Department of Mathematics, University of Texas at Arlington, Arlington, USA.
Department of Mathematics, East China University of Science and Technology, Shanghai, China.
J Transl Med. 2019 Sep 6;17(1):306. doi: 10.1186/s12967-019-2030-0.
Identifying how pain transitions from acute to chronic is critical in designing effective prevention and management techniques for patients' well-being, physically, psychosocially, and financially. There is an increasingly pressing need for a quantitative and predictive method to evaluate how low back pain trajectories are classified and, subsequently, how we can more effectively intervene during these progression stages.
In order to better understand pain mechanisms, we investigated, using computational modeling, how best to describe pain trajectories by developing a platform by which we studied the transition of acute chronic pain.
The present study uses a computational neuroscience-based method to conduct such trajectory research, motivated by the use of hypothalamic-pituitary-adrenal (HPA) axis activity-history over a time-period as a way to mimic pain trajectories. A numerical simulation study is presented as a "proof of concept" for this modeling approach.
This model and its simulation results have highlighted the feasibility and the potential of developing such a broader model for patient evaluations.
在设计针对患者身心健康、社会心理和经济方面的有效预防和管理技术时，确定疼痛如何从急性转变为慢性至关重要。因此，我们迫切需要一种定量和预测的方法来评估如何对腰痛轨迹进行分类，以及如何在这些进展阶段更有效地进行干预。
为了更好地了解疼痛机制，我们通过开发一个研究急性慢性疼痛转变的平台，利用计算建模研究如何更好地描述疼痛轨迹。
本研究使用基于计算神经科学的方法来进行这种轨迹研究，这是受到使用下丘脑-垂体-肾上腺（HPA）轴活动历史作为模拟疼痛轨迹的一种方式的启发。提出了一项数值模拟研究作为该建模方法的“概念验证”。
该模型及其模拟结果突出了为患者评估开发这种更广泛模型的可行性和潜力。
