后急性COVID-19综合症（PACS）与严重器官损伤有关。鉴定和分层面临SARS-CoV-2感染危险的个体对于提供适当的护理至关重要。这项探索性研究使用手动和机器学习方法，寻找PACS的潜在液体生物检测信号。使用高清晰度单细胞荧光染色工作流程对液体生物检测进行分析，我们分析了100例后COVID患者和19例前流行前正常供体（ND）对照组。在我们的患者队列中，73例在感染SARS-CoV-2之前接种了至少1剂疫苗。我们将COVID患者分成25例无症状，22例有症状的COVID-19但不怀疑PACS和53例怀疑PACS。本研究调查的所有COVID-19患者均在2020年4月至2022年1月间被诊断，血液采样时间的中位数为243天（范围为16-669天）。我们对外周血中的罕见事件进行了组织病理学检查，并使用机器学习模型评估PACS的预测因素。手动分类发现，在PACS怀疑队列中有一些罕见的细胞和无细胞事件，与内皮细胞和血小板结构的特征一致。与ND相比，涉及假设事件的三种类别在PACS怀疑队列中的发生率明显更高（p值<0.05）。机器学习分类器在将ND与后COVID区分开时表现良好，准确率达到90.1%，但在将怀疑和不怀疑PACS的患者区分开时表现差，准确率为58.7%。手动和机器学习模型均发现后COVID队列和ND之间存在差异，表明在活跃的SARS-CoV-2感染后存在液体生物检测信号。需要更多研究来分层PACS及其亚综合征。这项工作全部或部分由Fulgent Genetics、Kathy和Richard Leventhal以及Vassiliadis Research Fund资助。本研究还得到了国家癌症研究所U54CA260591的支持。 版权所有©2023作者。由Elsevier B.V.出版。保留所有权利。

Post-acute COVID-19 syndrome (PACS) is linked to severe organ damage. The identification and stratification of at-risk SARS-CoV-2 infected individuals is vital to providing appropriate care. This exploratory study looks for a potential liquid biopsy signal for PACS using both manual and machine learning approaches.Using a high definition single cell assay (HDSCA) workflow for liquid biopsy, we analysed 100 Post-COVID patients and 19 pre-pandemic normal donor (ND) controls. Within our patient cohort, 73 had received at least 1 dose of vaccination prior to SARS-CoV-2 infection. We stratified the COVID patients into 25 asymptomatic, 22 symptomatic COVID-19 but not suspected for PACS and 53 PACS suspected. All COVID-19 patients investigated in this study were diagnosed between April 2020 and January 2022 with a median 243 days (range 16-669) from diagnosis to their blood draw. We did a histopathological examination of rare events in the peripheral blood and used a machine learning model to evaluate predictors of PACS.The manual classification found rare cellular and acellular events consistent with features of endothelial cells and platelet structures in the PACS-suspected cohort. The three categories encompassing the hypothesised events were observed at a significantly higher incidence in the PACS-suspected cohort compared to the ND (p-value < 0.05). The machine learning classifier performed well when separating the NDs from Post-COVID with an accuracy of 90.1%, but poorly when separating the patients suspected and not suspected of PACS with an accuracy of 58.7%.Both the manual and the machine learning model found differences in the Post-COVID cohort and the NDs, suggesting the existence of a liquid biopsy signal after active SARS-CoV-2 infection. More research is needed to stratify PACS and its subsyndromes.This work was funded in whole or in part by Fulgent Genetics, Kathy and Richard Leventhal and Vassiliadis Research Fund. This work was also supported by the National Cancer InstituteU54CA260591.Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.