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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...
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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

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SERS-ATB:用于抗生素SERS光谱可视化和深度学习识别的全面数据库服务器.

Quan Yuan1, Jia-Wei Tang2, Jie Chen3

  • 1School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China; Department of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, Shandong Province, China.

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概括

为了打击抗生素污染,创建了一个新的数据库,包含200种抗生素的12800种表面增强拉曼光谱 (SERS) 光谱. 一个卷积神经网络 (CNN) 模型实现了98.94%的识别准确度,有助于环境监测.

关键词:
抗生素 抗生素是一种抗生素.卷积神经网络是一种卷积神经网络.机器学习算法 机器学习算法拉曼光谱是拉曼光谱中的一个.表面增强的拉曼光谱学

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科学领域:

  • 环境科学 环境科学
  • 分析化学 分析化学
  • 频谱学是一种光谱学.

背景情况:

  • 水源中的抗生素污染是一个日益严重的环境和健康问题.
  • 抗生素耐药性的传播因环境污染而加剧.
  • 表面增强拉曼光谱 (SERS) 提供敏感的抗生素识别,但缺乏全面的光谱数据库.

研究的目的:

  • 使用SERS开发一个大规模的,开放的抗生素光谱数据库.
  • 从SERS数据建立可靠的机器学习模型,用于从SERS数据中识别抗生素.
  • 促进对抗生素污染的环境监测和管理.

主要方法:

  • 对200种环境相关的抗生素进行了12800个SERS光谱的系统收集.
  • 开发一个基于Web的,开放式访问的SERS光谱数据库.
  • 机器学习算法的比较和验证,包括卷积神经网络 (CNN),用于光谱识别.

主要成果:

  • 为200种抗生素建立了一个开放的SERS光谱数据库 (http://sers.test.bniu.net/).
  • 一个CNN模型在数据库中识别抗生素时达到98.94%的准确性.
  • 对CNN模型的外部验证显示了82.8%的准确性,证明了它的实际实用性.

结论:

  • SERS光谱数据库和CNN模型为环境抗生素检测提供了一个新的,可扩展的资源.
  • 该资源增强了SERS在环境监测计划中的整合.
  • 这项研究支持改善抗生素污染管理和缓解抗生素耐药性的研究.