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新冠病毒感染中高凝状态的计算建模

Computational modeling of hypercoagulability in COVID-19.

作者信息

Zhu Ge, Modepalli Susree, Anand Mohan, Li He

机构信息

Center for Biomedical Engineering, Brown University, Providence, USA.

School of Medicine, Georgetwon University, Washington D.C, USA.

出版信息

Comput Methods Biomech Biomed Engin. 2023 Feb;26(3):338-349. doi: 10.1080/10255842.2022.2124858. Epub 2022 Sep 26.

Abstract

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected more than 100 million people worldwide and claimed millions of lives. While the leading cause of mortality in COVID-19 patients is the hypoxic respiratory failure from acute respiratory distress syndrome, there is accumulating evidence that shows excessive coagulation also increases the fatalities in COVID-19. Thus, there is a pressing demand to understand the association between COVID-19-induced hypercoagulability and the extent of formation of undesired blood clots. Mathematical modeling of coagulation has been used as an important tool to identify novel reaction mechanisms and to identify targets for new drugs. Here, we employ the coagulation factor data of COVID-19 patients reported from published studies as inputs for two mathematical models of coagulation to identify how the concentrations of coagulation factors change in these patients. Our simulation results show that while the levels of many of the abnormal coagulation factors measured in COVID-19 patients promote the generation of thrombin and fibrin, two key components of blood clots, the increased level of fibrinogen and then the reduced level of antithrombin are the factors most responsible for boosting the level of fibrin and thrombin, respectively. Altogether, our study demonstrates the potential of mathematical modeling to identify coagulation factors responsible for the increased clot formation in COVID-19 patients where clinical data is scarce.

摘要

2019年冠状病毒病(COVID-19)由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引起,已在全球感染了超过1亿人,并夺走了数百万人的生命。虽然COVID-19患者的主要死亡原因是急性呼吸窘迫综合征导致的低氧性呼吸衰竭,但越来越多的证据表明,过度凝血也会增加COVID-19的死亡率。因此,迫切需要了解COVID-19诱导的高凝状态与不良血栓形成程度之间的关联。凝血的数学建模已被用作识别新反应机制和确定新药靶点的重要工具。在这里,我们将已发表研究报告的COVID-19患者的凝血因子数据作为两种凝血数学模型的输入,以确定这些患者中凝血因子的浓度如何变化。我们的模拟结果表明,虽然在COVID-19患者中测得的许多异常凝血因子水平促进了凝血酶和纤维蛋白(血栓的两个关键成分)的生成,但纤维蛋白原水平的升高以及随后抗凝血酶水平的降低分别是导致纤维蛋白和凝血酶水平升高的最主要因素。总之,我们的研究证明了在临床数据稀缺的情况下,数学建模在识别导致COVID-19患者血栓形成增加的凝血因子方面的潜力。

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