Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Catalonia, Spain.
Departament de Física, Universitat Politècnica de Catalunya, Castelldefels, Barcelona, Catalonia, Spain.
PLoS Comput Biol. 2020 May 20;16(5):e1007772. doi: 10.1371/journal.pcbi.1007772. eCollection 2020 May.
Tuberculosis (TB) is an infectious disease that still causes more than 1.5 million deaths annually. The World Health Organization estimates that around 30% of the world's population is latently infected. However, the mechanisms responsible for 10% of this reserve (i.e., of the latently infected population) developing an active disease are not fully understood, yet. The dynamic hypothesis suggests that endogenous reinfection has an important role in maintaining latent infection. In order to examine this hypothesis for falsifiability, an agent-based model of growth, merging, and proliferation of TB lesions was implemented in a computational bronchial tree, built with an iterative algorithm for the generation of bronchial bifurcations and tubes applied inside a virtual 3D pulmonary surface. The computational model was fed and parameterized with computed tomography (CT) experimental data from 5 latently infected minipigs. First, we used CT images to reconstruct the virtual pulmonary surfaces where bronchial trees are built. Then, CT data about TB lesion' size and location to each minipig were used in the parameterization process. The model's outcome provides spatial and size distributions of TB lesions that successfully reproduced experimental data, thus reinforcing the role of the bronchial tree as the spatial structure triggering endogenous reinfection. A sensitivity analysis of the model shows that the final number of lesions is strongly related with the endogenous reinfection frequency and maximum growth rate of the lesions, while their mean diameter mainly depends on the spatial spreading of new lesions and the maximum radius. Finally, the model was used as an in silico experimental platform to explore the transition from latent infection to active disease, identifying two main triggering factors: a high inflammatory response and the combination of a moderate inflammatory response with a small breathing amplitude.
结核病（TB）是一种传染病，每年仍导致超过 150 万人死亡。世界卫生组织估计，全球约有 30%的人口受到潜伏感染。然而，导致这一储备量的 10%（即潜伏感染人群）中的 10%发展为活动性疾病的机制尚未完全了解。动态假说表明，内源性再感染在维持潜伏感染方面起着重要作用。为了检验该假说的可证伪性，我们在一个计算支气管树中实现了一个结核病病变的生长、合并和增殖的基于代理的模型，该模型使用分枝算法生成支气管分叉和管腔，应用于虚拟 3D 肺部表面内。计算模型使用来自 5 头潜伏感染小型猪的 CT 实验数据进行了补充和参数化。首先，我们使用 CT 图像来重建构建支气管树的虚拟肺部表面。然后，将每个小型猪的 TB 病变大小和位置的 CT 数据用于参数化过程。该模型的结果提供了 TB 病变的空间和大小分布，成功再现了实验数据，从而加强了支气管树作为触发内源性再感染的空间结构的作用。对模型的敏感性分析表明，病变的最终数量与内源性再感染频率和病变的最大增长率密切相关，而其平均直径主要取决于新病变的空间传播和最大半径。最后，该模型被用作一种虚拟实验平台，探索从潜伏感染到活动性疾病的转变，确定了两个主要触发因素：强烈的炎症反应和中度炎症反应与小呼吸幅度的结合。
