Safety, immunogenicity, and efficacy of the mRNA vaccine CS-2034 as a heterologous booster versus homologous booster with BBIBP-CorV in adults aged ≥18 years: a randomised, double-blind, phase 2b trial.

BACKGROUND: Heterologous boosting is suggested to be of use in populations who have received inactivated COVID-19 vaccines. We aimed to assess the safety and immunogenicity of a heterologous vaccination with the mRNA vaccine CS-2034 versus the inactivated BBIBP-CorV as a fourth dose, as well as the efficacy against the SARS-CoV-2 omicron (BA.5) variant. METHODS: This trial contains a randomised, double-blind, parallel-controlled study in healthy participants aged 18 years or older (group A) and an open-label cohort in participants 60 years and older (group B), who had received three doses of inactivated whole-virion vaccines at least 6 months before enrolment. Pregnant women and people with major chronic illnesses or a history of allergies were excluded. Eligible participants in group A were stratified by age (18-59 years and ≥60 years) and then randomised by SAS 9.4 in a ratio of 3:1 to receive a dose of the mRNA vaccine (CS-2034, CanSino, Shanghai, China) or inactivated vaccine (BBIBP-CorV, Sinopharm, Beijing, China). Safety and immunogenicity against omicron variants of the fourth dose were evaluated in group A. Participants 60 years and older were involved in group B for safety observations. The primary outcome was geometric mean titres (GMTs) of the neutralising antibodies against omicron and seroconversion rates against BA.5 variant 28 days after the boosting, and incidence of adverse reactions within 28 days. The intention-to-treat group was involved in the safety analysis, while all patients in group A who had blood samples taken before and after the booster were involved in the immunogenicity analysis. This trial was registered at the Chinese Clinical Trial Registry Centre (ChiCTR2200064575). FINDINGS: Between Oct 13, and Nov 22, 2022, 320 participants were enrolled in group A (240 in the CS-2034 group and 80 in the BBIBP-CorV group) and 113 in group B. Adverse reactions after vaccination were more frequent in CS-2034 recipients (158 [44·8%]) than BBIBP-CorV recipients (17 [21·3%], p<0·0001). However, most adverse reactions were mild or moderate, with grade 3 adverse reactions only reported by eight (2%) of 353 participants receiving CS-2034. Heterologous boosting with CS-2034 elicited 14·4-fold (GMT 229·3, 95% CI 202·7-259·4 vs 15·9, 13·1-19·4) higher concentration of neutralising antibodies to SARS-CoV-2 omicron variant BA.5 than did homologous boosting with BBIBP-CorV. The seroconversion rates of SARS-CoV-2-specific neutralising antibody responses were much higher in the mRNA heterologous booster regimen compared with BBIBP-CorV homologous booster regimen (original strain 47 [100%] of 47 vs three [18·8%] of 16; BA.1 45 [95·8%] of 48 vs two [12·5%] 16; and BA.5 233 [98·3%] of 240 vs 15 [18·8%] of 80 by day 28). INTERPRETATION: Both the administration of mRNA vaccine CS-2034 and inactivated vaccine BBIBP-CorV as a fourth dose were well tolerated. Heterologous boosting with mRNA vaccine CS-2034 induced higher immune responses and protection against symptomatic SARS-CoV-2 omicron infections compared with homologous boosting, which could support the emergency use authorisation of CS-2034 in adults. FUNDING: Science and Technology Commission of Shanghai, National Natural Science Foundation of China, Jiangsu Provincial Science Fund for Distinguished Young Scholars, and Jiangsu Provincial Key Project of Science and Technology Plan. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

SARS-CoV-2 epitope-specific T cells: Immunity response feature, TCR repertoire characteristics and cross-reactivity.

The devastating COVID-19 pandemic caused by SARS-CoV-2 and multiple variants or subvariants remains an ongoing global challenge. SARS-CoV-2-specific T cell responses play a critical role in early virus clearance, disease severity control, limiting the viral transmission and underpinning COVID-19 vaccine efficacy. Studies estimated broad and robust T cell responses in each individual recognized at least 30 to 40 SARS-CoV-2 antigen epitopes and associated with COVID-19 clinical outcome. Several key immunodominant viral proteome epitopes, including S protein- and non-S protein-derived epitopes, may primarily induce potent and long-lasting antiviral protective effects. In this review, we summarized the immune response features of immunodominant epitope-specific T cells targeting different SRAS-CoV-2 proteome structures after infection and vaccination, including abundance, magnitude, frequency, phenotypic features and response kinetics. Further, we analyzed the epitopes immunodominance hierarchy in combination with multiple epitope-specific T cell attributes and TCR repertoires characteristics, and discussed the significant implications of cross-reactive T cells toward HCoVs, SRAS-CoV-2 and variants of concern, especially Omicron. This review may be essential for mapping the landscape of T cell responses toward SARS-CoV-2 and optimizing the current vaccine strategy.

Development of SARS-CoV-2 animal vaccines using a stable and efficient NDV expression system.

With the continuation of the coronavirus disease 2019 pandemic and the emergence of new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants, the control of the spread of the virus remains urgent. Various animals, including cats, ferrets, hamsters, nonhuman primates, minks, tree shrews, fruit bats, and rabbits, are susceptible to SARS-CoV-2 infection naturally or experimentally. Therefore, to avoid animals from becoming mixing vessels of the virus, vaccination of animals should be considered. In the present study, we report the establishment of an efficient and stable system using Newcastle disease virus (NDV) as a vector to express SARS-CoV-2 spike protein/subunit for the rapid generation of vaccines against SARS-CoV-2 in animals. Our data showed that the S and S1 protein was sufficiently expressed in rNDV-S and rNDV-S1-infected cells, respectively. The S protein was incorporated into and displayed on the surface of rNDV-S viral particles. Intramuscular immunization with rNDV-S was found to induce the highest level of binding and neutralizing antibodies, as well as strong S-specific T-cell response in mice. Intranasal immunization with rNDV-S1 provoked a robust T-cell response but barely any detectable antibodies. Overall, the NDV-vectored vaccine candidates were able to induce profound humoral and cellular immunity, which will provide a good system for developing vaccines targeting both T-cell and antibody responses.

Coronavirus disease 2019 death prediction by electrocardiographic abnormalities and elevated D-dimer levels.

Background: Electrocardiography (ECG) plays a very important role in various cardiovascular diseases and elevated D-dimer in serum associated with thrombosis. In patients with coronavirus disease 2019 (COVID-19), immense pieces of evidence showed that ECG abnormalities or elevated D-dimer in serum occurred frequently. However, it remains unclear whether ECG abnormalities combined with elevated D-dimer could be a new risk predictor in patients with COVID-19. Methods and results: This retrospective cohort study enrolled 416 patients with COVID-19 at Wuhan Tongji Hospital from 1 February to 20 March 2020. ECG manifestations, D-dimer levels, and in-hospital deaths were recorded for all patients. Logistic regression analysis was performed to examine the association between ECG manifestations and in-hospital mortality in patients with elevated D-dimer levels. In patients hospitalized for COVID-19, ST-T abnormalities (34.3%) were the most frequent ECG manifestations, whereas sinus tachycardia (ST) (13.3%) and atrial arrhythmias with rapid rhythms (8.5%) were the two most common cardiac arrhythmias. Compared to severely ill patients with COVID-19, ST-T abnormalities, ST and atrial arrhythmias (p<0.001) with rapid rhythms, D-dimer levels, and in-hospital deaths were significantly more frequent in critically ill patients with COVID-19. Moreover, elevated D-dimer levels were observed in all the patients who died. In the subgroup of 303 patients with elevated serum D-dimer levels, the patient's age, the incidence of ST-T abnormalities, ST, atrial fibrillation (AF), and atrial premature beat were significantly higher than those in the non-elevated D-dimer subgroup. Multivariate logistic regression analysis further revealed that ST and AF were risk factors for in-hospital mortality in COVID-19 patients with elevated D-dimer levels. Conclusions: ECG abnormalities and elevated D-dimer levels were associated with a higher risk of critical illness and death in patients hospitalized for COVID-19. ECG abnormalities, including ST and AF, combined with elevated D-dimer levels, can be used to predict death in COVID-19.

Coronavirus disease 2019 death prediction by electrocardiographic abnormalities and elevated D-dimer levels

Background Electrocardiography (ECG) plays a very important role in various cardiovascular diseases and elevated D-dimer in serum associated with thrombosis. In patients with coronavirus disease 2019 (COVID-19), immense pieces of evidence showed that ECG abnormalities or elevated D-dimer in serum occurred frequently. However, it remains unclear whether ECG abnormalities combined with elevated D-dimer could be a new risk predictor in patients with COVID-19. Methods and results This retrospective cohort study enrolled 416 patients with COVID-19 at Wuhan Tongji Hospital from 1 February to 20 March 2020. ECG manifestations, D-dimer levels, and in-hospital deaths were recorded for all patients. Logistic regression analysis was performed to examine the association between ECG manifestations and in-hospital mortality in patients with elevated D-dimer levels. In patients hospitalized for COVID-19, ST-T abnormalities (34.3%) were the most frequent ECG manifestations, whereas sinus tachycardia (ST) (13.3%) and atrial arrhythmias with rapid rhythms (8.5%) were the two most common cardiac arrhythmias. Compared to severely ill patients with COVID-19, ST-T abnormalities, ST and atrial arrhythmias (p<0.001) with rapid rhythms, D-dimer levels, and in-hospital deaths were significantly more frequent in critically ill patients with COVID-19. Moreover, elevated D-dimer levels were observed in all the patients who died. In the subgroup of 303 patients with elevated serum D-dimer levels, the patient's age, the incidence of ST-T abnormalities, ST, atrial fibrillation (AF), and atrial premature beat were significantly higher than those in the non-elevated D-dimer subgroup. Multivariate logistic regression analysis further revealed that ST and AF were risk factors for in-hospital mortality in COVID-19 patients with elevated D-dimer levels. Conclusions ECG abnormalities and elevated D-dimer levels were associated with a higher risk of critical illness and death in patients hospitalized for COVID-19. ECG abnormalities, including ST and AF, combined with elevated D-dimer levels, can be used to predict death in COVID-19.

SARS-CoV-2-specific CD4+ T cells are associated with long-term persistence of neutralizing antibodies.

Understanding the decay and maintenance of long-term SARS-CoV-2 neutralizing antibodies in infected or vaccinated people and how vaccines protect against other SARS-CoV-2 variants is critical for assessing public vaccination plans. Here, we measured different plasm antibody levels 2 and 12 months after disease onset, including anti-RBD, anti-N, total neutralizing antibodies, and two neutralizing-antibody clusters. We found that total neutralizing antibodies declined more slowly than total anti-RBD and anti-N IgG, and the two neutralizing-antibody clusters decayed even more slowly than total neutralizing antibodies. Interestingly, the level of neutralizing antibodies at 12 months after disease onset was significantly lower than that at 2 months but more broadly neutralized SARS-CoV-2 variants, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Lambda (C.37). Significant immune escape by the Omicron variant (B.1.1.529) was also observed 2 months post-recovery. Furthermore, we revealed that a high percentage of virus-specific CD4+ T cells and cTfh1 were associated with a slower decline in humoral immunity, accompanied by higher levels of CXCR3 ligands such as CXCL9 and CXCL10, higher frequency of cTfh1, and lower levels of cTfh2 and cTfh17. Our data highlight the importance of coordinating T-cell and humoral immunity to achieve long-term protective immunity.

Influencing factors of burnout and its dimensions among mental health workers during the COVID-19 pandemic.

AIM: To examine the demographic and work characteristics of mental health workers associated with burnout during the COVID-19 epidemic and to examine the relationship between burnout and humanistic care ability. DESIGN: Online cross-sectional design. METHODS: 270 mental health workers in Chongqing, China, were recruited via WeChat from 1 to 31 December 2020. Online self-administered questionnaires were used to collect data. Data were analyzed by t-tests and one-way analyses of variance, Pearson's correlation analysis, and multiple linear regression analysis. RESULTS: During the COVID-19 pandemic, mental health workers had a high prevalence of burnout and a low level of humanistic care ability. Work factors including profession, work shift, work pressure, work-family conflict, practice environment satisfaction, salary satisfaction, and humanistic care ability were significantly associated with burnout and its subdimension.

Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study.

BACKGROUND: Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. METHODS: We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. RESULTS: Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. CONCLUSIONS: County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

Heterologous boosting with third dose of coronavirus disease recombinant subunit vaccine increases neutralizing antibodies and T cell immunity against different severe acute respiratory syndrome coronavirus 2 variants.

Waned vaccine-induced immunity and emerging severe acute respiratory syndrome coronavirus 2 variants with potential for immune escape pose a major threat to the coronavirus disease (COVID-19) pandemic. Here, we showed that humoral immunity components, including anti-S + N, anti-RBD IgG, and neutralizing antibodies (NAbs), gradually waned and decreased the neutralizing capacity against emerging Omicron variants at 3 and 6 months after two inactivated COVID-19 vaccinations. We evaluated two boosting strategies with either a third dose of inactivated vaccine (homologous, I-I-I) or a recombinant subunit vaccine (heterologous, I-I-S). Both strategies induced the production of high levels of NAbs with a broad neutralizing capacity and longer retention. Interestingly, I-I-S induced 3.5-fold to 6.8-fold higher NAb titres than I-I-I, with a broader neutralizing capacity against six variants of concern, including Omicron. Further immunological analysis revealed that the two immunization strategies differ considerably, not only in the magnitude of total NAbs produced, but also in the composite pattern of NAbs and the population of virus-specific CD4+ T cells produced. Additionally, in some cases, heterologous boosted immunity induced the production of more effective epitopes than natural infection. The level of I-I-S-induced NAbs decreased to 48% and 18% at 1 and 3 months after booster vaccination, respectively. Overall, our data provide important evidence for vaccination strategies based on available vaccines and may help guide future global vaccination plans.

Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: A cohort study.

BACKGROUND AND OBJECTIVE: Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. METHODS: 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. RESULTS: There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923-0.9952), AUC ROC score (0.9999 95% CI 0.9998-1.0000), sensitivity (0.9938 95% CI 0.9910-0.9965) and specificity (0.9979 95% CI 0.9970-0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440-0.9508), AUC ROC score (0.9762 95% CI 0.9848-0.9865), sensitivity (0.9482 95% CI 0.9393-0.9578) and specificity (0.9835 95% CI 0.9806-0.9863). CONCLUSIONS: Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.

Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: a cohort study

Background and Objective Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. Methods 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. Results There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923–0.9952), AUC ROC score (0.9999 95% CI 0.9998–1.0000), sensitivity (0.9938 95% CI 0.9910–0.9965) and specificity (0.9979 95% CI 0.9970–0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440–0.9508), AUC ROC score (0.9762 95% CI 0.9848–0.9865), sensitivity (0.9482 95% CI 0.9393–0.9578) and specificity (0.9835 95% CI 0.9806–0.9863). Conclusions Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.

Exposure to SARS-CoV-2 generates T-cell memory in the absence of a detectable viral infection.

T-cell immunity is important for recovery from COVID-19 and provides heightened immunity for re-infection. However, little is known about the SARS-CoV-2-specific T-cell immunity in virus-exposed individuals. Here we report virus-specific CD4+ and CD8+ T-cell memory in recovered COVID-19 patients and close contacts. We also demonstrate the size and quality of the memory T-cell pool of COVID-19 patients are larger and better than those of close contacts. However, the proliferation capacity, size and quality of T-cell responses in close contacts are readily distinguishable from healthy donors, suggesting close contacts are able to gain T-cell immunity against SARS-CoV-2 despite lacking a detectable infection. Additionally, asymptomatic and symptomatic COVID-19 patients contain similar levels of SARS-CoV-2-specific T-cell memory. Overall, this study demonstrates the versatility and potential of memory T cells from COVID-19 patients and close contacts, which may be important for host protection.

Electrocardiogram analysis of patients with different types of COVID-19.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 causes acute myocardial damage and arrhythmia in coronavirus disease 2019 (COVID-19) patients. Studying the changes of electrocardiogram is of great significance for the diagnosis of patients with COVID-19. METHODS: A retrospective analysis method was adopted to compare the electrocardiogram changes between COVID-19 critically severe and severe patients. Univariate and multivariate logistic regression were used to analyze the correlation of the levels of serum indexes and past medical history with ST-T changes and atrial fibrillation. And the correlation of ECG parameters with in-hospital death and ventilator use were investigated by using the same methods. RESULTS: The incidence of male, stroke, elevated cardiac troponin I (cTnI), N-terminal of the prohormone brain natriuretic peptide (NT-proBNP), d-dimer, high-sensitivity C-reactive protein (hs-CRP), hyperkalemia, and hypocalcemia in the critically ill patients was higher than that in severe patients. There were differences in ST-T changes, sinus tachycardia, atrial fibrillation, and atrial tachycardia between the two groups. Multivariate logistic regression analysis showed that elevated cTnI and NT-proBNP were the independent risk factors of ST-T changes. Elevated NT-proBNP and age were the independent risk factors of atrial fibrillation. Sinus tachycardia and atrial fibrillation were the independent risk factors of in-hospital death and ventilator use. CONCLUSION: ST-T changes, sinus tachycardia, and atrial fibrillation are with great significance in the diagnosis of the severity, myocardia injury, and cardiac insufficiency of COVID-19 patients. Sinus tachycardia and atrial fibrillation could be used as independent variables predicting in-hospital death and ventilator use.