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呼吸道病毒感染中共同感染病原体致死协同作用的数学建模:综述

Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review.

作者信息

Mochan Ericka, Sego T J

机构信息

Department of Computational and Chemical Sciences, Carlow University, Pittsburgh, PA 15213, USA.

Department of Medicine, University of Florida, Gainesville, FL 32611, USA.

出版信息

Microorganisms. 2023 Dec 13;11(12):2974. doi: 10.3390/microorganisms11122974.

Abstract

Influenza A virus (IAV) infections represent a substantial global health challenge and are often accompanied by coinfections involving secondary viruses or bacteria, resulting in increased morbidity and mortality. The clinical impact of coinfections remains poorly understood, with conflicting findings regarding fatality. Isolating the impact of each pathogen and mechanisms of pathogen synergy during coinfections is challenging and further complicated by host and pathogen variability and experimental conditions. Factors such as cytokine dysregulation, immune cell function alterations, mucociliary dysfunction, and changes to the respiratory tract epithelium have been identified as contributors to increased lethality. The relative significance of these factors depends on variables such as pathogen types, infection timing, sequence, and inoculum size. Mathematical biological modeling can play a pivotal role in shedding light on the mechanisms of coinfections. Mathematical modeling enables the quantification of aspects of the intra-host immune response that are difficult to assess experimentally. In this narrative review, we highlight important mechanisms of IAV coinfection with bacterial and viral pathogens and survey mathematical models of coinfection and the insights gained from them. We discuss current challenges and limitations facing coinfection modeling, as well as current trends and future directions toward a complete understanding of coinfection using mathematical modeling and computer simulation.

摘要

甲型流感病毒(IAV)感染是一项重大的全球健康挑战,并且常常伴有包括继发病毒或细菌在内的合并感染,从而导致发病率和死亡率上升。合并感染对临床的影响仍知之甚少,关于死亡率的研究结果相互矛盾。在合并感染期间,分离每种病原体的影响以及病原体协同作用的机制具有挑战性,并且宿主和病原体的变异性以及实验条件会使其进一步复杂化。细胞因子失调、免疫细胞功能改变、黏液纤毛功能障碍以及呼吸道上皮细胞变化等因素已被确定为致死率增加的原因。这些因素的相对重要性取决于病原体类型、感染时间、顺序和接种量等变量。数学生物学建模在阐明合并感染的机制方面可以发挥关键作用。数学建模能够对宿主内免疫反应中难以通过实验评估的方面进行量化。在这篇叙述性综述中,我们重点介绍了IAV与细菌和病毒病原体合并感染的重要机制,并概述了合并感染的数学模型以及从中获得的见解。我们讨论了合并感染建模目前面临的挑战和局限性,以及利用数学建模和计算机模拟全面理解合并感染的当前趋势和未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf13/10745501/20062c633ec2/microorganisms-11-02974-g001.jpg

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