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通过嗅觉技术对 SARS-CoV-2 感染患者和对照者的化学呼吸谱进行比较分析。

Comparative analysis of chemical breath-prints through olfactory technology for the discrimination between SARS-CoV-2 infected patients and controls.

机构信息

Department of Pharmacy, Health Sciences Division, University of Quintana Roo, Quintana Roo, Mexico.

Faculty of Medicine-Center for Applied Research on Environment and Health (CIAAS), Autonomous University of San Luis Potosí, Avenida Sierra Leona No. 550, CP 78210, Colonia Lomas Segunda Sección, San Luis Potosí, SLP, Mexico.

出版信息

Clin Chim Acta. 2021 Aug;519:126-132. doi: 10.1016/j.cca.2021.04.015. Epub 2021 Apr 24.

Abstract

BACKGROUND

We identified a global chemical pattern of volatile organic compounds in exhaled breath capable of discriminating between COVID-19 patients and controls (without infection) using an electronic nose.

METHODS

The study focused on 42 SARS-CoV-2 RT-qPCR positive subjects as well as 42 negative subjects. Principal component analysis indicated a separation of the study groups and provides a cumulative percentage of explanation of the variation of 98.3%.

RESULTS

The canonical analysis of principal coordinates model shows a separation by the first canonical axis CAP1 (r = 0.939 and 95.23% of correct classification rate), the cut-off point of 0.0089; 100% sensitivity (CI 95%:91.5-100%) and 97.6% specificity (CI 95%:87.4-99.9%). The predictive model usefulness was tested on 30 open population subjects without prior knowledge of SARS-CoV-2 RT-qPCR status. Of these 3 subjects exhibited COVID-19 suggestive breath profiles, all asymptomatic at the time, two of which were later shown to be SARS-CoV-2 RT-qPCR positive. An additional subject had a borderline breath profile and SARS-CoV-2 RT-qPCR positive. The remaining 27 subjects exhibited healthy breath profiles as well as SARS-CoV-2 RT-qPCR test results.

CONCLUSIONS

In all, the use of olfactory technologies in communities with high transmission rates as well as in resource-limited settings where targeted sampling is not viable represents a practical COVID-19 screening approach capable of promptly identifying COVID-19 suspect patients and providing useful epidemiological information to guide community health strategies in the context of COVID-19.

摘要

背景

我们使用电子鼻识别出了新冠病毒患者与对照(无感染)呼出气体中的挥发性有机化合物的全球化学模式,能够将两者区分开来。

方法

本研究聚焦于 42 例 SARS-CoV-2 RT-qPCR 阳性患者以及 42 例阴性患者。主成分分析表明研究组之间存在分离,并提供了 98.3%的变异解释的累积百分比。

结果

主坐标典范分析模型显示通过第一典范轴 CAP1 (r = 0.939 和 95.23%的正确分类率)进行分离,截断值为 0.0089;100%的敏感性(95%置信区间:91.5-100%)和 97.6%的特异性(95%置信区间:87.4-99.9%)。在没有 SARS-CoV-2 RT-qPCR 状态先验知识的 30 名开放人群受试者中测试了预测模型的实用性。其中 3 名受试者表现出 COVID-19 提示性呼吸特征,当时均无症状,其中 2 名后来被证实 SARS-CoV-2 RT-qPCR 阳性。另外 1 名受试者呼吸特征接近阳性,且 SARS-CoV-2 RT-qPCR 阳性。其余 27 名受试者表现出健康的呼吸特征以及 SARS-CoV-2 RT-qPCR 检测结果。

结论

总之,嗅觉技术在高传播率社区以及资源有限的环境中具有实用性,因为在这些环境中无法进行靶向采样,它代表了一种实用的 COVID-19 筛查方法,能够迅速识别 COVID-19 疑似患者,并提供有用的流行病学信息,以指导 COVID-19 背景下的社区卫生策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/03d5/8064814/8de4a3d2dbfc/gr1_lrg.jpg

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