关键词: DNA damage Descriptive statistics Genotoxicity Historical control data In vivo mammalian alkaline comet assay OECD test guideline 489 Rat Summarizing strategies Variance components analysis

Mesh : Animals Comet Assay / methods DNA Damage Reproducibility of Results Mutation Research Design

来  源:   DOI:10.1016/j.yrtph.2024.105583

Abstract:
The alkaline comet assay is frequently used as in vivo follow-up test within different regulatory environments to characterize the DNA-damaging potential of different test items. The corresponding OECD Test guideline 489 highlights the importance of statistical analyses and historical control data (HCD) but does not provide detailed procedures. Therefore, the working group \"Statistics\" of the German-speaking Society for Environmental Mutation Research (GUM) collected HCD from five laboratories and >200 comet assay studies and performed several statistical analyses. Key results included that (I) observed large inter-laboratory effects argue against the use of absolute quality thresholds, (II) > 50% zero values on a slide are considered problematic, due to their influence on slide or animal summary statistics, (III) the type of summarizing measure for single-cell data (e.g., median, arithmetic and geometric mean) may lead to extreme differences in resulting animal tail intensities and study outcome in the HCD. These summarizing values increase the reliability of analysis results by better meeting statistical model assumptions, but at the cost of information loss. Furthermore, the relation between negative and positive control groups in the data set was always satisfactorily (or sufficiently) based on ratio, difference and quantile analyses.
摘要:
碱性彗星测定经常用作不同调节环境中的体内后续测试,以表征不同测试项目的DNA损伤潜力。相应的OECD测试指南489强调了统计分析和历史控制数据(HCD)的重要性,但没有提供详细的程序。因此,德语环境突变研究协会(GUM)的"统计学"工作组从5个实验室收集了HCD和>200个彗星试验研究,并进行了多项统计分析.关键结果包括(I)观察到的大型实验室间效应与使用绝对质量阈值相反,(II)幻灯片上>50%的零值被认为是有问题的,由于它们对幻灯片或动物摘要统计的影响,(III)单细胞数据汇总度量的类型(例如,中位数,算术和几何平均值)可能会导致HCD中产生的动物尾巴强度和研究结果的极端差异。这些汇总值通过更好地满足统计模型假设来增加分析结果的可靠性,但以信息丢失为代价。此外,数据集中阴性和阳性对照组之间的关系总是令人满意(或充分)基于比率,差异和分位数分析。
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