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COVID-19 疫苗的蛋白质亚基数学模型。

A mathematical model of protein subunits COVID-19 vaccines.

机构信息

Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, Toronto, Ontario, Canada.

Modelling Infection & Immunity Lab, Centre for Disease Modelling, Mathematics & Statistics, York University, Toronto, Ontario, Canada.

出版信息

Math Biosci. 2023 Apr;358:108970. doi: 10.1016/j.mbs.2023.108970. Epub 2023 Feb 10.

Abstract

We consider a general mathematical model for protein subunit vaccine with a focus on the MF59-adjuvanted spike glycoprotein-clamp vaccine for SARS-CoV-2, and use the model to study immunological outcomes in the humoral and cell-mediated arms of the immune response from vaccination. The mathematical model is fit to vaccine clinical trial data. We elucidate the role of Interferon-γ and Interleukin-4 in stimulating the immune response of the host. Model results, and results from a sensitivity analysis, show that a balance between the T1 and T2 arms of the immune response is struck, with the T1 response being dominant. The model predicts that two-doses of the vaccine at 28 days apart will result in approximately 85% humoral immunity loss relative to peak immunity approximately 6 months post dose 1.

摘要

我们考虑了一种用于蛋白质亚单位疫苗的通用数学模型,重点是针对 SARS-CoV-2 的 MF59 佐剂 Spike 糖蛋白夹疫苗,并使用该模型研究疫苗接种后体液和细胞介导免疫反应中的免疫结果。该数学模型适合疫苗临床试验数据。我们阐明了干扰素-γ和白细胞介素-4在刺激宿主免疫反应中的作用。模型结果和敏感性分析结果表明,在免疫反应的 T1 和 T2 臂之间取得了平衡,T1 反应占主导地位。该模型预测,相隔 28 天接种两剂疫苗,与第 1 剂后约 6 个月的峰值免疫相比,大约会导致 85%的体液免疫丧失。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ba6/9911981/6b195c2551d1/gr1_lrg.jpg

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