut flora reduced fecal mercury excretion and the dietary differences in whole body retention of mercury. Tissue mercury concentrations and proportion of organic mercury in feces, cecal contents, liver, and kidneys were increased by antibiotic treatment of mice fed the pelleted or synthetic diets. These results are consistent with the theory that demethylation of methylmercury by intestinal microflora is a major factor determining the excretion rate of mercury.

A number of methods have previously been considered for the statistical comparison of flow cytometric frequency distributions. For two distributions, the foremost of these is the Kolmogorov-Smirnov (K-S) test, which has been criticized as "too sensitive."We discuss some alternative methods based on the Poisson distribution. The assumption of Poisson variation within channels allows the use of channel-by-channel confidence intervals and chi-square tests. These are simple and more appropriate for discrete data than the K-S test. Graphical displays of these and other techniques are presented. We also attempt to set the problem in an appropriate context. We argue that any statistical procedure must rest on a reasonable understanding of the nature of the variability in the system. This understanding takes the form of an appropriate probability model, which may be approximate but must provide a reasonably accurate description of the data. Incomplete Understanding of the data can lead to inappropriate analysis. We discuss the assumptions that underlie our techniques and consider extensions to more complex situations.

Sickle cell anemia is a disease for which there is currently no effective treatment. One method of evaluating clinical status is the counting of cell types based on morphology. There is a need for a rapid, reproducible method, superior to human inspection, for classification of these cells. Quantitative digital-image analysis is being applied to this need. Blood from 24 patients with sickle cell anemia (SS) and SC disease and ten hematologically normal volunteers (AA) was stressed by bubbling with nitrogen. One hundred fifty cells were analyzed from each sickle specimen, and 100 were analyzed from each nonsickle specimen. Expert observers classified each cell as normal (N), sickle (S), or other abnormal (A). Cells were analyzed with a custom, high-resolution image-analysis instrument. A total of 42 features including metric, optical density-derived, and textural features were extracted. The metric feature Form Factor (4~ArealPerimeter~) was selected by recursive partitioning analysis as the sole feature needed for segregating cells into the classes of N, A, and S. The agreement of automated classification (using cutpoints determined by recursive partitioning analysis) with a human expert for specimens from individuals with sickle cell anemia was 89% for N-, 73% for A-, and 92% for S-classified cells. For specimens from AA individuals, the agreement was 92% for N and 76% for A. For specimens from individuals with sickle cell anemia, rates of agreement between two human experts were compared and found to be 86% for N, 84% for A, and 80% for S. For specimens from AA individuals, the agreement was 90% for N and 87% for A. o 1994 Wiley-Liss, Inc.

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