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首页> 外文期刊>Annals of Biomedical Engineering: The Journal of the Biomedical Engineering Society >Mathematical modeling of guided neurite extension in an engineered conduit with multiple concentration gradients of nerve growth factor (NGF).
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Mathematical modeling of guided neurite extension in an engineered conduit with multiple concentration gradients of nerve growth factor (NGF).

机译:具有多个神经生长因子(NGF)浓度梯度的工程导管中导向神经突延伸的数学模型。

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摘要

Neurotrophic factors such as nerve growth factor (NGF) provide essential cues to navigate growing axon toward their targets. Concentration and concentration gradient of NGF are key parameters affecting the growth rate and direction of neurites and axons. However, the maximum distance for guided nerve growth under stimulation of a single concentration gradient is limited and is thus unfavorable in nerve regeneration. Since the sensitivity of PC12 cells to NGF signals is restorable even after brief removal of the factors, exposure to multiple concentration gradients of the factor can achieve longer distances and greater rates of guided growth. In this study, a mathematical model simulating nerve growth in a virtually constructed nerve conduit incorporating multiple NGF concentration gradients is established. Using a genetic algorithm, optimized initial profiles of NGF able to achieve 4.5 cm of guided growth with a significantly improved growth rate has been obtained. The model also predicts an inverse relationship between the diffusion coefficient of the factor and the neurite growth rate. This model provides a useful tool for evaluating various conduit designs before fabrication and evaluation.
机译:神经营养因子(例如神经生长因子(NGF))为引导生长的轴突朝向其靶标提供了必要的线索。 NGF的浓度和浓度梯度是影响神经突和轴突生长速度和方向的关键参数。然而,在单个浓度梯度的刺激下引导神经生长的最大距离受到限制,因此不利于神经再生。由于即使短暂去除因子后,PC12细胞对NGF信号的敏感性仍可恢复,因此暴露于因子的多个浓度梯度可以实现更长的距离和更大的引导生长速率。在这项研究中,建立了一个模拟模型,该模型在包含多个NGF浓度梯度的虚拟构造的神经导管中模拟了神经的生长。使用遗传算法,已获得了能够实现4.5 cm指导生长且生长速率显着提高的NGF的优化初始轮廓。该模型还预测了因子的扩散系数与神经突生长速率之间的反比关系。该模型为在制造和评估之前评估各种导管设计提供了有用的工具。

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