Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review.
Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review.
复制标题
呼吸道病毒感染中共感染病原体致死协同作用的数学建模：综述。
DOI：
10.3390/microorganisms11122974
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发表时间：
2023-12-13
期刊：
影响因子：
4.5
通讯作者：
中科院分区：
文献类型：
--
作者：
甲型流感病毒（IAV）感染是一项重大的全球卫生挑战，通常伴有涉及继发性病毒或细菌的共感染，导致发病率和死亡率增加。合并感染的临床影响仍然知之甚少，关于致死率的研究结果相互矛盾。在共感染过程中，分离每种病原体的影响和病原体协同作用机制具有挑战性，并且因宿主和病原体的可变性和实验条件而进一步复杂化。细胞因子失调、免疫细胞功能改变、纤毛粘膜功能障碍和呼吸道上皮改变等因素已被确定为导致致死率增加的因素。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。数学生物学模型可以在揭示共感染机制方面发挥关键作用。数学建模可以量化宿主内免疫反应的各个方面，而这些方面很难通过实验进行评估。在这篇叙述性综述中，我们强调了IAV与细菌和病毒病原体共同感染的重要机制，并调查了共同感染的数学模型及其见解。我们讨论了当前共同感染建模面临的挑战和局限性，以及使用数学建模和计算机模拟全面了解共同感染的当前趋势和未来方向。
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.
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DOI：
10.1086/650204
发表时间：
2010-02-15
期刊：
The Journal of infectious diseases
影响因子：
--
作者：
Lee MH;Arrecubieta C;Martin FJ;Prince A;Borczuk AC;Lowy FD
通讯作者：
Lowy FD
流感病毒通过IFIT蛋白的两波表达抑制RSV感染。
DOI：
10.3390/v12101171
发表时间：
2020-10-16
期刊：
Viruses
影响因子：
--
作者：
Drori Y;Jacob-Hirsch J;Pando R;Glatman-Freedman A;Friedman N;Mendelson E;Mandelboim M
通讯作者：
Mandelboim M
DOI：
10.3390/v7102875
发表时间：
2015-10-12
期刊：
Viruses
影响因子：
--
作者：
Boianelli A;Nguyen VK;Ebensen T;Schulze K;Wilk E;Sharma N;Stegemann-Koniszewski S;Bruder D;Toapanta FR;Guzmán CA;Meyer-Hermann M;Hernandez-Vargas EA
通讯作者：
Hernandez-Vargas EA
影响因子：
5.4
作者：
Avadhanula, V;Rodriguez, CA;Adderson, EE
通讯作者：
Adderson, EE
影响因子：
4.7
作者：
Anestad, Gabriel;Nordbo, Svein Arne
通讯作者：
Nordbo, Svein Arne