Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans.

Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation.

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

Potent NTD-Targeting Neutralizing Antibodies against SARS-CoV-2 Selected from a Synthetic Immune System.

The majority of neutralizing antibodies (NAbs) against SARS-CoV-2 recognize the receptor-binding domain (RBD) of the spike (S) protein. As an escaping strategy, the RBD of the virus is highly variable, evolving mutations to thwart a natural immune response or vaccination. Targeting non-RBD regions of the S protein thus provides a viable alternative to generating potential, robust NAbs. Using a pre-pandemic combinatorial antibody library of 1011, through an alternate negative and positive screening strategy, 11 non-RBD-targeting antibodies are identified. Amongst one NAb that binds specifically to the N-terminal domain of the S protein, SA3, shows mutually non-exclusive binding of the angiotensin-converting enzyme 2 receptor with the S protein. SA3 appears to be insensitive to the conformational change and to interact with both the "open" and "closed" configurations of the trimeric S protein. SA3 shows compatible neutralization as S-E6, an RBD-targeting NAb, against the wild type and variant of concern (VOC) B.1.351 (Beta) of the SARS-CoV-2 pseudo virus. More importantly, the combination of SA3 with S-E6 is synergistic and recovers from the 10-fold loss in neutralization efficacy against the VOC B.1.351 pseudo virus.

The influence of deliberate rumination on the post-traumatic growth of college students during the COVID-19 pandemic and the moderating role of self-efficacy.

Objective: To understand the relationship between deliberate rumination and post-traumatic growth and the mechanisms affecting this relationship, we constructed an adjustment model to test the impact of deliberate rumination on the post-traumatic growth of college students and the moderating role of self-efficacy during the 2019 COVID-19 pandemic. Study design and setting: A total of 881 college students from a university of science and technology in Guangdong Province, China, completed a questionnaire that measured deliberate rumination, post-traumatic growth, and self-efficacy. SPSS (version 26) and the PROCESS plug-in (version 4.0) were used for correlation and moderation analyses. Results: The correlation analysis showed that deliberate rumination was positively correlated with post-traumatic growth (r = 0.353, P < 0.01) and self-efficacy (r = 0.261, P < 0.01). Self-efficacy was also positively correlated with post-traumatic growth (r = 0.466, P < 0.01). In addition, we found that self-efficacy had a regulatory effect on the relationship between deliberate rumination and post-traumatic growth (R 2 = 0.287, P < 0.001) and that this effect was significant. Conclusion: The results show that deliberate rumination can be a positive predictor of post-traumatic growth and can play a certain role in fostering such growth. In addition, self-efficacy is a moderator that plays a buffer role between deliberate rumination and post-traumatic growth. These results contribute to a more comprehensive understanding of the mechanisms that affect post-traumatic growth.

A numerical study of COVID-19-laden droplets dispersion in aircraft cabin ventilation system.

Ventilation systems for aircraft cabins are mainly used to maintain a comfortable environment in the cabin and ensure the health of passengers. This study evaluates the decontamination performance of two cabin ventilation systems, the displacement ventilation (DV) system and the mixing ventilation (MV) system, in preventing contamination by virus (COVID-19)-laden droplets. The Euler-Lagrange method was used to computationally model droplet dispersion of different diameters and their behavior in the two systems was contrastively analyzed. Statistics on droplet suspension ratios and duration as well as the infection probability of each passenger were also computed. It was found that11.07% fewer droplet remained suspended in the DV system were than those in the MV system 10s from droplet release. In addition, the number of droplets extracted from the exhausts in the DV system was 13.15% more than the MV system at the 400s mark. In the DV system, higher ambient wind velocities were also found to locally increase infection probability for passengers in certain locations.

pH-Sensitive and Biodegradable Mn3(PO4)2·3H2O Nanoparticles as an Adjuvant of Protein-Based Bivalent COVID-19 Vaccine to Induce Potent and Broad-Spectrum Immunity.

Developing a novel and potent adjuvant with great biocompatibility for immune response augmentation is of great significance to enhance vaccine efficacy. In this work, we prepared a long-term stable, pH-sensitive, and biodegradable Mn3(PO4)2·3H2O nanoparticle (nano-MnP) by simply mixing MnCl2/NaH2PO4/Na2HPO4 solution for the first time and employed it as an immune stimulant in the bivalent COVID-19 protein vaccine comprised of wild-type S1 (S1-WT) and Omicron S1 (S1-Omicron) proteins as antigens to elicit a broad-spectrum immunity. The biological experiments indicated that the nano-MnP could effectively activate antigen-presenting cells through the cGAS-STING pathway. Compared with the conventional Alum-adjuvanted group, the nano-MnP-adjuvanted bivalent vaccine elicited approximately 7- and 8-fold increases in IgG antibody titers and antigen-specific IFN-γ secreting T cells, respectively. Importantly, antisera of the nano-MnP-adjuvanted group could effectively cross-neutralize the SARS-CoV-2 and its five variants of concern (VOCs) including Alpha, Beta, Gamma, Delta, and Omicron, demonstrating that this bivalent vaccine based on S1-WT and S1-Omicron proteins is an effective vaccine design strategy to induce broad-spectrum immune responses. Collectively, this nano-MnP material may provide a novel and efficient adjuvant platform for various prophylactic and therapeutic vaccines and provide insights for the development of the next-generation manganese adjuvant.

Enhance antibiotic resistance and human health risks in aerosols during the COVID-19 pandemic.

Aerosols are an important route for the transmission of antibiotic resistance genes (ARGs). Since the 2019 (COVID-19) pandemic, the large-scale use of disinfectants has effectively prevented the spread of environmental microorganisms, but studies regarding the antibiotic resistance of airborne bacteria remain limited. This study focused on four functional urban areas (commercial areas, educational areas, residential areas and wastewater treatment plant) to study the variations in ARG abundances, bacterial community structures and risks to human health during the COVID-19 pandemic in aerosol. The results indicated the abundance of ARGs during the COVID-19 period were up to approximately 13-fold greater than before the COVID-19 period. Large-scale disinfection resulted in a decrease in total bacterial abundance. However, chlorine-resistant bacteria tended to be survived. Among the four functional areas, the diversity and abundance of aerosol bacteria were highest in commercial aera. Antibiotic susceptibility assays suggested elevated resistance of isolated bacteria to several tested antibiotics due to disinfection exposure. The potential exposure risks of ARGs to human health were 2 times higher than before the COVID-19 pandemic, and respiratory intake was the main exposure route. The results highlighted the elevated antibiotic resistance of bacteria in aerosols that were exposed to disinfectants after the COVID-19 pandemic. This study provides theoretical guidance for the rational use of disinfectants and control of antimicrobial resistance.

Association Between Ischemic Stroke and COVID-19 in China: A Population-Based Retrospective Study.

Background and Purpose: To investigate the effect of prior ischemic stroke on the outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19), and to describe the incidence, clinical features, and risk factors of acute ischemic stroke (AIS) following COVID-19. Methods: In this population-based retrospective study, we included all the hospitalized positive patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020. Clinical data were extracted from administrative datasets coordinated by the Wuhan Health Commission. The propensity score matching and multivariate logistic regression analyses were used to adjust the confounding factors. Results: There are 36,358 patients in the final cohort, in which 1,160 (3.2%) had a prior stroke. After adjusting for available baseline characteristics, patients with prior stroke had a higher proportion of severe and critical illness and mortality. We found for the first time that the premorbid modified Rankin Scale (MRS) grouping (odds ratio [OR] = 1.796 [95% CI 1.334-2.435], p < 0.001) and older age (OR = 1.905 [95% CI 1.211-3.046], p = 0.006) imparted increased risk of death. AIS following COVID-19 occurred in 124 (0.34%) cases, and patients with prior stroke had a much higher incidence of AIS (3.4%). Logistic regression analyses confirmed an association between the severity of COVID-19 with the incidence of AIS. COVID-19 patients with AIS had a significantly higher mortality compared with COVID-19 patients without stroke and AIS patients without COVID-19. Conclusions: Coronavirus disease 2019 patients with prior stroke, especially those with the higher premorbid MRS or aged, have worse clinical outcomes. Furthermore, COVID-19 increases the incidence of AIS, and the incidence is positively associated with the severity of COVID-19.

Self-Adjuvanting Protein Vaccine Conjugated with a Novel Synthetic TLR4 Agonist on Virus-Like Liposome Induces Potent Immunity against SARS-CoV-2.

Exploring potent adjuvants and new vaccine strategies is crucial for the development of protein vaccines. In this work, we synthesized a new TLR4 agonist, structurally simplified lipid A analogue GAP112, as a potent built-in adjuvant to improve the immunogenicity of SARS-CoV-2 spike RBD protein. The new TLR4 agonist GAP112 was site-selectively conjugated on the N-terminus of RBD to construct an adjuvant-protein conjugate vaccine in a liposomal formulation. It is the first time that a TLR4 agonist is site-specifically and quantitatively conjugated to a protein antigen. Compared with an unconjugated mixture of GAP112/RBD, a two-dose immunization of the GAP112-RBD conjugate vaccine strongly activated innate immune cells, elicited a 223-fold increase in RBD-specific antibodies, and markedly enhanced T-cell responses. Antibodies induced by GAP112-RBD also effectively cross-neutralized SARS-CoV-2 variants (Delta/B.1.617.2 and Omicron/B.1.1.529). This conjugate strategy provides an effective method to greatly enhance the immunogenicity of antigen in protein vaccines against SARS-CoV-2 and other diseases.

Dynamic white matter changes in recovered COVID-19 patients: a two-year follow-up study.

Background and purpose: Long COVID with regard to the neurological system deserves more attention, as a surge of treated patients are being discharged from the hospital. As the dynamic changes in white matter after two years remain unknown, this characteristic was the focus of this study. Methods: We investigated 17 recovered COVID-19 patients at two years after discharge. Diffusion tensor imaging, neurite orientation dispersion and density imaging were performed to identify white matter integrity and changes from one to two years after discharge. Data for 13 revisited healthy controls were collected as a reference. Subscales of the Wechsler Intelligence scale were used to assess cognitive function. Repeated-measures ANOVA was used to detect longitudinal changes in 17 recovered COVID-19 patients and 13 healthy controls after one-year follow-up. Correlations between diffusion metrics, cognitive function, and other clinical characteristics (i.e., inflammatory factors) were also analyzed. Results: Longitudinal analysis showed the recovery trends of large-scale brain regions, with small-scale brain region deterioration from one year to two years after SARS-CoV-2 infection. However, persistent white matter abnormalities were noted at two years after discharge. Longitudinal changes of cognitive function showed no group difference. But cross-sectional cognitive difference between recovered COVID-19 patients and revisited HCs was detected. Inflammation levels in the acute stage correlated positively with white matter abnormalities and negatively with cognitive function. Moreover, the more abnormal the white matter was at two years, the greater was the cognitive deficit present. Conclusion: Recovered COVID-19 patients showed longitudinal recovery trends of white matter. But also had persistent white matter abnormalities at two years after discharge. Inflammation levels in the acute stage may be considered predictors of cognition and white matter integrity, and the white matter microstructure acts as a biomarker of cognitive function in recovered COVID-19 patients. These findings provide an objective basis for early clinical intervention.

Predictive effect of resilience on self-efficacy during the COVID-19 pandemic: The moderating role of creativity.

Introduction: To appraise the relationship and mechanism between resilience and self-efficacy in the context of the COVID-19 pandemic, we proposed a model to investigate the effect of resilience on self-efficacy and the moderating effect of creativity in this regard. Methods: Scales that measured resilience, creative behavior, and self-efficacy were rated by 881 college students in China to establish the moderating model. Results: The results showed that resilience and self-efficacy of participating college students were positively correlated, which meant that high resilience could predict a high level of self-efficacy. Moderating analysis using the SPSS PROCESS plug-in showed that creativity was an important element of resilience that positively affected self-efficacy and that this moderating effect was more significant in participants with a high degree of creativity. Discussion: These findings can provide a better understanding of the relationship between resilience and self-efficacy in demonstrating the traumatic impact of the COVID-19 pandemic on adolescent mental health and academic performance.

Force majeure and changed circumstances during the COVID-19 pandemic: the case of sports service contracts and judicial responses in China

This paper aims to explore the impact of COVID-19 on the performance of sports service contracts in China from a legal and judicial perspective. It attempts to contribute to the discussion on the applicability of the doctrines of force majeure and change of circumstances in the cases of impracticability of performance or obstructed performance due to COVID-19 and consequent government enforced pandemic control policies and measures. It reveals that courts in China have adopted a differentiated, pluralist, and practical approach according to the degree of the impact of COVID-19 preventive and control measures on the performance of contracts. The juridical responses by judges tend to favour the amendment of contract under the principle of changed circumstances with a view to balancing the interests of both parties to the contract and reducing the impact on the operations of the sport service industry. China’s experience suggests that it is important that courts adhere to the principle of fairness, the principle of balance of interests, and the principle of encouraging transaction in dealing with contract performance disputes caused by COVID-19, while the applicability of force majeure should be carefully examined in judicial practice.

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population.

Background: Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection. Methods: Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score. Results: The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count. Conclusion: The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Proteomic and Metabolomic Characterization of SARS-CoV-2-Infected Cynomolgus Macaque at Early Stage.

Although tremendous effort has been exerted to elucidate the pathogenesis of severe COVID-19 cases, the detailed mechanism of moderate cases, which accounts for 90% of all patients, remains unclear yet, partly limited by lacking the biopsy tissues. Here, we established the COVID-19 infection model in cynomolgus macaques (CMs), monitored the clinical and pathological features, and analyzed underlying pathogenic mechanisms at early infection stage by performing proteomic and metabolomic profiling of lung tissues and sera samples from COVID-19 CMs models. Our data demonstrated that innate immune response, neutrophile and platelet activation were mainly dysregulated in COVID-19 CMs. The symptom of neutrophilia, lymphopenia and massive "cytokines storm", main features of severe COVID-19 patients, were greatly weakened in most of the challenged CMs, which are more semblable as moderate patients. Thus, COVID-19 model in CMs is rational to understand the pathogenesis of moderate COVID-19 and may be a candidate model to assess the safety and efficacy of therapeutics and vaccines against SARS-CoV-2 infection.

RNA G-quadruplex formed in SARS-CoV-2 used for COVID-19 treatment in animal models.

The ongoing COVID-19 pandemic has continued to affect millions of lives worldwide, leading to the urgent need for novel therapeutic strategies. G-quadruplexes (G4s) have been demonstrated to regulate life cycle of multiple viruses. Here, we identify several highly conservative and stable G4s in SARS-CoV-2 and clarify their dual-function of inhibition of the viral replication and translation processes. Furthermore, the cationic porphyrin compound 5,10,15,20-tetrakis-(N-methyl-4-pyridyl)porphine (TMPyP4) targeting SARS-CoV-2 G4s shows excellent antiviral activity, while its N-methyl-2-pyridyl positional isomer TMPyP2 with low affinity for G4 has no effects on SARS-CoV-2 infection, suggesting that the antiviral activity of TMPyP4 attributes to targeting SARS-CoV-2 G4s. In the Syrian hamster and transgenic mouse models of SARS-CoV-2 infection, administration of TMPyP4 at nontoxic doses significantly suppresses SARS-CoV-2 infection, resulting in reduced viral loads and lung lesions. Worth to note, the anti-COVID-19 activity of TMPyP4 is more potent than remdesivir evidenced by both in vitro and in vivo studies. Our findings highlight SARS-CoV-2 G4s as a novel druggable target and the compelling potential of TMPyP4 for COVID-19 therapy. Different from the existing anti-SARS-CoV-2 therapeutic strategies, our work provides another alternative therapeutic tactic for SARS-CoV-2 infection focusing on targeting the secondary structures within SARS-CoV-2 genome, and would open a new avenue for design and synthesis of drug candidates with high selectivity toward the new targets.

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Background Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection. Methods Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman’s rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score. Results The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count. Conclusion The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Proteomic and Metabolomic Characterization of SARS-CoV-2-Infected Cynomolgus Macaque at Early Stage

Although tremendous effort has been exerted to elucidate the pathogenesis of severe COVID-19 cases, the detailed mechanism of moderate cases, which accounts for 90% of all patients, remains unclear yet, partly limited by lacking the biopsy tissues. Here, we established the COVID-19 infection model in cynomolgus macaques (CMs), monitored the clinical and pathological features, and analyzed underlying pathogenic mechanisms at early infection stage by performing proteomic and metabolomic profiling of lung tissues and sera samples from COVID-19 CMs models. Our data demonstrated that innate immune response, neutrophile and platelet activation were mainly dysregulated in COVID-19 CMs. The symptom of neutrophilia, lymphopenia and massive “cytokines storm”, main features of severe COVID-19 patients, were greatly weakened in most of the challenged CMs, which are more semblable as moderate patients. Thus, COVID-19 model in CMs is rational to understand the pathogenesis of moderate COVID-19 and may be a candidate model to assess the safety and efficacy of therapeutics and vaccines against SARS-CoV-2 infection.

Families coping with the COVID-19 pandemic: Risk perceptions associated with preventive intention across three generations of Chinese families

While generational differences in coping with the threat of the global COVID-19 crisis were widely discussed in Western societies, a more careful look from the family level is needed in collectivistic societies like China. This study conducted an online survey among three generations of Chinese families between late January and late March in 2020. The study examined 1380 individuals (college students [G1]: N = 762, M-age = 20.47 + 2.45, 78.1% female;parents [G2]: N = 386, M-age = 47.64 + 4.08, 51.3% female;grandparents [G3]: N = 232, M-age = 73.50 + 8.57, 54.3% female) and their cognitions, affect, and preventive intentions toward COVID-19. The investigation ultimately yielded 226 pairs of family data. The results showed generational differences in the above variables. Perceived severity showed a significant total effect on preventive intention for all three generations, and perceived societal risk showed a significant (total) effect on preventive intention only for G3. Perceived severity was linked to preventive intentions through negative affect for those with lower self-efficacy in G1 and G2. Perceived societal risk was also linked to preventive intention through negative affect for those with low self-efficacy for G2. Moreover, cluster analyses identified three types of families with different epidemic coping patterns: stand-by families (48.23%), precautious families (35.40%), and insensitive families (16.37%). This research provides theoretical and practical implications for understanding the disparities in epidemic prevention between different generations and families. Findings show insights for improving the government's communication strategies.

Adjuvant-Protein Conjugate Vaccine with Built-In TLR7 Agonist on S1 Induces Potent Immunity against SARS-CoV-2 and Variants of Concern.

With the global pandemic of the new coronavirus disease (COVID-19), a safe, effective, and affordable mass-produced vaccine remains the current focus of research. Herein, we designed an adjuvant-protein conjugate vaccine candidate, in which the TLR7 agonist (TLR7a) was conjugated to S1 subunit of SARS-CoV-2 spike protein, and systematically compared the effect of different numbers of built-in TLR7a on the immune activity for the first time. As the number of built-in TLR7a increased, a bell-shaped reaction was observed in three TLR7a-S1 conjugates, with TLR7a(10)-S1 (with around 10 built-in adjuvant molecules on one S1 protein) eliciting a more potent immune response than TLR7a(2)-S1 and TLR7a(18)-S1. This adjuvant-protein conjugate strategy allows the built-in adjuvant to provide cluster effects and prevents systemic toxicity and facilitates the co-delivery of adjuvant and antigen. Vaccination of mice with TLR7a(10)-S1 triggered a potent humoral and cellular immunity and a balanced Th1/Th2 immune response. Meanwhile, the vaccine induces effective neutralizing antibodies against SARS-CoV-2 and all variants of concern (B.1.1.7/alpha, B.1.351/beta, P.1/gamma, B.1.617.2/delta, and B.1.1.529/omicron). It is expected that the adjuvant-protein conjugate strategy has great potential to construct a potent recombinant protein vaccine candidate against various types of diseases.