COVID-19 detection method based on SVRNet and SVDNet in lung x-rays.

Purpose: To detect and diagnose coronavirus disease 2019 (COVID-19) better and faster, separable VGG-ResNet (SVRNet) and separable VGG-DenseNet (SVDNet) models are proposed, and a detection system is designed, based on lung x-rays to diagnose whether patients are infected with COVID-19. Approach: Combining deep learning and transfer learning, 1560 lung x-ray images in the COVID-19 x-ray image database (COVID-19 Radiography Database) were used as the experimental data set, and the most representative image classification models, VGG16, ResNet50, InceptionV3, and Xception, were fine-tuned and trained. Then, two new models for lung x-ray detection, SVRNet and SVDNet, were proposed on this basis. Finally, 312 test set images (including 44 COVID-19 and 268 normal images) were used as input to evaluate the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet models. Results: In the classification experiment of lung x-rays that tested positive and negative for COVID-19, the classification accuracy, sensitivity, and specificity of SVRNet and SVDNet are 99.13%, 99.14%, 99.12% and 99.37%, 99.43%, 99.31%, respectively. Compared with the VGG16 network, SVRNet and SVDNet increased by 3.07%, 2.84%, 3.31% and 3.31%, 3.13%, 3.50%, respectively. On the other hand, the parameters of SVRNet and SVDNet are 5.65×106 and 6.57×106 , respectively. These are 61.56% and 55.31% less than VGG16, respectively. Conclusions: The SVRNet and SVDNet models proposed greatly reduce the number of parameters, while improving the accuracy and increasing the operating speed, and can accurately and quickly detect lung x-rays containing COVID-19.

Unmatched Virus Preservation Solution may Impede One-Step Reagent for Detection of 2019-nCoV RNA.

BACKGROUND AND METHODS: 2019 Corona Virus Disease (COVID-19) caused by SARS-CoV-2 is still pandemic now. RT-qPCR detection was the most common method for the diagnosis of SARS-CoV-2 infection, facilitated by amounts of nucleic acid testing kits. However, the accuracy of nucleic acid detection is affected by various factors such as specimen collection, specimen preparation, reagents deficiency, and personnel quality. RESULTS: In this study, we found that unmatched virus preservation solution will inhibit N gene and OFR-1ab gene (two independent genes of SARS-CoV-2) amplification in one-step detection reagent. CONCLUSIONS: Despite just being a particular phenomenon we found in our work to fight 2019-nCoV, we concluded that unmatched virus preservation solution may have an inhibitory effect on SARS-CoV-2 nucleic acid detection which may lead to incorrect clinical diagnosis.

Dynamic monitoring of immune function indexes in COVID-19 patients.

We conducted a retrospective analysis of the clinical characteristics and dynamic variations of immune indexes in nine COVID-19 patients in Zigong, China. We used flow cytometry and enzyme-linked immunosorbent assays to measure the absolute levels of CD4 and CD8 lymphocytes and SARS-CoV-2 antibodies, respectively. We found that CRP, LDH, HBDH, CD4/CD8 and IgE levels were increased in 6/9 patients, while PA and the absolute numbers of CD4 and CD8 lymphocytes decreased in 7/9 patients. From disease onset through 63 days of follow-up, SARS-CoV-2 IgG levels were consistently higher than those of SARS-CoV-2 IgM, reaching peaks on days 28 and 13, respectively. IgM levels decreased to normal 35 days after disease onset, while IgG levels remained elevated through day 63. IgE levels varied similarly to SARS-CoV-2 IgM. Our results suggest that SARS-CoV-2 may elicit allergic immune responses in patients and that the levels of CRP, PA, LDH, and HBDH, as well as the absolute numbers of CD4 and CD8 lymphocytes could be used as early diagnostic markers of SARS-CoV-2 infection. Lastly, the dynamic variation of SARS-CoV-2 antibodies could guide the timing of blood collection for plasma exchange.

Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China.

As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.