Community-acquired pneumonia (CAP) has been brought to the forefront of global health priorities due to the COVID-19 pandemic. However, classification of viral versus bacterial pneumonia etiology remains a significant clinical challenge. To this end, we have engineered a panel of activity-based nanosensors that detect the dysregulated activity of pulmonary host proteases implicated in the response to pneumonia-causing pathogens and produce a urinary readout of disease. The nanosensor targets were selected based on a human protease transcriptomic signature for pneumonia etiology generated from 33 unique publicly available study cohorts. Five mouse models of bacterial or viral CAP were developed to assess the ability of the nanosensors to produce etiology-specific urinary signatures. Machine learning algorithms were used to train diagnostic classifiers that could distinguish infected mice from healthy controls and differentiate those with bacterial versus viral pneumonia with high accuracy. This proof-of-concept diagnostic approach demonstrates a way to distinguish pneumonia etiology based solely on the host proteolytic response to infection.

中文翻译：

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宿主蛋白酶活性对肺炎病因进行分类。

由于 COVID-19 大流行，社区获得性肺炎 (CAP) 已成为全球卫生优先事项的重中之重。然而，病毒性和细菌性肺炎病因的分类仍然是一个重大的临床挑战。为此，我们设计了一组基于活性的纳米传感器，用于检测肺宿主蛋白酶的失调活性，这些蛋白酶与对引起肺炎的病原体的反应有关，并产生疾病的尿液读数。纳米传感器目标是根据 33 个独特的公开研究队列产生的肺炎病因的人类蛋白酶转录组学特征选择的。开发了五种细菌或病毒 CAP 小鼠模型，以评估纳米传感器产生病因特异性尿液特征的能力。机器学习算法被用来训练诊断分类器，这些分类器可以区分受感染的小鼠和健康对照，并以高精度区分细菌性肺炎和病毒性肺炎。这种概念验证诊断方法展示了一种仅基于宿主对感染的蛋白水解反应来区分肺炎病因的方法。