Probing the mechanical properties of TNF-α stimulated endothelial cell with atomic force microscopy.

TNF-α (tumor necrosis factor-α) is a potent pro-inflammatory cytokine that regulates the permeability of blood and lymphatic vessels. The plasma concentration of TNF-α is elevated (> 1 pg/mL) in several pathologies, including rheumatoid arthritis, atherosclerosis, cancer, pre-eclampsia; in obese individuals; and in trauma patients. To test whether circulating TNF-α could induce similar alterations in different districts along the vascular system, three endothelial cell lines, namely HUVEC, HPMEC, and HCAEC, were characterized in terms of 1) mechanical properties, employing atomic force microscopy; 2) cytoskeletal organization, through fluorescence microscopy; and 3) membrane overexpression of adhesion molecules, employing ELISA and immunostaining. Upon stimulation with TNF-α (10 ng/mL for 20 h), for all three endothelial cells, the mechanical stiffness increased by about 50% with a mean apparent elastic modulus of E ~5 ± 0.5 kPa (~3.3 ± 0.35 kPa for the control cells); the density of F-actin filaments increased in the apical and median planes; and the ICAM-1 receptors were overexpressed compared with controls. Collectively, these results demonstrate that sufficiently high levels of circulating TNF-α have similar effects on different endothelial districts, and provide additional information for unraveling the possible correlations between circulating pro-inflammatory cytokines and systemic vascular dysfunction.

THE ESTROGENIC PROPERTIES OF O,P'-DDT

o,p'-DDT is a major component of the pesticide DDT (dichlorodiphenyltrichloro ethane, technical grade). Although possessing little insecticidal ability, the o,p'- isomer has two major biological activities which affect mammalian reproductive systems: it is estrogenic, and it induces hepatic mixed function oxidase enzymes. The focus of this work is the characterization of the estrogenic properties of o,p'-DDT in rodents.^ Initial studies examined the ability of o,p'-DDT to bind to and interact with elements of the estrogen receptor system. In an in vitro assay, DDT was shown to compete with 17(beta)-estradiol (E(,2)) for binding to cytoplasmic estrogen receptors (R(,c)) from normal and neoplastic tissues in two rodent species. The following phenomena were studied by measuring receptor levels from uteri (whole uteri and/or uterine cell types) taken from immature ovariectomized rats given one acute injection of o,p'-DDT or E(,2): the translocation of the R(,c) to the nucleus, nuclear receptor (R(,n)) retention patterns, and the subsequent reappearance of R(,c) in the cytoplasm.^ The magnitude and temporal patterns of the biological responses of uteri from similar immature rats were compared following o,p'-DDT and E(,2) exposure. The responses examined included increased "Induced Protein" synthesis (in vitro); and uterine wet weight, DNA synthesis and mitosis (in vivo).^ From dose-response data, correlations were made between R(,n) levels and levels of subsequent biological responses. The aim was to lend support to the premise that biological responses to o,p'-DDT exposure occur as a result of its interaction with the classical estrogen receptor system. Correlation coefficients of 0.95 to 0.98 were obtained between R(,n) levels and levels of responses examined, strongly supporting this hypothesis.^ Finally, o,p'-DDT was shown to be as effective as E(,2) in supporting the growth of a transplantable estrogen-responsive mammary tumor in adult rats (although it was unable to support the growth of a transplantable estrogen-dependent renal tumor in hamsters). While the positive result cannot be directly extrapolated to human or animal exposure to environmental estrogens, it suggests that hyperplastic responses of estrogen sensitive tissues should be considered as a possible toxicity of o,p'-DDT, related compounds having estrogenic properties, and other environmental estrogens. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^