论文标题：A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection

期刊：Applied Network Science

作者：Michael J. Pitcher, Ruth Bowness, Simon Dobson and Stephen H. Gillespie

发表时间：2018/08/23

数字识别码：10.1007/s41109-018-0091-2

原文链接：https://appliednetsci.springeropen.com/articles/10.1007/s41109-018-0091-2?utm_source=other&utm_medium=other&utm_content=null&utm_campaign=BSCN_2_DD_SO_Arti_Scinet

微信链接：https://mp.weixin.qq.com/s/6gdTVmczEv0AkqEBCRgbNg

虽然我们早就能够成功地治愈结核病，但治疗的持续时间很长。这导致病人往往难以严格遵守治疗方案，也意味着这种疾病的致死率居高不下。在接下来的这篇客座文章中，Michael J. Pitcher向我们介绍了他最近发表在Applied Network Science上的研究，该研究表明，模拟结核病感染的空间分布可能有助于开发出替代性的、为期更短的治疗方案。

虽然早在数十年前就有了针对结核病的有效疗法，但是每年还会有超过100万人死于结核病。目前标准的治疗方案是进行6个月的多药化疗，这一漫长的疗程带来了一个问题：患者由于难以获得治疗资源以及所用药物的副作用，因此很难坚持完成整个治疗方案。

无法坚持完成治疗是一个很棘手的难题，这不仅会导致疾病复发，更会导致残余细菌产生抗生素耐药性。因此，通过缩短治疗时间来保证患者能够完成治疗显得极为重要。

新疗法的试验是一个漫长而昂贵的过程，并且不一定能保证成功。因此，对研究人员来说，很重要的一点是预测哪种方案更有可能成功。但进行这样的预测就需要更深入地了解病理。

了解感染的分布和散播

结核病在感染过程中表现出了不同的分布情况。初期感染通常发生在肺部较浅、空气较多的区域。这种感染通常能够得到控制（但无法根除），使得疾病以无症状的方式潜伏。后初期或“再活化”的结核病发生在肺尖。了解了疾病分布的原因以及细菌在肺部环境中的传播路径，将有助于阐明结核病感染背后的复杂动力学，并使我们能够更准确地预测出哪种治疗方案会起效。

研究人员已经开发出了一种体内计算模型，该模型利用复合种群网络结构模拟整个肺环境结构，包括环境特征如气体流通、血液灌注和氧张力的空间差异性以及细菌传播路径，从而研究它们对结核病的传播和规模的影响。

肺结核模型应当考虑到空间异质性

该研究结果表明，肺内的差异会导致肺尖含有更利于细菌生长的环境，即氧张力的增加导致细菌复制代谢加快，血液灌注的减少则相对削弱了免疫反应，从而使细菌可以肆意地快速生长。

了解结核病发生的环境是改善治疗的重要一步。本研究表明，在结核病的肺模型中引入微小的空间异质性能够带来显著的影响，未来的模型应该考虑到肺内空间分布，以便更准确地模拟肺内病理学。

摘要：

Tuberculosis (TB) is an ancient disease that, although curable, still accounts for over 1 million deaths worldwide. Shortening treatment time is an important area of research but is hampered by the lack of models that mimic the full range of human pathology. TB shows distinct localisations during different stages of infection, the reasons for which are poorly understood. Greater understanding of how heterogeneity within the human lung influences disease progression may hold the key to improving treatment efficiency and reducing treatment times.

In this work, we present a novel in silico software model which uses a networked metapopulation incorporating both spatial heterogeneity and dissemination possibilities to simulate a TB infection over the whole lung and associated lymphatics. The entire population of bacteria and immune cells is split into a network of patches: members interact within patches and are able to move between them. Patches and edges of the lung network include their own environmental attributes which influence the dynamics of interactions between the members of the subpopulations of the patches and the translocation of members along edges.

In this work, we detail the initial findings of a whole-organ model that incorporates distinct spatial heterogeneity features which are not present in standard differential equation approaches to tuberculosis modelling. We show that the inclusion of heterogeneity within the lung landscape when modelling TB disease progression has significant outcomes on the bacterial load present: a greater differential of oxygen, perfusion and ventilation between the apices and the basal regions of the lungs creates micro-environments at the apex that are more preferential for bacteria, due to increased oxygen availability and reduced immune activity, leading to a greater overall bacterial load present once latency is established.

These findings suggest that further whole-organ modelling incorporating more sophisticated heterogeneities within the environment and complex lung topologies will provide more insight into the environments in which TB bacteria persist and thus help develop new treatments which are factored towards these environmental conditions.

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https://appliednetsci.springeropen.com/articles/10.1007/s41109-018-0091-2?utm_source=other&utm_medium=other&utm_content=null&utm_campaign=BSCN_2_DD_SO_Arti_Scinet

期刊介绍:

Applied Network Science (ANS) (https://appliednetsci.springeropen.com/) is an open-access and strictly peer-reviewed journal giving researchers and practitioners in the field the ability to reach a larger audience. ANS encompasses all established and emerging fields that have been or can be shown to benefit from quantitative network-based modeling. Contributions from all fields of science, technology, medicine and humanities will be considered, in particular from newly emerging research areas formed and developing at the interfaces of presently established sub-disciplines.

（来源：科学网）