2015
DOI: 10.1089/ars.2015.6398
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Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation

Abstract: Significance: Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models. Recent Advances: Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these set… Show more

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Cited by 75 publications
(93 citation statements)
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References 158 publications
(185 reference statements)
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“…To induce stroke lesions in rodents, quite extensive surgical intervention is required in comparison to the spontaneous induction of the lesions in humans. Surgical trauma can cause systemic activation of the innate immune system, which also may lead to nonspecific activation of adaptive immune responses (39). This should be kept in mind as a caveat, when translating effects of immunomodulatory treatments in experimental models to human therapy.…”
mentioning
confidence: 99%
“…To induce stroke lesions in rodents, quite extensive surgical intervention is required in comparison to the spontaneous induction of the lesions in humans. Surgical trauma can cause systemic activation of the innate immune system, which also may lead to nonspecific activation of adaptive immune responses (39). This should be kept in mind as a caveat, when translating effects of immunomodulatory treatments in experimental models to human therapy.…”
mentioning
confidence: 99%
“…Modeling methods and similar computational tools have been reviewed in detail elsewhere [35][36][37][38][39][40][41]. Here, some selections are explained in more detail.…”
Section: Dynamic Data-driven Modeling Methodsmentioning
confidence: 99%
“…One major use for this information is to identify relationships between and among variables in order to separate them effectively into groups. Another is to efficiently and objectively reduce the dimensionality of a dataset by eliminating from further investigation variables shown to be least important to the overall information content of a time-varying, multivariate dataset [39,42]. In this way, PCA can be used as a filter for other analyses that have constraints on the number of variables that can be input [43].…”
Section: Principal Component Analysis (Pca)mentioning
confidence: 99%
“…Although these methods do not provide detailed mechanistic insight, they can be used to understand abstract features of the response, such as the presence of nonlinearities and the order of the response. The main drawback of this class of models is that they are often lack mechanistic insight, and can be over-fit to the data on which they were trained (19,23).…”
Section: Dynamic Inference Of Sepsis Clinical Criteriamentioning
confidence: 99%