Machine learning chained neural network analysis of oxygen transport amplifies the physiological relevance of vascularized microphysiological systems.
Machine learning chained neural network analysis of oxygen transport amplifies the physiological relevance of vascularized microphysiological systems.
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DOI：
10.1002/btm2.10582
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发表时间：
2023-11
影响因子：
7.4
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中科院分区：
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由于每个生物系统都需要毛细血管来支持其氧合，因此此类系统的工程化临床前模型（例如，血管化微生理系统（vMPS））的设计已经引起了人们的关注，从而增强了人类生物学和治疗的生理相关性。但是，vMPS中成形血管的生理学和功能目前通过非标准化、用户依赖性和简单的形态学指标进行评估，这些指标与器官氧合的基本功能关系不大。在这里，链式神经网络使用从代表影响组织的血管网络架构的因素的随机组合的vMPS的不同集合导出的形态度量来设计和训练。这种机器学习算法输出一个奇异的度量，称为血管网络质量指数（VNQI）。形态学指标和VNQI与vMPS内测得的氧气水平的交叉相关性显示，VNQI与氧气测量值的相关性最大。VNQI对血管网络的决定因素敏感，并且它始终比单独的形态学指标更好地与测得的氧相关。最后，VNQI与细胞移植治疗的功能结果呈正相关，如缺氧挑战的血管化胰岛芯片所示。因此，采用这种工具将扩大预测，并使器官芯片，移植模型和其他细胞生物系统标准化。
Since every biological system requires capillaries to support its oxygenation, design of engineered preclinical models of such systems, for example, vascularized microphysiological systems (vMPS) have gained attention enhancing the physiological relevance of human biology and therapies. But the physiology and function of formed vessels in the vMPS is currently assessed by non‐standardized, user‐dependent, and simple morphological metrics that poorly relate to the fundamental function of oxygenation of organs. Here, a chained neural network is engineered and trained using morphological metrics derived from a diverse set of vMPS representing random combinations of factors that influence the vascular network architecture of a tissue. This machine‐learned algorithm outputs a singular measure, termed as vascular network quality index (VNQI). Cross‐correlation of morphological metrics and VNQI against measured oxygen levels within vMPS revealed that VNQI correlated the most with oxygen measurements. VNQI is sensitive to the determinants of vascular networks and it consistently correlates better to the measured oxygen than morphological metrics alone. Finally, the VNQI is positively associated with the functional outcomes of cell transplantation therapies, shown in the vascularized islet‐chip challenged with hypoxia. Therefore, adoption of this tool will amplify the predictions and enable standardization of organ‐chips, transplant models, and other cell biosystems.