In vitro metabolism and toxicity assessment of toxin T-514 (Peroxisomicine A1) of Karwinskia humboldtiana in microsomes and primary cultured hepatocytes.

T-514 (Peroxisomicine A(1)) from Karwinskia humboldtiana is a dimeric hydroxyanthracenone with a highly selective cytotoxic effect on tumor cells. We evaluated the metabolism of this compound in two in vitro systems (liver microsomes and hepatocytes) and assessed the cytotoxicity of its metabolites on normal and tumor cells. Microsomes (12.5, 125 and 250 microg of protein/ml) and hepatocytes (1 x 10(6) cells/ml) were incubated with the toxin (25 microM) for 0.5, 1, 3, 6, 9, 12 and 24 h and the samples were examined using chromatographic analysis and UV spectra. Two metabolites (M1 and M2) were detected in the rat microsomes and one (M1) in the monkey microsomes. The retention times and UV spectra of the peaks were very similar to those of the toxin T-514. M1 was isolated and identified as a mixture of two isomers. The cytotoxicity of the metabolites was evaluated in Chang liver and Hep G2 cells but they did not show the selective cytotoxic effect on tumor cells seen in the original compound.     

Is there a role for reconstructive techniques to prevent periodontal defects after third molar surgery?

PURPOSE: Among patients at high risk for second molar (M2) periodontal defects after third molar (M3) removal, does active treatment at the time of extraction, when compared with no treatment, alter the risk of postextraction M2 periodontal defects? MATERIALS AND METHODS: We used a prospective cohort study design and a sample composed of subjects at high risk for developing M2 periodontal defects after M3 extraction, that is, age > or = 26 years, pre-existing periodontal defects (attachment level [AL] > or = 3 mm), and mesioangular or horizontal M3 impaction. The predictor variable was treatment status of the M3 extraction site. The M3 extraction sites were reconstructed with demineralized bone powder (DBP), bioresorbable guided tissue regeneration (GTR) therapy, or no treatment. The outcome variable was ALs measured at the M2 distobuccal line angle preoperatively and 26 weeks after extraction. Appropriate univariate, bivariate, and multivariate statistics were computed, and statistical significance was set at a value P < .05. RESULTS: The cohort was composed of 12 subjects contributing 18 high-risk M3s. Twenty-six weeks after M3 removal, the ALs for GTR-treated (3.0 +/- 1.2 mm), DBP-treated (1.4 +/- 0.5 mm), and control (3.8 +/- 0.9) M3 sites were statistically significantly different ( P = .002). Tukey post-hoc comparisons revealed a statistically significant difference between control and DBP ALs ( P = .001) and GTR-treated and DBP-treated ALs ( P = .037). There was no statistically significant difference in ALs between control and GTR-treated M3s ( P = .35). CONCLUSIONS: The results of this study suggest that subjects at high risk for developing M2 periodontal defects after M3 removal may benefit from the use of DBP placed at the time of M3 extraction to enhance periodontal healing.     

Diabetes mellitus as a contributory factor in oral candidosis.

It has been reported that poor glycaemic control predisposes to oral candidal infection in diabetic patients. For instance, the carriage of Candida species and the density of candidal growth in the oral cavity is frequently claimed to be increased in patients with diabetes mellitus. However, the validity of these observations remains controversial. Hence, we review and discuss here the clinical data in the literature on the relationship between diabetes and oral candidal carriage and infection, and possible mechanisms associated with its pathogenicity.     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.