Your browser doesn't support javascript.
loading
Occurrence and Distribution of Antibacterial Quaternary Ammonium Compounds in Chinese Estuaries Revealed by Machine Learning-Assisted Mass Spectrometric Analysis.
Su, Wenyuan; Li, Pengyang; Zhong, Laijin; Liang, Wenqing; Li, Tingyu; Liu, Jiyan; Ruan, Ting; Jiang, Guibin.
Afiliación
  • Su W; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Li P; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Zhong L; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Liang W; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Li T; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Liu J; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Ruan T; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Jiang G; University of Chinese Academy of Sciences, Beijing 100049, China.
Environ Sci Technol ; 58(26): 11707-11717, 2024 Jul 02.
Article en En | MEDLINE | ID: mdl-38871667
ABSTRACT
Antimicrobial resistance (AMR) undermines the United Nations Sustainable Development Goals of good health and well-being. Antibiotics are known to exacerbate AMR, but nonantibiotic antimicrobials, such as quaternary ammonium compounds (QACs), are now emerging as another significant driver of AMR. However, assessing the AMR risks of QACs in complex environmental matrices remains challenging due to the ambiguity in their chemical structures and antibacterial activity. By machine learning prediction and high-resolution mass spectrometric analysis, a list of antibacterial QACs (n = 856) from industrial chemical inventories is compiled, and it leads to the identification of 50 structurally diverse antibacterial QACs in sediments, including traditional hydrocarbon-based compounds and new subclasses that bear additional functional groups, such as choline, ester, betaine, aryl ether, and pyridine. Urban wastewater, aquaculture, and hospital discharges are the main factors influencing QAC distribution patterns in estuarine sediments. Toxic unit calculations and metagenomic analysis revealed that these QACs can influence antibiotic resistance genes (particularly sulfonamide resistance genes) through cross- and coresistances. The potential to influence the AMR is related to their environmental persistence. These results suggest that controlling the source, preventing the co-use of QACs and sulfonamides, and prioritizing control of highly persistent molecules will lead to global stewardship and sustainable use of QACs.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estuarios / Aprendizaje Automático / Compuestos de Amonio Cuaternario / Antibacterianos País/Región como asunto: Asia Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estuarios / Aprendizaje Automático / Compuestos de Amonio Cuaternario / Antibacterianos País/Región como asunto: Asia Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos