Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Anal Chem. 2024 May 14;96(19):7360-7366. doi: 10.1021/acs.analchem.4c00291. Epub 2024 May 2.
During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.
在全球范围内已经见证超过 7.72 亿例确诊病例和超过 600 万人死亡的 2019 年冠状病毒病（COVID-19）大流行期间，COVID-19 的爆发成为了一个影响富贫国家的重大医学挑战。因此，迫切需要探索疾病机制并实施快速检测方法。为了解决这个问题，我们使用解吸分离电离（DSI）装置结合质谱仪来高效检测和筛选 COVID-19 尿液样本。该研究纳入了 COVID-19 患者、健康对照（HC）和其他类型肺炎（OP）患者，以评估他们的尿液代谢组学图谱。随后，我们确定了 COVID-19 患者中差异表达的代谢物，并认识到氨基酸代谢是主要涉及的代谢途径。此外，多种已建立的机器学习算法验证了代谢物在区分 COVID-19 组与健康受试者方面的出色表现，在盲测试集的曲线下面积为 0.932。这项研究共同表明，使用 DSI 设备从尿液中检测到的小分子代谢物可以快速筛查 COVID-19，每个样本只需三分钟。这种方法有可能扩展我们对 COVID-19 病理生理机制的理解，并为通过机器学习算法快速筛查 COVID-19 患者提供了一种途径。
