Persistent Topological Laplacian Analysis of SARS-CoV-2 Variants

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its inability to handle heterogeneous information, such as multiple types of geometric objects;being qualitative rather than quantitative, e.g., counting a 5-member ring the same as a 6-member ring, and a failure to describe nontopological changes, such as homotopic changes in protein–protein binding. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the limitations of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD). First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on a topological deep learning paradigm and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science. [ FROM AUTHOR] Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data. To alleviate the difficulties, we propose a co-evolutionary transfer learning (CETL) method for predicting the demands of a set of medical materials, which is important in COVID-19 prevention and control. CETL reuses material demand knowledge not only from other epidemics, such as severe acute respiratory syndrome (SARS) and bird flu but also from natural and manmade disasters. The knowledge or data of these related tasks can also be relatively few and imbalanced. In CETL, each prediction task is implemented by a fuzzy deep contractive autoencoder (CAE), and all prediction networks are cooperatively evolved, simultaneously using intrapopulation evolution to learn task-specific knowledge in each domain and using interpopulation evolution to learn common knowledge shared across the domains. Experimental results show that CETL achieves high prediction accuracies compared to selected state-of-the-art transfer learning and multitask learning models on datasets during two stages of COVID-19 spreading in China.

Serum procalcitonin level is independently associated with mechanical ventilation and case-fatality in hospitalized COVID-19-positive US veterans-A potential marker for disease severity.

The Coronavirus-19 disease (COVID-19) has claimed over 6.8 million lives since first being reported in late 2019. The virus that causes COVID-19 disease is highly contagious and spreads rapidly. To date, there are no approved prognostic tools that could predict why some patients develop severe or fatal disease outcomes. Early COVID-19 studies found an association between procalcitonin (PCT) and hospitalization or duration of mechanical ventilation and death but were limited by the cohort sizes. Therefore, this study was designed to confirm the associations of PCT with COVID-19 disease severity outcomes in a large cohort. For this retrospective data analysis study, 27,154 COVID-19-positive US veterans with post-infection PCT laboratory test data and their disease severity outcomes were accessed using the VA electronic healthcare data. Cox regression models were used to test the association between serum PCT levels and disease outcomes while controlling for demographics and relevant confounding variables. The models demonstrated increasing disease severity (ventilation and death) with increasing PCT levels. For PCT serum levels above 0.20 ng/ml, the unadjusted risk increased nearly 2.3-fold for mechanical ventilation (hazard ratio, HR, 2.26, 95%CI: 2.11-2.42) and in-hospital death (HR, 2.28, 95%CI: 2.16-2.41). Even when adjusted for demographics, diabetes, pneumonia, antibiotic use, white blood cell count, and serum C-reactive protein levels, the risks remained relatively high for mechanical ventilation (HR, 1.80, 95%CI: 1.67-1.94) and death (HR, 1.76, 95%CI: 1.66-1.87). These data suggest that higher PCT levels have independent associations with ventilation and in-hospital death in veterans with COVID-19 disease, validating previous findings. The data suggested that serum PCT level may be a promising prognostic tool for COVID-19 severity assessment and should be further evaluated in a prospective clinical trial.

Changes in Antigen-Reactive Antibody Level at Various Time Spans after COVID-19 Vaccination Using Machine Learning (preprint)

Vaccines trigger a complicated immunological response that includes B and T cells, with B cells producing antibodies. SARS-CoV-2 immunity weakens over time after vaccination. Discovering key changes in antigen-reactive antibodies over time after vaccination could help improve vaccine efficiency. In this study, we collected data on blood antibody levels in a cohort of healthcare workers vaccinated for COVID-19 and obtained 73 antigens in samples from four groups according to the duration after vaccination, including 104 unvaccinated healthcare workers, 534 healthcare workers within 60 days after vaccination, 594 healthcare workers between 60 and 180 days after vaccination, and 141 healthcare workers over 180 days after vaccination. An efficient machine learning based framework containing four feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and maximum relevance minimum redundancy) and four classification algorithms (decision tree, k-nearest neighbor, random forest, and support vector machine) was designed to screen out essential antigens. Several efficient classifiers with weighted F1 value around 0.75 were constructed. This study revealed that S1+S2, S1.mFcTag, S1.HisTag, S1, S2, Spike.RBD.His.Bac, Spike.RBD.rFc, and S1.RBD.mFc were most highly ranked among all features, where S1 and S2 are the subunits of Spike, and the suffixes represent the tagging information of different recombinant proteins. Meanwhile, the classification rules were extracted from the optimal decision tree to explain quantitatively the roles of antigens in the classification. This study identified antibodies associated with decreased clinical immunity based on populations with different time spans after vaccination. These antibodies have important implications for maintaining long-term immunity to SARS-CoV-2.

Emerging Dominant SARS-CoV-2 Variants.

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants, Omicron (BA.1), BA.2, and BA.4/BA.5, were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. On the basis of newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BP.1, BL*, BA.2.75*, BQ.1*, and particularly BN.1* have a high potential to become the new dominant variants to drive the next surge. Our key projection about these variants dominance made on Oct. 18, 2022 (see arXiv:2210.09485) became reality in late November 2022.

Model selection with decision support model for US natural gas consumption forecasting

Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account.

A potential alternative systemic treatment option for epithelioid sarcoma (preprint)

Background: Epithelioid sarcoma is a rare soft tissue sarcoma characterized by SMARCB1/INI1 deficiency. Much attention has been paid to the selective EZH2 inhibitor tazemetostat, where other systemic treatments are generally ignored. To explore alternative treatment options, we studied the effects of irinotecan-based chemotherapy in a series of epithelioid sarcoma patients. Methods: We retrospectively reviewed data from patients with metastatic or unresectable epithelioid sarcoma at the Peking University People’s Hospital treated with irinotecan (50 mg/m2/d d1-5 Q3W) in combination with Anlotinib (12 mg Qd, 2 weeks on and 1 week off) from July 2015 to November 2021. Results: A total of 54 courses were administered. With a median follow up of 21.2 months (95% CI, 12.2, 68.1), the 5-year overall survival rate was 83.3%. Five of eight (62.5%) patients presented with unresectable localized lesions, including local tumor thrombosis and lymphatic metastasis. The other patients had unresectable pulmonary metastases. Six of eight (75%) patients had progressed following two lines of systemic therapy. The objective response rate reached 37.5% (three of eight patients) while stabilized disease was observed in 62.5% (five of eight) of patients. No patient had progressed at initial evaluation. At the last follow up, two patients were still using the combination and three patients had ceased the therapy due to toxicities such as diarrhea, nausea, and emesis. One patient changed to tazemetostat for maintenance and one patient stopped treatment due to coronavirus disease 2019 (COVID-19). Another patient stopped therapy as residual lesions had been radiated. Conclusions: The combination of irinotecan and Anlotinib as a salvage regimen may be considered another effective treatment option for refractory epithelioid sarcoma. Trial registration: This trial was approved in the Medical Ethics Committee of Peking University People’s Hospital on October 28, 2022 (No.: 2022PHD015-002). The trial was registered in Clinicaltrials.gov with identifier no. NCT05656222.

A novel RBD-protein/peptide vaccine elicits broadly neutralizing antibodies and protects mice and macaques against SARS-CoV-2.

The development of safe and effective vaccines to respond to COVID-19 pandemic/endemic remains a priority. We developed a novel subunit protein-peptide COVID-19 vaccine candidate (UB-612) composed of: (i) receptor binding domain of SARS-CoV-2 spike protein fused to a modified single-chain human IgG1 Fc; (ii) five synthetic peptides incorporating conserved helper and cytotoxic T lymphocyte (Th/CTL) epitopes derived from SARS-CoV-2 structural proteins (three from S2 subunit, one from membrane and one from nucleocapsid), and one universal Th peptide; (iii) aluminum phosphate as adjuvant. The immunogenicity and protective immunity induced by UB-612 vaccine were evaluated in four animal models: Sprague-Dawley rats, AAV-hACE2 transduced BALB/c mice, rhesus and cynomolgus macaques. UB-612 vaccine induced high levels of neutralizing antibody and T-cell responses, in all animals. The immune sera from vaccinated animals neutralized the SARS-CoV-2 original wild-type strains and multiple variants of concern, including Delta and Omicron. The vaccination significantly reduced viral loads, lung pathology scores, and disease progression after intranasal and intratracheal challenge with SARS-CoV-2 in mice, rhesus and cynomolgus macaques. UB-612 has been tested in primary regimens in Phase 1 and Phase 2 clinical studies and is currently being evaluated in a global pivotal Phase 3 clinical study as a single dose heterologous booster.

Hospitalization, mechanical ventilation, and case-fatality outcomes in US veterans with COVID-19 disease between years 2020-2021.

PURPOSE: Although veterans represent a significant proportion (7%) of the USA population, the COVID-19 disease impact within this group has been underreported. To bridge this gap, this study was undertaken. METHOD: A total of 419,559 veterans, who tested positive for COVID-19 disease in the Veterans Affairs hospital system from March 1st, 2020 to December 31st, 2021 with 60-days follow-up, was included in this retrospective review. Primary outcome measures included age-adjusted incidences and relative incidences of COVID-19 hospitalization, mechanical ventilation, and case-fatality outcomes. RESULTS: Of this veteran cohort with COVID-19 disease, predominately 85.7% were male, 59.1% were White veterans, 27.5% were ages 50-64, and 40.5% were obese. Although Black veterans were at 63% higher relative risk (RR) for hospitalization incidences, they had a similar risk RR for in-hospital deaths compared to the White-veteran referent. Asian, American Indian/Alaska Native races, advanced age ≥65, and the underweight were at high RR for mechanical ventilator and/or in-hospital deaths compared to respective referent groups. Veterans who are ≥85 years old had a nearly 5-fold higher incidence of death compared respective referent group. The monthly outcomes for hospitalization, ventilation, and case-fatality data showed decreasing trends with time. CONCLUSION: An increased incidence of death was associated with age ≥65 years and underweight veterans compared to the referent group. Age-adjusted data, however, did not show any increased incidence of death in Black veterans compared to White veterans. RATINGS OF THE QUALITY OF THE EVIDENCE: 3 (Case-control studies; retrospective cohort study).

Humoral Immune Response to Inactivated COVID-19 Vaccination at the 3rd Month among People Living with HIV (preprint)

Background: Research on the immune response to inactivated COVID-19 vaccination among people living with HIV (PLWH) is limited, especially among those with low CD4+ T lymphocyte (CD4 cell) count. This cross-sectional study aimed to assess the humoral immune response to inactivated COVID-19 vaccination among PLWH compared to HIV negative controls (HNC) and to determine the impact of CD4 cell count on vaccine response among PLWH.Methods: The neutralizing antibodies (nAbs) and the specific IgM and IgG-binding antibody responses to the inactivated COVID-19 vaccine at the third month after the second dose of inactivated COVID-19 vaccination were measured among 138 PLWH and 35 HNC. Multivariable logistic regression and multiple linear regression models were conducted to identify factors associated with the seroconversion rate of antibodies and the magnitude of anti-SARS-CoV-2 antibody titers, respectively.Results: At the end of the third month after two doses of vaccination, the seroconversion rates of IgG were comparable between PLWH (8.7%; 95%CI, 3.9-13.5%) and HNC (11.4%; 95%CI, 0.3-22.5%), respectively. The median titers and seroconversion rate of nAbs among PLWH were 0.57 (IQR: 0.30-1.11) log10 BAU/mL and 29.0% (95%CI: 21.3-36.8%), respectively, both lower than those in HNC (P＜0.05). After adjusting for age, sex, comorbidities, and CD4 cell count, the titers and seroconversion rate of nAbs were comparable between PLWH and HNC (P＞0.05). Multivariable regression analyses showed that CD4 cell count＜200 /μL was independently associated with lower titers and seroconversion rate of nAbs among PLWH (P＜0.05). A positive correlation was observed between the CD4 cell count and nAbs titers in PLWH (Spearman'sρ=0.25, P=0.034). Conclusion: Our study concluded that the immune response to inactivated COVID-19 vaccination among PLWH was independently associated with CD4 cell count, PLWH with lower CD4 cell count showed a weaker humoral immune response, especially those with CD4 cell count＜200 /μL. This finding suggests that expanding COVID-19 vaccination coverage among PLWH is impendency. In addition, aggressive ART should be carried out for PLWH, especially for those with low CD4 cell count, to improve the immune response to vaccines.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Omicron BA.2 (B.1.1.529.2): High Potential for Becoming the Next Dominant Variant.

The Omicron variant has three subvariants: BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3). BA.2 is found to be able to alarmingly reinfect patients originally infected by Omicron BA.1. An important question is whether BA.2 or BA.3 will become a new dominating "variant of concern". Currently, no experimental data has been reported about BA.2 and BA.3. We construct a novel algebraic topology-based deep learning model to systematically evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and antibody resistance. Our comparative analysis of all main variants, namely, Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2 is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape current vaccines. Therefore, we project that Omicron BA.2 is on a path to becoming the next dominant variant. We forecast that like Omicron BA.1, BA.2 will also seriously compromise most existing monoclonal antibodies. All key predictions have been nearly perfectly confirmed before the official publication of this work.

Emerging Vaccine-Breakthrough SARS-CoV-2 Variants.

The surge of COVID-19 infections has been fueled by new SARS-CoV-2 variants, namely Alpha, Beta, Gamma, Delta, and so forth. The molecular mechanism underlying such surge is elusive due to the existence of 28 554 unique mutations, including 4 653 non-degenerate mutations on the spike protein. Understanding the molecular mechanism of SARS-CoV-2 transmission and evolution is a prerequisite to foresee the trend of emerging vaccine-breakthrough variants and the design of mutation-proof vaccines and monoclonal antibodies. We integrate the genotyping of 1 489 884 SARS-CoV-2 genomes, a library of 130 human antibodies, tens of thousands of mutational data, topological data analysis, and deep learning to reveal SARS-CoV-2 evolution mechanism and forecast emerging vaccine-breakthrough variants. We show that prevailing variants can be quantitatively explained by infectivity-strengthening and vaccine-escape (co-)mutations on the spike protein RBD due to natural selection and/or vaccination-induced evolutionary pressure. We illustrate that infectivity strengthening mutations were the main mechanism for viral evolution, while vaccine-escape mutations become a dominating viral evolutionary mechanism among highly vaccinated populations. We demonstrate that Lambda is as infectious as Delta but is more vaccine-resistant. We analyze emerging vaccine-breakthrough comutations in highly vaccinated countries, including the United Kingdom, the United States, Denmark, and so forth. Finally, we identify sets of comutations that have a high likelihood of massive growth: [A411S, L452R, T478K], [L452R, T478K, N501Y], [V401L, L452R, T478K], [K417N, L452R, T478K], [L452R, T478K, E484K, N501Y], and [P384L, K417N, E484K, N501Y]. We predict they can escape existing vaccines. We foresee an urgent need to develop new virus combating strategies.

Omicron Variant (B.1.1.529): Infectivity, Vaccine Breakthrough, and Antibody Resistance.

The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529) has ushered panic responses around the world due to its contagious and vaccine escape mutations. The essential infectivity and antibody resistance of the SARS-CoV-2 variant are determined by its mutations on the spike (S) protein receptor-binding domain (RBD). However, a complete experimental evaluation of Omicron might take weeks or even months. Here, we present a comprehensive quantitative analysis of Omicron's infectivity, vaccine breakthrough, and antibody resistance. An artificial intelligence (AI) model, which has been trained with tens of thousands of experimental data and extensively validated by experimental results on SARS-CoV-2, reveals that Omicron may be over 10 times more contagious than the original virus or about 2.8 times as infectious as the Delta variant. On the basis of 185 three-dimensional (3D) structures of antibody-RBD complexes, we unveil that Omicron may have an 88% likelihood to escape current vaccines. The U.S. Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly may be seriously compromised. Omicron may also diminish the efficacy of mAbs from AstraZeneca, Regeneron mAb cocktail, Celltrion, and Rockefeller University. However, its impacts on GlaxoSmithKline's sotrovimab appear to be mild. Our work calls for new strategies to develop the next generation mutation-proof SARS-CoV-2 vaccines and antibodies.

Immune response and safety to inactivated COVID-19 vaccine: A comparison between People Living with HIV and HIV-naive individuals (preprint)

Background: Multi-types COVID-19 vaccines have shown safety and efficacy against COVID-19 in adults. Although current guidelines encourage people living with HIV(PLWH) to take COVID-19 vaccines, whether their immune response to COVID-19 vaccines is distinct from HIV-free individuals is still unclear. Methods: Between March to June 2021, 48 PLWH and 40 HNC, aged 18 to 59 years, were enrolled in the study in Wuchang district of Wuhan city. All of them received inactivated COVID-19 vaccine at day 0 and the second dose at day 28. The primary safety outcome was the combined adverse reactions within 7days after each injection. The primary immunogenicity outcomes were neutralizing antibodies (nAbs) responses by chemiluminescence and total specific IgM and IgG antibodies responses by ELISA and colloidal gold at baseline (day 0), day 14, day 28, day 42, and day 70. Results: In total, the study included 46 PLWH and 38 HNC who finished 70 days’ follow-up. The frequency of adverse reactions to the first and second dose was not different between PLWH (30% and 11%) vs HNC (32% and 24%). There were no serious adverse events. NAbs responses among PLWH peaked at day 70, while among HNC peaked at day 42. At day 42, the geometric mean concentration (GMC) and seroconversion rate of nAbs among PLWH were 4.46 binding antibody units (BAU)/mL (95% CI, 3.18-5.87) and 26% (95% CI, 14-41), which were lower than that among HNC [GMC (18.28 BAU/mL, 95% CI, 10.33-32.33), seroconversion rate (63%, 95% CI, 44-79)]. IgG responses among both PLWH and HNC peaked at day 70. At day 70, the geometric mean ELISA units (GMEU) and seroconversion rate of IgG among PLWH were 0.193 ELISA units (EU)/mL (95% CI, 0.119-0.313) and 51% (95% CI, 34-69), which was lower than that among HNC [GMEU (0.379 BAU/mL, 95% CI, 0.224-0.653), seroconversion rate (86%, 95% CI, 64-97)]. Conclusion: Early humoral immune response to the inactivated COVID-19 vaccine was weaker and delayed among the PLWH population than that among HNC. This observation remained consistent regardless of a high CD4 count and a low HIV viral load suppressed by antiretroviral therapy (ART).

Mechanisms of SARS-CoV-2 Evolution Revealing Vaccine-Resistant Mutations in Europe and America.

The importance of understanding SARS-CoV-2 evolution cannot be overlooked. Recent studies confirm that natural selection is the dominating mechanism of SARS-CoV-2 evolution, which favors mutations that strengthen viral infectivity. Here, we demonstrate that vaccine-breakthrough or antibody-resistant mutations provide a new mechanism of viral evolution. Specifically, vaccine-resistant mutation Y449S in the spike (S) protein receptor-binding domain, which occurred in co-mutations Y449S and N501Y, has reduced infectivity compared to that of the original SARS-CoV-2 but can disrupt existing antibodies that neutralize the virus. By tracking the evolutionary trajectories of vaccine-resistant mutations in more than 2.2 million SARS-CoV-2 genomes, we reveal that the occurrence and frequency of vaccine-resistant mutations correlate strongly with the vaccination rates in Europe and America. We anticipate that as a complementary transmission pathway, vaccine-breakthrough or antibody-resistant mutations, like those in Omicron, will become a dominating mechanism of SARS-CoV-2 evolution when most of the world's population is either vaccinated or infected. Our study sheds light on SARS-CoV-2 evolution and transmission and enables the design of the next-generation mutation-proof vaccines and antibody drugs.

Perspectives on SARS-CoV-2 Main Protease Inhibitors.

The main protease (Mpro) plays a crucial role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication and is highly conserved, rendering it one of the most attractive therapeutic targets for SARS-CoV-2 inhibition. Currently, although two drug candidates targeting SARS-CoV-2 Mpro designed by Pfizer are under clinical trials, no SARS-CoV-2 medication is approved due to the long period of drug development. Here, we collect a comprehensive list of 817 available SARS-CoV-2 and SARS-CoV Mpro inhibitors from the literature or databases and analyze their molecular mechanisms of action. The structure-activity relationships (SARs) among each series of inhibitors are discussed. Additionally, we broadly examine available antiviral activity, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and animal tests of these inhibitors. We comment on their druggability or drawbacks that prevent them from becoming drugs. This Perspective sheds light on the future development of Mpro inhibitors for SARS-CoV-2 and future coronavirus diseases.

Network-Based Analysis of The Genetic Effects of SARS-CoV-2 Infection To Patients With Exacerbation of Virus-Induced Asthma (VAE) (preprint)

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel RNA virus that emerged in late 2019 and was responsible for coronavirus disease (COVID-19). The WHO has declared the COVID-19 in the world pandemic. The most exacerbations of asthma are triggered by viral infections. However, the genetic effects of COVID-19 on asthma need to be further studied.Results Eighty-eight common differentially expressed genes (cDEGs) were identified in datasets GSE147507 and GSE30326. Function analysis showed that cDEGs has antiviral activity, histone kinase activity, chemokine activity and viral protein interaction with cytokine activity. protein–protein interactions (PPIs) network revealed that the proteins encoded by CDEGs interact with each other at a high frequency. Hub genes and essential modules were detected based on the PPIs network. Transcription factors (TF) and miRNA interaction with cDEGs are identified. Drug molecules such as suloctidil HL60 UP and Yu Ping Feng San were recommended for the treatment of novel coronavirus-induced exacerbation of asthma.Conclusions COVID-19 has a genetic effect on virus-induced exacerbation of asthma, and the hub genes we screened may be a potential therapeutic target.

Adverse Events of Inactivated COVID-19 Vaccine in HIV-Infected Adults (preprint)

This study aims to evaluate the safety of inactivated COVID-19 vaccineamong adult people living with HIV (PLWH). In total, 259 PLWH who received at least one dose of inactivated COVID-19 vaccine were enrolled, and post-vaccination adverse events (AEs) were evaluated seven days following each vaccination dose. The overall AE frequency was 22.8% after dose one, which was higher than after dose two(10.2%)(P＜0.001). No severe side event or vaccine safety concern was observed. Our finding was essential in reducing vaccine hesitancy among PLWH.