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从呼气中检测 COVID-19。

COVID-19 detection from exhaled breath.

机构信息

Politecnico di Torino, Torino, Italy.

NanoTech Analysis Srl, Torino, Italy.

出版信息

Sci Rep. 2024 Oct 6;14(1):23245. doi: 10.1038/s41598-024-74104-1.

Abstract

The SARS-CoV-2 coronavirus emerged in 2019 causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next 4 years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast, and non-invasive COVID-19 detection system, which exploits only exhaled breath. Specifically, provided an air sample, the mass spectra in the 10-351 mass-to-charge range are measured using an original micro and nano-sampling device coupled with a high-precision spectrometer; then, the raw spectra are processed by custom software algorithms; the clean and augmented data are eventually classified using state-of-the-art machine-learning algorithms. An uncontrolled clinical trial was conducted between 2021 and 2022 on 302 subjects who were concerned about being infected, either due to exhibiting symptoms or having recently recovered from illness. Despite the simplicity of use, our system showed a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19 (that is, 95% accuracy, 94% recall, 96% specificity, and 92% [Formula: see text]-score). In light of these outcomes, we think that the proposed system holds the potential for substantial contributions to routine screenings and expedited responses during future epidemics, as it yields results comparable to state-of-the-art methods, providing them in a more rapid and less invasive manner.

摘要

2019 年出现了一种新型冠状病毒(SARS-CoV-2),引发了 COVID-19 大流行,在接下来的 4 年中,全球报告的确诊病例达到 77 亿,死亡人数达到 700 万。这场全球卫生紧急事件需要前所未有的努力来监测和降低感染率,推动了新诊断方法的研究。在本文中,我们介绍了一种廉价、快速、非侵入性的 COVID-19 检测系统,该系统仅利用呼出的气体。具体来说,提供一个空气样本,使用原始的微纳采样装置和高精度光谱仪测量 10-351 质量荷比范围内的质谱;然后,使用定制的软件算法处理原始光谱;最后,使用最先进的机器学习算法对清洁和增强的数据进行分类。在 2021 年至 2022 年期间,我们对 302 名关注自身感染风险的受试者进行了一项非控制临床试验,这些受试者可能出现了症状,或者最近刚从疾病中康复。尽管使用简便,但我们的系统在识别 COVID-19 病例方面的性能与传统的聚合酶链反应和抗原检测相当(即准确率为 95%,召回率为 94%,特异性为 96%,F1-分数为 92%)。鉴于这些结果,我们认为该系统具有在未来的疫情中进行常规筛查和快速响应的潜力,因为它能够提供与最先进方法相当的结果,并且以更快、更非侵入性的方式提供结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/555a/11456604/b84b18ccdc42/41598_2024_74104_Fig1_HTML.jpg

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