Changes in interferon-γ release assay readout after COVID-19 vaccination: A prospective cohort study.

OBJECTIVES: Interferon-γ release assays (IGRAs) are widely used in public health practice to diagnose latent tuberculosis. During the COVID-19 pandemic and rollout of COVID-19 vaccination, it has remained unclear whether COVID-19 vaccines interfere with IGRA readouts. METHODS: We prospectively recruited healthcare workers during their annual occupational health examinations in 2021. Baseline IGRA readouts were compared with follow-up data after the participants had received two doses of COVID-19 vaccination. RESULTS: A total of 134 baseline IGRA-negative cases (92 with ChAdOx1 vaccine, 27 with mRNA-1273 vaccine, and 15 with heterologous vaccination) and seven baseline IGRA-positive cases were analyzed. Among the baseline IGRA-negative cases, there were decreased interferon-γ concentrations over the Nil (P = 0.005) and increased Mitogen-Nil (P < 0.001) values after vaccination. For TB2-Nil value, a similar trend (P = 0.057) of increase was observed. Compared with the 0.35 IU/ml threshold, the baseline and follow-up readout differences were less than |± 0.10| IU/ml over the TB1-Nil and TB2-Nil values in >90% baseline IGRA-negative cases. No significant readout difference was observed among baseline IGRA-positive cases. CONCLUSION: COVID-19 vaccination did not change IGRA interpretation in most cases. Cases showing conversion/borderline IGRA readouts should be given special consideration.

Neural Network-Based Ultra-High-Definition Video Live Streaming Optimization Algorithm

Online live streaming has been widely used in distant teaching, online live shopping, and so on. Particularly, online teaching live streaming breaks the time and space boundary of teaching and has better interactivity, which is a new distant education mode. As a new online sales model, online live shopping promotes the rapid development of Internet economy. However, the quality of live video affects the user experience. This paper studies the optimization algorithm of ultra-high-definition live streaming, focusing on superresolution technology. Convolutional neural network (CNN) is a multilayer artificial neural network designed to process two-dimensional input data. It takes advantage of CNN in image processing. This paper proposes an image superresolution algorithm based on hybrid dilated convolution and Laplacian pyramid. By mixing the dilated convolution module, the receptive field of the network can be improved more effectively to obtain more context information so that the high-frequency features of the image can be extracted more effectively. Experiment was running on Set5, Set14, Urban100, and BSD100 datasets, and the results reveal that the proposed algorithm outperforms baselines with respect to peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), and image quality.

High-Specificity Targets in SARS-CoV-2 N Protein for Serological Antibody Detection (preprint)

Background The current coronavirus disease (COVID-19) pandemic has created a pressing need to diagnose and screen a large number of close contacts of confirmed and suspected cases. Numerous nucleic acid detection kits are being rapidly developed and approved for viral etiological diagnosis; however, these are limited by the number of false negatives produced in clinical practice. Therefore, there is an urgent need to establish serological detection methods to serve as supplementary diagnostics.Methods We (1) performed a conservation and specificity analysis of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein, which is the main target of serological diagnosis; (2) integrated various B-cell epitope prediction methods to obtain possible dominant epitope regions for the N protein; (3) applied ELISA to analyze differences in the serological antibody levels for different epitopes; and (4) identified N protein epitopes for IgG and IgM with high specificity.Results SARS-CoV-2 strains showed low mutation rates for the N protein, and the construction of a phylogeny was a good characterization of its molecular evolutionary lineage in relation to other coronaviruses. SARS-CoV-2 showed the closest genetic relationship with SARS-CoV, which showed multiple consecutive long conserved regions at the amino acid level, but differed substantially from other coronaviruses. Tests targeting the SARS-CoV-2 N protein produced strong positive results in SARS-CoV patients in recovery. Of the five epitope dominant regions, using N18-39 and N183-197 for IgG and IgM detection, respectively, can effectively overcome the limitations of cross-reactivity.Conclusions The patients infected with both SARS viruses may exhibit cross-reactivity when using the N protein for antibody detection. However, there are regions of the N protein that can be used for antibody detection and some of these regions showed good specificity even between SARS-CoV-2 and SARS-CoV, and the antibody levels detected were consistent with those detected by the complete N protein. These findings provide a basis for serological diagnosis of SARS-CoV-2 patients, and research ideas for developing vaccines.

Association of CT Findings with clinical severity in Patients with COVID-19, a multicenter Cohort observational study (preprint)

Background 2019 Novel Coronavirus disease (COVID-19) may cause critical illness including severe pneumonia and acute respiratory distress syndrome. Our purpose is to was to analyze the radiological features of COVID-19 pneumonia and its association with clinical severity.Methods This retrospective study included 212 patients (122 males, Mean age, 45.6 ± 12.8 years) from 10 hospitals. Chest CT, chest X-ray (CXR), clinical and laboratory data at admission and follow-up CT were collected. Chest CT and CXR were reviewed and CT score of the involved lung was calculated.Results 94.3% patients had pneumonia on the baseline CT at admission. The most CT findings were as follows: GGO (140/200), GGO with consolidation (38/200) and consolidation (16/200) most involving the lower lobes with a predilection for the peripheral aspects. The CT score negatively correlated with Lymphocyte count while it positively correlated with C-reactive protein. ROC curve showed an optimal cutoff value of the CT score of 15 had a sensitivity of 70% and a specificity of 96.5% for the prediction of severe status. Series CT showed GGO or consolidation gradually reduced in 52 patients while 6 patients had reticular opacities. 14 patients showed the normal CXR while GGO were found on CT.Conclusion COVID-19 pneumonia manifests as focal, multifocal ground-glass opacities with/without consolidations. Higher CT score correlated severe clinical status. CXR is yet insufficient for evaluation of COVID-19 pneumonia.