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Ensemble Models of Neutrophil Trafficking in Severe Sepsis

机译:严重败血症中性粒细胞贩运的集合模型

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A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture)-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about of the treated population) that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental studies to advance understanding of the complex biological response to severe infection, a problem of growing magnitude in humans.
机译:严重败血症的标志是全身性炎症,它会激活白细胞,并可能导致其误导。这既导致迁移到感染部位的受损,又增加了对健康组织的浸润。为了更好地了解所涉及的病理生理机制,我们建立了大鼠CLP(盲肠结扎和穿刺)引起的脓毒症急性炎症反应的粗粒现象学模型。该模型结合了对炎性刺激的独特的嗜中性白细胞动力学反应以及分隔的炎性反应的成分之间的动态相互作用。使用马尔可夫链蒙特卡洛抽样统计地生成与实验观察结果一致的模型参数集的集合。对模型状态下的预测不确定性进行了量化,从而得出了合奏参数集。参数的正演模拟成功捕获了实验特征,并预测系统激活的循环中性粒细胞显示出向组织的迁移受损和肺中的中性粒细胞隔离,从而导致组织损伤和死亡。从幸存者和非幸存者队列估计的参数集合的主成分和多元回归分析提供了对指示败血症结果的病理机制的深入了解。此外,该模型被扩展以包含假设机制,通过该机制,利用体外血液净化进行的免疫调节可导致败血症大鼠的预后得到改善。模拟确定了受益于血液净化的亚人群(约占治疗人群)。幸存者显示出嗜中性粒细胞向组织的迁移增加,肺中性粒细胞的螯合减少,从而改善了结局。本文介绍的模型集合提供了一个平台,可用于生成和测试计算机模拟的假设,以及激发进一步的实验研究,以增进对严重感染这一复杂的生物反应的理解,人类对这一问题的关注日益严重。

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