ABSTRACT
Understanding the dynamic changes in antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for evaluating the effectiveness of the vaccine and the stage for the recovery of the COVID-19 disease. A rapid and accurate method for the detection of SARS-CoV-2-specific antibodies is still urgently needed. Here, we developed a novel fluorescent lateral flow immunoassay (LFA) platform for the detection of SARS-CoV-2-specific IgM and IgG by the aggregation-induced emission carbon dots conjugated with the SARS-CoV-2 spike protein (SSP). The aggregation-induced emission carbon dots (AIE-CDs) are one of the best prospect fluorescent probe materials for exhibiting high emission efficiency in both aggregate and solid states. The AIE-CDs were synthesized and displayed dual fluorescence emission, which provides a new perspective for the design of a high sensitivity testing system. In this work, the novel LFA platform adopted the AIE carbon dots, which are used to detect SARS-CoV-2-specific IgM and IgG conveniently. Furthermore, this sensor had a low LOD of 100 pg/ml. Therefore, this newly developed strategy has potential applications in the areas of public health for the advancement of clinical research.
ABSTRACT
Understanding the dynamic changes in antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for evaluating the effectiveness of the vaccine and the stage for the recovery of the COVID-19 disease. A rapid and accurate method for the detection of SARS-CoV-2-specific antibodies is still urgently needed. Here, we developed a novel fluorescent lateral flow immunoassay (LFA) platform for the detection of SARS-CoV-2-specific IgM and IgG by the aggregation-induced emission carbon dots conjugated with the SARS-CoV-2 spike protein (SSP). The aggregation-induced emission carbon dots (AIE-CDs) are one of the best prospect fluorescent probe materials for exhibiting high emission efficiency in both aggregate and solid states. The AIE-CDs were synthesized and displayed dual fluorescence emission, which provides a new perspective for the design of a high sensitivity testing system. In this work, the novel LFA platform adopted the AIE carbon dots, which are used to detect SARS-CoV-2-specific IgM and IgG conveniently. Furthermore, this sensor had a low LOD of 100 pg/ml. Therefore, this newly developed strategy has potential applications in the areas of public health for the advancement of clinical research.
ABSTRACT
The coronavirus disease of 2019 (COVID-19) has evolved into a worldwide pandemic. Although CT is sensitive in detecting lesions and assessing their severity, these works mainly depend on radiologists' subjective judgment, which is inefficient in case of a large-scale outbreak. This work focuses on developing a CT-based radiomics model to assess whether COVID-19 patients are in the early, progressive, severe, or absorption stages of the disease. We retrospectively analyzed the CT images of 284 COVID-19 patients. All of the patients were divided into four groups (0-3): early (n = 75), progressive (n = 58), severe (n = 75), and absorption (n = 76) groups, according to the progression of the disease and the CT features. Meanwhile, they were split randomly to training and test datasets with the fixed ratio of 7 : 3 in each category. Thirty-eight radiomic features were nominated from 1688 radiomic features after using select K-best method and the ElasticNet algorithm. On this basis, a support vector machine (SVM) classifier was trained to build this model. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic performance of various models. The precision, recall, and f 1-score of the classification model of macro- and microaverage were 0.82, 0.82, 0.81, 0.81, 0.81, and 0.81 for the training dataset and 0.75, 0.73, 0.73, 0.72, 0.72, and 0.72 for the test dataset. The AUCs for groups 0, 1, 2, and 3 on the training dataset were 0.99, 0.97, 0.96, and 0.93, and the microaverage AUC was 0.97 with a macroaverage AUC of 0.97. On the test dataset, AUCs for each group were 0.97, 0.86, 0.83, and 0.89 and the microaverage AUC was 0.89 with a macroaverage AUC of 0.90. The CT-based radiomics model proved efficacious in assessing the severity of COVID-19.