Machine Learning-Assisted 3D SERS Chip with Acoustic Enrichment for High-Accuracy Diagnosis of Respiratory Viruses and Emerging Pathogens.

IF 9.1 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Yingjin Ma,Man-Chung Wong,Menglin Song,Pui Wang,Yuan Liu,Yifei Zhao,Honglin Chen,Juewen Liu,Jianhua Hao
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Abstract

Outbreaks of SARS-CoV-2, first investigated as an unknown pathogen, have reflected the severe threat that pathogen X poses to public health and social security. Early and precise diagnosis and classification of infectious respiratory diseases with similar symptoms are essential for the risk assessment of public health or epidemiological investigations. Current technologies are limited to detect known viruses, leading to false negatives for novel or mutated pathogens. Here, we propose an ML-assisted SERS strategy for screening various types of respiratory viruses and potential pathogen X in cases with similar infectious symptoms. A label-free 3D plasmonic Au-PS SERS chip was designed to amplify the Raman signal over 103-fold compared to a conventional Au substrate. An ensemble ML model was developed to analyze SERS data for effectively distinguishing between healthy individuals, SARS-CoV-2, RSV, and influenza A and B, as well as identifying newly emerging pathogens. Our experiments demonstrated that the ensemble model integrated with SERS spectra achieved a remarkable classification accuracy of 100%. Notably, the model exhibited excellent performance in detecting mixed viral infections and simulated pathogen X, with a reliable detection range of viral concentrations from 5 × 102 to 106 PFU/mL under acoustic enrichment. This approach holds significant promise for the early screening and detection of emerging and known respiratory pathogens.
基于声学富集的机器学习辅助3D SERS芯片用于呼吸道病毒和新发病原体的高精度诊断。
SARS-CoV-2首次作为一种未知病原体被调查,它的爆发反映了X病原体对公共卫生和社会安全构成的严重威胁。具有相似症状的传染性呼吸道疾病的早期准确诊断和分类对于公共卫生风险评估或流行病学调查至关重要。目前的技术仅限于检测已知的病毒,导致对新的或突变的病原体产生假阴性。在此,我们提出了一种ml辅助的SERS策略,用于筛选具有相似感染症状的各种呼吸道病毒和潜在病原体X。设计了一种无标签的3D等离子体Au- ps SERS芯片,与传统的Au衬底相比,该芯片可将拉曼信号放大103倍以上。建立了一个集成ML模型来分析SERS数据,以有效区分健康个体、SARS-CoV-2、RSV以及甲型和乙型流感,并识别新出现的病原体。我们的实验表明,集成了SERS光谱的集合模型达到了100%的分类精度。值得注意的是,该模型在检测混合病毒感染和模拟病原体X方面表现出色,在声富集条件下,病毒浓度的可靠检测范围为5 × 102 ~ 106 PFU/mL。这种方法对新出现的和已知的呼吸道病原体的早期筛查和检测具有重要的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Sensors
ACS Sensors Chemical Engineering-Bioengineering
CiteScore
14.50
自引率
3.40%
发文量
372
期刊介绍: ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.
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