Predictive capacity of a non-radioisotopic local lymph node assay using flow cytometry, LLNA: BrdU-FCM: Comparison of a cutoff approach and inferential statistics
- 주제(키워드) Descriptive and inferential statistics , LLNA:BrdU-FCM , Local lymph node assay , Prediction model , Predictive capacity , Skin sensitization
- 등재 SCIE, SCOPUS
- 발행기관 Elsevier Inc.
- 발행년도 2016
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000122655
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.vascn.2015.12.001
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
In order for a novel test method to be applied for regulatory purposes, its reliability and relevance, i.e., reproducibility and predictive capacity, must be demonstrated. Here, we examine the predictive capacity of a novel non-radioisotopic local lymph node assay, LLNA:BrdU-FCM (5-bromo-2'-deoxyuridine-flow cytometry), with a cutoff approach and inferential statistics as a prediction model. 22 reference substances in OECD TG429 were tested with a concurrent positive control, hexylcinnamaldehyde 25%(PC), and the stimulation index (SI) representing the fold increase in lymph node cells over the vehicle control was obtained. The optimal cutoff SI (2.7 ≤ cutoff < 3.5), with respect to predictive capacity, was obtained by a receiver operating characteristic curve, which produced 90.9% accuracy for the 22 substances. To address the inter-test variability in responsiveness, SI values standardized with PC were employed to obtain the optimal percentage cutoff (42.6 ≤ cutoff < 57.3% of PC), which produced 86.4% accuracy. A test substance may be diagnosed as a sensitizer if a statistically significant increase in SI is elicited. The parametric one-sided t-test and non-parametric Wilcoxon rank-sum test produced 77.3% accuracy. Similarly, a test substance could be defined as a sensitizer if the SI means of the vehicle control, and of the low, middle, and high concentrations were statistically significantly different, which was tested using ANOVA or Kruskal-Wallis, with post hoc analysis, Dunnett, or DSCF (Dwass-Steel-Critchlow-Fligner), respectively, depending on the equal variance test, producing 81.8% accuracy. The absolute SI-based cutoff approach produced the best predictive capacity, however the discordant decisions between prediction models need to be examined further. © 2015 Elsevier Inc.
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