A vaccine delivery system promotes strong immune responses against SARS-CoV-2 variants.

Global coronavirus disease 2019 (COVID-19) pandemics highlight the need of developing vaccines with universal and durable protection against emerging SARS-CoV-2 variants. Here we developed an extended-release vaccine delivery system (GP-diABZI-RBD), consisting the original SARS-CoV-2 WA1 strain receptor-binding domain (RBD) as the antigen and diABZI stimulator of interferon genes (STING) agonist in conjunction with yeast ß-glucan particles (GP-diABZI) as the platform. GP-diABZI-RBD could activate STING pathway and inhibit SARS-CoV-2 replication. Compared to diABZI-RBD, intraperitoneal injection of GP-diABZI-RBD elicited robust cellular and humoral immune responses in mice. Using SARS-CoV-2 GFP/ΔN transcription and replication-competent virus-like particle system (trVLP), we demonstrated that GP-diABZI-RBD-prototype vaccine exhibited the strongest and durable humoral immune responses and antiviral protection; whereas GP-diABZI-RBD-Omicron displayed minimum neutralization responses against trVLP. By using pseudotype virus (PsVs) neutralization assay, we found that GP-diABZI-RBD-Prototype, GP-diABZI-RBD-Delta, and GP-diABZI-RBD-Gamma immunized mice sera could efficiently neutralize Delta and Gamma PsVs, but had weak protection against Omicron PsVs. In contrast, GP-diABZI-RBD-Omicron immunized mice sera displayed the strongest neutralization response to Omicron PsVs. Taken together, the results suggest that GP-diABZI can serve as a promising vaccine delivery system for enhancing durable humoral and cellular immunity against broad SARS-CoV-2 variants. Our study provides important scientific basis for developing SARS-CoV-2 VOC-specific vaccines.

Association of Primary and Booster Vaccination and Prior Infection With SARS-CoV-2 Infection and Severe COVID-19 Outcomes.

Importance: Data about the association of COVID-19 vaccination and prior SARS-CoV-2 infection with risk of SARS-CoV-2 infection and severe COVID-19 outcomes may guide prevention strategies. Objective: To estimate the time-varying association of primary and booster COVID-19 vaccination and prior SARS-CoV-2 infection with subsequent SARS-CoV-2 infection, hospitalization, and death. Design, Setting, and Participants: Cohort study of 10.6 million residents in North Carolina from March 2, 2020, through June 3, 2022. Exposures: COVID-19 primary vaccine series and boosters and prior SARS-CoV-2 infection. Main Outcomes and Measures: Rate ratio (RR) of SARS-CoV-2 infection and hazard ratio (HR) of COVID-19-related hospitalization and death. Results: The median age among the 10.6 million participants was 39 years; 51.3% were female, 71.5% were White, and 9.9% were Hispanic. As of June 3, 2022, 67% of participants had been vaccinated. There were 2 771 364 SARS-CoV-2 infections, with a hospitalization rate of 6.3% and mortality rate of 1.4%. The adjusted RR of the primary vaccine series compared with being unvaccinated against infection became 0.53 (95% CI, 0.52-0.53) for BNT162b2, 0.52 (95% CI, 0.51-0.53) for mRNA-1273, and 0.51 (95% CI, 0.50-0.53) for Ad26.COV2.S 10 months after the first dose, but the adjusted HR for hospitalization remained at 0.29 (95% CI, 0.24-0.35) for BNT162b2, 0.27 (95% CI, 0.23-0.32) for mRNA-1273, and 0.35 (95% CI, 0.29-0.42) for Ad26.COV2.S and the adjusted HR of death remained at 0.23 (95% CI, 0.17-0.29) for BNT162b2, 0.15 (95% CI, 0.11-0.20) for mRNA-1273, and 0.24 (95% CI, 0.19-0.31) for Ad26.COV2.S. For the BNT162b2 primary series, boosting in December 2021 with BNT162b2 had the adjusted RR relative to primary series of 0.39 (95% CI, 0.38-0.40) and boosting with mRNA-1273 had the adjusted RR of 0.32 (95% CI, 0.30-0.34) against infection after 1 month and boosting with BNT162b2 had the adjusted RR of 0.84 (95% CI, 0.82-0.86) and boosting with mRNA-1273 had the adjusted RR of 0.60 (95% CI, 0.57-0.62) after 3 months. Among all participants, the adjusted RR of Omicron infection compared with no prior infection was estimated at 0.23 (95% CI, 0.22-0.24) against infection, and the adjusted HRs were 0.10 (95% CI, 0.07-0.14) against hospitalization and 0.11 (95% CI, 0.08-0.15) against death after 4 months. Conclusions and Relevance: Receipt of primary COVID-19 vaccine series compared with being unvaccinated, receipt of boosters compared with primary vaccination, and prior infection compared with no prior infection were all significantly associated with lower risk of SARS-CoV-2 infection (including Omicron) and resulting hospitalization and death. The associated protection waned over time, especially against infection.

Reliably Assessing Duration of Protection for Coronavirus Disease 2019 Vaccines.

Decision making about vaccination and boosting schedules for coronavirus disease 2019 (COVID-19) hinges on reliable methods for evaluating the longevity of vaccine protection. We show that modeling of protection as a piecewise linear function of time since vaccination for the log hazard ratio of the vaccine effect provides more reliable estimates of vaccine effectiveness at the end of an observation period and also detects plateaus in protective effectiveness more reliably than the standard method of estimating a constant vaccine effect over each time period. This approach will be useful for analyzing data pertaining to COVID-19 vaccines and other vaccines for which rapid and reliable understanding of vaccine effectiveness over time is desired.

Verifying the Effectiveness of Sports Event Policies for a City’s Sustainable Growth: Focusing on the Multiple Effects

This article presents empirical evidence that suggests that there are multiple effects of local government sports event hosting policies. This study is predicated on the notion that the attraction of sports events is a feature of city-level policies. The empirical analysis used a multiple effects model, and the research employed a dual model approach: (a) a sponsorship effect model and (b) a tourism effect model. A questionnaire was administered online, and 383 cases were used for data processing. Confirmatory factor analysis and structural equation modeling were performed using SPSS 25.0 and AMOS 25.0. (a) In the “business model”, it was confirmed that event satisfaction affected sustainable purchase intention only through the sponsor’s social image. (b) The “tourism model” confirmed that event satisfaction affected the intention to engage in positive word of mouth to recommend the destination through both forming a psychological attachment and experiencing emotional satisfaction. Among the event satisfaction factors, service satisfaction was identified as more important than facility satisfaction. As shown by the above results, satisfaction with sports events had simultaneous effects on the persistence of the sponsorship effects model and the persistence of tourism effects. The study concluded that attractive sports events promoted sustainable urban growth.

Evaluating Vaccine Efficacy Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Although interim results from several large, placebo-controlled, phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic coronavirus disease 2019 (COVID-19), it is unknown how effective the vaccines are in preventing people from becoming asymptomatically infected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between 2 antibody or reverse-transcription polymerase chain reaction (RT-PCR) tests. Additional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment of participants or crossover of placebo recipients to the vaccine arm before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, staggered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies that mimic the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates.

Mathematical modeling and stability analysis of the COVID-19 with quarantine and isolation.

The present paper focuses on the modeling of the COVID-19 infection with the use of hospitalization, isolation and quarantine. Initially, we construct the model by spliting the entire population into different groups. We then rigorously analyze the model by presenting the necessary basic mathematical features including the feasible region and positivity of the problem solution. Further, we evaluate the model possible equilibria. The theoretical expression of the most important mathematical quantity of major public health interest called the basic reproduction number is presented. We are taking into account to study the disease free equilibrium by studying its local and global asymptotical analysis. We considering the cases of the COVID-19 infection of Pakistan population and find the parameters using the estimation with the help of nonlinear least square and have R 0 ≈ 1 . 95 . Further, to determine the influence of the model parameters on disease dynamics we perform the sensitivity analysis. Simulations of the model are presented using estimated parameters and the impact of various non-pharmaceutical interventions on disease dynamics is shown with the help of graphical results. The graphical interpretation justify that the effective utilization of keeping the social-distancing, making the quarantine of people (or contact-tracing policy) and to make hospitalization of confirmed infected people that dramatically reduces the number of infected individuals (enhancing the quarantine or contact-tracing by 50% from its baseline reduces 84% in the predicted number of confirmed infected cases). Moreover, it is observed that without quarantine and hospitalization the scenario of the disease in Pakistan is very worse and the infected cases are raising rapidly. Therefore, the present study suggests that still, a proper and effective application of these non-pharmaceutical interventions are necessary to curtail or minimize the COVID-19 infection in Pakistan.

Effectiveness of Covid-19 Vaccines over a 9-Month Period in North Carolina.

BACKGROUND: The duration of protection afforded by coronavirus disease 2019 (Covid-19) vaccines in the United States is unclear. Whether the increase in postvaccination infections during the summer of 2021 was caused by declining immunity over time, the emergence of the B.1.617.2 (delta) variant, or both is unknown. METHODS: We extracted data regarding Covid-19-related vaccination and outcomes during a 9-month period (December 11, 2020, to September 8, 2021) for approximately 10.6 million North Carolina residents by linking data from the North Carolina Covid-19 Surveillance System and the Covid-19 Vaccine Management System. We used a Cox regression model to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), and Ad26.COV2.S (Johnson & Johnson-Janssen) vaccines in reducing the current risks of Covid-19, hospitalization, and death, as a function of time elapsed since vaccination. RESULTS: For the two-dose regimens of messenger RNA (mRNA) vaccines BNT162b2 (30 µg per dose) and mRNA-1273 (100 µg per dose), vaccine effectiveness against Covid-19 was 94.5% (95% confidence interval [CI], 94.1 to 94.9) and 95.9% (95% CI, 95.5 to 96.2), respectively, at 2 months after the first dose and decreased to 66.6% (95% CI, 65.2 to 67.8) and 80.3% (95% CI, 79.3 to 81.2), respectively, at 7 months. Among early recipients of BNT162b2 and mRNA-1273, effectiveness decreased by approximately 15 and 10 percentage points, respectively, from mid-June to mid-July, when the delta variant became dominant. For the one-dose regimen of Ad26.COV2.S (5 × 1010 viral particles), effectiveness against Covid-19 was 74.8% (95% CI, 72.5 to 76.9) at 1 month and decreased to 59.4% (95% CI, 57.2 to 61.5) at 5 months. All three vaccines maintained better effectiveness in preventing hospitalization and death than in preventing infection over time, although the two mRNA vaccines provided higher levels of protection than Ad26.COV2.S. CONCLUSIONS: All three Covid-19 vaccines had durable effectiveness in reducing the risks of hospitalization and death. Waning protection against infection over time was due to both declining immunity and the emergence of the delta variant. (Funded by a Dennis Gillings Distinguished Professorship and the National Institutes of Health.).

A Comprehensive Mathematical Model for SARS-CoV-2 in Caputo Derivative

In the present work, we study the COVID-19 infection through a new mathematical model using the Caputo derivative. The model has all the possible interactions that are responsible for the spread of disease in the community. We first formulate the model in classical differential equations and then extend it into fractional differential equations using the definition of the Caputo derivative. We explore in detail the stability results for the model of the disease-free case when R0<1. We show that the model is stable locally when R0<1. We give the result that the model is globally asymptotically stable whenever R0≤1. Further, to estimate the model parameters, we consider the real data of the fourth wave from Pakistan and provide a reasonable fitting to the data. We estimate the basic reproduction number for the proposed data to be R0=1.0779. Moreover, using the real parameters, we present the numerical solution by first giving a reliable scheme that can numerically handle the solution of the model. In our simulation, we give the graphical results for some sensitive parameters that have a large impact on disease elimination. Our results show that taking into consideration all the possible interactions can describe COVID-19 infection.

Modeling the impact of COVID-19 on air quality in southern California: implications for future control policies

In response to the coronavirus disease of 2019 (COVID-19), California issued statewide stay-at-home orders, bringing about abrupt and dramatic reductions in air pollutant emissions. This crisis offers us an unprecedented opportunity to evaluate the effectiveness of emission reductions in terms of air quality. Here we use the Weather Research and Forecasting model with Chemistry (WRF-Chem) in combination with surface observations to study the impact of the COVID-19 lockdown measures on air quality in southern California. Based on activity level statistics and satellite observations, we estimate the sectoral emission changes during the lockdown. Due to the reduced emissions, the population-weighted concentrations of fine particulate matter (PM2.5) decrease by 15 % in southern California. The emission reductions contribute 68 % of the PM2.5 concentration decrease before and after the lockdown, while meteorology variations contribute the remaining 32 %. Among all chemical compositions, the PM2.5 concentration decrease due to emission reductions is dominated by nitrate and primary components. For O3 concentrations, the emission reductions cause a decrease in rural areas but an increase in urban areas;the increase can be offset by a 70 % emission reduction in anthropogenic volatile organic compounds (VOCs). These findings suggest that a strengthened control on primary PM2.5 emissions and a well-balanced control on nitrogen oxides and VOC emissions are needed to effectively and sustainably alleviate PM2.5 and O3 pollution in southern California.

Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19.

PURPOSE: Differentiating COVID-19 from other acute infectious pneumonias rapidly is challenging at present. This study aims to improve the diagnosis of COVID-19 using computed tomography (CT). METHOD: COVID-19 was confirmed mainly by virus nucleic acid testing and epidemiological history according to WHO interim guidance, while other infectious pneumonias were diagnosed by antigen testing. The texture features were extracted from CT images by two radiologists with 5 years of work experience using modified wavelet transform and matrix computation analyses. The random forest (RF) classifier was applied to identify COVID-19 patients and images. RESULTS: We retrospectively analysed the data of 95 individuals (291 images) with COVID-19 and 96 individuals (279 images) with other acute infectious pneumonias, including 50 individuals (160 images) with influenza A/B. In total, 6 texture features showed a positive association with COVID-19, while 4 features were negatively associated. The mean AUROC, accuracy, sensitivity, and specificity values of the 5-fold test sets were 0.800, 0.722, 0.770, and 0.680 for image classification and 0.858, 0.826, 0.809, and 0.842 for individual classification, respectively. The feature 'Correlation' contributed most both at the image level and individual level, even compared with the clinical factors. In addition, the texture features could discriminate COVID-19 from influenza A/B, with an AUROC of 0.883 for images and 0.957 for individuals. CONCLUSIONS: The developed texture feature-based RF classifier could assist in the diagnosis of COVID-19, which could be a rapid screening tool in the era of pandemic.

Fast automated detection of COVID-19 from medical images using convolutional neural networks.

Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.

Whole Genome Identification of Potential G-Quadruplexes and Analysis of the G-Quadruplex Binding Domain for SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has become a global public health emergency. G-quadruplex, one of the non-canonical secondary structures, has shown potential antiviral values. However, little is known about the G-quadruplexes of the emerging SARS-CoV-2. Herein, we characterized the potential G-quadruplexes in both positive and negative-sense viral strands. The identified potential G-quadruplexes exhibited similar features to the G-quadruplexes detected in the human transcriptome. Within some bat- and pangolin-related betacoronaviruses, the G-tracts rather than the loops were under heightened selective constraints. We also found that the amino acid sequence similar to SUD (SARS-unique domain) was retained in SARS-CoV-2 but depleted in some other coronaviruses that can infect humans. Further analysis revealed that the amino acid residues related to the binding affinity of G-quadruplexes were conserved among 16,466 SARS-CoV-2 samples. Moreover, the dimer of the SUD-homology structure in SARS-CoV-2 displayed similar electrostatic potential patterns to the SUD dimer from SARS. Considering the potential value of G-quadruplexes to serve as targets in antiviral strategy, our fundamental research could provide new insights for the SARS-CoV-2 drug discovery.

Analysis of the molecular mechanism of Pudilan (PDL) treatment for COVID-19 by network pharmacology tools.

BACKGROUND: Pudilan (PDL), a four-herb prescription with the traditional function of heat-clearing and detoxifying, has been clinically used as an anti-SARS-CoV-2 infectory agent in China. PDL might also have therapeutic potentials for COVID-19 while the underlying mechanisms remain to be clarified. METHODS: We used network pharmacology analysis and selected 68 co-targeted genes/proteins as targets of both PDL and COVID-19. These co-targeted genes/proteins were predicted by SwissDock Server for their high-precision docking simulation, and analyzed by STRING for proteins to protein interaction (PPI), pathway and GO (gene ontology) enrichment. The therapeutic effect for PDL treatment on COVID-19 was validated by the TCMATCOV (TCM Anti COVID-19) platform. RESULTS: PDL might prevent the entrance of SARS-CoV-2 entry into cells by blocking the angiotensin-converting enzyme 2 (ACE2). It might inhibit the cytokine storm by affecting C-reactive protein (CRP), interferon-γ (IFN-γ), interleukin- 6 (IL-6), interleukin- 10 (IL-10), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), C-C motif chemokine ligand 5 (CCL5), transforming growth factor-ß1 (TGFß1), and other proteins. PDL might moderate the immune system to shorten the course of the disease, delay disease progression, and reduce the mortality rate. CONCLUSION: PDL might have a therapeutic effect on COVID-19 through three aspects, including the moderate immune system, anti-inflammation, and anti-virus entry into cells.

Analysis of the susceptibility of lung cancer patients to SARS-CoV-2 infection.

Recent studies have reported that COVID-19 patients with lung cancer have a higher risk of severe events than patients without cancer. In this study, we investigated the gene expression of angiotensin I-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) with prognosis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Lung cancer patients in each age stage, subtype, and pathological stage are susceptible to SARS-CoV-2 infection, except for the primitive subtype of LUSC. LUAD patients are more susceptible to SARS-CoV-2 infection than LUSC patients. The findings are unanimous on tissue expression in gene and protein levels.

[Study on treatment of "cytokine storm" by anti-2019-nCoV prescriptions based on arachidonic acid metabolic pathway].

Since the outbreak of 2019-nCoV, the epidemic has developed rapidly and the situation is grim. LANCET figured out that the 2019-nCoV is closely related to "cytokine storm". "Cytokine storm" is an excessive immune response of the body to external stimuli such as viruses and bacteria. As the virus attacking the body, it stimulates the secretion of a large number of inflammatory factors: interleukin(IL), interferon(IFN), C-X-C motif chemokine(CXCL) and so on, which lead to cytokine cascade reaction. With the exudation of inflammatory factors, cytokines increase abnormally in tissues and organs, interfering with the immune system, causing excessive immune response of the body, resulting in diffuse damage of lung cells, pulmonary fibrosis, and multiple organ damage, even death. Arachidonic acid(AA) metabolic pathway is principally used to synthesize inflammatory cytokines, such as monocyte chemotactic protein 1(MCP-1), tumor necrosis factor(TNF), IL, IFN, etc., which is closely related to the occurrence, development and regression of inflammation. Therefore, the inhibition of AA metabolism pathway is benefit for inhibiting the release of inflammatory factors in the body and alleviating the "cytokine storm". Based on the pharmacophore models of the targets on AA metabolic pathway, the traditional Chinese medicine database 2009(TCMD 2009) was screened. The potential herbs were ranked by the number of hit molecules, which were scored by pharmacophore fit value. In the end, we obtained the potential active prescriptions on "cytokine storm" according to the potential herbs in the "National novel coronavirus pneumonia diagnosis and treatment plan(trial version sixth)". The results showed that the hit components with the inhibitory effect on AA were magnolignan Ⅰ, lonicerin and physcion-8-O-ß-D-glucopy-ranoside, which mostly extracted from Magnoliae Officinalis Cortex, Zingiberis Rhizoma Recens, Lonicerae Japonicae Flos, Rhei Radix et Rhizoma, Salviae Miltiorrhizae Radix et Rhizoma, Scutellariae Radix, Gardeniae Fructus, Ginseng Radix et Rhizoma, Arctii Fructus, Dryopteridis Crassirhizomatis Rhizoma, Paeoniaeradix Rubra, Dioscoreae Rhizoma. Finally the anti-2019-nCoV prescriptions were analyzed to obtain the potential active prescriptions on AA metabolic pathway, Huoxiang Zhengqi Capsules, Jinhua Qinggan Granules, Lianhua Qingwen Capsules, Qingfei Paidu Decoction, Xuebijing Injection, Reduning Injection and Tanreqing Injection were found that may prevent 2019-nCoV via regulate cytokines. This study intends to provide reference for clinical use of traditional Chinese medicine to resist new coronavirus.