Lafci Büyükkahraman Mehtap, Chen Houjia, Chen-Charpentier Benito M, Liao Jun, Kojouharov Hristo V
Department of Mathematics, Uşak University, Uşak 64200, Türkiye.
Department of Bioengineering, The University of Texas at Arlington, Arlington, TX 76010-0138, USA.
Bioengineering (Basel). 2025 Feb 13;12(2):177. doi: 10.3390/bioengineering12020177.
After myocardial infarction (MI), the heart undergoes necrosis, inflammation, scar formation, and remodeling. While restoring blood flow is crucial, it can cause ischemia-reperfusion (IR) injury, driven by reactive oxygen species (ROSs), which exacerbate cell death and tissue damage. This study introduces a mathematical model capturing key post-MI dynamics, including inflammatory responses, IR injury, cardiac remodeling, and stem cell therapy. The model uses nonlinear ordinary differential equations to simulate these processes under varying conditions, offering a predictive tool to understand MI pathophysiology better and optimize treatments.
After myocardial infarction (MI), left ventricular remodeling progresses through three distinct yet interconnected phases. The first phase captures the immediate dynamics following MI, prior to any medical intervention. This stage is mathematically modeled using the system of ordinary differential equations: The second and third stages of the remodeling process account for the system dynamics of medical treatments, including oxygen restoration and subsequent stem cell injection at the injury site.
We simulate heart tissue and immune cell dynamics over 30 days for mild and severe MI using the novel mathematical model under medical treatment. The treatment involves no intervention until 2 h post-MI, followed by oxygen restoration and stem cell injection at day 7, which is shown experimentallyand numerically to be optimal. The simulation incorporates a baseline ROS threshold (Rc) where subcritical ROS levels do not cause cell damage.
This study presents a novel mathematical model that extends a previously published framework by incorporating three clinically relevant parameters: oxygen restoration rate (ω), patient risk factors (γ), and neutrophil recruitment profile (δ). The model accounts for post-MI inflammatory dynamics, ROS-mediated ischemia-reperfusion (IR) injury, cardiac remodeling, and stem cell therapy. The model's sensitivity highlights critical clinical insights: while oxygen restoration is vital, excessive rates may exacerbate ROS-driven IR injury. Additionally, heightened patient risk factors (e.g., smoking, obesity) and immunodeficiency significantly impact tissue damage and recovery. This predictive tool offers valuable insights into MI pathology and aids in optimizing treatment strategies to mitigate IR injury and improve post-MI outcomes.
心肌梗死(MI)后,心脏会经历坏死、炎症、瘢痕形成和重塑过程。虽然恢复血流至关重要,但它会引发由活性氧(ROS)驱动的缺血再灌注(IR)损伤,进而加剧细胞死亡和组织损伤。本研究引入了一个数学模型,该模型捕捉了心肌梗死后的关键动态变化,包括炎症反应、IR损伤、心脏重塑和干细胞治疗。该模型使用非线性常微分方程来模拟不同条件下的这些过程,为更好地理解心肌梗死病理生理学和优化治疗提供了一种预测工具。
心肌梗死后,左心室重塑通过三个不同但相互关联的阶段进行。第一阶段捕捉心肌梗死后在任何医疗干预之前的即时动态变化。这个阶段使用常微分方程组进行数学建模:重塑过程的第二和第三阶段考虑了医疗治疗的系统动态变化,包括氧气恢复和随后在损伤部位注射干细胞。
我们使用新型数学模型在医疗治疗情况下模拟了轻度和重度心肌梗死30天内心脏组织和免疫细胞的动态变化。治疗方法是在心肌梗死后2小时内不进行干预,然后在第7天进行氧气恢复和干细胞注射,实验和数值结果均表明这是最佳方案。模拟纳入了一个基线ROS阈值(Rc),低于该阈值的ROS水平不会导致细胞损伤。
本研究提出了一个新型数学模型,该模型通过纳入三个临床相关参数扩展了先前发表的框架:氧气恢复率(ω)、患者风险因素(γ)和中性粒细胞募集情况(δ)。该模型考虑了心肌梗死后的炎症动态变化、ROS介导的缺血再灌注(IR)损伤、心脏重塑和干细胞治疗。该模型的敏感性突出了关键的临床见解:虽然氧气恢复至关重要,但过高的恢复率可能会加剧ROS驱动的IR损伤。此外,患者风险因素增加(如吸烟、肥胖)和免疫缺陷会显著影响组织损伤和恢复。这个预测工具为心肌梗死病理学提供了有价值的见解,并有助于优化治疗策略以减轻IR损伤并改善心肌梗死后的预后。