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.

Mobile Phone Dependency and Sleep Quality in College Students during COVID-19 Outbreak: The Mediating Role of Bedtime Procrastination and Fear of Missing Out (preprint)

Objective The purpose of the study was to examine the associations between mobile phone dependency and sleep quality among college students during COVID-19 outbreak. The potential mediation roles of bedtime procrastination and fear of missing out (FoMO) were investigated.Methods A total of 881 college students completed an online survey in May 2022 in Shanghai, China. Mobile Phone Involvement Questionnaire, Bedtime Procrastination Scale, Bedtime Procrastination Scale and Pittsburgh Sleep Quality Index were used to assess mobile phone dependency, bedtime procrastination, fear of missing out, and sleep quality, respectively. Multiple linear regression and mediation analysis were conducted.Results The correlation analyses indicated mobile phone dependency was positively associated with fear of missing out, bedtime procrastination, and sleep quality among college students. The structural equation modeling analyses revealed that mobile phone dependency had significant indirect effects on sleep quality through bedtime procrastination (indirect effect: 0.030, 95%CI: 0.022–0.041) and fear of missing out (indirect effect: 0.013, 95%CI: 0.003–0.023).Conclusion The findings indicated that bedtime procrastination and fear of missing out partially mediated on the association between mobile phone dependency and sleep quality. Bedtime procrastination and fear of missing out should be considered as potential intervention targets for reducing mobile phone dependency and improving sleep quality in college students.

Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic

Current research on carbon emissions and economic development has tended to apply more homogeneous low-frequency data to construct VAR models with impulse responses, ignoring some of the sample information in high-frequency data. This study constructs a MIDAS model to forecast GDP growth rate based on monthly carbon emission data and quarterly GDP data in the context of the COVID-19 pandemic. The results show that: (1) The MIDAS model has smaller RMSE than the VAR model in short-term forecasting, and provides more stable real-time forecasts and short-term forecasts of quarterly GDP growth rates, which can provide more accurate reference intervals;(2) China's future macroeconomic growth rate has recently declined due to the impact of the sudden epidemic, but the trend is generally optimistic. By improving urban planning and other methods, the authorities can achieve the two-carbon goal of carbon capping and carbon neutrality at an early date. In the context of the impact of COVID-19 on China's economic development, we need to strike a balance between ensuring stable economic growth and ecological protection, and build environmentally friendly cities, so as to achieve sustainable economic and ecological development and enhance human well-being.

Structural basis of a two-antibody cocktail exhibiting highly potent and broadly neutralizing activities against SARS-CoV-2 variants including diverse Omicron sublineages (preprint)

SARS-CoV-2 variants of concern (VOCs), especially the latest Omicron, have exhibited severe antibody evasion. Broadly neutralizing antibodies with high potency against Omicron are urgently needed for understanding working mechanisms and developing therapeutic agents. In this study, we characterized previously reported F61, which was isolated from convalescent patients infected with prototype SARS-CoV-2, as a broadly neutralizing antibody against all VOCs including Omicron BA.1, BA.1.1, BA.2, BA.3 and BA.4 sublineages by utilizing antigen binding and cell infection assays. We also identified and characterized another broadly neutralizing antibody D2 with epitope distinct from that of F61. More importantly, we showed that a combination of F61 with D2 exhibited synergy in neutralization and protecting mice from SARS-CoV-2 Delta and Omicron BA.1 variants. Cryo-EM structures of the spike-F61 and spike-D2 binary complexes revealed the distinct epitopes of F61 and D2 at atomic level and the structural basis for neutralization. Cryo-EM structure of the Omicron-spike-F61-D2 ternary complex provides further structural insights into the synergy between F61 and D2. These results collectively indicated F61 and F61-D2 cocktail as promising therapeutic antibodies for combating SARS-CoV-2 variants including diverse Omicron sublineages.

A Class of Semiparametric Models with Homogeneous Structure for Panel Data Analysis (preprint)

Stimulated by the analysis of a dataset from China about Covid-19, we propose a class of semiparametric models for panel data analysis. The proposed models account for both homogeneity and heterogeneity among the individuals of a panel data. They strike a nice balance between parsimony and risk of misspecification. Although stimulated by the analysis of a particular dataset, the proposed models apply to very broad range of panel data analysis, they are powerful in exploring nonlinear dynamic patterns of impacts of covariates or transformed covariates. An estimation procedure is presented, and its asymptotic properties are established. Intensive simulation studies are also conducted to demonstrate how well the estimation procedure works and the risk of ignoring homogeneity or heterogeneity among individuals in panel data analysis. Finally, we apply the proposed models and estimation procedure to the Covid-19 data from China, and reveal some interesting dynamic patterns of the impacts of some important factors.

Potential mechanism of Astragalus membranaceus in the prevention of COVID-19 based on network pharmacology and molecular docking

Objective: To explore the potential molecular mechanism of Astragalus membranaceus in the treatment of coronavirus disease 2019(COVID-19)based on the network pharmacology and molecular docking. Methods The traditional Chinese medicine systems pharmacology database and analysis platform(TCMSP)and the related literature were searched to obtain the active ingredients and predictive targets of Astragalus membranaceus. The herbal targets were selected based on STRING database for PPI network construction and the results were displayed by Cytoscape software. The key targets were screened through the algorithm of network topology and the network modules were analyzed. Gene Ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were carried out on key target genes using Gene Ontology Enrichment Analysis Software Toolkit(GOEAST)and The Database for Annotation, Visualization and Integrated Discovery(DAVID)online tools. Combined with relevant literature, the mechanism of Astragalus membranaceus in the treatment of COVID-19 was analyzed. Results A total of 19 candidate active components and 889 predictive targets of Astragalus membranaceus were selected by oral bioavailability(OB)and drug-likeness(DL)values. The preventive mechanism of Astragalus membranaceus might be closely related to the signal pathways involved in the body's living nerve ligand receptor interaction, calcium signal, T cell receptor, cAMP signal pathway and chemokines. Conclusion Astragalus membranaceus mainly plays roles in many kinds of targets through multi-approach and multi-signaling pathways.

China's River Chief Policy and the Sustainable Development Goals: Prefecture-Level Evidence from the Yangtze River Economic Belt

The River Chief Policy (RCP), an institutional innovation in China by which top party and government officials assume responsibility for water management, shapes the incentive structure of local governments and may have a huge influence on the Sustainable Development Goals (SDGs). Using a staggered difference-in-difference approach and panel data from 91 cities in the Yangtze River Economic Belt, we estimate the impact of the RCP on an SDG index with eight local-specific indicators. The estimation results show that the RCP has improved the overall SDG index and significantly improved the levels of innovation, education, and consumption. Heterogeneity tests show that more affluent regions are more committed to investing in education, raising consumption, and increasing wages under the RCP. These results suggest that local governments in China have responded rationally and strategically to the RCP. In general, economic growth remains the central goal of local governments, while the strengthening of other responsibilities such as environmental protection will lead to more effort being made to achieve the SDGs during and after the COVID-19 pandemic.

Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: findings from the UK Biobank cohort (preprint)

BackgroundSleep has been associated with aging and relevant health outcomes, but their causal relationship remains inconclusive. MethodsIn this study, we investigated the associations of sleep behaviors with biological ages (BAs) among 363,886 middle and elderly-aged adults from UK Biobank. Sleep index (0 [worst]-6 [best]) of each participant was retrieved from six sleep behaviors: snoring, chronotype, daytime sleepiness, sleep duration, insomnia, and difficulties in getting up. Two BAs, the KDM-biological age and PhenoAge, were estimated by corresponding algorithms based on clinical traits, and their discrepancies with chronological age were defined as the age accelerations (AAs). ResultsWe first observed negative associations between the sleep index and the two AAs, and demonstrated that the change of AAs could be the consequence of sleep quality using Mendelian randomization with genetic risk scores of sleep index and BAs. Particularly, one unit increase in sleep index was associated with 0.105- and 0.125-year decreases in KDM-biological age acceleration and PhenoAge acceleration, respectively. Furthermore, we observed significant independent and joint effects of sleep and air pollution (i.e. PM2.5 and NO2), another key driver of aging, on BAs. Sleep quality also showed modifying effect on the associations of elevated PM2.5 and NO2 levels with accelerated aging. For instance, an interquartile range increase in PM2.5 level was associated with 0.011-, 0.047-, and 0.078-year increase in PhenoAge acceleration among people with high (5-6), medium (3-4), and low (0-2) sleep index, respectively. ConclusionsOur findings elucidate that better sleep quality could lessen accelerated biological aging resulting from exogenous exposures including air pollution. FundingPeking University Start-up Grant (BMU2021YJ044)

SARS-CoV-2 Seroprevalence and Risk Factors among Convalescents in Sichuan Province, China: a retrospective cohort study (preprint)

Objectives: To understand SARS-CoV-2 seroprevalence of convalescents and assess their the immunity. Furthermore, we intend to explore the association between antibody levels and with demographic factors. Methods: 177 COVID-19 convalescents in Sichuan Province were voluntarily participated in our study. 363 serum samples were collected from June, 2020 to November, 2020. Duration of seroprevelance in these convalescents and their demographic characteristics were described, and the risk factors to antibody levels were analysed. Results: Men had more than twice the odds of having IgM antibody positive compared with women (OR=2.419, 95% CI:[1.232, 4.751]). Participants without symptoms were nearly 0.5 times IgG seropositive than those with symptoms (OR=0.455, 95% CI: [0.220, 0.940]). People aged[≥]60 years were nearly 3 times IgG seropositive than those who aged < 20 years (OR=2.986, 95% CI: [1.058, 8.432]). Seroprevalence in asymptomatic declined quicker than symptomatic. Conclusions: Age and gender may affect the antibody levels and seroprevalence. Asymptomatic appeared more easier to turn to seronegative than symptomatic. Keywords: Seroprevalence; SARS-CoV-2; Risk Factors

Azithromycin Plus Zinc Sulfate Rapidly and Synergistically Suppresses IκBα-Mediated In Vitro Human Airway Cell ACE2 Expression for SARS-CoV-2 Entry (preprint)

Large-scale efforts have been persistently undertaken for medical prophylaxis and treatment of COVID-19 disasters worldwide. A variety of novel viral spike protein-targeted vaccine preparations have recently been clinically distributed based on accelerated approval. We revisited the early but inconclusive clinical interest in the combination of azithromycin and zinc sulfate repurposing with safety advantages. In vitro proof of concept was provided for rapid and synergistic suppression of ACE2 expression following treatments in human airway cells, Calu-3 and H322M. The two representative ACE2-expressing human airway cells indicate the upper and lower respiratory tracts. Prophylactic and early therapeutic roles of azithromycin combined with zinc are proposed for virus cellular entry prevention potential bridging to effective antibody production.

The translational landscape of SARS-CoV-2 and infected cells (preprint)

SARS-CoV-2, a betacoronavirus with a positive-sense RNA genome, has caused the ongoing COVID-19 pandemic. Although a large number of transcriptional profiling studies have been conducted in SARS-CoV-2 infected cells, little is known regarding the translational landscape of host and viral proteins. Here, using ribosome profiling in SARS-CoV-2-infected cells, we identify structural elements that regulate viral gene expression, alternative translation initiation events, as well as host responses regulated by mRNA translation. We found that the ribosome density was low within the SARS-CoV-2 frameshifting element but high immediately downstream, which suggests the utilization of a highly efficient ribosomal frameshifting strategy. In SARS-CoV-2-infected cells, although many chemokine, cytokine and interferon stimulated genes were upregulated at the mRNA level, they were not translated efficiently, suggesting a translational block that disarms host innate host responses. Together, these data reveal the key role of mRNA translation in SARS-CoV-2 replication and highlight unique mechanisms for therapeutic development.

Identification, Isolation, Propagation And Inactivation Of SARS-CoV2 Isolated From Egypt (preprint)

Severe Acute Respiratory Syndrome Coronavirus 2 causes the novel pandemic Pneumonia disease. It is a positive single strand ssRNA virus that infect human. COVID-19 appeared in Egypt in Feb 2020. The samples were taken from patients with COVID-19 symptoms at military hospital in Egypt and transported to the main chemical laboratories under all the biosafety measures according to WHO guidelines. All samples were tested with RT-PCR. Positive samples were cultured using VeroE6 cell lines. The propagated virus was isolated and inactivated. The isolated virus was sequenced using next generation sequencing and submitted into gene bank. This study provides an isolation, propagation and inactivation methodology which is valuable for production of inactivated vaccines against SARS-CoV2 in Egypt.

Epidemiological and clinical features of imported and local patients with coronavirus disease 2019 (COVID-19) in Hainan, China (preprint)

Background: Hainan Island, which is a popular tourist destination, received many imported cases of Coronavirus disease 2019 (COVID-19) but successfully contained the epidemic within one month. We described the epidemiological and clinical characteristics of COVID-19 in Hainan and compared these features between imported and local cases to provide information for other international epidemic areas. Methods: : We included 91 patients (56 imported and 35 local cases) from two designated hospitals for COVID-19 in Haikou, China, from January 20 to February 19, 2020. Data on the demographic, epidemiological, clinical and laboratory characteristics were extracted from medical records. Patients were followed until April 21, 2020, and the levels of antibodies at the follow-ups were also analyzed. Results: : Of the 91 patients, 78 (85.7%) patients were diagnosed within the first three weeks after the first case was identified (Day 1: Jan 22, 2020), while the number of local cases started to increase during the third week. No new cases occurred after Day 29. Fever and cough were two main clinical manifestations. In total, 15 (16.5%) patients were severe, 14 (15.4%) had complicated infections, nine (9.9%) were admitted to the ICU, and three died. The median duration of viral shedding in feces was longer than that in nasopharyngeal swabs (19 days vs 16 days, P =0.007). Compared with local cases, imported cases were older and had a higher incidence of fever and concurrent infections. There was no difference in outcomes between the two groups. IgG was positive in 92.8% patients (77/83) in the follow-up at week 2 after discharge, while 88.4% patients (38/43) had a reduction in IgG levels in the follow-up at week 4 after discharge, and the median level was lower than that in the follow-up at week 2 (10.95 S/CO vs 15.02 S/CO, P<0.001). Conclusion: Imported cases were more severe than local cases but had similar prognoses. The level of IgG antibodies declined from week 6 to week 8 after onset. The short epidemic period in Hainan suggests that the epidemic could be quickly brought under control if proper timely measures were taken.

Neutrophil-to-Lymphocyte Ratio on Admission Predicts In-hospital Mortality in Patients with COVID-19 (preprint)

Background: A novel coronavirus caused an outbreak of acute infectious pneumonia are spreading over the globe. However, studies predicting prognosis are limited. We predicted outcomes of patients with coronavirus disease 2019 (COVID-19) using the neutrophil-to-lymphocyte ratio (NLR) on admission.Methods: We retrospectively analyzed the characteristics of COVID-19 patients diagnosed from February 6 to March 1. The outcomes, including the occurrence of in-hospital mortality, acute kidney injury (AKI), and endotracheal intubation (ETI), were recorded. The relationships of neutrophils, lymphocytes, C-reactive protein, lactate dehydrogenase, and NLR with outcomes were assessed using multivariate regression model. P-values for trends across quartiles of NLR was examined.Results: A total of 182 patients were included. 37 (20.3%) patients died during the hospitalization, 41 (22.5%) developed AKI, and 36 (19.8%) received ETI. The NLR had a superior predictive performance than others. Using an NLR cutoff of 11.4, the area under the curves (AUC) were 0.766 for in-hospital mortality, 0.755 for AKI, and 0.733 for ETI. In multivariate analysis, NLR >11.4 was further identified as an independent prognostic factor. Following stratification with quartiles of NLR, a positive trend between the increasing quartiles of NLR and the three outcomes were observed (p-values for trends across quartiles were 0.043, <0.001, and 0.041, respectively). The multivariate adjusted odds ratio (OR) in the highest quartile vs. the lowest quartile were 5.738 for mortality, 25.307 for AKI, and 5.136 for ETI.Conclusions: Increasing NLR obtained on admission is a powerful predictor for inpatient mortality, AKI, and ETI in COVID-19 patients.

Unified statistical inference for a novel nonlinear dynamic functional/longitudinal data model (preprint)

In light of recent work studying massive functional/longitudinal data, such as the resulting data from the COVID-19 pandemic, we propose a novel functional/longitudinal data model which is a combination of the popular varying coefficient (VC) model and additive model. We call it Semi-VCAM in which the response could be a functional/longitudinal variable, and the explanatory variables could be a mixture of functional/longitudinal and scalar variables. Notably some of the scalar variables could be categorical variables as well. The Semi-VCAM simultaneously allows for both substantial flexibility and the maintaining of one-dimensional rates of convergence. A local linear smoothing with the aid of an initial B spline series approximation is developed to estimate the unknown functional effects in the model. To avoid the subjective choice between the sparse and dense cases of the data, we establish the asymptotic theories of the resultant Pilot Estimation Based Local Linear Estimators (PEBLLE) on a unified framework of sparse, dense and ultra-dense cases of the data. Moreover, we construct unified consistent tests to justify whether a parsimony submodel is sufficient or not. These test methods also avoid the subjective choice between the sparse, dense and ultra dense cases of the data. Extensive Monte Carlo simulation studies investigating the finite sample performance of the proposed methodologies confirm our asymptotic results. We further illustrate our methodologies via analyzing the COVID-19 data from China and the CD4 data.