研究设计:Covid-19-19肺疾病的人工智能引导的导航。(从上到下)步骤0:使用称为布尔等效相关群集(BECC133)的机器学习工具(BECC133)挖掘了超过45,000人,小鼠和大鼠基因表达数据库,以识别不变的宿主对病毒大流传学(VIP)的反应。在没有足够数量的COVID-19数据集中,在COVID-19大流行病开始时,这些VIP签名仅在过去的大流行中的两个数据集上进行了培训(流感和禽流感; GSE47963,GSE47963,n = 438;GSE113211,n = 118),没有进一步训练来预期分析当前大流行的样品(即Covid-19; n = 727个来自不同数据集的样品)。20代分类疾病严重程度的子集称为严重VIP(SVIP)签名。VIP签名似乎捕获了“不变”的宿主反应,即所有病毒大流行病引起的宿主免疫反应的共同基本性质,包括COVID-19。步骤1:对代表大量肺部疾病的多种转录组数据集进行了分析的VIP/SVIP特征和COV-LUNG特定13基因特征。这些努力确定COVID-19肺疾病是最接近特发性肺纤维化(IPF)的肺部疾病。两种条件都诱导了一系列基因特征。步骤2:先前在IPF27中验证的临床上有用的全血和PBMC衍生的预后特征显示在Covid-19中显示了交叉疗效,反之亦然。 Step 3: Gene signatures of alveolar type II (AT2) cytopathic changes that are known to fuel IPF were analyzed in COVID-19 lung, and predicted shared features were validated in human and hamster lungs and lung-organoid derived models. Step 4: Protein- protein interaction (PPI) network built using sViP and AT2 cytopathy-related signatures was analyzed to pinpoint ER stress as a major shared feature in COVID-19 lung disease and IPF, which was subsequently validated in human and hamster lungs. Credit:ebiomedicine(2022)。doi:10.1016/j.ebiom.2022.104185