Research and application progress of COVID-19 vaccine

The coronavirus disease 2019 (COVID-19) pandemic is a serious threat to human life, health and social development. In response to this public health event, various COVID-19 vaccines have been rapidly developed around the world. To date, 41 vaccines have been approved for emergency use, and the use of vaccines has significantly reduced the morbidity and mortality of COVID-19. However, with the continuous mutants, especially the emergence of the Omicron variant, challenges to vaccine-induced immune protection are appearing, there is still a long way to go for the continued development of COVID-19vaccines. This article briefly reviews the research progress of COVID-19 vaccine and its effect on mutant virus strains.

Development and validation of an LC-MS/MS method for simultaneous determination of remdesivir and its hydrolyzed metabolite and nucleoside, and its application in a pharmacokinetic study of normal and diabetic nephropathy mice.

Remdesivir (RDV), a phosphoramidate prodrug, has broad-spectrum antiviral activity. It is the first antiviral drug approved by the US Food and Drug Administration (FDA) for the treatment of COVID-19. Remdesivir is rapidly metabolized in the body to produce derivatives: alanine metabolite (RM-442) and RDV C-nucleoside (RN). Here, the phosphatase inhibitor PhosSTOP and carboxylesterase inhibitor 5,5'-dithiobis-2-nitrobenzoic acid were used to improve stability of RDV in mouse blood. We developed a rapid and sensitive LC-MS/MS method to simultaneously quantify RDV, RM-442 and RN in mouse blood. Chromatographic separation was achieved by gradient elution on an Acquity HSS T3 column. The run time was 3.2 min. The linearity ranges of the analytes were 0.5-1,000 ng/ml for RDV and 5-10,000 ng/ml for both RM-442 and RN. The method had an acceptable precision (RSD < 8.4% for RDV, RSD < 10.7% for RM-442 and RSD < 7.2% for RN) and accuracy (91.0-106.3% for RDV, 92.5-98.6% for RM-442 and 87.5-98.4% for RN). This method was successfully applied to analyze RDV, RM-442 and RN in the blood of normal and diabetic nephropathy DBA/2 J mice after intravenous injection of RDV at 20 mg/kg. The area under the concentration-time curve of RN between the normal and diabetic nephropathy mice showed a significant difference (P < 0.01).

The Impact of Age Difference on the Efficacy and Safety of COVID-19 Vaccines: A Systematic Review and Meta-Analysis.

Objective: This meta-analysis compared the efficacy and safety of five kinds of COVID-19 vaccines in different age groups (young adults and older adults), aiming to analyze the difference of adverse events (AEs) rate and virus geometric mean titer (GMT) values between young and older people, in order to find a specific trend, and explore the causes of this trend through meta-analysis. Method: Meta-analysis was used to analyze the five eligible articles. The modified Jadad scoring scale was used to evaluate the quality of eligible literature with a scoring system of 1 to 7. The primary endpoint of the effectiveness index was GMT. The primary endpoints of the safety index were the incidence of local AEs and systemic AEs. Stata 12.0 software was used for meta-analysis. Revman 5.0 software was used to map the risk of publication bias, and Egger's test was used to analyze publication bias. Results: The GMT values of young adults were higher than older adults (SMD = 1.40, 95% CI (0.79, 2.02), P<0.01). There was a higher incidence of local and systemic AEs in young people than in the elderly (OR = 1.10, 95% CI (1.08, 1.12), P<0.01; OR = 1.18, 95% CI (1.14, 1.22), P<0.01). Conclusion: The immune effect of young people after being vaccinated with COVID-19 vaccines was better than that of the elderly, but the safety was worse than that of old people, the most common AEs were fever, rash, and local muscle pain, which were tolerable for young people. As the AEs of the elderly were lower, they can also be vaccinated safely; the reason for the low level of GMT in the elderly was related to Immunosenescence. The vaccine tolerance of people of different ages needs to be studied continuously.

Safety of reopening universities and colleges using a combined strategy during coronavirus disease 2019 in China: a cross-sectional study (preprint)

Background There is no consensus as to when and how to reopen schools during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to evaluate the safety of reopening universities and colleges using a combined strategy in China.Methods This cross-sectional study included 13,116 staffs and postgraduate students who have returned to the four campuses of the University of Science and Technology of China from 17 February (students returned from 12 May) to 2 July 2020. The returning to school was guided by a combined strategy including use of personal protective equipment, management of transportation, serological and nucleic acid tests for COVID-19, quarantine, and restrictions in and out of campus. Epidemiology history and COVID-19 related symptoms (fever, cough, and dyspnoea) were recorded in a subset of participants using an online questionnaire.Results Among 13,116 participants, 4067 tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid and no positive results were identified. Of 9049 participants who chose to conduct antibody tests, 28 (0.3%) tested positive but no one was confirmed by the additional viral nucleic acid tests. Online questionnaires were collected from 5741 participants (mean 25.1 years, 35% female). High-risk exposures and COVID-19 related symptoms were reported in 8.3% and 7.4% of participants, respectively. Comorbidities (hypertension, diabetes, chronic pulmonary disease, and chronic kidney disease) were rare (0.2%-1.5%). Conclusions Using a combined strategy for COVID-19 prevention and control, safely reopening of universities and colleges in low-risk regions is possible and laboratory screening for SARS-CoV-2 infection may not be necessary. Further studies need to cautiously evaluate the safety of reopening schools, if any, in the middle- and high-risk regions.

Safety of Reopening Universities and Colleges Using a Combined Strategy During Coronavirus Disease 2019 in China: Cross Sectional Study (preprint)

Background: There is no consensus as to when and how to reopen schools during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to evaluate the safety of reopening universities and colleges using a combined strategy in China.Methods: This cross-sectional study included 13,116 staffs and postgraduate students who have returned to the four campuses of the University of Science and Technology of China from 17 February (students returned from 12 May) to 2 July 2020. The returning to school was guided by a combined strategy including use of personal protective equipment, management of transportation, serological and nucleic acid tests for COVID-19, quarantine, and restrictions in and out of campus. Epidemiology history and COVID-19 related symptoms (fever, cough, and dyspnoea) were recorded in a subset of participants using an online questionnaire.Findings: Among 13,116 participants, 4067 tested for SARS-CoV-2 nucleic acid and no positive results were identified. Of 9049 participants who chose to conduct antibody tests, 28 (0.3%) tested positive but no one was confirmed by the additional viral nucleic acid tests. Online questionnaires were collected from 5741 participants (mean 25.1 years, 35% female). High-risk exposures and COVID-19 related symptoms were reported in 8.3% and 7.4% of participants, respectively. Comorbidities (hypertension, diabetes, chronic pulmonary disease, and chronic kidney disease) were rare (0.2%-1.5%). Interpretation: Using a combined strategy for COVID-19 prevention and control, safely reopening of universities and colleges in low-risk regions is possible and laboratory screening for SARS-CoV-2 infection may not be necessary. Further studies need to cautiously evaluate the safety of reopening schools, if any, in the middle- and high-risk regions.Funding: This research received grants from the Fundamental Research Funds for the Central Universities (Grant number: YD9110002008, YD9110004001, and YD9110002002).Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: All participants who participated the online survey provided informed consent. Informed consent was waived for other participants as this study did not collect their individual data. This study was approved by the Research Ethics Commissions of the First Affiliated Hospital of USTC.

Association of Population Migration and Coronavirus Disease 2019 Epidemic Control (preprint)

Background and Objective To analyze the impact of different patterns of migration flow in two cities, Hefei and Shenzhen, on the epidemic and disease control of Coronavirus Disease 2019 (COVID-19), in order to provide insight for making differentiated controlling policies. Methods We collected demographic and epidemiological information of confirmed COVID-19 cases in Hefei and Shenzhen between January 19 and February 11, 2020, from data officially published by the provincial and municipal Centers for Disease Control and Prevention (CDC). From these data we calculated basic reproduction number R0 to reflect the rate of spread of COVID-19 in these cities. Aggregated data of population migration during the same period was extracted from Baidu Migration. The change of R0 in the two cites were analyzed and compared. Spearman correlation analysis between R0 and population inflow from epidemic focus were performed. Results A total of 157 confirmed cases was identified in Hefei by 24:00 February 11, 2020, with an average age of 44.4{+/-}15.6 years, 74 female (47.1%) and 386 confirmed cases were identified in Shenzhen, with an average age of 45.15{+/-}17.99 years, 202 female (52.3%). Significant difference in the proportion of imported cases between the two cities was observed (Hefei vs Shenzhen, 24.2% vs 74.9%, p=0.000). Before January 31 2020, during the initial stage of the Level 1 Response to Major Public Health Emergencies, there was no significant association observed in Shenzhen between R0 and the proportion of population inflow from the epidemic focus (P =0.260, r=-0.452); meanwhile in Hefei, such association was strong (P =0.000, r=1.0). However, after the initial stage of response, the situation reversed. A weak association was observed in Shenzhen between be R0 and the proportion of population inflow from the epidemic focus (P=0.073, r=0.536) but not in Hefei (P =0.498, r=0.217). Conclusion Following Level 1 Response, consistent decline of R0 of COVID-19 was observed in both Hefei and Shenzhen. Different patterns of disease spread were observed in the two cities, driven by different patterns of population migration. This indicated that population migration should be taken into consideration when we set controlling policy of a novel infectious disease.