Wang Hao, Jia Chenhao, Li Hongzhao, Yin Rui, Chen Jiang, Li Yan, Yue Min
Institute of Preventive Veterinary Sciences & Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, China.
Hainan Institute of Zhejiang University, Sanya, China.
Front Mol Biosci. 2022 Aug 12;9:976705. doi: 10.3389/fmolb.2022.976705. eCollection 2022.
The antimicrobial resistance (AMR) crisis from bacterial pathogens is frequently emerging and rapidly disseminated during the sustained antimicrobial exposure in human-dominated communities, posing a compelling threat as one of the biggest challenges in humans. The frequent incidences of some common but untreatable infections unfold the public health catastrophe that antimicrobial-resistant pathogens have outpaced the available countermeasures, now explicitly amplified during the COVID-19 pandemic. Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes. Integrated with reliable diagnostic tools and powerful analytic approaches, a collaborative and systematic surveillance platform with high accuracy and predictability should be established and implemented, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.
在人类主导的社区中,持续使用抗菌药物期间,细菌病原体引发的抗菌药物耐药性(AMR)危机频繁出现且迅速传播,这构成了人类面临的最大挑战之一,是一个紧迫的威胁。一些常见但无法治疗的感染频繁发生,揭示了公共卫生灾难,即抗菌药物耐药性病原体已经超过了现有的应对措施,在2019冠状病毒病大流行期间,这种情况更是明显加剧。如今,生物技术和机器学习的进步有助于人们更深入地了解AMR细菌适应和进化过程中独特的时空动态。结合可靠的诊断工具和强大的分析方法,应建立并实施一个具有高精度和可预测性的协作式系统监测平台,这不仅是为了制定有效的AMR控制策略,也是为了保护现有和未来宝贵抗菌药物的使用寿命。