在基因组抗菌素耐药性监测方面的创新
Nicole E Wheeler1, Vivien Price2, Edward Cunningham-Oakes3
1Institute of Microbiology and Infection, University of Birmingham, Birmingham, Edgbaston, UK.
The Lancet. Microbe
|November 17, 2023
概括
抗微生物耐药性 (AMR) 的基因组监测可以通过使用未充分利用的创新技术,如元基因组学和机器学习来改进. 具有成本效益的应用程序和数据标准化是全球采用的关键,特别是在低收入国家.
科学领域:
- 基因组学就是基因组学.
- 流行病学 流行病学
- 计算生物学 计算生物学
背景情况:
- 全基因组测序对于抗菌素耐药性 (AMR) 监测至关重要,特别是在高收入国家.
- 在低收入和中等收入国家,采用先进的基因组技术对抗药物耐药性监测面临挑战.
- 测序和分析方面的创新为AMR流行病学提供了变革性的潜力.
研究的目的:
- 进行对基因组AMR监测中未充分利用的创新进行情境分析.
- 确定改善全球AMR监测的关键领域.
- 为采用新型基因组技术提供建议.
主要方法:
- 进行了专家研讨会和在线咨询.
- 进行了基因组AMR监测的情境分析.
- 发现了包括临床和环境元基因组学,基因/等离子体跟踪和机器学习在内的不足使用的创新.
主要成果:
- 针对AMR监测的几项有前途的基因组创新仍未得到充分利用.
- 需要具有成本效益的使用案例和与临床结果的明确联系来证明投资的合理性.
- 方法的协调和公平的数据共享框架至关重要.
结论:
- 实施先进的基因组监测需要对能力,培训和标准化方法进行战略投资.
- 开发具有成本效益的元基因组学和机器学习应用程序可以增强全球抗菌耐药性监测.
- 公平的数据治理和共享对于全球成功采用基因组AMR监测工具至关重要.
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