Safety and immunogenicity of inactivated COVID-19 vaccine CoronaVac and the RBD-dimer-based COVID-19 vaccine ZF2001 in chronic hepatitis B patients.

Background and aims: Although COVID-19 vaccination is recommended for the patients with chronic liver disease, the clinical outcomes of COVID-19 vaccinated in patients with chronic hepatitis B (CHB) has not been well characterized. The study aimed to explore the safety and specific antibody responses following COVID-19 vaccination among CHB patients. Methods: Patients with CHB were included. All patients were vaccinated with two doses of inactivated vaccine (CoronaVac) or three doses of adjuvanted protein subunit vaccine (ZF2001). The adverse events were recorded and neutralizing antibody (NAb) were determined 14 days following the whole-course vaccination. Results: A total of 200 patients with CHB were included. Specific NAb against SARS-CoV-2 were positive in 170 (84.6%) patients. The median (IQR) concentrations of NAb were 16.32 (8.44-34.10) AU/ml. Comparison of immune responses between CoronaVac and ZF2001 vaccines showed no significant differences in neither the concentrations of NAb nor the seropositive rates (84.4 vs. 85.7%). Moreover, we observed lower immunogenicity in older patients and in patients with cirrhosis or underlying comorbidities. The incidences of adverse events were 37 (18.5%) with the most common adverse event as injection side pain [25 (12.5%)], followed by fatigue [15 (7.5%)]. There were no differences in the frequencies of adverse between CoronaVac and ZF2001 (19.3% vs. 17.6%). Almost all of the adverse reactions were mild and self-resolved within a few days after vaccination. Severe adverse events were not observed. Conclusions: COVID-19 vaccines, CoronaVac and ZF2001 had a favorable safety profile and induced efficient immune response in patients with CHB.

Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

An emissions inventory using flight information reveals the long-term changes of aviation CO2 emissions in China

The low-carbon development of air transport industry is of great significance for China to achieve the commitment of carbon peak and carbon neutrality goals. In order to improve the basic data of aviation CO2 emissions, this study continuously collected full flight information in China from January 2017 to December 2020, and established a flight information database and an aircraft-engine parameter database. On the basis of IPCC's Tier 3B accounting method, this study established a long-term aviation CO2 emissions inventory of China from 2017 to 2020 by calculating and accumulating CO2 emissions of each flight. And aviation CO2 emissions of various provinces and cities in China were calculated combined with spatial allocation method. The results showed that aviation CO2 emissions in China was 104.1, 120.1, 136.9, and 88.3 Mt in 2017, 2018, 2019, and 2020, respectively, with annual growth rates of 15.4%, 14.0%, and −35.3% in 2018, 2019, and 2020, respectively. Affected by the COVID-19 pandemic, aviation CO2 emissions in all 31 provinces and 93% of cities decreased in 2020 compared with 2019. China is in the stage of rapid development of air transport industry, and aviation fossil energy consumption and CO2 emissions have continued to grow in recent years.

Immunogenicity of a vaccinia virus-based severe acute respiratory syndrome coronavirus 2 vaccine candidate

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines provide essential tools for the control of the COVID-19 pandemic. A number of technologies have been employed to develop SARS-CoV-2 vaccines, including the inactivated SARS-CoV-2 particles, mRNA to express viral spike protein, recombinant spike proteins, and viral vectors. Here, we report the use of the vaccinia virus Tiantan strain as a vector to express the SARS-CoV-2 spike protein. When it was used to inoculate mice, robust SARS-CoV-2 spike protein-specific antibody response and T-cell response were detected. Sera from the vaccinated mice showed strong neutralizing activity against the ancestral Wuhan SARS-CoV-2, the variants of concern (VOCs) B.1.351, B.1.617.2, and the emerging B.1.1.529 (omicron). This finding supports the possibility of developing a new type of SARS-CoV-2 vaccine using the vaccinia virus vector.

Safety and Immunogenicity of Inactivated and Recombinant Protein SARS-CoV-2 Vaccines in Patients With Thyroid Cancer.

Background: This study aimed at assessing the safety and immunogenicity of SARS-CoV-2 vaccines in patients with thyroid cancer. Methods: This observational study included thyroid cancer patients between April 1, 2021, and November 31, 2021, in the Second Affiliated Hospital of Chongqing Medical University. All participants received at least one dose of the SARS-CoV-2 vaccine. SARS-CoV-2 IgG was tested, and the interval time between the last dose and humoral response test ranged from <1 to 8 months. The complications after SARS-CoV-2 vaccines were recorded. Results: A total of 115 participants at least received one dose of SARS-CoV-2 vaccines with a 67.0% IgG-positive rate. Among them, 98 cases had completed vaccination, and the positivity of SARS-CoV-2 IgG antibodies was 96% (24/25) with three doses of ZF2001. SARS-CoV-2 IgG antibodies' positivity was 63.0% (46/73) of two doses of CoronaVac or BBIBP-CorV vaccine. Additionally, after 4 months of the last-dose vaccination, the IgG-positive rate (31.6%, 6/19) significantly decreased in thyroid cancer patients. The IgG-positive rate (81.0%, 64/79) was satisfactory within 3 months of the last-dose vaccination. Ten (10.2%) patients had side effects after SARS-CoV-2 vaccination. Among them, two (2.0%) patients had a fever, five (5.1%) patients had injection site pain, one (1.0%) patient felt dizzy, and one patient felt dizzy and had injection site pain at the same time. Conclusion: SARS-CoV-2 vaccines (CoronaVac, BBIBP-CorV, and ZF2001) are safe in thyroid cancer patients. The regression time of SARS-CoV-2 IgG is significantly shorter in thyroid cancer patients than in healthy adults. Therefore, a booster vaccination dose may be earlier than the systematic strategy for thyroid cancer patients.

SARS-CoV-2 spike protein-induced cell fusion activates the cGAS-STING pathway and the interferon response.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the unprecedented coronavirus disease 2019 (COVID-19) pandemic. Critical cases of COVID-19 are characterized by the production of excessive amounts of cytokines and extensive lung damage, which is partially caused by the fusion of SARS-CoV-2-infected pneumocytes. Here, we found that cell fusion caused by the SARS-CoV-2 spike (S) protein induced a type I interferon (IFN) response. This function of the S protein required its cleavage by proteases at the S1/S2 and the S2' sites. We further showed that cell fusion damaged nuclei and resulted in the formation of micronuclei that were sensed by the cytosolic DNA sensor cGAS and led to the activation of its downstream effector STING. Phosphorylation of the transcriptional regulator IRF3 and the expression of IFNB, which encodes a type I IFN, were abrogated in cGAS-deficient fused cells. Moreover, infection with VSV-SARS-CoV-2 also induced cell fusion, DNA damage, and cGAS-STING-dependent expression of IFNB. Together, these results uncover a pathway underlying the IFN response to SARS-CoV-2 infection. Our data suggest a mechanism by which fused pneumocytes in the lungs of patients with COVID-19 may enhance the production of IFNs and other cytokines, thus exacerbating disease severity.