Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
PLoS One. 2012;7(6):e39677. doi: 10.1371/journal.pone.0039677. Epub 2012 Jun 22.
There is a growing realisation that neuro-inflammation plays a fundamental role in the pathology of Traumatic Brain Injury (TBI). This has led to the search for biomarkers that reflect these underlying inflammatory processes using techniques such as cerebral microdialysis. The interpretation of such biomarker data has been limited by the statistical methods used. When analysing data of this sort the multiple putative interactions between mediators need to be considered as well as the timing of production and high degree of statistical co-variance in levels of these mediators. Here we present a cytokine and chemokine dataset from human brain following human traumatic brain injury and use principal component analysis and partial least squares discriminant analysis to demonstrate the pattern of production following TBI, distinct phases of the humoral inflammatory response and the differing patterns of response in brain and in peripheral blood. This technique has the added advantage of making no assumptions about the Relative Recovery (RR) of microdialysis derived parameters. Taken together these techniques can be used in complex microdialysis datasets to summarise the data succinctly and generate hypotheses for future study.
人们越来越认识到,神经炎症在创伤性脑损伤(TBI)的病理学中起着根本性的作用。这导致人们寻找生物标志物,使用脑微透析等技术来反映这些潜在的炎症过程。这些生物标志物数据的解释受到所使用的统计方法的限制。在分析此类数据时,需要考虑介质之间的多种假定相互作用,以及这些介质产生的时间和高度统计协方差。在这里,我们展示了人类创伤性脑损伤后人类大脑中的细胞因子和趋化因子数据集,并使用主成分分析和偏最小二乘判别分析来展示 TBI 后产生的模式、体液炎症反应的不同阶段以及大脑和外周血中不同的反应模式。这种技术的额外优点是不对微透析衍生参数的相对回收率(RR)做出任何假设。总之,这些技术可用于复杂的微透析数据集,以简洁地总结数据并为未来的研究生成假设。