16S rRNA基因测序和机器学习揭示了药物滥用和人类宿主肠道微生物群之间的相关性
Yunting Liu1, Pei Zhang2, Hongmei Sheng1
1Lanzhou University, Lanzhou, China.
Addiction biology
|September 27, 2023
概括
药物使用障碍与肠道微生物群多样性的改变有关. 机器学习模型可以根据便样本识别药物滥用,显示未来查的潜力.
科学领域:
- 微生物学 微生物学
- 神经科学是一个神经科学.
- 数据科学数据科学数据科学
背景情况:
- 越来越多的证据将肠道微生物群与健康和成联系起来.
- 大脑-肠轴越来越多地被研究为物质使用障碍 (SUD).
- 以前的研究表明,酒精,食物和香烟成与肠道微生物群之间的联系.
研究的目的:
- 探索肠道微生物群与药物滥用之间的相关性.
- 开发一种机器学习模型,用于识别与药物相关的便样本.
主要方法:
- 从中国三个地区的476名参与者收集了便样本.
- 利用16S rRNA基因测序用于肠道微生物群分析.
- 开发了一种用于物质使用障碍识别的机器学习模型.
主要成果:
- SUD参与者表现出下肠道微生物群的多样性和同质性.
- 与SUD相关的关键生物标志物包括Bacteroides,Prevotella_9,Faecalibacterium和Blautia.
- 机器学习模型实现了0.9的AUC,用于区分物质使用者和非使用者.
结论:
- 肠道微生物群的改变与物质使用障碍有关.
- 开发的机器学习模型显示了预测和选SUD的前景.
- 进一步的研究可以利用这些发现为基于社区的SUD干预措施.
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