相关实验视频

Updated: Jul 19, 2026

Rapid Antimicrobial Susceptibility Testing by Stimulated Raman Scattering Imaging of Deuterium Incorporation in a Single Bacterium

Rapid Antimicrobial Susceptibility Testing by Stimulated Raman Scattering Imaging of Deuterium Incorporation in a Single Bacterium

Published on: February 14, 2022

使用机器学习的快速方法来量化自然水生环境中抗菌素耐药性的相对负担

Peng Jiang¹, Shuyi Sun², Shin Giek Goh³

¹Department of Industrial Engineering and Management, Business School, Sichuan University, Chengdu 610064, China; NUS Environmental Research Institute, National University of Singapore, Singapore 117411, Singapore.

|July 24, 2024

概括

一个新的机器学习框架快速量化水生环境中的抗菌素耐药性 (AMR). 这种方法可以从几天到几个小时大幅加快分析速度,改善环境监测和管理策略.

科学领域:

环境科学环境科学
微生物学微生物学
数据科学数据科学数据科学

背景情况:

过度使用抗生素导致水生生态系统的抗菌素耐药性 (AMR).
目前的抗菌耐药性量化方法缓慢且昂贵,阻碍了有效的监测.
了解AMR剂量反应机制仍然不完整.

研究的目的:

开发一种快速的方法来量化自然水生环境中AMR的相对负担.
调查AMR负担与可访问的环境/土地使用变量之间的关联.
增强抗药性负担分析和管理实践.

主要方法:

集成机器学习框架使用适应性随机森林模型.
对抗药物耐药性负担与环境变量 (溶解氧,电阻) 和土地利用模式 (绿地) 之间的关联分析.
与后勤回归,决策树和基本随机森林模型进行比较.

主要成果:

量化时间从3-7天减少到大约0.5小时.
所有五个AMR相对负担指标都超过了85%的准确性,F1得分>0.92.
适应性随机森林模型显示出比其他方法更好的准确性和可解释性.
溶解氧气,电阻力和绿色区域比例被确定为关键预测变量.

结论:

开发的机器学习框架可以快速准确地量化水生AMR.
这种方法克服了传统耗时和昂贵的方法的局限性.
这些发现支持改善水生抗菌素耐药性的负担分析和管理.

关键词:

抗生素耐药性基因是抗生素耐药性的基因.抗生素耐药的细菌可以抵抗抗生素.负担预测负担预测机器学习是机器学习.自然环境自然环境自然环境系统建模系统建模

更多相关视频

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes

Published on: March 3, 2023

Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics

Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics

Published on: July 19, 2024

相关概念视频

Rapid Identification of Pathogens

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...