FightAMR: Novel global One Health surveillance approach to fight AMR using Artificial Intelligence and big data mining

FightAMR：利用人工智能和大数据挖掘对抗 AMR 的新型全球统一健康监测方法

• 批准号: MR/Y034422/1
• 负责人: Tania Dottorini
• 金额: $ 46.46万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY034422%2F1
• 关键词: FightAMR; Novel; global; Health; surveillance

Understanding the risk and direction of antimicrobial resistance (AMR) spread through food-borne routes, and developing of interventions to limit the spread of AMR within and between humans, animals, environment and food is a significant challenge, requiring a 360-degree investigation of a complex, interconnected system of humans, animals, environment on one hand and geographical, societal and climate-related variables on the other. This project will develop a monitoring system using AI and advanced tech to detect AMR spread in the interconnected human-animal-environment-food system ('One Health').First, we will analyse the heterogeneous corpus of historical AMR-related public data. This will improve our understanding of what data (monitorable biomarkers) should be collected to identify the conditions leading to a higher risk of AMR spread. This knowledge will be used to guide a large-scale multi-country sampling collection campaign of a large amount of heterogeneous and interconnected data from farms, wet markets, food, environment. Data will include results of microbiological analysis, whole-genome sequencing, metagenomics, phenotyping, documentation of on-farm management practices, and environmental sensor data (temperature, humidity, etc). An innovative AI-powered data mining pipeline will be used to unravel previously unknown correlations between observable animal, human, environment, food variables and a core set of resistome, microbiome, and microbial genomics variables, highlighting new routes for surveillance deployable in low-to-high-income countries.

了解抗菌素耐药性 (AMR) 通过食源性途径传播的风险和方向，并制定干预措施来限制 AMR 在人类、动物、环境和食品内部以及之间的传播是一项重大挑战，需要对抗菌素耐药性进行 360 度调查这是一个复杂且相互关联的系统，一方面由人类、动物、环境组成，另一方面由地理、社会和气候相关变量组成。该项目将开发一个利用人工智能和先进技术的监测系统，以检测互联的人类-动物-环境-食品系统（“One Health”）中的AMR传播。首先，我们将分析历史AMR相关公共数据的异构语料库。这将提高我们对应收集哪些数据（可监测生物标志物）的理解，以确定导致 AMR 传播风险较高的条件。这些知识将用于指导大规模多国抽样收集活动，收集来自农场、生鲜市场、食品和环境的大量异构且相互关联的数据。数据将包括微生物分析、全基因组测序、宏基因组学、表型分析、农场管理实践记录以及环境传感器数据（温度、湿度等）的结果。创新的人工智能驱动的数据挖掘管道将用于揭示可观察的动物、人类、环境、食物变量与一组核心的抗药性、微生物组和微生物基因组变量之间以前未知的相关性，突出显示可在低至低水平部署的监测新途径。 - 高收入国家。