Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death.
Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death.
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DOI：
10.3389/fphar.2016.00342
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发表时间：
2016
影响因子：
5.6
通讯作者：
Vodovotz Y
中科院分区：
文献类型：
--
作者：
Abboud A;Mi Q;Puccio A;Okonkwo D;Buliga M;Constantine G;Vodovotz Y
创伤性脑损伤（TBI）引起的炎症是发病率和死亡率的复杂中介。我们之前已经证明了数据驱动和机制模型在创伤性损伤环境中的效用。我们假设不同的动态炎症程序是TBI幸存者与非幸存者的特征，并试图利用计算建模来获得对这种生/死分岔的新见解。使用Luminex™在31例TBI患者5天的脑脊液（CSF）样品中检测13种炎症细胞因子和趋化因子。在该队列中，5例为非幸存者（Glasgow Outcome Scale [GOS] score = 1）， 26例为幸存者（GOS bbb1）。初始损伤（格拉斯哥昏迷量表[GCS]）与GOS的Pearson相关性分析表明，幸存者和非幸存者对损伤有不同的临床反应轨迹。在TBI幸存者与非幸存者之间，在5天内观察到白细胞介素(IL)-4、IL-5、IL-6、IL-8、IL-13和肿瘤坏死因子-α (TNF-α)的统计学差异。主成分分析和动态贝叶斯网络推断表明趋化因子TNF-α、IL-6和IL-10在TBI中的差异作用，并在此基础上建立了TBI的常微分方程模型。该模型分别根据TBI幸存者与非幸存者的时间过程数据作为初始GCS的函数进行校准。对这些模型模拟的综合参数值的分析表明，创伤性脑损伤幸存者与非幸存者在小胶质细胞和损伤反应方面存在差异。这些研究表明，在人类TBI的背景下，数据驱动和机制模型相结合的效用。
Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1) and 26 were survivors (GOS > 1). A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS]) vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α) were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.
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影响因子：
29.7
作者：
Davis BK;Wen H;Ting JP
通讯作者：
Ting JP
影响因子：
8.8
作者：
Abboud A;Namas RA;Ramadan M;Mi Q;Almahmoud K;Abdul-Malak O;Azhar N;Zaaqoq A;Namas R;Barclay DA;Yin J;Sperry J;Peitzman A;Zamora R;Simmons RL;Billiar TR;Vodovotz Y
通讯作者：
Vodovotz Y
影响因子：
5.7
作者：
Namas, Rami A.;Namas, Rajaie;Vodovotz, Yoram
通讯作者：
Vodovotz, Yoram
影响因子：
2
作者：
Kumar, R;Clermont, G;Chow, CC
通讯作者：
Chow, CC
影响因子：
17.1
作者：
Brown, David;Namas, Rami A.;Vodovotz, Yoram
通讯作者：
Vodovotz, Yoram