日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Kinetically-derived maximal dose (KMD) confirms lack of human relevance for high-dose effects of octamethylcyclotetrasiloxane (D4)

动力学衍生最大剂量 (KMD) 证实八甲基环四硅氧烷 (D4) 高剂量效应与人类无关

Borgert, Christopher J; Burgoon, Lyle D; Fuentes, Claudio

Octamethylcyclotetrasiloxane (D4) lacks endocrine disruptive potential via estrogen pathways

八甲基环四硅氧烷(D4)不具有通过雌激素途径干扰内分泌的潜力

Borgert, Christopher J; Burgoon, Lyle D

Challenges and Opportunities in the Development and Adoption of New Approach Methods (NAMs)

新方法论(NAMs)开发和应用中的挑战与机遇

Levine, Steven L; Riter, Leah S; Lagadic, Laurent; Bejarano, Adriana C; Burden, Natalie; Burgoon, Lyle D; Becker, Richard A; Ryman, Jessica P; Armbrust, Kevin L

Kinetically-derived maximal dose (KMD) indicates lack of human carcinogenicity of ethylbenzene

动力学衍生最大剂量 (KMD) 表明乙苯对人类无致癌性。

Burgoon, Lyle D; Borgert, Christopher J; Fuentes, Claudio; Klaunig, James E

The physiological and biochemical basis of potency thresholds modeled using human estrogen receptor alpha: implications for identifying endocrine disruptors

利用人雌激素受体α模型构建效力阈值的生理和生化基础:对识别内分泌干扰物的意义

Borgert, Christopher J; Burgoon, Lyle D; Matthews, John C

A novel approach to calculating the kinetically derived maximum dose

一种计算动力学衍生最大剂量的新方法

Burgoon, Lyle D; Fuentes, Claudio; Borgert, Christopher J

Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways

利用基于总体暴露和不良结局路径的框架,整理与化学相互作用机制相关的信息

Price, Paul S; Jarabek, Annie M; Burgoon, Lyle D

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)

利用不良结局通路贝叶斯网络(AOPBN)预测化学物质引起脂肪变性的概率

Burgoon, Lyle D; Angrish, Michelle; Garcia-Reyero, Natalia; Pollesch, Nathan; Zupanic, Anze; Perkins, Edward

Different as night and day: Behavioural and life history responses to varied photoperiods in Daphnia magna

截然不同:大型蚤对不同光周期的行为和生活史反应

Gust, Kurt A; Kennedy, Alan J; Laird, Jennifer G; Wilbanks, Mitchell S; Barker, Natalie D; Guan, Xin; Melby, Nicolas L; Burgoon, Lyle D; Kjelland, Michael E; Swannack, Todd M

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

引入 WikiPathways 作为数据源,以支持化学品和纳米材料监管风险评估中的不良结局路径

Martens, Marvin; Verbruggen, Tim; Nymark, Penny; Grafström, Roland; Burgoon, Lyle D; Aladjov, Hristo; Torres Andón, Fernando; Evelo, Chris T; Willighagen, Egon L