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RaSizeNet:一个可解释的多模式深度学习模型,集成SERS光谱和惯性微流体大小分布,用于精确检测复杂的病原体

Guanwen Su1, Wenrui Yang1, Guozhao Liu1

  • 1School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.

Journal of hazardous materials
|February 18, 2026
PubMed
概括

一个新的深度学习模型,RaSizeNet,使用综合光谱和微流体学准确识别细菌. 这一突破提高了复杂样本中的病原体检测,以改善公共卫生.

关键词:
惯性微流体的使用没有标签的检测检测.多式联络深度学习多式联络深度学习精确的病原体识别 精确的病原体识别表面增强的拉曼散射.

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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...