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用于发现治疗策略的癫痫发生中神经免疫相互作用的数学模型。

A mathematical model of neuroimmune interactions in epileptogenesis for discovering treatment strategies.

作者信息

Batulin Danylo, Lagzi Fereshteh, Vezzani Annamaria, Jedlicka Peter, Triesch Jochen

机构信息

Frankfurt Institute for Advanced Studies, Frankfurt 60438, Germany.

Faculty of Computer Science and Mathematics, Goethe University, Frankfurt 60486, Germany.

出版信息

iScience. 2022 May 4;25(6):104343. doi: 10.1016/j.isci.2022.104343. eCollection 2022 Jun 17.

Abstract

The development of epilepsy (epileptogenesis) involves a complex interplay of neuronal and immune processes. Here, we present a first-of-its-kind mathematical model to better understand the relationships among these processes. Our model describes the interaction between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model reproduces the available data from three animal models. The model successfully describes characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries or the existence of qualitatively different outcomes for varying injury intensity. In line with the concept of degeneracy, our simulations reveal multiple routes toward epilepsy with neuronal loss as a sufficient but non-necessary component. Finally, we show that our model allows for predictions of therapeutic strategies, revealing injury-specific therapeutic targets and optimal time windows for intervention.

摘要

癫痫的发展(癫痫发生)涉及神经元和免疫过程的复杂相互作用。在此,我们提出了首个此类数学模型,以更好地理解这些过程之间的关系。我们的模型描述了神经炎症、血脑屏障破坏、神经元丢失、电路重塑和癫痫发作之间的相互作用。该模型被公式化为一个非线性微分方程组,再现了来自三种动物模型的现有数据。该模型成功地描述了癫痫发生的特征,例如尽管损伤短暂且瞬时,但癫痫发生的时间尺度却长得惊人(长达数十年),或者不同损伤强度会导致性质不同的结果。符合简并性概念,我们的模拟揭示了导致癫痫的多种途径,其中神经元丢失是一个充分但非必要的因素。最后,我们表明我们的模型能够预测治疗策略,揭示针对特定损伤的治疗靶点和最佳干预时间窗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0f/9121278/3fa2731fd7c8/fx1.jpg

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