BACKGROUND
Biomarkers have been widely explored for COVID-19 diagnosis. Both viral RNA or antigens (Ag) in the respiratory system and antibodies (Ab) in blood are used to identify active infection, transmission risk, and immune response but have limitations. This study investigated the diagnostic utility of SARS-CoV-2 nucleocapsid protein (N-Ag) in serum.
METHODS
We retrospectively studied 208 randomly-selected cases with SARS-CoV-2 infection confirmed by viral RNA test in swabs. N-Ag concentrations were measured in remnant serum samples, compared to viral RNA or Ab results, and correlated to electronic health records for clinical value evaluation.
RESULTS
Serum N-Ag was detected during active infection as early as day 2 from symptom onset with a diagnostic sensitivity of 81.5%. Within one week of symptom onset, the diagnostic sensitivity and specificity reached 90.9% (95% CI, 85.1–94.6%) and 98.3% (95% CI, 91.1–99.9%), respectively. Moreover, serum N-Ag concentration closely correlated to disease severity, reflected by highest level of care, medical interventions, chest imaging, and the length of hospital stays. Longitudinal analysis revealed the simultaneous increase of Abs and decline of N-Ag.
CONCLUSIONS
Serum N-Ag is a biomarker for SARS-CoV-2 acute infection with high diagnostic sensitivity and specificity compared to viral RNA in the respiratory system. There is a correlation between serum N-Ag concentrations and disease severity and an inverse relationship of N-Ag and Abs. The diagnostic value of serum N-Ag, as well as technical and practical advantages it could offer, may meet unsatisfied diagnostic and prognostic needs during the pandemic.
Background: Bacterial vaginosis (BV) is caused by the excessive and imbalanced growth of bacteria in vagina, affecting 30-50% of women in their lives. Gram stain followed by Nugent scoring based on bacterial morphotypes under the microscope (NS) has been considered the golden standard for BV diagnosis, which is often labor-intensive, time-consuming, and variable results from person to person.
Methods: We developed and optimized a convolutional neural networks (CNN) model, and evaluated its ability to automatically identify and classify three categories of Nugent scores from microscope images. The CNN model was first established with a panel of microscopic images with Nugent scores determined by experts. The model was trained by minimizing the cross entropy loss function and optimized by using a momentum optimizer. The separate test sets of images collected from three hospitals were evaluated by the CNN models.
Results: The CNN model consisted of 25 convolutional layers, 2 pooling layers, and a fully connected layer. The model obtained 82.4% sensitivity and 96.6% specificity on the 5,815 validation images when considered altered vaginal flora and BV as the positive samples, which was better than the top-level technologists and obstetricians in China. The ability of generalization for our model was strong that it obtained 75.1% accuracy of three categories of Nugent scores on the independent test set of 1082 images, which was 6.6% higher than the average of three technologists, who are with a bachelor degree in medicine and eligible making diagnostic decisions. When three technologists ran one specimen in triplicate, the precision of three categories of Nugent scores was 54.0%. 103 samples diagnosed by two technologists at different days showed repeatability of 90.3%.
Conclusion: The CNN model over-performed human healthcare practitioners on accuracy and stability for three categories of Nugent scores diagnosis. The deep learning model may offer translational applications in automating diagnosis of bacterial vaginosis with proper supporting hardware.
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