在细菌感染中尽量减少治疗引起的抗生素耐药性的出现
Mathew Stracy1,2, Olga Snitser1, Idan Yelin1
1Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel.
概括
抗生素耐药性可能是由于再感染而产生的, 不仅仅是进化. 机器学习可以通过为患者个性化选择抗生素来预测和最小化这种风险,从而减少耐药细菌的传播.
科学领域:
- 微生物学
- 传染性疾病
- 计算生物学
背景情况:
- 目前的抗生素策略优先考虑病原体易感性,但忽视治疗诱导的耐药性.
- 抗生素耐药性的出现对有效的细菌感染治疗构成重大威胁.
研究的目的:
- 研究在治疗期间导致抗生素耐药性的机制.
- 开发治疗诱导耐药性的预测模型.
- 探索个性化抗生素推,以尽量减少耐药性的出现.
主要方法:
- 1113个细菌分离物的全基因组测序 (处理前和后).
- 机器学习分析尿路感染 (140,349) 和伤口感染 (7365) 的大型数据集.
- 分析患者感染史以预测复发情况.
主要成果:
- 治疗诱导的耐药性是常见的,通常是由于耐药菌株的再感染而不是新的进化.
- 机器学习模型可以预测个体患者的抗药性.
- 基于患者病史的个性化抗生素推可以最大限度地减少耐药性的发展.
结论:
- 在治疗期间出现抗生素耐药性是可以预测和预防的.
- 个人化医疗方法利用机器学习和基因组数据对抗抗生素耐药性至关重要.
- 减少耐药性复发可以减轻耐药性病原体的传播.
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