Targeting ARF4-mediated intracellular transport as broad-spectrum antivirals (preprint)

Host factors that regulate cellular vesicular trafficking also contribute to progeny virions’ destination, thus representing as potential antiviral drug targets. Here we demonstrate that genetic deletion of ARF4, a regulator in vesicle transport, repressed multiple pathogenic RNA viral infections including Zika virus (ZIKV), influenza A virus (IAV), SARS-CoV-2 and Vesicular Stomatitis virus (VSV). ARF4 activation was stimulated upon viral infection, and viral production was rescued when reconstituted with the activated ARF4, but not the inactivated mutants. Mechanically, ARF4 deletion obstructed viral normal translocation into Golgi complex, but led to mis-sorting for lysosomal degradation, consequently caused the blockage of final release. More importantly, ARF4 targeting peptides achieved significant therapeutic efficacy against ZIKV and IAV challenge in mice by blocking ARF4 activation. Hence, we clarify the critical role of ARF4 during viral infection, providing a broad-spectrum antiviral target and the basis for further pharmaceutical development.

Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 Across U.S. States and Selected Countries (preprint)

This paper proposes a structural econometric approach to estimating the basic reproduction number ($\mathcal{R}_{0}$) of Covid-19. This approach identifies $\mathcal{R}_{0}$ in a panel regression model by filtering out the effects of mitigating factors on disease diffusion and is easy to implement. We apply the method to data from 48 contiguous U.S. states and a diverse set of countries. Our results reveal a notable concentration of $\mathcal{R}_{0}$ estimates with an average value of 4.5. Through a counterfactual analysis, we highlight a significant underestimation of the $\mathcal{R}_{0}$ when mitigating factors are not appropriately accounted for.

A pseudovirus-based method to dynamically mimic SARS-CoV-2-associated cell-to-cell fusion and transmission (preprint)

SARS-CoV-2 has caused the global tremendous loss and continues to evolve to generate variants. Entry of SARS-CoV-2 into the host cells is primarily mediated by Spike (S), which binds to the host receptor hACE2 and initiates virus-cell membrane fusion. Cell fusion contributes to viral entry, cell-to-cell transmission and tissue damage in COVID-19 patients. Many reporter assays have been developed to study S-mediated cell fusion by equally coculturing S-expressing cells and hACE2-positive cells. However, these strategies cannot fully simulate cell-to-cell fusion and transmission of SARS-CoV-2 infection, in which virions from a single target cell transmit to the neighbor cells and induce syncytia formation. Here, we design a pseudovirus-based method to dynamically mimic cell-to-cell fusion and transmission of SARS-CoV-2. We coculture a small number of pseudovirus-producing 293FT cells and a large number of hACE2-expressing 293T cells, and demonstrate that a single cell producing S-pseudotyped virions can induce significant syncytia of hACE2-positive cells. This pseudovirus-based method is a powerful tool to screen and estimate potential inhibitors of S-driven syncytia. Moreover, this strategy can also be utilized to explore fusogenic ability of SARS-CoV-2 variants. Together, the pseudovirus-based method we report here will be beneficial to drug screening and scientific research against SARS-CoV-2 or future emerging coronavirus.

Diversity and independent evolutionary profiling of rodent-borne pathogens in tropical island, China (preprint)

The risk of emerging infectious diseases (EID) is increasing globally. More than 60% of EIDs worldwide are caused by animal-borne pathogens, and most viral pathogens are rodent-borne. This study aimed to characterise the virome and analyse the phylogenetic evolution and diversity of rodent-borne viruses in Hainan Province, China. We collected 588 anal and throat samples from rodents, combined them into 28 pools according to their species and location, and processed them for next-generation sequencing and bioinformatics analysis. The diverse viral reads closely related to mammals were assigned to 15 viral families. Molecular clues of the important rodent-borne viruses were further identified by polymerase chain reaction for phylogenetic analysis and annotation of genetic characteristics such as coronavirus, arenavirus, picornavirus. We identified a pestivirus in Leopoldoms edwardsi and two bocaviruses in Rattus andamanensis and Leopoldoms edwardsi from the national nature reserves of Jianfengling and Bangxi with low amino acid identity to known pathogens are proposed as the novel species, and their rodent hosts have not been previously reported to carry these viruses. These results expand our knowledge of viral classification and host range and suggest that there are highly diverse, undiscovered viruses that have evolved independently in their unique wildlife hosts in inaccessible areas, which may cause zoonosis if they cross their host barrier. Our virome and phylogenetic analyses of rodent-borne viruses provide basic data for the prevention and control of human infectious diseases caused by rodent-borne viruses in the subtropical area of China.

Influenza vaccine uptake among children and older adults in China: A secondary analysis of a quasi-experimental study (preprint)

Background: Influenza vaccination is the key to reducing the influenza-related disease burden, especially among high-risk populations. However, influenza vaccine uptake is low in China. This secondary analysis of a quasi-experimental trial in Guangdong Province aimed to understand factors associated with influenza vaccine uptake among children and older people stratified by funding context. Methods: : A total of 225 children (aged 0.5-8 years) and 225 older people (aged 60 years or above) were recruited from three clinics (rural, suburban, urban). Participants were allocated into two groups based on different funding contexts: self-paid group (N=150, including 75 children and 75 older adults) in which participants paid full market price for their vaccination; and subsidized group (N=300, including 150 children and 150 older adults) in which varying levels of financial support was provided. Univariable and multivariable logistic regressions were conducted stratified by funding contexts. Results: : Overall, 75.0% (225/300) of participants in the subsidized group and 36.7% (55/150) in the self-paid group got vaccinated. Older adults had lower vaccination rates than children in both funding groups, while both age groups showed much higher uptake in the subsidized group than in the self-paid group (86.7% vs 53.3% among children; 63.3% vs 20.0% among older people). In the self-paid group, participants living with children (aOR:2.61, 95%CI: 1.06-6.42) or older people (aOR:4.76, 95%CI: 1.08-20.90) having prior influenza vaccination in the same household were more likely to be vaccinated; trust in providers’ advice (aOR=4.95, 95%CI:1.99, 12.43) or effectiveness of the vaccine (aOR: 12.18, 95%CI: 5.21-28.50), and experienced influenza-like illnesses in the family (aOR=46.52, 4.10, 533.78) were associated with higher vaccine uptake in the subsidized group. Conclusions: Older people had suboptimal vaccine uptake compared to children in both contexts and need more attention in future efforts to enhance influenza vaccination. Tailoring interventions to different vaccine funding contexts may help improve influenza vaccine uptake: In self-paid context, measures to motivate people to accept their first ever influenza vaccination may be a promising strategy. In subsidized context, strategies to improve public confidence in vaccine effectiveness and providers’ advice would be useful. Trial registration: ChiCTR2000040048. Registered on November 19, 2020.

Immunogenicity and safety of the CoronaVac inactivated SARS-CoV-2 vaccine in people with underlying medical conditions in China: a retrospective study (preprint)

People living with chronic disease, particularly seniors older than 60 years old, are lagging behind in the national COVID-19 vaccination campaign in China due to the uncertainty of vaccine safety and effectiveness. However, this special population made up of most severe symptom and death cases among SARS-CoV-2 infected patients and should be prioritized in vaccination program. Thus, safety and immunogenicity data of COVID-19 vaccines in people with underlying medical conditions are needed to address the vaccine hesitancy in this special population. Here, we report a retrospective cohort study evaluating the immunogenicity and safety of the inactivated COVID-19 vaccine, CoronaVac, in people with at least one of the six common diseases, focusing on seniors (N = 969). We found that CoronaVac is as safe in people with chronic diseases as that in healthy control, without serious adverse event reported in this study. By day 14-28 post vaccination, we observed no significant difference for the antibody responses between disease groups and healthy control, except for the coronary artery disease (p=0.03) and chronic respiratory disease group (p=0.04) showing moderate reduction. Such difference diminished by day 90 and 180, as neutralizing antibodies significantly reduced in all participants. Most people showed detectable SARS-CoV-2-specific T cell response at day 90 and day 180 without significant difference between disease groups and healthy control. Overall, our results highlight the comparable safety, immunogenicity and cellular immunity memory of CoronaVac in seniors and people living with chronic diseases, addressing vaccine hesitancy for this special population.

scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment (preprint)

Cell-to-cell variability is orchestrated by transcriptional variations participating in different biological processes. However, the dissection of transcriptional variability in specific biological process at single-cell level remains unavailable. Here, we present a deep generative model scPheno to integrate scRNA-seq with disease phenotypes to unravel the invisible phenotype-related transcriptional variations. We applied scPheno on COVID-19 blood scRNA-seq to separate transcriptional variations in regulating COVID-19 host immunity and transcriptional variations in maintaining cell-type identity. In silico, we found CLU+IFI27+S100A9+ monocyte as the efficient cellular marker for the prediction of COVID-19 diagnosis. Inspiringly, using only 4 genes upregulated in CLU+IFI27+S100A9+ monocytes can predict the COVID-19 diagnosis of individuals from different country with an accuracy up to 81.3%. We also found C1+CD163+ monocyte and 8 C1+CD163+ monocyte-upregulated genes as the efficient biomarkers for the prediction of severity assessment. Overall, scPheno is an effective method in dissecting the transcriptional basis of phenotype variations at single-cell level.

Immunogenicity and safety of the CoronaVac inactivated SARS-CoV-2 vaccine in people with underlying medical conditions: a retrospective study (preprint)

Background: People living with chronic disease, particularly seniors older than 60 years old, are lagging behind in the national vaccination campaign in China due to uncertainty of safety and effectiveness. However, this special population made up of most severe symptom and death cases among infected patients and should be prioritized in vaccination program. In this retrospective study, we assessed the safety and immunogenicity of the CoronaVac inactivated vaccines in people with underlying medical conditions to address the vaccine hesitation in this special population. Methods: In this cohort study, volunteers aged 40 years and older, had received two doses of CoronaVac inactivated vaccines (3-5 weeks interval), been healthy or with at least one of the six diseases: coronary heart disease (CAD), hypertension, diabetes mellitus (DM), chronic respiratory disease (CRD), obesity and cancer, were recruited from 4 study sites in China. The primary safety outcome was the incidence of adverse events within 14 days after each dose of vaccination. The primary immunogenic outcome was geometric mean titer (GMT) of neutralizing antibodies to living SARS-CoV-2 virus at 14-28 days, 3 months, and 6 months after full two-dose vaccination. This study is registered with ChiCTR.org.cn (ChiCTR2200058281) and is active but no longer recruiting. Findings: Among 1,302 volunteers screened between Jul 5 and Dec 30, 2021, 969 were eligible and enrolled in our cohort, including 740 living with underlying medical conditions and 229 as healthy control. All of them formed the safety cohort. The overall incidence of adverse reactions was 150 (20.27%) of 740 in the comorbidities group versus 32 (13.97%) of 229 in the healthy group, with significant difference (P=0.0334). The difference was mainly contributed by fatigue and injection-site pain in some groups. Most adverse reactions were mild (Grade 1). We did not observe any serious adverse events related to vaccination. By day 14-28 post vaccination, the seroconversion rates and GMT of neutralizing antibody showed no significant difference between disease group and healthy group, except CAD group (P=0.03) and CRD group (P=0.04) showed slight reduction. By day 90, the neutralizing antibody GMTs were significantly reduced in each group, with no significant difference between diseases and healthy group. By day 180, the neutralizing antibody continued to decrease in each group, but with slower declination. Interpretation: For people living with chronic disease especially seniors older than 60 years, the CoronaVac vaccines are as safe as in healthy people. Although the immunogenicity is slightly different in subgroup of some diseases compared with that of the healthy population, the overall trend was consistent. Our findings highlight the evidence to address vaccine hesitancy for seniors and people living with chronic diseases. Funding: Yunnan Provincial Science and Technology Department (202102AA100051 and 202003AC100010, China), Sinovac Biotech Ltd (PRO-nCOV-4004).

The statistical evidence missing from the Swedish decision-making of COVID-19 strategy during the early period: A longitudinal observational analysis.

A controversy about the Swedish strategy of dealing with COVID-19 during the early period is how decision-making was based on evidence, which refers to data and data analysis. During the earliest period of the pandemic, the Swedish decision-making was based on subjective perspective. However, when more data became available, the decision-making stood on mathematical and descriptive analyses. The mathematical analysis aimed to model the condition for herd immunity while the descriptive analysis compared different measures without adjustment of population differences and updating pandemic situations. Due to the dubious interpretations of these analyses, a mild measure was adopted in Sweden upon the arrival of the second wave, leading to a surge of poor public health outcomes compared to the other Nordic countries (Denmark, Norway, and Finland). In this article, using data available during the first wave, we conduct longitudinal analysis to investigate the consequence of the shred of evidence in the Swedish decision-making for the first wave, where the study period is between January 2020 and August 2020. The design is longitudinal observational study. The linear regressions based on the Poisson distribution and the binomial distribution are employed for the analysis. We found that the early Swedish measure had a long-term and significant effect on general mortality and COVID-19 mortality and a certain mitigating effect on unemployment in Sweden during the first wave; here, the effect was measured by an increase of general deaths, COVID-19 deaths or unemployed persons under Swedish measure relative to the measures adopted by the other Nordic countries. These pieces of statistical evidence were not studied in the mathematical and descriptive analyses but could play an important role in the decision-making at the second wave. In conclusion, a timely longitudinal analysis should be part of the decision-making process for containing the current pandemic or a future one.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence (preprint)

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Matching Theory and Evidence on Covid-19 using a Stochastic Network SIR Model (preprint)

This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under-reporting of infected cases. Empirical evidence on six European countries matches the simulated outcomes once the under-reporting of infected cases is addressed. It is estimated that the number of actual cases could be between 4 to 10 times higher than the reported numbers in October 2020 and declined to 2 to 3 times in April 2021. The calibrated models are used in the counterfactual analyses of the impact of social distancing and vaccination on the epidemic evolution, and the timing of early interventions in the UK and Germany.

Screening of cell-virus, cell-cell, gene-gene interactions among kingdoms of life at single cell resolution (preprint)

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) issued a significant and urgent threat to global health. The exact animal origin of SARS-CoV-2 remains obscure and understanding its host range is vital for preventing interspecies transmission. Previously, we have assessed the target cell profiles of SARS-CoV-2 in pets, livestock, poultry and wild animals. Herein, we expand this investigation to a wider range of animal species and viruses to provide a comprehensive source for large-scale screening of potential virus hosts. Single cell atlas for several mammalian species (alpaca, hamster, hedgehog, chinchilla etc.), as well as comparative atlas for lung, brain and peripheral blood mononuclear cells (PBMC) for various lineages of animals were constructed, from which we systemically analyzed the virus entry factors for 113 viruses over 20 species from mammalians, birds, reptiles, amphibians and invertebrates. Conserved cellular connectomes and regulomes were also identified, revealing the fundamental cell-cell and gene-gene cross-talks between these species. Overall, our study could help identify the potential host range and tissue tropism of SARS-CoV-2 and a diverse set of viruses and reveal the host-virus co-evolution footprints.

Machine Learning Supports Clinical Decision in Optimized Treatment for COVID-19 Patients (preprint)

Background: It has been difficult to distinguish between mild, moderate, and severe cases at an early stage for COVID-19 patients, making it challenging to decide an optimized treatment for each patient. This machine learning system could predict the clinical course and be used to develop a novel method to provide optimal treatment based on risk. Method: We applied machine learning techniques to international clinical data from a large cohort of patients with COVID-19 at 15 hospitals in Japan and three hospitals in New York City from January 1, 2020 to March 30, 2021. We analyzed clinical information of over 2000 COVID-19 patients comprising various races and ethnicities and built a severity and mortality prediction model. Furthermore, using a severity index with machine learning allowed early detection of patients most at-risk for developing severe illness to support the decision for the patient to receive optimized therapy. Findings: We developed an international COVID-19 early prediction model for use at the time of hospital admission that predicts disease severity and mortality with high accuracy, 0.88 (AUC). Using the novel method of severity-matched analysis to assess treatment effectiveness, in the high-risk group, the Kaplan–Meier estimates of mortality by Day 30 were 26% in the dexamethasone treatment group and 63% in the non-treatment group. The Kaplan–Meier estimates of mortality were low at 3% with remdesivir and dexamethasone in combination and 49% with no remdesivir and dexamethasone treatment by Day 30. There may be an add-on effect of remdesivir to conventional dexamethasone. Interpretation: The severity prediction index can be calculated, which can assist with an optimized treatment for COVID-19 patients in each risk group. The severity-matched treatment system could support the recommendation of optimized treatments, such as dexamethasone, remdesivir, or heparin, in high-risk groups by calculating the severity index predicted at the time of the first visit.Trial Registration Details: The trial registration number was 2020142NI. Funding Information: K.T., K.I., and Y.D. received funding from the Japan Agency for Medical Research and Development (AMED) (19fk0108153h0001). K.T. received funding from AMED (19jm0610015h0001). K.T. received funding from the Healthy Longevity Global Grand Challenge, Catalyst Award.Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The study protocol was centrally reviewed by the Institutional Review Board of Tokyo University. The requirement for consent was waived given the retrospective and non-interventional nature of the study.

Calibrating multi-dimensional complex ODE from noisy data via deep neural networks (preprint)

Ordinary differential equations (ODEs) are widely used to model complex dynamics that arises in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally very difficult. In this work, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary kernel method, and then feed them into a sparsely connected deep neural network with ReLU activation function. Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure. When the ODE possesses a general modular structure, with each modular component involving only a few input variables, and the network architecture is properly chosen, our method is proven to be consistent. Theoretical properties are corroborated by an extensive simulation study that demonstrates the validity and effectiveness of the proposed method. Finally, we use our method to simultaneously characterize the growth rate of Covid-19 infection cases from 50 states of the USA.

Effect of the COVID-19 pandemic on the status of overweight and obesity among children and adolescents: a retrospective study in Chengdu, China (preprint)

Background: The prevalence of overweight and obesity among children and adolescents is steadily increasing and has become a public health concern. Lifestyle changes due to the COVID-19 pandemic may have an impact on the status of overweight and obesity among children and adolescents. This study aimed to analyze the effect of the COVID-19 pandemic on the status of overweight and obesity among children and adolescents. Methods: We retrospectively analyzed the children and adolescents who visited the West China Second University Hospital, Sichuan University from January 1st, 2018 to June 30st, 2020. We included obese children who met the criteria and divided them into 5 groups with 6 months as the unit according to the time of their visit. The national lockdown time was used as a segmentation point to study the changes of obesity status in the same children before and after lockdown. Results: A total of 140,526 children and adolescents visited the outpatient department from January 1st, 2018 to June 30st, 2020, and 1,740 of them were diagnosed as overweight or obese at the time of their first visit. The study found that there was a significant difference in the obesity rate among the groups (P < 0.01). However, there was no difference between January to June, 2020 and the previous period. Except for the increased incidence of VD deficiency (P < 0.01), the severity of obesity, insulin resistance and dyslipidemia of obese children did not change before and after COVID-19 (P=0.303, 0.663, 0.106, respectively). A total of 65 obese children were followed up in the outpatient department before and after COVID-19 lockdown. There were no significant differences in BMI-SDS, HOMA-IR and 25(OH)VD among obese children before and after lockdown (p = 0.626, 0.386, 0.251, respectively). Conclusions: The available evidence cannot prove that the COVID-19 pandemic affects the status of overweight and obesity among children and adolescents who visited hospitals. It may be related to the multiple effects of the COVID-19 pandemic on children.

mRNA vaccination compared to infection elicits an IgG-predominant response with greater SARS-CoV-2 specificity and similar decrease in variant spike recognition (preprint)

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, new vaccine strategies including lipid nanoparticle delivery of antigen encoding RNA have been deployed globally. The BioNTech/Pfizer mRNA vaccine BNT162b2 encoding SARS-CoV-2 spike protein shows 95% efficacy in preventing disease, but it is unclear how the antibody responses to vaccination differ from those generated by infection. Here we compare the magnitude and breadth of antibodies targeting SARS-CoV-2, SARS-CoV-2 variants of concern, and endemic coronaviruses, in vaccinees and infected patients. We find that vaccination differs from infection in the dominance of IgG over IgM and IgA responses, with IgG reaching levels similar to those of severely ill COVID-19 patients and shows decreased breadth of the antibody response targeting endemic coronaviruses. Viral variants of concern from B.1.1.7 to P.1 to B.1.351 form a remarkably consistent hierarchy of progressively decreasing antibody recognition by both vaccinees and infected patients exposed to Wuhan-Hu-1 antigens.