综合性人体干预研究,用于毒素生物标志物发现和毒动力学表征
Lia Visintin1, Eugenio Alladio2,3, María García Nicolás4,5
1Centre of Excellence in Mycotoxicology and Public Health, Faculty of Pharmaceutical Sciences, Ghent University, B-9000, Ghent, Belgium. lia.visintin@ugent.be.
Scientific reports
|November 7, 2025
概括
这项研究引入了一个新的框架,用于寻找人类生物标志物,用于毒素暴露. 它使用机器学习来分析来自非侵入性样本的数据,改进健康风险评估.
科学领域:
- 毒理学 毒理学 毒理学
- 生物标志物发现发现
- 人类健康 人类健康 人类健康
背景情况:
- 毒素暴露对健康构成风险,但经过验证的人类暴露生物标志物很少.
- 准确识别暴露生物标志物对于风险评估和公共卫生至关重要.
研究的目的:
- 开发一个完整的框架,用于在人类中发现菌毒素生物标志物和毒动力学 (TK) 特性.
- 通过非和最小侵入性采样来识别新的生物标志物.
- 建立一个基于机器学习 (ML) 的数据分析和生物标志物验证工作流.
主要方法:
- 综合框架,结合生物标志物发现和TC分析.
- 利用机器学习 (ML) 来进行数据分析,分类和回归建模.
- 采用贝叶斯方法进行人口-TK建模以估计ADME参数.
主要成果:
- 开发并验证了用于识别和表征菌毒素生物标志物的标准化框架.
- 证明了ML在分析复杂生物标志物数据中的实用性.
- 成功估计了关键的吸收,分布,新陈代谢和分泌 (ADME) 特性.
结论:
- 拟议的框架允许精确准确地识别和验证人类真菌毒素生物标志物.
- 这种方法有助于更好地了解人类的真菌毒素毒动力学.
- 由于伦理考虑,该研究重点关注IARC第三组真菌毒素.
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