SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

中文翻译：

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COVID-19 血浆 MALDI-TOF 质谱分析的预后准确性。

SARS-CoV-2 感染造成全球健康危机。在全世界不断努力寻找治疗解决方案的同时，迫切需要改善 COVID-19 的预后。在此，我们报告了血浆蛋白质组指纹图谱，该指纹图谱可预测高风险（住院）和低风险（门诊）的 COVID-19 病例，该平台通过将机器学习与基质辅助激光解吸电离质谱分析相结合的平台进行识别。样品制备、MS 和数据分析参数经过优化，在没有特征选择的情况下，数据集中的总体准确度为 92%，灵敏度为 93%，特异性为 92%。我们在 MALDI-TOF 图谱中识别出属于相同蛋白质型的两个不同区域。SDS-PAGE 和定量自下而上的蛋白质组学分析相结合，可以鉴定血清淀粉样蛋白 A-1 和 A-2 的完整形式和截短形式，这两种蛋白都已被描述为急性期病毒感染的生物标志物。使用目前临床使用的技术对高风险和低风险的 COVID-19 患者进行公正的区分可能会在 COVID-19 的无创预后中得到迅速应用。进一步的验证将巩固其临床实用性。