Induction of SCEs and DNA fragmentation in bovine peripheral lymphocytes by in vitro exposure to tolylfluanid-based fungicide

The potential for genotoxic and cytotoxic effects of tolylfluanid-based fungicide (50% active agent) was evaluated using sister chromatid exchange (SCE) and proliferation indices (PI) in cultured bovine peripheral lymphocytes. For the detection of possible genetic damage, DNA fragmentation assay was also applied. Bovine lymphocytes cultured for 72 h were treated with the fungicide at the final concentrations of 1.75, 3.5, 8.75, and 17.5 μg/mL for the last 24 and 48 h of culture without S9 metabolic activation, and during the last 2 h of culture with S9 metabolic activation. In the SCE assays no evidence for genotoxic activity of the fungicide was found in treatments of 24 h without and 2 h with S9. After the 24 h exposure to tolylfluanid, a weak decrease in the PI was observed. With the prolonged exposure time (48 h), dose dependence in the increase of SCE frequencies was observed. Moreover, after 48 h exposure slight fragmentation of DNA at the concentrations of 3.5 and 8.75 μg/mL was demonstrated. SCE quantification is the most widely used approach for the assessment of genotoxic/cytogenetic effects of chemical compounds. Positive results in the assay at 48 h exposure indicated a potential of the fungicide to increase frequency of chromosomal damage (replication injuries) that is the confirmation of early effect of exposure

Accuracy and precision of variance components in occupational posture recordings : a simulation study of different data collection strategies

Background: Information on exposure variability, expressed as exposure variance components, is of vital use in occupational epidemiology, including informed risk control and efficient study design. While accurate and precise estimates of the variance components are desirable in such cases, very little research has been devoted to understanding the performance of data sampling strategies designed specifically to determine the size and structure of exposure variability. The aim of this study was to investigate the accuracy and precision of estimators of between-subjects, between-days and within-day variance components obtained by sampling strategies differing with respect to number of subjects, total sampling time per subject, number of days per subject and the size of individual sampling periods. Methods: Minute-by-minute values of average elevation, percentage time above 90 degrees and percentage time below 15 degrees were calculated in a data set consisting of measurements of right upper arm elevation during four full shifts from each of 23 car mechanics. Based on this parent data, bootstrapping was used to simulate sampling with 80 different combinations of the number of subjects (10, 20), total sampling time per subject (60, 120, 240, 480 minutes), number of days per subject (2, 4), and size of sampling periods (blocks) within days (1, 15, 60, 240 minutes). Accuracy (absence of bias) and precision (prediction intervals) of the variance component estimators were assessed for each simulated sampling strategy. Results: Sampling in small blocks within days resulted in essentially unbiased variance components. For a specific total sampling time per subject, and in particular if this time was small, increasing the block size resulted in an increasing bias, primarily of the between-days and the within-days variance components. Prediction intervals were in general wide, and even more so at larger block sizes. Distributing sampling time across more days gave in general more precise variance component estimates, but also reduced accuracy in some cases. Conclusions: Variance components estimated from small samples of exposure data within working days may be both inaccurate and imprecise, in particular if sampling is laid out in large consecutive time blocks. In order to estimate variance components with a satisfying accuracy and precision, for instance for arriving at trustworthy power calculations in a planned intervention study, larger samples of data will be required than for estimating an exposure mean value with a corresponding certainty