ABSTRACT
BACKGROUND: Since the outbreak of COVID-19 has resulted in over 313,000,000 confirmed cases of infection and over 5,500,000 deaths, substantial research work has been conducted to discover agents/ vaccines against COVID-19. Undesired adverse effects were observed in clinical practice and common vaccines do not protect the nasal tissue. An increasing volume of direct evidence based on clinical studies of traditional Chinese medicines (TCM) in the treatment of COVID-19 has been reported. However, the safe anti-inflammatory and anti-fibrotic proprietary Chinese medicines nasal spray, designated as Allergic Rhinitis Nose Drops (ARND), and its potential of re-purposing for suppressing viral infection via SARS-CoV-2 RBD (Delta)- angiotensin converting enzyme 2 (ACE2) binding have not been elucidated. PURPOSE: To characterize ARND as a potential SARS-CoV-2 entry inhibitor for its possible preventive application in anti-virus hygienic agent. METHODS: Network pharmacology analysis of ARND was adopted to asacertain gene targets which were commonly affected by COVID-19. The inhibitory effect of ARND on viral infection was determined by an in vitro pseudovirus assay. Furthermore, ARND was confirmed to have a strong binding affinity with ACE2 and SARS-CoV-2 spike-RBD (Delta) by ELISA. Finally, inflammatory and fibrotic cell models were used in conjunction in this study. RESULTS: The results suggested ARND not only inhibited pseudovirus infection and undermined the binding affinity between ACE2 and the Spike protein (Delta), but also attenuated the inflammatory response upon infection and may lead to a better prognosis with a lower risk of pulmonary fibrosis. The data in this study also provide a basis for further development of ARND as an antiviral hygienic product and further investigations on ARND in the live virus, in vivo and COVID-19 patients. ARND holds promise for use in the current COVID-19 outbreak as well as in future pandemics. CONCLUSION: ARND could be considered as a safe anti-SARS-CoV-2 agent with potential to prevent SARS-CoV-2 coronavirus infection.
ABSTRACT
It's urgently needed to assess the COVID-19 epidemic under the "dynamic zero-COVID policy" in China, which provides a scientific basis for evaluating the effectiveness of this strategy in COVID-19 control. Here, we developed a time-dependent susceptible-exposed-asymptomatic-infected-quarantined-removed (SEAIQR) model with stage-specific interventions based on recent Shanghai epidemic data, considering a large number of asymptomatic infectious, the changing parameters, and control procedures. The data collected from March 1st, 2022 to April 15th, 2022 were used to fit the model, and the data of subsequent 7 days and 14 days were used to evaluate the model performance of forecasting. We then calculated the effective regeneration number (R t) and analyzed the sensitivity of different measures scenarios. Asymptomatic infectious accounts for the vast majority of the outbreaks in Shanghai, and Pudong is the district with the most positive cases. The peak of newly confirmed cases and newly asymptomatic infectious predicted by the SEAIQR model would appear on April 13th, 2022, with 1963 and 28,502 cases, respectively, and zero community transmission may be achieved in early to mid-May. The prediction errors for newly confirmed cases were considered to be reasonable, and newly asymptomatic infectious were considered to be good between April 16th to 22nd and reasonable between April 16th to 29th. The final ranges of cumulative confirmed cases and cumulative asymptomatic infectious predicted in this round of the epidemic were 26,477 â¼ 47,749 and 402,254 â¼ 730,176, respectively. At the beginning of the outbreak, R t was 6.69. Since the implementation of comprehensive control, R t showed a gradual downward trend, dropping to below 1.0 on April 15th, 2022. With the early implementation of control measures and the improvement of quarantine rate, recovery rate, and immunity threshold, the peak number of infections will continue to decrease, whereas the earlier the control is implemented, the earlier the turning point of the epidemic will arrive. The proposed time-dependent SEAIQR dynamic model fits and forecasts the epidemic well, which can provide a reference for decision making of the "dynamic zero-COVID policy".
ABSTRACT
BACKGROUND: COVID-19 pandemic is a global crisis which results in millions of deaths and causes long-term neurological sequelae, such as Alzheimer's disease (AD). OBJECTIVE: We aimed to explore the interaction between COVID-19 and AD by integrating bioinformatics to find the biomarkers which lead to AD occurrence and development with COVID-19 and provide early intervention. METHODS: The differential expressed genes (DEGs) were found by GSE147507 and GSE132903, respectively. The common genes between COVID-19 and AD were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactions (PPI) network analysis were carried out. Hub genes were found by cytoscape. A multivariate logistic regression model was constructed. NetworkAnalyst was used for the analysis of TF-gene interactions, TF-miRNA coregulatory network, and Protein-chemical Interactions. RESULTS: Forty common DEGs for AD and COVID-19 were found. GO and KEGG analysis indicated that the DEGs were enriched in the calcium signal pathway and other pathways. A PPI network was constructed, and 5 hub genes were identified (ITPR1, ITPR3, ITPKB, RAPGEF3, MFGE8). Four hub genes (ITPR1, ITPR3, ITPKB, RAPGEF3) which were considered as important factors in the development of AD that were affected by COVID-19 were shown by nomogram. Utilizing NetworkAnalyst, the interaction network of 4 hub genes and TF, miRNA, common AD risk genes, and known compounds is displayed, respectively. CONCLUSION: COVID-19 patients are at high risk of developing AD. Vaccination is required. Four hub genes can be considered as biomarkers for prediction and treatment of AD development caused by COVID-19. Compounds with neuroprotective effects can be used as adjuvant therapy for COVID-19 patients.