The Impact of Early Antibiotic Use on Clinical Outcomes of Patients Hospitalized with COVID-19: A Propensity Score-Matched Analysis (preprint)

Background: Early empiric antibiotics were prescribed to numerous patients during the Coronavirus disease 2019(COVID-19) pandemic. However, the potential impact of empiric antibiotic therapy on the clinical outcomes of patients hospitalized with COVID-19 is yet unknown. Methods: We conducted a retrospective cohort study in West China Hospital of Sichuan University between Dec 2022 to Mar 2023. The 1:2 propensity score matched patient populations were further developed to adjust confounding factors. Results: We included a total of 1472 COVID-19 hospitalized patients, of whom 87.4% (1287 patients) received early antibiotic prescriptions. In propensity-score-matched datasets, our analysis comprised 139 patients withnon-antibiotic use(with 278 matched controls) and 27 patients withdeferred-antibiotic use(with 54 matched controls). Patients with older ages, multiple comorbidities, severe and critical COVID-19 subtypes, higher serum infection indicators and inflammatory indicators at admission were more likely to receive early antibiotic prescriptions. After adjusting confounding factors likely to influence the prognosis, no significant difference in all-cause mortality(HR=1.000(0.246-4.060), p=1.000) and ICU admission(HR=0.436(0.093-2.04), p=0.293)), need for mechanical ventilation(HR=0.723(0.296-1.763), p=0.476)) and tracheal intubation(HR=1.338(0.221-8.103), p=0.751)) were observed between early antibiotics use cohort and non-antibiotic use cohort. Conclusions: Early antibiotics were frequently prescribed to patients in more severe disease condition at admission. However, early antibiotic treatment failed to demonstrate better clinical outcomes in hospitalized patients with COVID-19 in the propensity-score-matched cohorts.

Estimating long-term and short-term influence of COVID-19 activity restriction on regional highway traffic demand: A case study in Zhejiang Province, China

Since the outbreak of COVID-19 in China in late 2019, government administrators have implemented traffic restriction policies to prevent the spread of COVID-19. However, highway traffic volumes obtained from ETC data in some provinces did not return to the levels of previous years after the end of the traffic restriction policy, suggesting that traffic restriction policy may have long-term effects. This paper proposed a method that analyzes traffic restriction policies' long-term and short-term impact on highway traffic volume under COVID-19. This method first analyzes the long-term and short-term impacts of traffic restriction policies on the highway traffic volume using the Prophet model combined with the concept of traffic volume loss. It further investigates the relationship between COVID-19 cases and the long-term and short-term impacts of the traffic restriction policy using Granger causality and the impulse response function of the Bayesian vector autoregressive (BVAR) model. The results showed that during the COVID-19 pandemic, highway traffic in Zhejiang Province decreased by about 95.5%, and the short-term impact of COVID-19 cases was most pronounced on the second day. However, the long-term effects were relatively small when the traffic restriction policy ended and was verified by data from other provinces. These results will provide decision support for traffic management and provide recommendations for future traffic impact assessments in the event of similar epidemics.

Breakthrough infection with Omicron subvariant BF.7 induces broadly neutralizing antibodies to SARS-CoV-2 variants including XBB lineage (preprint)

Since China eased its COVID-19 response strategies in late 2022, we have been witnessing a rapid and wide spread of SARS-CoV-2 infection across the major cities, including capital Beijing, where Omicron subvariant BF.7 has been dominating the infection. Here, we show that such expansion is unlikely due to a higher binding affinity of BF.7 to human receptor angiotensin-converting enzyme 2 (ACE2) as the similar binding activities were found for other Omicron subvariants tested such as BA.1, BA.5.2, BQ.1, BQ.1.1, XBB, and XBB.1. Additionally, through study of antibody response among six different clinical cohorts, we found that primary infection with BF.7 among the unvaccinated individuals only elicited type-specific neutralizing antibodies to the infecting virus and its close related strains. By a distinct contrast, breakthrough infection with BF.7 among the vaccinated individuals, particularly those severe cases, induced strong and broadly neutralizing antibodies to a diverse panel of SARS-CoV-2 variants and Omicron subvariants including the XBB lineage. A deeper understanding of how these broadly neutralizing antibodies were generated or boosted by BF.7 breakthrough infection will hold the key for augmenting antibody immunity against diverse SARS-CoV-2 variants.

Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on emission estimates of NOx. We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NOx emissions in China. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018 and evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5 ± 3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less-densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu “Qianxi” mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji (JJJ) and the Yangtze River Delta (YRD) mega-regions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data-assimilation approaches for air quality applications.

Tourism policy responses to COVID-19 and first-stage tourism recovery in China

In order to find a coordinated approach to support tourism recovery following the impacts of COVID-19, this research examines the experiences of mainland China, the first country whose domestic tourism recovered in the first stage (the first year after the pandemic outbreak). Through the content analysis of tourism policy documents at national, provincial, and city levels, we generated the features of the policy responses from the supply and demand sides, and the policy trends before and after the first peak of the recovery. Next, we summarized the three steps which make up the first stage, and describe the effective policy focus for each step. This process-oriented policy analysis can guide other countries in how to cope with tourism recovery during the first stage.

Who spread COVID-19 (mis)information online? Differential informedness, psychological mechanisms, and intervention strategies☆☆☆

Based on a regional survey conducted in five cities of China (Beijing, Shanghai, Guangzhou, Chengdu, and Wuhan) in January 2020 and a national survey experiment conducted in 31 provinces of China in December 2020 during the COVID-19 pandemic, we investigated the intentions for the misinformed, uninformed, and informed individuals to spread COVID-19 related (mis)information online and the psychological factors affecting their distinct sharing behaviors. We found that (1) both misinformed and uninformed individuals were more likely to spread misinformation and less likely to share fact as compared with the informed ones;(2) the reasons for the misinformed individuals to spread misinformation resembled those for the informed ones to share truth, but the uninformed ones shared misinformation based on different motivations;and (3) information that arouses positive emotions were more likely to go viral than that arouses negative feelings in the context of COVID-19, regardless of facticity. The implications of these findings were discussed in terms of how people react to misinformation when coping with risk, and intervention strategies were proposed to combat COVID-19 or other types of misinformation in risk scenarios.

PF-D-Trimer, a broadly protective SARS-CoV-2 subunit vaccine: immunogenicity and application (preprint)

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had and still has a considerable impact on global public health. One of the characteristics of SARS-CoV-2 is a surface homotrimeric spike protein, the primary responsible for the host immune response upon infection. Here we show the preclinical studies of a broad protective SARS-CoV-2 subunit vaccine developed from our Trimer Domain platform using the Delta spike protein, from antigen design to purification, vaccine evaluation and manufacturability. The prefusion trimerized Delta spike protein, PF-D-Trimer, was highly expressed in Chinese hamster ovary (CHO) cells, purified by a rapid one-step anti-Trimer Domain monoclonal antibody immunoaffinity process and prepared as a vaccine formulation with an adjuvant. The immunogenicity studies demonstrated that this vaccine candidate induces robust immune responses in mouse, rat and Syrian hamster models. It also protects K18-hACE2 transgenic mice in a homologous virus challenge. The neutralizing antibodies induced by this vaccine display a cross-reactive capacity against the ancestral WA1 and Delta variants as well as different Omicron, including BA.5.2. The Trimer Domain platform was proven to be a key technology in the rapid production of the PF-D-Trimer vaccine and may be crucial to accelerate the development of updated versions of SARS-CoV-2 vaccines.

Status of sleep and exercise during remote learning period for primary school students in one city during the outbreak of COVID-19

Objective: To explore the relationship between sleep and exercise among grade 1-6 students in a certain city during the period of home-based online courses in the epidemic, and to provide reference basis for the government and relevant departments to make relevant policies on student health promotion.

An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C) (preprint)

While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usability. In this study, we proposed an open natural language processing development framework. We evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The corpora were derived from texts from three different institutions (Mayo Clinic, University of Kentucky, University of Minnesota). The gold standard annotations were tested with a single institution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study and adoption. Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.

Reinforcing the Supply Chain of Umifenovir and Other Antiviral Drugs with Retrosynthetic Software (preprint)

The global disruption caused by the 2020 coronavirus pandemic stressed the supply chain of many products, including pharmaceuticals. Multiple drug repurposing studies for COVID-19 are now underway. If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it, will be available in the quantities required. We used retrosynthetic software to arrive at alternate chemical supply chains for the antiviral drug umifenovir, as well as eleven other antiviral and anti-inflammatory drugs. We have experimentally validated four routes to umifenovir and one route to bromhexine. In several instances, the software utilizes C–H functionalization logic, and one route to umifenovir employs functionalization of six C–H bonds. The general strategy we apply can be used to identify distinct starting materials, and relieve stress on existing supply chains.

ADAR mediated A-to-I RNA editing affects SARS-CoV-2 characteristics and fuels its evolution (preprint)

Upon SARS-CoV-2 infection, viral intermediates activate the Type I interferon (IFN) response through MDA5-mediated sensing and accordingly induce ADAR1 p150 expression, which might lead to A-to-I RNA editing of SARS-CoV-2. Here, we developed an RNA virus-specific editing identification pipeline, surveyed 7622 RNA-seq data from diverse types of samples infected with SARS-CoV-2, and constructed an atlas of A-to-I RNA editing sites in SARS-CoV-2. We found that A-to-I editing was dynamically regulated, and on average, approximately 91 editing events were deposited at viral dsRNA intermediates per sample. Moreover, editing hotspots were observed, including recoding sites in the spike gene that affect viral infectivity and antigenicity. Finally, we provided evidence that RNA editing accelerated SARS-CoV-2 evolution in humans. Collectively, our data suggest that SARS-CoV-2 hijacks components of the host antiviral machinery to edit its genome and fuel its evolution.

Digital Transformation: Reshaping China's Development Landscape

Keywords: digital transformation, shift from old to new growth drivers, "dual circulations" development landscape, re-industrialization of output value, service-based employment JEL Classification Code: O33 DOI: 10.19602/j.chinaeconomist.2021.05.01 1.Introduction Since the beginning of industrialization, the world has experienced four major stages of industrial development, from mechanization (industry 1.0) to electrification (industry 2.0), automation (industry 3.0), and, finally, digitalization (industry 4.0). Flexible manufacturing enables supply chain connectivity, cuts operational costs, and increases the efficiency of capital by enhancing forecasting and automating maintenance, thus reducing downtime and excess inventory, and accelerating cash turnover. [...]digital transformation will ultimately induce the entire industrial chain to upgrade. According to Torste (2021), a country should encourage technology diffusion throughout industrial chains as it strives to catch up with more advanced countries.

Aglycone polyether ionophores as broad-spectrum agents inhibit multiple enveloped viruses including SARS-CoV-2 in vitro and successfully cure JEV infected mice (preprint)

Infections with zoonotic viruses, such as flaviviruses, influenza virus, and the SARS-CoV-2 pandemic coronavirus constitute an increasing global risk. Hence, an urgent need exists for the development of broad-spectrum antivirals to prevent such outbreaks. Here, we show that the maduramycin and CP-80,219 aglycone polyether ionophores exhibit effective broad-spectrum antiviral activity, against various viruses, including Japanese encephalitis virus (JEV), Dengue virus (DENV), Zika virus (ZIKV), and Chikungunya virus (CHIKV), while also exhibiting promising activity against PR8 influenza virus and SARS-CoV-2. Moreover, liposome-encapsulated maduramycin and CP-80,219 provide full protection for mice from infection with JEV in vivo. Mechanistic studies suggest that aglycone polyether ionophores primarily inhibit the viral replication step without blocking endosome acidification to promote the fusion between viral and cellular membranes. The successful application of liposomes containing aglycone polyether ionophores in JEV-infected mice offers hope to the development of broad-spectrum antiviral drugs like penicillin back to 1940s.

Drug Repurposing for COVID-19 via Knowledge Graph Completion (preprint)

Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1=0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. Conclusion: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.

Atmospheric pressure and population density as super-factors influencing the transmission of coronavirus disease 2019 (COVID-19) (preprint)

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease (COVID-19), recently emerged and led to a global pandemic with enormous consequent losses to global health and economies. To date, more than 30 million cases have been reported globally and have affected almost every with varying degrees. Meteorological and non-meteorological factors such as temperature, relative humidity, atmospheric pressure, population density, and latitude, are considered critical in virus transmission. To explore the correlation of environmental factors with the transmission of SARS-CoV-2 based on parameters including infection rate, effective reproduction number, and compound growth rate, we analyzed data of confirmed cases from 487 counties in the United States. We found a small impact of temperature and humidity on virus transmission, but observed a considerable positive influence of atmospheric pressure and population density on virus transmission. Geographic areas and seasons (autumn and winter), with exposure to higher atmospheric pressure, are more likely at higher risk of an outbreak. Social distancing and other measures could be effective strategies to combat COVID-19 outbreaks in densely populated areas. Additional studies are needed to explore the mechanisms underlying the relationship between meteorological parameters and transmission of SARS-CoV-2.

Analysis of clinical characteristics, laboratory findings and therapy for 134 cases of COVID-19: a retrospective analysis (preprint)

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread worldwide. As of October 27th, 2020, the number of worldwide laboratory-confirmed cases had reached 43,340,710, with 1,157,496 deaths. We sought to analyze the clinical characteristics, laboratory findings and therapy for a set of COVID-19 cases.Methods: For this retrospective study, we extracted data for 134 patients with laboratory-confirmed COVID-19 at our hospital from January 16th to April 24th, 2020. Cases were confirmed by real-time RT-PCR and abnormal radiologic findings. Outcomes were followed up until May 1st, 2020.Results: An outcome of death or severe COVID-19 was more likely to occur with coinfection and severe underlying diseases. Age above 60 years old, male sex and symptoms such as fever, cough, chest tightness, headaches and fatigue were also related to severe COVID-19 and an outcome of death. In addition, high temperature, high blood leukocyte and neutrophil counts, C-reactive protein and D-dimer levels, and alanine aminotransferase, aspartate aminotransferase, α-hydroxybutyrate dehydrogenase, lactate dehydrogenase and creatine kinase activities were related to severe COVID-19 and an outcome of death, as was a low lymphocyte count. The administration of gamma globulin appeared to be helpful for reducing mortality in patients with severe COVID-19; however, as the P value was greater than 0.05 (P=0.180), studies of the same conditions with larger samples are needed.Conclusion: Multiple factors are related to severe COVID-19 and an outcome of death. The administration of gamma globulin seemed to be helpful for reducing mortality in severe cases. More related studies are needed in the future.Clinical trial registrationNot applicable

Analysis of clinical characteristics, laboratory findings and therapy of 134 cases of COVID-19 in Wuhan, China: a retrospective analysis. (preprint)

Background：As everyone knows, the pandemic COVID-19 is spreading in the whole world. The number of laboratory-confirmed cases reached 28,637,211 and that of the death cases was 917,404 in the world as of September 13th, 2020. We sought to analyse the clinical characteristics, laboratory findings and therapy of some cases with COVID-19.Methods: In this retrospective study, we extracted the data on 134 patients with laboratory-confirmed COVID-19 in Wuhan Xinzhou District People's Hospital from January 16th to April 24th , 2020. Cases were confirmed by real-time RT-PCR and abnormal radiologic findings. Outcomes were followed up until May 1th , 2020. Results：Co-infection and severe underlying diseases made it easier for a case with COVID-19 to develop to be a severe one or reach an outcome of death. Age above 60 years old, male and symptoms such as fever, cough, chest tightness, headaches and fatigue were related to severe COVID-19 and an outcome of death. In addition, higher temperature, blood leukocyte count, neutrophil count, C-reactive protein level, D-dimer level, alanine aminotransferase activity, aspartate aminotransferase activity,α-hydroxybutyrate dehydrogenase activity, lactate dehydrogenase activity and creatine kinase activity were also related to severe COVID-19 and an outcome of death, and so was lower lymphocyte count. Administration of gamma globulin seemed helpful for reducing the mortality of patients with severe COVID-19, however the P value was greater than 0.05 (P=0.180), which mean under the same condition, studies of larger samples are needed in the future.                 Conclusion: Multiple factors were related to severe COVID-19 and an outcome of death.  Administration of gamma globulin seemed helpful for reducing the mortality of severe cases. More related studies are needed in the future.

SARS-CoV-2 manipulates the SR-B1-mediated HDL uptake pathway for its entry (preprint)

The recently emerged pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly, leading to a global COVID-19 pandemic. Binding of the viral spike protein (SARS-2-S) to cell surface receptor angiotensin-converting enzyme 2 (ACE2) mediates host cell infection. In the present study, we demonstrate that in addition to ACE2, the S1 subunit of SARS-2-S binds to HDL and that SARS-CoV-2 hijacks the SR-B1-mediated HDL uptake pathway to facilitate its entry. SR-B1 facilitates SARS-CoV-2 entry into permissive cells by augmenting virus attachment. MAb (monoclonal antibody)-mediated blocking of SARS-2-S-HDL binding and SR-B1 antagonists strongly inhibit HDL-enhanced SARS-CoV-2 infection. Notably, SR-B1 is co-expressed with ACE2 in human pulmonary and extrapulmonary tissues. These findings revealed a novel mechanism for SARS-CoV-2 entry and could provide a new target to treat SARS-CoV-2 infection.

Reinforcing the Supply Chain of COVID-19 Therapeutics with Expert-Coded Retrosynthetic Software (preprint)

Supply chains become stressed when demand for essential products increases rapidly in times of crisis. This year, the scourge of coronavirus highlighted the fragility of diverse supply chains, affecting the world’s pipeline of hand sanitizer, 1 toilet paper,2 and pharmaceutical starting materials. 3 Many drug repurposing studies are now underway. 4 If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it,5 will be available in the quantities required to satisfy global demand. We show the use of a retrosynthetic artificial intelligence (AI) 6-10 to navigate multiple parallel synthetic sequences, and arrive at plausible alternate reagent supply chains for twelve investigational COVID-19 therapeutics. In many instances, the AI utilizes C–H functionalization logic, 11-13 and we have experimentally validated several syntheses, including a route to the antiviral umifenovir that requires functionalization of six C–H bonds. This general solution to chemical supply chain reinforcement will be useful during global disruptions, such as during a pandemic.

Elevated serum ferritin level effectively discriminates severity illness and predicts prognosis of COVID-19 patients (preprint)

During coronavirus disease 2019 (COVID-19) pandemic, medical resources in every country is in shortage. Efficacious indicators of discriminating severe illness and predicting outcome is in urgent need. We collected data and clinical records from 79 COVID-19 patients admitted between January 12, 2020 and February 21, 2020 at Wuhan Union hospital, China. Spearman’s correlation analysis, receiver operating characteristic (ROC) curve, logistic regression model, and Kaplan-Meier survival curves were employed in the analysis. Of 79 patients enrolled, 2 died in hospital, 8 were transferred to other hospitals, and 69 were discharged. Patients with elevated ferritin levels (> 200 ng/mL) had a higher incidence of severity illness when compared with those with normal ferritin levels (≤ 200 ng/mL) (50.0% vs 2.9%). In addition, severity illness manifested significantly higher level of ferritin as compared with non-severe ones (median 921.3 vs 130.7 ng/mL, p < 0.001). Furthermore, ferritin could effectively discriminate severity and non-severity, with an area under the ROC curve (AUC) reaching 0.873 (sensitivity 96%, specificity 70%), larger than that of age (0.697), C-reactive protein (0.730) and lymphocytes% (0.717). Combined model incorporating multivariate revealed a similar manner with ferritin alone (p = 0.981). Furthermore, elevated ferritin group showed longer viral clearance time (median 16 vs 6 days, p < 0.001) and in-hospital length (median 18 vs 10 days, p < 0.001). Our results suggest that ferritin could act as a simple and efficacious complementary tool to identify severe COVID-19 patients at early stage and predict their outcome. This indicator would provide guidance for subsequent clinical practice, alleviate the medical stress and reduce the mortality.