A combined approach of MALDI-TOF mass spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.
A combined approach of MALDI-TOF mass spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.
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DOI：
10.1016/j.jviromet.2020.113991
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发表时间：
2020-12
影响因子：
3.1
通讯作者：
Baumeister E
中科院分区：
文献类型：
--
作者：
Rocca MF;Zintgraff JC;Dattero ME;Santos LS;Ledesma M;Vay C;Prieto M;Benedetti E;Avaro M;Russo M;Nachtigall FM;Baumeister E
2019年冠状病毒病，称为COVID-19，由严重急性呼吸道综合征冠状病毒2（SARS-CoV-2）引起。SARS-CoV-2病毒的早期、敏感和特异性检测被广泛认为是应对持续爆发的关键点。目前，诊断是基于分子真实的时间RT-PCR技术，虽然他们的实施受到威胁，由于世界各地的供应的特殊需求。这就是为什么开发替代和/或补充测试变得如此重要。在这里，我们利用质谱技术结合机器学习算法的潜力，直接从鼻咽拭子样本中检测COVID-19阳性和阴性蛋白质谱。根据获得的初步结果，准确性= 67.66%，灵敏度= 61.76%，特异性= 71.72%，尽管这些参数仍需改进以用作筛选技术，质谱法-的方法加上多变量分析表明，这是一个有趣的工具，值得探索作为一种补充诊断方法，由于低成本和快速性能。然而，应考虑采取进一步的步骤，如分析大量样品，以确定所开发方法的适用性。
Coronavirus disease 2019, known as COVID-19, is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early, sensitive and specific detection of SARS-CoV-2 virus is widely recognized as the critical point in responding to the ongoing outbreak. Currently, the diagnosis is based on molecular real time RT-PCR techniques, although their implementation is being threatened due to the extraordinary demand for supplies worldwide. That is why the development of alternative and / or complementary tests becomes so relevant. Here, we exploit the potential of mass spectrometry technology combined with machine learning algorithms, for the detection of COVID-19 positive and negative protein profiles directly from nasopharyngeal swabs samples. According to the preliminary results obtained, accuracy = 67.66 %, sensitivity = 61.76 %, specificity = 71.72 %, and although these parameters still need to be improved to be used as a screening technique, mass spectrometry-based methods coupled with multivariate analysis showed that it is an interesting tool that deserves to be explored as a complementary diagnostic approach due to the low cost and fast performance. However, further steps, such as the analysis of a large number of samples, should be taken in consideration to determine the applicability of the method developed.
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