Progress of the COVID-19: Persistence, Effectiveness, and Immune Escape of the Neutralizing Antibody in Convalescent Serum.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a new coronavirus causing Coronavirus Disease 2019 (COVID-19), is a major topic of global human health concern. The Delta and Omicron variants have caused alarming responses worldwide due to their high transmission rates and a number of mutations. During a one-year follow-up (from June 2020 to June 2021), we included 114 patients with SARS-CoV-2 infection to study the long-term dynamics and the correlative factors of neutralizing antibodies (NAbs) in convalescent patients. The blood samples were collected at two detection time points (at 6 and 12 months after discharge). We evaluated the NAbs response of discharged patients by performing a micro-neutralization assay using a SARS-CoV-2 wild type. In addition, a total of 62 serum samples from discharged COVID-19 patients with Alpha, Beta, Delta, and Omicron variants of infection were enrolled to perform cross-neutralization tests using the original SARS-CoV-2 strain and VOCs variants (including Alpha, Beta, Gamma, Delta, and Omicron variants) and to assess the ability of NAbs against the SARS-CoV-2 variants. NAbs seroconversion occurred in 91.46% of patients (n = 82) in the first timepoint and in 89.29% of patients (n = 84) in the second detection point, and three kinds of NAbs kinetics curves were perceived. The NAbs levels in young patients had higher values than those in elder patients. The kinetics of disease duration was accompanied by an opposite trend in NAbs levels. Despite a declining NAbs response, NAbs activity was still detectable in a substantial proportion of recovered patients one year after discharge. Compared to the wild strain, the Omicron strain could lead to a 23.44-, 3.42-, 8.03-, and 2.57-fold reduction in neutralization capacity in "SAlpha", "SBeta", "SDelta", and "SOmicron", respectively, and the NAbs levels against the Omicron strain were significantly lower than those of the Beta and Delta variants. Remarkably, the NAbs activity of convalescent serum with Omicron strain infection was most obviously detectable against six SARS-CoV-2 strains in our study. The role of the vaccination history in NAbs levels further confirmed the previous study that reported vaccine-induced NAbs as the convincing protection mechanism against SARS-CoV-2. In conclusion, our findings highlighted the dynamics of the long-term immune responses after the disappearance of symptoms and revealed that NAbs levels varied among all types of convalescent patients with COVID-19 and that NAbs remained detectable for one year, which is reassuring in terms of protection against reinfection. Moreover, a moderate correlation between the duration of disease and Nabs titers was observed, whereas age was negatively correlated with Nabs titers. On the other hand, compared with other VOCs, the Omicron variant was able to escape the defenses of the immune system more significantly, and the convalescent serum infected with the Omicron variant played a critical part in protection against different SARS-CoV-2 variants. Recovery serum from individuals vaccinated with inactivated vaccine preceding infection with the Omicron strain had a high efficacy against the original strain and the VOCs variants, whereas the convalescent serum of persons vaccinated by inactivated vaccine prior to infection with the Delta variant was only potent against the wild-type strain.

Single-dose rAAV5-based vaccine provides long-term protective immunity against SARS-CoV-2 and its variants.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus and its variants, has posed unprecedented challenges worldwide. Existing vaccines have limited effectiveness against SARS-CoV-2 variants. Therefore, novel vaccines to match mutated viral lineages by providing long-term protective immunity are urgently needed. We designed a recombinant adeno-associated virus 5 (rAAV5)-based vaccine (rAAV-COVID-19) by using the SARS-CoV-2 spike protein receptor binding domain (RBD-plus) sequence with both single-stranded (ssAAV5) and self-complementary (scAAV5) delivery vectors and found that it provides excellent protection from SARS-CoV-2 infection. A single-dose vaccination in mice induced a robust immune response; induced neutralizing antibody (NA) titers were maintained at a peak level of over 1:1024 more than a year post-injection and were accompanied by functional T-cell responses. Importantly, both ssAAV- and scAAV-based RBD-plus vaccines produced high levels of serum NAs against the circulating SARS-CoV-2 variants, including Alpha, Beta, Gamma and Delta. A SARS-CoV-2 virus challenge showed that the ssAAV5-RBD-plus vaccine protected both young and old mice from SARS-CoV-2 infection in the upper and lower respiratory tracts. Whole genome sequencing demonstrated that AAV vector DNA sequences were not found in the genomes of vaccinated mice one year after vaccination, demonstrating vaccine safety. These results suggest that the rAAV5-based vaccine is safe and effective against SARS-CoV-2 and several variants as it provides long-term protective immunity. This novel vaccine has a significant potential for development into a human prophylactic vaccination to help end the global pandemic.

Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network.

PURPOSE: COVID-19 is a hot issue right now, and it's causing a huge number of infections in people, posing a grave threat to human life. Deep learning-based image diagnostic technology can effectively enhance the deficiencies of the current main detection method. This paper proposes a multi-classification model diagnosis based on segmentation and classification multi-task. METHOD: In the segmentation task, the end-to-end DRD U-Net model is used to segment the lung lesions to improve the ability of feature reuse and target segmentation. In the classification task, the model combined with WGAN and Deep Neural Network classifier is used to effectively solve the problem of multi-classification of COVID-19 images with small samples, to achieve the goal of effectively distinguishing COVID-19 patients, other pneumonia patients, and normal subjects. RESULTS: Experiments are carried out on common X-ray image and CT image data sets. The results display that in the segmentation task, the model is optimal in the key indicators of DSC and HD, and the error is increased by 0.33% and reduced by 3.57 mm compared with the original network U-Net. In the classification task, compared with SMOTE oversampling method, accuracy increased from 65.32% to 73.84%, F-measure increased from 67.65% to 74.65%, G-mean increased from 66.52% to 74.37%. At the same time, compared with other classical multi-task models, the results also have some advantages. CONCLUSION: This study provides new possibilities for COVID-19 image diagnosis methods, improves the accuracy of diagnosis, and hopes to provide substantial help for COVID-19 diagnosis.