With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first real-time, reagent-free, portable analysis platform for viral detection in liquid saliva, using COVID-19 as a proof-of-concept. We show that vibrational molecular spectroscopy and machine learning (ML) detect biomolecular changes consistent with the presence of viral infection. Saliva samples were collected from 470 individuals, including 65 that were infected with COVID-19 (28 from hospitalized patients and 37 from a walk-in testing clinic) and 251 that had a negative polymerase chain reaction (PCR) test. A further 154 were collected from healthy volunteers. Saliva measurements were achieved in 6 minutes or less and led to machine learning models predicting COVID-19 infection with sensitivity and specificity reaching 90%, depending on volunteer symptoms and disease severity. Machine learning models were based on linear support vector machines (SVM). This platform could be deployed to manage future pandemics using the same hardware but using a tunable machine learning model that could be rapidly updated as new viral strains emerge.

中文翻译：

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基于液体唾液的拉曼光谱设备具有板载机器学习功能，可实时检测 COVID-19 感染


随着人口密度的增加，病毒爆发达到大流行状态的可能性正在增加。然而，目前的病毒筛查技术使用特异性试剂，随着病毒的变异，检测准确性会降低。在这里，我们展示了第一个用于液体唾液中病毒检测的实时、免试剂、便携式分析平台，使用 COVID-19 作为概念验证。我们表明，振动分子光谱和机器学习 （ML） 可以检测到与病毒感染一致的生物分子变化。从 470 人身上收集了唾液样本，其中包括 65 名感染 COVID-19 的人（28 名来自住院患者，37 名来自步入式检测诊所）和 251 名聚合酶链反应 （PCR） 检测阴性的人。另外 154 例是从健康志愿者中收集的。唾液测量在 6 分钟或更短的时间内完成，并导致机器学习模型预测 COVID-19 感染，灵敏度和特异性达到 90%，具体取决于志愿者症状和疾病严重程度。机器学习模型基于线性支持向量机 （SVM）。该平台可以部署以使用相同的硬件来管理未来的大流行病，但使用可调机器学习模型，该模型可以随着新病毒株的出现而快速更新。