Digital Magnetic Detection of Biomolecular Interactions with Single Nanoparticles.

Biomolecular interactions compose a fundamental element of all life forms and are the biological basis of many biomedical assays. However, current methods for detecting biomolecular interactions have limitations in sensitivity and specificity. Here, using nitrogen-vacancy centers in diamond as quantum sensors, we demonstrate digital magnetic detection of biomolecular interactions with single magnetic nanoparticles (MNPs). We first developed a single-particle magnetic imaging (SiPMI) method on 100 nm-sized MNPs with negligible magnetic background, high signal stability, and accurate quantification. The single-particle method was performed on biotin-streptavidin interactions and DNA-DNA interactions in which a single-base mismatch was specifically differentiated. Subsequently, SARS-CoV-2-related antibodies and nucleic acids were examined by a digital immunomagnetic assay derived from SiPMI. In addition, a magnetic separation process improved the detection sensitivity and dynamic range by more than 3 orders of magnitude and also the specificity. This digital magnetic platform is applicable to extensive biomolecular interaction studies and ultrasensitive biomedical assays.

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model.

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.