Targeted next-generation sequencing of pathogens reveals the profile of secondary infections in COVID-19 patients (preprint)

PURPOSE: To use targeted next-generation sequencing (tNGS) of pathogens for analysing the etiological distribution of secondary infections in patients with severe and critical novel coronavirus pneumonia (COVID-19), to obtain microbial epidemiological data on secondary infections in patients with COVID-19, and to provide a reference for early empirical antibiotic treatment of such patients. METHODS: Patients with infections secondary to severe and critical COVID-19 and hospitalised at the First Affiliated Hospital of Shandong First Medical University between 1 December 2022 and 30 June 2023 were included in the study. The characteristics and etiological distribution of secondary infections in these patients were analysed using tNGS. RESULTS: A total of 95 patients with COVID-19 secondary infections were included in the study, of whom 87.37% had one or more underlying diseases. Forty-eight pathogens were detected, the most common being HSV-4, Candida albicans, Klebsiella pneumoniae, Enterococcus faecium, HSV-1, Staphylococcus aureus, Aspergillus fumigatus, Acinetobacter baumannii, HSV-5, and Stenotrophomonas maltophilia, with Pneumocystis jirovecii being detected in 14.29% of cases. The majority (76.84%) of COVID-19 secondary infections were mixed infections, with mixed viral-bacterial-fungal infections being the most common (28.42%). CONCLUSION: Most secondary infections in severe and critical COVID-19 patients are mixed, with high rates of viral and fungal infections. In clinical settings, monitoring for reactivation or secondary infections by Herpesviridae viruses is crucial; additionally, these patients have a significantly higher rate of P. jirovecii infection. tNGS testing on bronchoalveolar lavage fluid can help determine the aetiology of secondary infections early in COVID-19 patients and assist in choosing appropriate antibiotics.

Real-world effectiveness of non-pharmaceutical interventions in containing COVID-19 pandemic after the roll-out of coronavirus vaccines: A systematic review (preprint)

BackgroundNon-pharmaceutical interventions (NPIs) have been widely used to control the transmission of infectious diseases. However, the current research evidence on the policy mechanisms of NPIs is still limited. This study aims to systematically identify, describe, and evaluate the existing literature for the real-world effectiveness of NPIs in containing COVID-19 pandemic after the roll-out of coronavirus vaccines, in order to search for optimal strategies for implementing NPIs. MethodsWe conducted a comprehensive search of relevant studies from January 1, 2021, to June 4, 2023 in PubMed, Embase, Web of science and MedRxiv. Two authors independently assessed eligibility and extracted data. Risk of bias assessment tool was used to evaluate the study design, statistical methodology, and quality of reporting. Data were collected, synthesised and analyzed through quantitative and qualitative approaches. The findings were presented using summary tables and figures, including information on the target countries and regions of the study, types of NPIs, and evidence quality. ResultsThe review included a total of seventeen studies that examined the real-world effectiveness of NPIs in containing the COVID-19 pandemic after the vaccine roll-out. These studies used five composite indicator that combined multiple NPIs and fourteen individual NPIs. The studies had an average quality assessment score of 13 (range: 10-16), indicating moderately high quality. Among the included studies, nine assessed the effectiveness of the composite indicator, with four of them also evaluating individual NPIs. Additionally, twelve studies investigated the effectiveness of individual NPIs. The most frequently evaluated individual NPIs were testing policy, restrictions on gathering, facial covering, and school closure. Workplace closures and stay-at-home requirements were also assessed. The effectiveness of NPIs varied depending on time frames, countries and regions. ConclusionIn summary, the research evidence suggests that NPIs remain effective in curbing the spread of COVID-19 even after the roll-out of vaccines. Studies based on different contexts had different viewpoints or conclusions regarding the effectiveness of NPIs in containing the COVID-19 pandemic. Further research is needed to understand the policy mechanisms and address potential future challenges.

Factors Influencing the Trajectory of COVID-19 Evolution: A Longitudinal Study of 12 Asian Countries (preprint)

Summary Background The effectiveness of different strategies in addressing the COVID-19 pandemic has been assessed, but there is still not enough evidence in Asian countries. This study aims to examine the factors influencing the trajectory of COVID-19 evolution in Asia, to provide insights for optimizing public health policies. Methods In this longitudinal analysis, we combined COVID-19 cases and vaccination percentages from Our Word in Data with the policy stringency index from the Oxford COVID-19 Government Response Tracker for 12 Asian countries between January 1, 2021, and September 30, 2022. An agglomerative hierarchical cluster analysis (HCA) was conducted to identify countries with similar COVID-19 evolution trajectories. We also investigated the potential impact of seasonal variations on the virus' trajectory. The relationship between the level of policy response, vaccination coverage, and COVID-19 cases was explored using Generalized Additive Models (GAMs). Findings There were noticeable differences in the evolution trajectory of COVID-19 among the countries. The 12 Asian countries were grouped into two clusters based on evolutionary similarities. Cluster 1 consisted of West Asian countries (Azerbaijan, Turkey, Bahrain, Israel and Lebanon); while Cluster 2 included Japan, South Korea, Singapore, Malaysia, Thailand, Cambodia and Indonesia. The analysis revealed that the stringency index and vaccination coverage were associated with a statistically significant impact (both P values < 0.0001) on the evolution trajectory of COVID-19 (adjR2=0.54). The dose-response relationships demonstrated that the continuous high levels of stringency index ([≥]87.6) or vaccination coverage ([≥] 42.0%) have led to a decrease in COVID-19 infection rates. In early 2021, the adjR2 increased to 0.93 for all countries. Furthermore, the adjR2 for Cluster 1 and Cluster 2 were 0.86 and 0.90 respectively. All GAMs models have significantly improved compared to null model (P values <0.0001). Interpretation By strengthening vaccination ahead of susceptible seasons and enhancing personal self-protection measures, the transmission of COVID-19 among the population can be reduced even during the highly infectious Omicron era.

Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020 (preprint)

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people's stance, which we consider as their online behavior in each topic-related event. Our results reveal a strong connection between the stances toward the three topical events and demonstrate the power of past behaviors in predicting one's future behavior.

Integrated network pharmacology analysis, molecular docking, LC-MS analysis and bioassays revealed the potential active ingredients and underlying mechanism of Scutellariae radix for COVID-19

Scutellariae radix (“Huang-Qin” in Chinese) is a well-known traditional herbal medicine and popular dietary supplement in the world, extensively used in prescriptions of TCMs as adjuvant treatments for coronavirus pneumonia 2019 (COVID-19) patients in China. According to the differences in its appearance, Scutellariae radix can be classified into two kinds: ZiQin (1∼3 year-old Scutellariae baicalensis with hard roots) and KuQin (more than 3 year-old S. baicalensis with withered pithy roots). In accordance with the clinical theory of TCM, KuQin is superior to ZiQin in cooling down the heat in the lung. However, the potential active ingredients and underlying mechanisms of Scutellariae radix for the treatment of COVID-19 remain largely unexplored. It is still not clear whether there is a difference in the curative effect of ZiQin and KuQin for the treatment of COVID-19. In this research, network pharmacology, LC-MS based plant metabolomics, and in vitro bioassays were integrated to explore both the potential active components and mechanism of Scutellariae radix for the treatment of COVID-19. As the results, network pharmacology combined with molecular docking analysis indicated that Scutellariae radix primarily regulates the MAPK and NF-κB signaling pathways via active components such as baicalein and scutellarin, and blocks SARS-CoV-2 spike binding to human ACE2 receptors. In vitro bioassays showed that baicalein and scutellarein exhibited more potent anti-inflammatory and anti-infectious effects than baicalin, the component with the highest content in Scutellariae radix. Moreover, baicalein inhibited SARS-CoV-2’s entry into Vero E6 cells with an IC50 value of 142.50 μM in a plaque formation assay. Taken together, baicalein was considered to be the most crucial active component of Scutellariae radix for the treatment of COVID-19 by integrative analysis. In addition, our bioassay study revealed that KuQin outperforms ZiQin in the treatment of COVID-19. Meanwhile, plant metabolomics revealed that baicalein was the compound with the most significant increase in KuQin compared to ZiQin, implying the primary reason for the superiority of KuQin over ZiQin in the treatment of COVID-19.

Detection of co-infection of porcine circovirus and porcine epidemic diarrhea virus

In this study, 650 tissue samples which were collected from 16 pig farms in Hubei Province, were used to detect porcine circovirus (PCV) and Porcine epidemic diarrhea virus (PEDV). The results showed that the positive rates of PCV1, PCV2, PCV3 and PEDV single infection were 1.08%, 4.15%, 2.46% and 6.46%, respectively. In the double infections, PEDV+PCV2 had the highest positive rate of 3.54%, followed by PCV2+PCV3, with a positive rate of 1.54%. In multiple infections, PEDV+PCV2+PCV3 had the highest positive rate of 2.00%. The results indicated that the positive rates of PEDV and PCV were decreased compared with the previous studies, but the prevalence of PEDV and PCV was still wide in Hubei Province, and most of which were co-infection.

BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images (preprint)

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively.

Seasonality and reduced nitric oxide titration dominated ozone increase during COVID-19 lockdown in eastern China

With improving PM2.5 air quality, the tropospheric ozone (O3) has become the top issue of China’s air pollution control. Here, we combine comprehensive observational data analysis with models to unveil the contributions of different processes and precursors to the change of O3 during COVID-19 lockdown in the Yangtze River Delta (YRD), one of the most urbanized megacity regions of eastern China. Despite a 44 to 47% reduction in volatile organic compounds (VOCs) and nitrogen oxides (NOx) emissions, maximum daily 8-h average (MDA8) ozone concentrations increase from 28 ppbv in pre-lockdown to 43 ppbv in lockdown period. We reproduce this transition with the WRF-Chem model, which shows that ~80% of the increase in MDA8 is due to meteorological factors (seasonal variation and radiation), and ~20% is due to emission reduction. We find that daytime photochemistry does not lead to an increase but rather a decrease of daytime O3 production during the lockdown. However, the reduced O3 production is overwhelmed by the weakened nitric oxide (NO) titration resulting in a net increase of O3 concentration. Although the emission reduction increases O3 concentration, it leads to a decrease in the Ox (O3 + NO2) concentration, suggesting reduced atmospheric oxidation capacity on a regional scale. The dominant effect of NO titration demonstrates the importance of prioritizing VOCs reduction, especially from solvent usage and the petrochemical industry with high emission ratios of VOCs/NOx.

Interaction of Insomnia and Somatization with Post-Traumatic Stress Disorder in Pregnant Women During the COVID-19 Pandemic.

INTRODUCTION: Post-traumatic stress disorder (PTSD) has an adverse impact on the emotional health of prenatal maternal women and their offspring. During the Coronavirus Disease 2019 (COVID-19) pandemic, pregnant women are vulnerable to traumatic events and are prone to PTSD symptoms. The aim of the study was to explore the predictive effects of insomnia and somatization on PTSD in pregnant women by utilizing generalized additive model (GAM). MATERIALS AND METHODS: A total of 1638 pregnant women from three local cities in China underwent online survey on sleep quality, somatization, and PTSD symptoms tested by the Insomnia Severity Index (ISI), the subscale somatization of Symptom Checklist-90 (SCL-90-S) and the Checklist for DSM-5 (PCL-5), respectively. RESULTS: Insomnia was positively correlated with PTSD symptoms in pregnant women (p = 1.79×10-5). Interestingly, insomnia and somatization showed a complex non-primary linear interaction in predicting PTSD (p = 2.00×10-16). CONCLUSION: Our results suggest that insomnia is a prominent predictor of PTSD symptoms in pregnant women in the context of public emergencies. In addition, the effects of insomnia and somatization on PTSD symptoms are characterized by complex non-primary linear relationships.

COVID-19 Impacts on Perinatal Care and Maternal Mental Health: A Geotemporal Analysis of Healthcare Disruptions and Emotional Well-Being Across the United States (preprint)

Background: Adequate perinatal care is essential for maternal and infant health. The novel coronavirus (COVID-19) pandemic is potentially the largest natural disruption to perinatal care in recent history, but these disruptions have yet to be characterized in a rigorous and systematic manner. Our goal was to document COVID-19 induced disruptions to perinatal care across the United States (US) using analyses sensitive to the temporal and geographical variability of the pandemic, and to examine the impact of these healthcare disruptions on maternal mental health.Methods: We performed an observational cross-sectional study of 1,922 postpartum and 3,868 pregnant individuals during the 2020 COVID-19 pandemic. Perinatal individuals were recruited from 15 academic institutions across the US, resulting in a geographically diverse sample. We conducted (1) descriptive analyses on the prevalence and timing of perinatal care disruptions, (2) group difference analyses to compare perinatal care disruptions depending on when and where individuals gave birth, (3) cross correlations to assess the temporal linkage between perinatal care disruptions and COVID-19 infection rates, and (4) hierarchical linear regressions to evaluate the impact of prenatal care and birth protocol disruptions on maternal psychological health.Findings: The COVID-19 pandemic significantly altered perinatal care across the US, both through restriction of in-person support and by shifting the focus of care. These changes occurred unevenly over time and across geographic locations. Changes in COVID-19 infection rates explained 65 to 78% of the variance in perinatal care disruptions from August 2019 to August 2020. Moreover, disruptions to perinatal care were robustly associated with heightened psychological distress in mothers, even after controlling for mental health history, number of pregnancy complications, and general stress about the COVID-19 pandemic.Interpretation: Our analyses reveal widespread disruptions to perinatal care across the US that fluctuated depending on where and when individuals gave birth, demonstrating reactivity and elasticity of the US healthcare system. In addition to influencing health outcomes, disruptions to perinatal care may also exacerbate mental health concerns during the COVID-19 pandemic.Funding Information: This research was supported by the NYU COVID-19 Research Catalyst rant, R34DA050287-S1, R34DA050287-S2, R34DA050254-01S2, R01MH126468, R01MH125870, the Nathaniel Wharton Fund, the Columbia University Population Research Center, R34DA050255, R34DA050255-01S2, the Fralin Biomedical Research Institute at VTC, the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR003015 and KL2TR003016, the University of Utah Center for Clinical and Translational Science COVID-19 Research Award, Virginia Commonwealth University School of Nursing Internal Grants Program, Sarah P. Farrell Legacy Research Endowment-Virginia Commonwealth University, 5R03HD096141-02, R01HD085990, R34DA050283-01S2, the USC Center for the Changing Family, the Stanford Institute for Research in the Social Sciences, R34DA050291, R01MH119070, R01MH117177, R34 DA050272-01S1, R01 MH113883, R01 DA046224, R21 MH111978, and R21 HD090493, R37 MH10149, UH3OD023279, and National Center for Advancing Translational Sciences (NCATS) Grant UL1TR001881Declaration of Interests: The authors report no conflicts of interest.Ethics Approval Statement: This study has received Institutional Review Board approval from theNYU Langone Health IRB as well as the local IRBs at each data collection site. All data was collected in accordance with the Helsinki Declaration.

Assessment of emission reduction effect in Beijing, Tianjin and surrounding 26 cities from January to March in 2020 during the epidemic of COVID-19

In order to evaluate the effect of air pollutions emission reduction in Beijing, Tianjin, Hebei and its surrounding 26 cities（"2 + 26" cities） from January to March in 2020 during the epidemic of COVID-19, the air quality model of nested air quality prediction modeling system（NAQPMS） was applied to conduct a few scenarios. The characteristics of air quality from January to March 2020, and during the periods before and after the epidemic of COVID-19 were investigated. The influences of meteorology, emergency emission reduction measures and social economic activities on ambient air quality as well as the uncertainties were elucidated and discussed. The results showed that the number of days achieving good and moderate air quality standard in "2+26" cities accounted for 59.6%, on average of 10.9% increase relative to the same period last year. The mean concentration of PM10、PM2.5、SO2、NO2、O3-8 h-90 per and CO-95 per in "2 + 26" cities from January to March in 2020 were 108, 76, 14, 109, 36μg/m3, and 2.3 mg/m3, respectively. During the epidemic period from January 24 to March 31, the concentrations of PM10, NO2, PM2.5, and CO decreased significantly compared with the period prior to the epidemic from January 1 to 23. In contrast to January to March in 2019, the PM2.5 concentrations of the cities along the Yan mountain and Taihang mountain increased by 1%～8% in 2020. However, the model simulations revealed that the emergency emission reduction measures potentially avoided twice of the regional heavy air pollution events, resulting in the quarterly mean PM2.5 concentration in "2 + 26" cities reduced by 6 to 26μg/m3. Due to the influence of the Spring Festival holiday and epidemic, the traffic emissions were reduced substantially. In contrast, the emissions from the industry such as coking and thermal power did not show large variations, and the negative impact of loose coal combustion on ambient air quality may become even more severe.

Behavioral coping phenotypes and psychosocial outcomes in a national U.S. sample of pregnant and postpartum women during the COVID-19 pandemic (preprint)

Maternal stress exposure during the COVID-19 pandemic may have transgenerational effects, adversely affecting both the pregnant woman and her offspring. Therefore, there is an urgent need to characterize the coping styles and psychosocial distress of pregnant and postpartum women during the COVID-19 pandemic to help mitigate lasting sequalae on both mothers and infants. Here we use latent profile analysis to examine patterns of behavioral coping strategies associated with risk and resiliency to adverse mental and physical health outcomes. Leveraging a large U.S. sample of perinatal women (N = 2,876 pregnant women, N = 1,536 postpartum women), we identified four behavioral phenotypes of coping strategies: (1) passive-coping, characterized by primarily engaging in high levels of screen time, social media use, and eating comfort foods; (2) active-coping, characterized by primarily engaging in high levels of self-care, social support, and limiting media exposure; (3) low-coping, characterized by low levels of all coping strategies; (4) high-coping, characterized by high levels of both active and passive coping strategies. Critically, we found that passive-coping phenotypes were associated with higher levels of depression and anxiety and worsening stress and energy levels in both pregnant and postpartum women. Supplementing passive coping strategies with high levels of active coping strategies (the high-coping profile) lessened adverse outcomes in postpartum women. These behavioral coping phenotypes highlight potential risk and protective factors for perinatal women, which is critical in helping to identify and treat perinatal women most at risk for experiencing mood and affective disorders resulting from the COVID-19 pandemic.

Robust Antibody and T Cell Responses in COVID-19 Patients Six Months After Infection (preprint)

Determining the longevity of natural immunity after SARS-CoV-2 infection is critical for understanding immune protection and optimizing vaccine design. Over six months have been passed since the emergence of COVID-19 in China. We evaluated humoral and cellular responses in 418 patients six months after infection. 95.9% and 98.1% of the patients maintained SARS-CoV-2-specific IgG and neutralizing antibodies. All severe patients were positive for IgG and neutralizing antibodies and had significantly higher titers than mild and moderate patients as well as asymptomatic individuals. The patients had a more robust SARS-CoV-2-specific CD4+ T cell response than CD8+ T cells six months after infection. Unexpectedly, sustained immune activation was observed, which displayed as the evaluated proinflammatory monocytes, non-classical NK cells, CD4+ Treg cells, and activated CD4+ T cells. Our findings indicate that SARS-CoV-2 gives rise to persisting and robust protective immunity, which provides a promising sign for prevention from reinfection and vaccination strategy.Funding: This work was supported by grants from the Natural Science Foundation of China (81773494 to M.J.M.), the National Major Project for Control and Prevention of Infectious Disease of China (2017ZX10303401-006 to M.J.M.), the Special National Project on Investigation of Basic Resources of China (2019FY101502 to M.J.M.).Conflict of Interest: The authors declare no competing interests.Ethical Approval: All patients provided written informed consent. The study was conducted following the Declaration of Helsinki, and the Institutional Review Board of the Academy of Military Medical Sciences approved the study protocol (IRB number: AF/SC-08/02.46).

The deubiquitinase USP44 promotes Treg function during inflammation by preventing FOXP3 degradation

The transcription factor forkhead box P3 (FOXP3) is essential for the development of regulatory T cells (Tregs) and their function in immune homeostasis. Previous studies have shown that in natural Tregs (nTregs), FOXP3 can be regulated by polyubiquitination and deubiquitination. However, the molecular players active in this pathway, especially those modulating FOXP3 by deubiquitination in the distinct induced Treg (iTreg) lineage, remain unclear. Here, we identify the ubiquitin-specific peptidase 44 (USP44) as a novel deubiquitinase for FOXP3. USP44 interacts with and stabilizes FOXP3 by removing K48-linked ubiquitin modifications. Notably, TGF-ß induces USP44 expression during iTreg differentiation. USP44 co-operates with USP7 to stabilize and deubiquitinate FOXP3. Tregs genetically lacking USP44 are less effective than their wild-type counterparts, both in vitro and in multiple in vivo models of inflammatory disease and cancer. These findings suggest that USP44 plays an important role in the post-translational regulation of Treg function and is thus a potential therapeutic target for tolerance-breaking anti-cancer immunotherapy.

Insights into the practical effectiveness of RT-PCR testing for SARS-CoV-2 from serologic data, a cohort study (preprint)

Background: Virologic detection of SARS-CoV-2 through Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) has limitations for surveillance. Serologic tests can be an important complementary approach. Objective: Assess the practical performance of RT-PCR based surveillance protocols, and the extent of undetected SARS-CoV-2 transmission in Shenzhen, China. Design: Cohort study nested in a public health response. Setting: Shenzhen, China; January-May 2020. Participants: 880 PCR-negative close-contacts of confirmed COVID-19 cases and 400 residents without known exposure (main analysis). Fifty-seven PCR-positive case contacts (timing analysis). Measurements: Virological testing by RT-PCR. Measurement of anti-SARS-CoV-2 antibodies in PCR-negative contacts 2-15 weeks after initial testing using total Ab ELISA. Rates of undetected infection, performance of RT-PCR over the course of infection, and characteristics of seropositive but PCR-negative individuals were assessed. Results: The adjusted seropositivity rate for total Ab among 880 PCR-negative close-contacts was 4.1% (95%CI, 2.9% to 5.7%), significantly higher than among residents without known exposure to cases (0.0%, 95%CI, 0.0% to 1.0%). PCR-positive cases were 8.0 times (RR; 95% CI, 5.3 to 12.7) more likely to report symptoms than the PCR-negative individuals who were seropositive, but otherwise similar. RT-PCR missed 36% (95%CI, 28% to 44%) of infected close-contacts, and false negative rates appear to be highly dependent on stage of infection. Limitations: No serological data were available on PCR-positive cases. Sample size was limited, and only 20% of PCR-negative contacts met inclusion criteria. Conclusion: Even rigorous RT-PCR testing protocols may miss a significant proportion of infections, perhaps in part due to difficulties timing testing of asymptomatics for optimal sensitivity. Surveillance and control protocols relying on RT-PCR were, nevertheless, able to contain community spread in Shenzhen.

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning (preprint)

Background Novel coronavirus disease (COVID-19) is an emerging, rapidly evolving situation. At present, the prognosis of severe and critically ill patients has become an important focus of attention. We strived to develop a prognostic prediction model for severe and critically ill COVID-19 patients.MethodsTo assess the factors associated with the prognosis of those patients, we retrospectively investigated the clinical, laboratory characteristics of confirmed 112 cases of COVID-19 admitted between 21 January to 6 March 2020 from Huangshi Central Hospital, Huangshi Hospital of Traditional Chinese Medicine, and Daye People’s Hospital. We applied machine learning method (survival random forest) to select predictors for 28-day survival and taken into account the dynamic trajectory of laboratory indicators. Results Fifteen candidate prognostic features, including 11 baseline measures (including platelet count (PLT), urea, creatine kinase (CK), fibrinogen, creatine kinase isoenzyme activity, aspartate aminotransferase (AST), activation of partial thromboplastin time (APTT), albumin, standard deviation of erythrocyte distribution width (RBC-SD), neutrophils (%) and red blood cell count (RBC)) and 4 trajectory clusters (changes during hospitalization in the white blood cell (WBC), PLT large cell ratio (P-LCR), PLT distribution width (PDW) and AST), combined with covariates achieved 100% (95%CI: 99%-100%) AUC and reached 87% (95%CI: 84%-91%) AUC in an external validation set. Conclusions Taking advantage of random forest technique and laboratory dynamic measures, we developed a forest model to predict survival outcome of COVID-19 patients, which achieved 87% AUC in the external validation set. Our online tool will help to facilitate the early recognition of patients with high risk. 

Clinical Features and Efficacy of Antiviral Drug, Arbidol in 220 Nonemergency COVID‐19 Patients from East-West-Lake Shelter Hospital in Wuhan: A Retrospective Case Series (preprint)

Objective. We aimed to describe the features of 220 nonemergency (mild or common type) COVID-19 patients from a shelter hospital, as well as evaluate the efficiency of antiviral drug, Arbidol in their disease progressions. Methods. Basic clinical characteristics were described and the efficacy of Arbidol was evaluated based on gender, age, maximum body temperature of the patients. Results. Basically, males had a higher risk of fever and more onset symptoms than females. Arbidol could accelerate fever recovery and viral clearance in respiratory specimens, particularly in males. Arbidol also contributed to shorter hospital stay without obvious adverse reactions.Conclusions. In the retrospective COVID-19 cohort, gender was one of the important factors affecting patient's conditions. Arbidol showed several beneficial effects in these patients, especially in males. This study brought more researches enlightenment in understanding the emerging infectious disease.

A novel biparatopic antibody-ACE2 fusion that blocks SARS-CoV-2 infection: implications for therapy (preprint)

In the absence of a proven effective vaccine preventing infection by SARS-CoV-2, or a proven drug to treat COVID-19, the positive results of passive immune therapy using convalescent serum provides a strong lead. We have developed a new class of tetravalent, biparatopic therapy, 89C8-ACE2. It combines the specificity of a monoclonal antibody (89C8) that recognizes the relatively conserved N-terminal domain (NTD) of the viral S glycoprotein, and the ectodomain of ACE2, which binds to the receptor-binding domain (RBD) of S. This molecule shows exceptional performance in vitro, inhibiting the interaction of recombinant S1 to ACE2 and transduction of ACE2-overexpressing cells by S-pseudotyped lentivirus with IC50s substantially below 100 pM, and with potency approximately 100-fold greater than ACE2-Fc itself. Moreover, 89C8-ACE2 was able to neutralize authentic virus infection in a standard assay at low nanomolar concentrations, making this class of molecule a promising lead for therapeutic applications.

An exploratory analysis of public opinion and sentiments towards COVID-19 pandemic using Twitter data. (preprint)

Objective: Twitter data have been increasingly used to address health-related issues. However, little is known about their potential for understanding public opinions and sentiments of the current COVID-19 pandemic. The present study explores public opinion and sentiments about the COVID-19 pandemic using Tweets from 3 popular Coronavirus-related hashtags (#COVID19, #Coronavirus, #SARSCoV2).Results: Of the 39,726 Tweets analysed, we found that over 60% of words used within Tweets in all hashtags (#COVID19, 63.9%; #Coronavirus: 65.6%; #SARSCoV2: 63.5%) conveyed a negative mood towards the pandemic. Our results also showed similar trends in Tweet volume in #COVID19 and #SARSCoV2, with a spike in the number of Tweets on the 3rd and 6th of April 2020. Further exploration of Tweets in both hashtags revealed similar Twitter discussions related to topics on “Hydroxychloroquine” and “Hospitalisations of the British Prime minister” and “ the attainment of 1 million cases of coronavirus globally”.The findings of this exploratory study indicate that there is potential for using data generated from Twitter to understand general public opinion and sentiments towards the COVID-19 pandemic. However, caution is needed due to several limitations in this study. It is also important for future studies to explore the context around Tweets.