Antimicrobial resistance(AMR)presents a multifaceted health threat to humans,animals,plants,food systems,and environments.In response,China initiated extensive research to understand and address AMR.However,there has ...Antimicrobial resistance(AMR)presents a multifaceted health threat to humans,animals,plants,food systems,and environments.In response,China initiated extensive research to understand and address AMR.However,there has been a lack of analysis and synthesis of research results at the national level.This study establishes a national AMR knowledge repository through the systematic analysis of over 44,000 scientific publications(2000–2024),employing a machine learning framework that combines transformer-based language models and cluster analysis.Natural language processing(NLP)was used to identify key AMR research topics,subtopics,and AMR detection methods across One Health sectors,including changes over time.Main findings include:(i)China's AMR research in human health aligns with societal disease burdens,yet gaps exist for pathogens like Clostridium difficile and Hepatitis B virus,despite their significant risks in China.(ii)While AMR research in probiotics is increasing,potential risks of AMR transmission associated with their use are often underestimated,particularly regarding the post-marketing surveillance and standardization of probiotic products.(iii)Discovery of new antimicrobial agents and alternative therapies is crucial for AMR prevention in China.(iv)Artificial intelligence(AI)methods are promising to guide and accelerate research,including exploration of natural products and plant extracts.Overall,while the AMR research in China aligns with One Health principles,with the plant health sector surpassing global counterparts,food systems require enhanced efforts and cross-sectoral research,particularly in the development of effective AMR detection and surveillance technologies.This work demonstrates a replicable methodological framework for establishing and sustaining country-specific scientific evidence platforms,offering valuable datadriven support for synthesizing findings,decision-making,and developing current and future action plans to manage AMR from a One Health perspective.展开更多
基金supported by the National Natural Science Foundation of China(42021005,21936006)Ningbo S&T project(2021-DST-004)。
文摘Antimicrobial resistance(AMR)presents a multifaceted health threat to humans,animals,plants,food systems,and environments.In response,China initiated extensive research to understand and address AMR.However,there has been a lack of analysis and synthesis of research results at the national level.This study establishes a national AMR knowledge repository through the systematic analysis of over 44,000 scientific publications(2000–2024),employing a machine learning framework that combines transformer-based language models and cluster analysis.Natural language processing(NLP)was used to identify key AMR research topics,subtopics,and AMR detection methods across One Health sectors,including changes over time.Main findings include:(i)China's AMR research in human health aligns with societal disease burdens,yet gaps exist for pathogens like Clostridium difficile and Hepatitis B virus,despite their significant risks in China.(ii)While AMR research in probiotics is increasing,potential risks of AMR transmission associated with their use are often underestimated,particularly regarding the post-marketing surveillance and standardization of probiotic products.(iii)Discovery of new antimicrobial agents and alternative therapies is crucial for AMR prevention in China.(iv)Artificial intelligence(AI)methods are promising to guide and accelerate research,including exploration of natural products and plant extracts.Overall,while the AMR research in China aligns with One Health principles,with the plant health sector surpassing global counterparts,food systems require enhanced efforts and cross-sectoral research,particularly in the development of effective AMR detection and surveillance technologies.This work demonstrates a replicable methodological framework for establishing and sustaining country-specific scientific evidence platforms,offering valuable datadriven support for synthesizing findings,decision-making,and developing current and future action plans to manage AMR from a One Health perspective.
