ABSTRACT
Background: SARS-CoV-2 antigen-based tests are well-calibrated to infectiousness and have a critical role to play in the COVID-19 public health response. We report the development and performance of a unique lateral flow immunoassay (LFA). Methods: Combinations of several monoclonal antibodies targeting multiple antigenic sites on the SARS-CoV-2 nucleocapsid protein (NP) were isolated, evaluated, and chosen for the development of a LFA termed CoV-SCAN (BioMedomics, Inc.). Clinical point-of-care studies in symptomatic and asymptomatic individuals were conducted to evaluate positive predictive agreement (PPA) and negative predictive agreement (NPA) with RT-PCR as comparator. Results: In laboratory testing, CoV-SCAN detected 14 recombinant N-proteins of SARS-CoV-2 variants with sensitivity in the range of 0.2-3.2 ng/mL, and 10 authentic SARS-CoV-2 variants with sensitivity in the range of 1.6-12.5 TCID50/swab. No cross reactivity was observed with other human coronaviruses or other respiratory pathogens. In clinical point-of-care testing on 148 individuals over age 2 with symptoms of ≤5 days, PPA was 87.2% (CI 95: 78.3-94.8%) and NPA was 100% (CI 95: 94.2-100%). In another 884 asymptomatic individuals, PPA was 85.7% (CI 95: 42.1-99.6%) and 99.7% (99.0-99.9%). Overall, CoV-SCAN detected over 97.2% of specimens with CT values <30 and 93.8% of nasal swab specimens with the Omicron variant, even within the first 2 days after symptom onset. Conclusions: The unique construction of CoV-SCAN using two pairs of monoclonal antibodies has resulted in a test with high performance that remains durable across multiple variants in both laboratory and clinical evaluations. CoV-SCAN should identify almost all individuals harboring infectious SARS-CoV-2. Summary: Unique construction of a point-of-care rapid antigen test using two pairs of monoclonal antibodies has led to good performance that remained durable across multiple variants in laboratory and clinical evaluations. Test should identify almost all individuals harboring infectious SARS-CoV-2.
ABSTRACT
Recent advances in the interdisciplinary scientific field of machine perception, computer vision, and biomedical engineering underpin a collection of machine learning algorithms with a remarkable ability to decipher the contents of microscope and nanoscope images. Machine learning algorithms are transforming the interpretation and analysis of microscope and nanoscope imaging data through use in conjunction with biological imaging modalities. These advances are enabling researchers to carry out real-time experiments that were previously thought to be computationally impossible. Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning, Darwin's Neural Network (DNN), to carry out morphometric analysis and classification of COVID19 and MERS-CoV collected in vivo and of multiple mammalian cell types in vitro.