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用于脑实质中压力驱动型流体输注的预测模型。

Predictive models for pressure-driven fluid infusions into brain parenchyma.

机构信息

Therataxis, LLC, JHU Eastern Complex, Suite B305, 1101 E 33rd St, Baltimore, MD 21218, USA.

出版信息

Phys Med Biol. 2011 Oct 7;56(19):6179-204. doi: 10.1088/0031-9155/56/19/003. Epub 2011 Sep 2.

DOI:10.1088/0031-9155/56/19/003
PMID:21891847
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3249646/
Abstract

Direct infusions into brain parenchyma of biological therapeutics for serious brain diseases have been, and are being, considered. However, individual brains, as well as distinct cytoarchitectural regions within brains, vary in their response to fluid flow and pressure. Further, the tissue responds dynamically to these stimuli, requiring a nonlinear treatment of equations that would describe fluid flow and drug transport in brain. We here report in detail on an individual-specific model and a comparison of its prediction with simulations for living porcine brains. Two critical features we introduced into our model-absent from previous ones, but requirements for any useful simulation-are the infusion-induced interstitial expansion and the backflow. These are significant determinants of the flow. Another feature of our treatment is the use of cross-property relations to obtain individual-specific parameters that are coefficients in the equations. The quantitative results are at least encouraging, showing a high fraction of overlap between the computed and measured volumes of distribution of a tracer molecule and are potentially clinically useful. Several improvements are called for; principally a treatment of the interstitial expansion more fundamentally based on poroelasticity and a better delineation of the diffusion tensor of a particle confined to the interstitial spaces.

摘要

直接将生物疗法输注到脑实质中用于治疗严重的脑部疾病已经在被考虑,并且正在被考虑。然而,个体大脑以及大脑内不同的细胞构筑区域在对流体流动和压力的反应方面存在差异。此外,组织对这些刺激会做出动态响应,这就需要对描述大脑中流体流动和药物传输的方程进行非线性处理。我们在这里详细报告了一个个体特异性模型,并将其预测与活体猪脑的模拟进行了比较。我们的模型引入了两个关键特征——以前的模型中没有这些特征,但对于任何有用的模拟都是必需的——即输注诱导的细胞间隙扩张和回流。这些是流动的重要决定因素。我们处理方法的另一个特征是使用交叉属性关系来获得个体特异性参数,这些参数是方程中的系数。定量结果至少令人鼓舞,表明示踪分子的计算和测量分布体积之间有很高的重叠部分,并且具有潜在的临床应用价值。需要进行一些改进;主要是更基于多孔弹性力学的细胞间隙扩张处理以及更好地描述限制在细胞间隙中的粒子的扩散张量。

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