对 COVID-19 重症和非重症患者的抗体动态进行拓扑数据分析。
Topological data analysis of antibody dynamics of severe and non-severe patients with COVID-19.
机构信息
Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, 83844-1103, USA; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, USA; Instituto de Matemáticas, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, Mexico.
Instituto de Matemáticas, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, Mexico.
出版信息
Math Biosci. 2023 Jul;361:109011. doi: 10.1016/j.mbs.2023.109011. Epub 2023 May 5.
The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system's role in the disease's severity level. Here, based on antibody kinetic data of severe and non-severe COVID-19 patients, topological data analysis (TDA) highlights that severity is not binary. However, there are differences in the shape of antibody responses that further classify COVID-19 patients into non-severe, severe, and intermediate cases of severity. Based on the results of TDA, different mathematical models were developed to represent the dynamics between the different severity groups. The best model was the one with the lowest average value of the Akaike Information Criterion for all groups of patients. Our results suggest that different immune mechanisms drive differences between the severity groups. Further inclusion of different components of the immune system will be central for a holistic way of tackling COVID-19.
COVID-19 大流行是一个重大的公共卫生威胁，人们对于免疫系统在疾病严重程度中的作用仍有许多未解之谜。在这里，基于严重和非严重 COVID-19 患者的抗体动力学数据，拓扑数据分析（TDA）强调了严重程度不是二元的。然而，抗体反应的形状存在差异，这进一步将 COVID-19 患者分为非严重、严重和中度严重病例。基于 TDA 的结果，开发了不同的数学模型来表示不同严重程度组之间的动态关系。对于所有患者组，具有最低平均 Akaike 信息准则值的模型是最佳模型。我们的结果表明，不同的免疫机制导致了严重程度组之间的差异。进一步纳入免疫系统的不同成分将是全面应对 COVID-19 的核心。
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