Department of Tumor Biotherapy (5th Ward of the Department of Oncology), Anhui Provincial Cancer Hospital, West District of The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230031, Hefei, Anhui Province, People's Republic of China.
Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230001, Hefei, Anhui Province, People's Republic of China.
ACS Nano. 2020 Sep 22;14(9):12125-12132. doi: 10.1021/acsnano.0c05657. Epub 2020 Aug 27.
This article reports on a noninvasive approach in detecting and following-up individuals who are at-risk or have an existing COVID-19 infection, with a potential ability to serve as an epidemic control tool. The proposed method uses a developed breath device composed of a nanomaterial-based hybrid sensor array with multiplexed detection capabilities that can detect disease-specific biomarkers from exhaled breath, thus enabling rapid and accurate diagnosis. An exploratory clinical study with this approach was examined in Wuhan, China, during March 2020. The study cohort included 49 confirmed COVID-19 patients, 58 healthy controls, and 33 non-COVID lung infection controls. When applicable, positive COVID-19 patients were sampled twice: during the active disease and after recovery. Discriminant analysis of the obtained signals from the nanomaterial-based sensors achieved very good test discriminations between the different groups. The training and test set data exhibited respectively 94% and 76% accuracy in differentiating patients from controls as well as 90% and 95% accuracy in differentiating between patients with COVID-19 and patients with other lung infections. While further validation studies are needed, the results may serve as a base for technology that would lead to a reduction in the number of unneeded confirmatory tests and lower the burden on hospitals, while allowing individuals a screening solution that can be performed in PoC facilities. The proposed method can be considered as a platform that could be applied for any other disease infection with proper modifications to the artificial intelligence and would therefore be available to serve as a diagnostic tool in case of a new disease outbreak.
本文报道了一种无创方法，用于检测和随访有 COVID-19 感染风险或现有感染的个体，具有作为疫情控制工具的潜力。该方法使用一种由纳米材料基混合传感器阵列组成的开发呼吸设备，该传感器阵列具有多重检测能力，能够从呼出的呼吸中检测到疾病特异性生物标志物，从而实现快速准确的诊断。在 2020 年 3 月，在中国武汉对该方法进行了探索性临床研究。研究队列包括 49 名确诊的 COVID-19 患者、58 名健康对照者和 33 名非 COVID 肺部感染对照者。在适用的情况下，对阳性 COVID-19 患者进行了两次采样：在疾病活动期和恢复期。基于纳米材料的传感器获得的信号的判别分析在不同组之间实现了非常好的测试区分。训练集和测试集数据分别在区分患者和对照组方面表现出 94%和 76%的准确率，在区分 COVID-19 患者和其他肺部感染患者方面表现出 90%和 95%的准确率。虽然还需要进一步的验证研究，但这些结果可以作为一种技术的基础，这种技术可以减少不必要的确认性测试的数量，减轻医院的负担，同时为个人提供可以在 PoC 设施中进行的筛查解决方案。该方法可以被视为一种平台，可以应用于任何其他疾病感染，只需对人工智能进行适当的修改，因此在新的疾病爆发时可以作为一种诊断工具。
