AI 中文总结
本研究构建多尺度计算框架，将ABM与PDE模型耦合，模拟NTM肺部感染中黏液纤毛清除与免疫动力学，识别关键调控参数并揭示黏液溶解疗法的非线性效应，为个性化治疗提供平台。
AI 中文摘要
非结核分枝杆菌（NTM）感染是囊性纤维化（CF）中的临床挑战，在该疾病中，尽管存在宿主免疫应答，但黏液纤毛清除功能受损和黏液流变学改变促进了细菌定植。由于细菌生长、免疫细胞动力学和黏液转运这些过程在空间和时间尺度上相互作用，因此理解它们如何调控感染进展十分困难。我们开发了一个计算框架，将NTM感染的机制性基于智能体模型（ABM）与空间分辨的偏微分方程（PDE）模型相衔接。该PDE模型在代表黏液和肺组织的双隔室几何结构中，耦合了细菌增殖、巨噬细胞趋化、免疫介导的清除、黏液降解和黏弹性转运。参数使用已建立的ABM数据进行校准，在保留细胞机制的同时产生高效的连续介质表示。该PDE模型再现了细菌和巨噬细胞动力学，并能够分析与黏液相关的机制和疗法。敏感性分析确定黏液黏度、细菌扩散率和巨噬细胞迁移率是通过黏液纤毛清除和肺组织定植调控细菌持续存在的关键因素。模拟揭示了黏液溶解疗法的非线性效应：增强的清除减少了黏液中的细菌负荷，而过度的黏度降低可能促进细菌向肺组织迁移，支持与抗菌治疗联合使用。该框架为研究肺部感染、评估疗法和开发患者特异性数字孪生提供了定量平台。
英文摘要
Non-tuberculous mycobacterial (NTM) infections are a clinical challenge in cystic fibrosis (CF), where impaired mucociliary clearance and altered mucus rheology promote bacterial colonization despite host immune responses. Understanding how bacterial growth, immune cell dynamics, and mucus transport regulate infection progression is difficult because these processes interact across spatial and temporal scales. We develop a computational framework bridging a mechanistic agent-based model (ABM) of NTM infection with a spatially resolved partial differential equation (PDE) model. The PDE model couples bacterial proliferation, macrophage chemotaxis, immune-mediated clearance, mucus degradation, and viscoelastic transport in a two-compartment geometry representing mucus and lung tissue. Parameters are calibrated using data from the established ABM, yielding an efficient continuum representation while preserving cellular mechanisms. The PDE model reproduces bacterial and macrophage dynamics and enables analyses of mucus-related mechanisms and therapies. Sensitivity analysis identifies mucus viscosity, bacterial diffusivity, and macrophage mobility as key regulators of bacterial persistence through mucociliary clearance and tissue colonization. Simulations reveal nonlinear effects of mucolytic therapies: enhanced clearance reduces bacterial burden in mucus, whereas excessive viscosity reduction may promote migration into lung tissue, supporting combination with antibacterial treatment. This framework provides a quantitative platform for studying pulmonary infections, evaluating therapies, and developing patient-specific digital twins.
