Prevalence of the prothrombin gene variant 20210 G -> A among patients with myocardial infarction

Objective: The aim of this study was to determine the prevalence of the prothrombin variant allele 20210A among survivors of myocardial infarction Background: The prothrombin gene variant has been identified as a novel genetic risk factor for venous thrombosis. However, the risk of developing arterial thrombosis as a result of the presence of this mutated allele is unknown. Methods: The G --> A transition at position 20210 of the 3'-untranslated region was determined in 220 survivors of myocardial infarction and in 295 individuals from the general population. Results: The prevalence of heterozygotes for the prothrombin mutated allele was 3% among patients with myocardial infarction and 0.7% in the general population (P = 0.03). No age-related difference in the prevalence of the mutated allele was observed. However, for individuals over 45 years old the prevalence among females was higher than among males (5% vs. 0%). Conclusion: These data suggest that being heterozygote for the allele variant 20210A of the prothrombin gene could be a genetic risk factor for developing myocardial infarction. (C) 1998 Elsevier Science B.V.371424

Critical Analysis of Dual-Probe Heat-Pulse Technique Applied to Measuring Thermal Diffusivity

The paper presents an analysis of the experimental parameters involved in application of the dual-probe heat pulse technique, followed by a critical review of methods for processing thermal response data (e.g., maximum detection and nonlinear least square regression) and the consequent obtainable uncertainty. Glycerol was selected as testing liquid, and its thermal diffusivitywas evaluated over the temperature range from −20 °C to 60 °C. In addition, Monte Carlo simulation was used to assess the uncertainty propagation for maximum detection. It was concluded that maximum detection approach to process thermal response data gives the closest results to the reference data inasmuch nonlinear regression results are affected by major uncertainties due to partial correlation between the evaluated parameters. Besides, the interpolation of temperature data with a polynomial to find the maximum leads to a systematic difference between measured and reference data, as put into evidence by the Monte Carlo simulations; through its correction, this systematic error can be reduced to a negligible value, about 0.8 %