吸入的弹性毛细血管网络模型
Elastocapillary network model of inhalation
摘要：
看似简单的吸气过程依赖于胸部肌肉收缩、单个肺分支中的弹性毛细血管相互作用、不同连接分支之间的空气传播以及进入肺部的总空气流量之间的复杂相互作用。这些过程发生在相当不同的长度和时间尺度上;因此，将它们与肺的生物力学特性联系起来，并量化它们如何共同控制吸入的时空特征，仍然是一个挑战。我们通过开发一个肺部的计算模型来解决这一挑战，该模型是一个分层的、分支的网络，由连接到粘弹性胸腔的液体衬里的柔性圆柱体组成。每个分支以由输入生物力学参数确定的速率和压力打开，使我们能够测试肺组织和分泌物的机械特性变化对吸入动力学的影响。通过对所有分支的动力学进行求和，我们量化了吸气过程中整体肺压和肺容量的演变，再现了测量的呼吸曲线的形状。使用这个模型，我们展示了肺肌肉收缩、粘液粘度和表面张力以及气道壁硬度的变化-许多呼吸系统疾病的特征，包括COVID-19、囊性纤维化、慢性阻塞性肺病、哮喘和肺气肿-如何极大地改变吸入肺容量和呼吸持续时间。因此，我们的工作有助于确定控制呼吸动力学的关键因素，并提供了一种方法来量化这些因素中疾病引起的变化如何导致呼吸窘迫。
英文摘要：
The seemingly simple process of inhalation relies on a complex interplay between muscular contraction in the thorax, elasto-capillary interactions in individual lung branches, propagation of air between different connected branches, and overall air flow into the lungs. These processes occur over considerably different length and time scales; consequently, linking them to the biomechanical properties of the lungs, and quantifying how they together control the spatiotemporal features of inhalation, remains a challenge. We address this challenge by developing a computational model of the lungs as a hierarchical, branched network of connected liquid-lined flexible cylinders coupled to a viscoelastic thoracic cavity. Each branch opens at a rate and a pressure that is determined by input biomechanical parameters, enabling us to test the influence of changes in the mechanical properties of lung tissues and secretions on inhalation dynamics. By summing the dynamics of all the branches, we quantify the evolution of overall lung pressure and volume during inhalation, reproducing the shape of measured breathing curves. Using this model, we demonstrate how changes in lung muscle contraction, mucus viscosity and surface tension, and airway wall stiffness---characteristic of many respiratory diseases, including those arising from COVID-19, cystic fibrosis, chronic obstructive pulmonary disease, asthma, and emphysema---drastically alter inhaled lung capacity and breathing duration. Our work therefore helps to identify the key factors that control breathing dynamics, and provides a way to quantify how disease-induced changes in these factors lead to respiratory distress.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
