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The Connected Steady State Model and the Interdependence of the CSF Proteome and CSF Flow Characteristics

机译:连通稳态模型以及CSF蛋白质组和CSF流动特性的相互依赖性

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摘要

Here we show that the hydrodynamic radii-dependent entry of blood proteins into cerebrospinal fluid (CSF) can best be modeled with a diffusional system of consecutive interdependent steady states between barrier-restricted molecular flux and bulk flow of CSF. The connected steady state model fits precisely to experimental results and provides the theoretical backbone to calculate the in-vivo hydrodynamic radii of blood-derived proteins as well as individual barrier characteristics. As the experimental reference set we used a previously published large-scale patient cohort of CSF to serum quotient ratios of immunoglobulins in relation to the respective albumin quotients. We related the inter-individual variances of these quotient relationships to the individual CSF flow time and barrier characteristics. We claim that this new concept allows the diagnosis of inflammatory processes with Reibergrams derived from population-based thresholds to be shifted to individualized judgment, thereby improving diagnostic sensitivity. We further use the source-dependent gradient patterns of proteins in CSF as intrinsic tracers for CSF flow characteristics. We assume that the rostrocaudal gradient of blood-derived proteins is a consequence of CSF bulk flow, whereas the slope of the gradient is a consequence of the unidirectional bulk flow and bidirectional pulsatile flow of CSF. Unlike blood-derived proteins, the influence of CSF flow characteristics on brain-derived proteins in CSF has been insufficiently discussed to date. By critically reviewing existing experimental data and by reassessing their conformity to CSF flow assumptions we conclude that the biomarker potential of brain-derived proteins in CSF can be improved by considering individual subproteomic dynamics of the CSF system.
机译:在这里,我们显示,血屏障蛋白的动力学动力学依赖于血液进入脑脊液(CSF)的进入,可以用屏障限制分子通量和脑脊液整体流量之间连续的相互依赖的稳态扩散系统进行建模。连接的稳态模型与实验结果完全吻合,并为计算血液衍生蛋白的体内流体动力学半径以及各个屏障特性提供了理论基础。作为实验参考集,我们使用了先前发表的大规模患者队列中的CSF与免疫球蛋白的血清商比相对于各自白蛋白商的比率。我们将这些商之间的个体差异与各个CSF流动时间和屏障特性相关联。我们声称,这个新概念可以将基于群体阈值的Reibergrams对炎症过程的诊断转移到个性化判断,从而提高诊断敏感性。我们进一步使用CSF中蛋白质的源依赖梯度模式作为CSF流动特征的内在示踪剂。我们假设血液衍生蛋白的尾脑尾端梯度是CSF整体流的结果,而梯度的斜率是CSF的单向整体流和双向脉动流的结果。迄今为止,与血液来源的蛋白质不同,脑脊液流动特性对脑脊液中脑源性蛋白质的影响尚未得到充分讨论。通过批判性地审查现有的实验数据并通过重新评估它们与CSF流量假设的一致性,我们得出结论,可以通过考虑CSF系统的个体蛋白质组学动力学来改善脑源性蛋白在CSF中的生物标志物潜力。

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