[Epidemiological investigation on the local epidemic situation in Zhengzhou High-Tech Zone caused by SARS-CoV-2 Delta variant].

This study collected epidemic data of COVID-19 in Zhengzhou from January 1 to January 20 in 2022. The epidemiological characteristics of the local epidemic in Zhengzhou High-tech Zone caused by the SARS-CoV-2 Delta variant were analyzed through epidemiological survey and big data analysis, which could provide a scientific basis for the prevention and control of the Delta variant. In detail, a total of 276 close contacts and 599 secondary close contacts were found in this study. The attack rate of close contacts and secondary close contacts was 5.43% (15/276) and 0.17% (1/599), respectively. There were 10 confirmed cases associated with the chain of transmission. Among them, the attack rates in close contacts of the first, second, third, fourth and fifth generation cases were 20.00% (5/25), 17.86% (5/28), 0.72% (1/139) and 14.81% (4/27), 0 (0/57), respectively. The attack rates in close contacts after sharing rooms/beds, having meals, having neighbor contacts, sharing vehicles with the patients, having same space contacts, and having work contacts were 26.67%, 9.10%, 8.33%, 4.55%, 1.43%, and 0 respectively. Collectively, the local epidemic situation in Zhengzhou High-tech Zone has an obvious family cluster. Prevention and control work should focus on decreasing family clusters of cases and community transmission.

Investigation on molecular characters of sars-cov-2 nsp8 gene and protein

Objective  To explore SARS-CoV-2 nsp8 genetic variation, Nsp8 protein structure, biological function and targeted drugs, and to lay foundation for establishing more effective prevention and control strategies.  Methods  Analyses of nsp8 genetic variability, physical and chemical characteristics, spatial structure, antigenic epitopes, biological function, and drug combined targets of Nsp8 were carried out using bioinformatics technology and large biological databases.  Results  Based on nsp8 sequences of 28 isolates of coronavirus of three species, evolutionary tree was successfully constructed. SARS-CoV-2 isolates showed 99%-100% conservation of nsp8 genes, less genetic distance to SARS than MERS isolates. Nsp8 had no signal peptide and transmembrane area. In reticulocytes in vitro, Nsp8 had a half-life of 4 h and was hydrophilic. A secondary model and a tertiary structure model were established. Linear B cell and CTL antigenic epitopes, phosphorylation and SUMB modification sites were found in Nsp8. Using the DrugBank database, four drugs targeted Nsp8 were obtained.  Conclusions  Nsp8 possesses the characteristics of typical antigens, participates in viral replication, and various isolates of the same species share high conservation of nsp8 gene, suggesting potential applications in researches on pathogenic mechanism, genotyping and prevention of this virus. Notably, this is the first report on Nsp8-targeted chemotherapeutic drugs, and the findings can be of considerable scientific significance and application value, under the conditions that measures with special effect for COVID-19 prevention and control are urgently needed. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Image-Based Prediction of Respiratory Diseases Including COVID-19 Using Convolutional Neural Networks

Respiratory diseases such as COVID-19, Pneumonia, SARS, and Streptococcus have caused severe worldwide public health concerns. Specifically, COVID-19, as an emerging worldwide pandemic, imposed the most critical challenge to all scientists and researchers for prognosis, diagnosis, and treatment of COVID-19 infection. This study aims to predict the aforementioned 4 respiratory diseases and normal people with chest X-ray and CT scan images using convolutional neural networks. A total of 1,156 images has been collected from 3 published databases. The combined dataset was enriched by empowering augmentation techniques and visual filters such as rotation and lung segmentation. The noises for augmentation include Gaussian and Speckle noises with zero mean and variance of 0.05, 0.10, and 0.20, and Salt and Pepper noise with 50% and 75% ratio. The customized convolutional neural network reached a prediction accuracy of 94% in classifying the test images into the normal and 4 disease categories, and 92%, 93%, and 92% as average precision, recall, and F1-score over all categories, respectively. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

[Preliminary analysis on COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province].

Objective: To analyze the characteristics of COVID-19 case spectrum and spread intensity in different provinces in China except Hubei province. Methods: The daily incidence data and case information of COVID-19 were collected from the official websites of provincial and municipal health commissions. The morbidity rate, severity rate, case-fatality rate, and spread ratio of COVID-19 were calculated. Results: As of 20 March, 2020, a total of 12 941 cases of COVID-19 had been conformed, including 116 deaths, and the average morbidity rate, severity rate and case-fatality rate were 0.97/100 000, 13.5% and 0.90%, respectively. The morbidity rates in Zhejiang (2.12/100 000), Jiangxi (2.01/100 000) and Beijing (1.93/100 000) ranked top three. The characteristics of COVID-19 case spectrum varied from province to province. The first three provinces (autonomous region, municipality) with high severity rates were Tianjin (45.6%), Xinjiang (35.5%) and Heilongjiang (29.5%). The case-fatality rate was highest in Xinjiang (3.95%), followed by Hainan (3.57%) and Heilongjiang (2.70%). The average spread ratio was 0.98 and the spread intensity varied from province to province. Tibet had the lowest spread ratio (0), followed by Qinghai (0.20) and Guangdong (0.23). Conclusion: The intervention measures were effective in preventing the spread of COVID-19 and improved treatment effect in China. However, there were significant differences among different regions in severity, case-fatality rate and spread ratio.

[The preliminary analysis on the characteristics of the cluster for the COVID-19].

Since December 2019, COVID-19, a new emerging infection disease, has spread in 27 countries and regions. The clusters of many cases were reported with the epidemic progresses. We collected currently available information for 377 COVID-19 clusters (1 719 cases), excluded the hospital clusters and Hubei cases, during the period from January 1 to February 20, 2020. There were 297 family clusters (79%), case median was 4; 39 clusters of dining (10%), case median was 5; 23 clusters of shopping malls or supermarkets (6%), case median was 13; 12 clusters of work units (3%), case median was 6, and 6 clusters of transportation. We selected 325 cases to estimate the incubation period and its range was 1 to 20 days, median was 7 days, and mode was 4 days. The analysis of the epidemic situation in a department store in China indicated that there was a possibility of patients as the source of infection during the incubation period of the epidemic. From February 5 to 21, 2020, 634 persons were infected on the Diamond Princess Liner. All persons are susceptible to the 2019 coronavirus. Age, patients during the incubation period and the worse environment might be the cause of the cases rising. The progress of the two typical outbreaks clearly demonstrated the spread of the early cases in Wuhan. In conclusion, screening and isolating close contacts remained essential other than clinical treatment during the epidemic. Especially for the healthy people in the epidemic area, isolation was the key.