纸质SERS芯片带有适应性注意力神经网络,用于病原体识别
Liyan Bi1, Huangruici Zhang2, Chenyu Mu3
1School of Special Education and Rehabilitation, Binzhou Medical University, Yantai 264003, China; Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai, Yantai 264005, China.
Journal of hazardous materials
|May 27, 2025
概括
本研究介绍了一种使用人工智能 (AI) 和表面增强拉曼散射 (SERS) 进行快速准确的病原体识别的基于纸张的新型生物传感器. 人工智能辅助的SERS芯片在区分各种细菌物种和菌株方面取得了很高的准确性.
科学领域:
- 生物技术是生物技术.
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 准确和快速的病原体识别对于公共卫生和患者护理至关重要.
- 现有的人工智能 (AI) 辅助的表面增强拉曼散射 (SERS) 生物传感器面临着精度和细菌指纹多样性有限的挑战.
- 需要改进的方法,以便迅速和可靠地进行病原体歧视.
研究的目的:
- 开发一种新的多分支自适应性注意力卷积神经网络 (MBAA-CNN) 辅助纸质SERS芯片,用于病原体识别.
- 为了增强拉曼光谱的多样性和改善病原体捕获,使用双重功能分子4-mercaptophenylboronic acid (4-MPBA).
- 为了实现高精度和可靠性,在区分各种病原体,包括抗生素耐药菌株.
主要方法:
- 开发一种基于纸张的SERS芯片,与一款新的MBAA-CNN集成.
- 使用4-mercaptophenylboronic酸 (4-MPBA) 捕获细菌和增强拉曼光谱 (标签模式).
- 使用K-means算法识别病原体,并将性能与无标签模式进行比较.
主要成果:
- 4-MPBA标记模式显示的精度明显高于无标记模式.
- MBAA-CNN在所有病原体物种中达到98.6%的准确率,在抗生素耐药和敏感菌株中达到99.5%的准确率.
- 在损失值,速度和准确性方面,MBAA-CNN的表现优于传统的机器学习模型.
结论:
- 开发的MBAA-CNN协助的纸质SERS芯片提供了一种快速可靠的病原体歧视方法.
- 这种方法显示出早期,无培养病原体诊断的潜力.
- 该技术可用于实时监测水环境中的微生物污染.
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