Individual bat viromes reveal the co-infection, spillover and emergence risk of potential zoonotic viruses (preprint)

Bats are reservoir hosts for many zoonotic viruses. Despite this, relatively little is known about the diversity and abundance of viruses within bats at the level of individual animals, and hence the frequency of virus co-infection and inter-species transmission. Using an unbiased meta-transcriptomics approach we characterised the mammalian associated viruses present in 149 individual bats sampled from Yunnan province, China. This revealed a high frequency of virus co-infection and species spillover among the animals studied, with 12 viruses shared among different bat species, which in turn facilitates virus recombination and reassortment. Of note, we identified five viral species that are likely to be pathogenic to humans or livestock, including a novel recombinant SARS-like coronavirus that is closely related to both SARS-CoV-2 and SARS-CoV, with only five amino acid differences between its receptor-binding domain sequence and that of the earliest sequences of SARS-CoV-2. Functional analysis predicts that this recombinant coronavirus can utilize the human ACE2 receptor such that it is likely to be of high zoonotic risk. Our study highlights the common occurrence of inter-species transmission and co-infection of bat viruses, as well as their implications for virus emergence.

Integrated Analysis of microRNAs and Metabolomics in Rat’s Serum Reveals Multi-action Modes of Qingfei Paidu Decoction for COVID-19 Treatment (preprint)

Background: During the fight against coronavirus disease 2019 (COVID-19) in China, Qingfei Paidu decoction (QFPDD) has been widely applied to treat COVID-19 patients. Retrospective studies showed that QFPDD could improve clinical outcomes of COVID-19. Thus, it is necessary and interesting to explore the action mode of QFPDD for further application and development. Methods: Sprague-Dawley (SD) rats were randomly divided into two groups, QFPDD (n=9) and control (n=10) groups. They were parallelly treated for 12 days with QFPDD and warm distilled water, respectively. At the endpoint, the microRNA (miRNA or miR) profiles in serum were detected to identify differently expressed miRNAs (DEMs). Then, the action mode of QFPDD were explored via review of potential roles of DEMs and functional enrichment analysis of their targets (e.g., GO enrichment and KEGG pathway analysis), especially focusing on the aspects of immunity, inflammation, virus infection and pulmonary fibrosis. Core genes were identified based on KEGG pathway analysis. Metabolomics were detected in serum and significantly changed metabolites (SCMs), especially the metabolic substrates and products of enzyme of core gene were identified as biomarkers to validate the regulation of DEMs to enzyme activity of core gene through metabolomic analysis and linear correlation analysis between SCMs and DEMs. Results: 23 DEMs were identified in the serum between QFPDD and control groups, with 1636 predicted genes. Reported evidence has showed that both the DEMs and their target genes involve regulation of immunity, inflammation, virus infection and pulmonary fibrosis. Phospholipase C, gamma 1 (Plcg1) was identified as a core gene and predicted to be upregulated attributed to downregulation of novel-89-mature. The levels of three SCMs, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)), PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) and PC(16:1(9Z)/16:1(9Z)), which were the metabolic substrates of phospholipase C, were significantly reduced in QFPDD group, in addition, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)) and PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) presented positively linear correlation with the expression level of novel-89-mature. The level of phosphorylcholine, a product of PCs metabolized by phospholipase C, was significantly elevated in QFPDD group. Conclusion: QFPDD can induce modification of miRNAs profile, and subsequently multi-regulate the immunity, inflammation, virus infection and pulmonary fibrosis in vivo, playing an important role for the positive outcomes of COVID-19 patients treated by QFPDD in China.

Changes in the Pathogenic Spectrum of Acute Respiratory Tract Infections During the COVID-19 Epidemic in Beijing, China (preprint)

Background: Public health measures against COVID-19 may influence other disease epidemics. Many countries have reported significant reductions in influenza activity in 2020–2021, but the prevalence of other respiratory pathogens during the COVID-19 pandemic has rarely been reported, especially in China.Methods: Data from the Respiratory Pathogen Surveillance System in Beijing were analyzed to compare pathogen infection rates before the COVID-19 (from 1 February 2015 to 31 January 2020) and during the COVID-19 (from 1 February 2020 to 31 January 2021).Findings: Among 41630 acute respiratory tract infections 13630 had at least one pathogen positive result, which decreased from 32·16% (95% CI 31·69%, 32·64%) before the COVID-19 to 10·97% (95% CI 10·03%, 11·96%) during the COVID-19, representing a 65·90% decrease (P<0·001). The positivity rate fluctuated with the strictness of public health measures. Before the COVID-19 epidemic, the top five of the pathogenic spectrums were IFV (26·27%), MP (19·30%), HPIV (11·80%), HRV (9·38%), and EV (8·38%), while during the COVID-19, the top five were seasonal HCoV (21·10%), HRV (18·99%), HPIV (14·98%), IFV (13·08%), and RSV (10.76%).Interpretation: The prevalence of respiratory pathogens decreased significantly during the COVID-19, closely relating to public health measures against COVID-19; these measures can serve as useful strategies for the prevention and control of other respiratory tract infections.Funding Statement: The National Major Science and Technology Project for Control and Prevention of Major Infectious Diseases in China (2017ZX10103004).Declaration of Interests: FH received funds from the National Major Science and Technology Project for Control and Prevention of Major Infectious Diseases in China (2017ZX10103004). All other authors declare no competing interests.Ethics Approval Statement: Ethics approval for the protocol of this study was obtained from the Ethics Committee of the BJCDC. Written informed consent was obtained.

Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis (preprint)

Background: There is limited information on the difference in epidemiology, clinical characteristics and outcomes of the initial outbreak of the coronavirus disease (COVID-19) in Wuhan (the epicenter) and Sichuan (the peripheral area) in the early phase of the COVID-19 pandemic. This study was conducted to investigate the differences in the epidemiological and clinical characteristics of patients with COVID-19 between the epicenter and peripheral areas of pandemic and thereby generate information that would be potentially helpful in formulating clinical practice recommendations to tackle the COVID-19 pandemic. Methods: The Sichuan & Wuhan Collaboration Research Group for COVID-19 established two retrospective cohorts that separately reflect the epicenter and peripheral area during the early pandemic. The epidemiology, clinical characteristics and outcomes of patients in the two groups were compared. Multivariate regression analyses were used to estimate the adjusted odds ratios (aOR) with regard to the outcomes. Results: The Wuhan (epicenter) cohort included 710 randomly selected patients, and the peripheral (Sichuan) cohort included 474 consecutive patients. A higher proportion of patients from the periphery had upper airway symptoms, whereas a lower proportion of patients in the epicenter had lower airway symptoms and comorbidities. Patients in the epicenter had a higher risk of death (aOR=7.64), intensive care unit (ICU) admission (aOR=1.66), delayed time from illness onset to hospital and ICU admission (aOR=6.29 and aOR=8.03, respectively), and prolonged duration of viral shedding (aOR=1.64). Conclusions: The worse outcomes in the epicenter could be explained by the prolonged time from illness onset to hospital and ICU admission. This could potentially have been associated with elevated systemic inflammation secondary to organ dysfunction and prolonged duration of virus shedding independent of age and comorbidities. Thus, early supportive care could achieve better clinical outcomes.

Can corticosteroids improve the outcomes of patients with Covid-19? A retrospective cohort study of patients within and outside the epicentre (preprint)

Background The novel coronavirus disease 2019 (Covid-19) has been a worldwide pandemic with more than 300,000 deaths. Corticosteroids have been used in some patients with severe Covid-19 in order to control the systemic inflammation or cytokine storm, however, their effects and safety have not yet been elucidated.Methods Patients with confirmed Covid-19 were retrospectively included from both the epicentre and out of the epicentre. Patients were classified into two groups according to the use of systemic corticosteroids, and the mortality and the rate of virus clearance were compared between the two groups. In addition, independent factors associated with death after corticosteroids treatment were also identified.Results A total of 775 patients were included in our final analysis, of which 238 (30.7%) patients received systemic corticosteroids treatment. Compared with patients without corticosteroids treatment, patients with corticosteroids treatment had significantly higher mortality (19.3% vs. 3.7%, P < 0.001) and lower rate of virus clearance (43.2% vs. 66.7%, P < 0.001) although along with increase of SpO2/FiO2 and blood lymphocytes in patients with severe Covid-19. Corticosteroids treatment was associated with longer hospital length of stays and delayed virus clearance time. In patients with corticosteroids treatment, blood lymphocytes (odds ratio (OR) 0.792, 95% confidence interval (CI) 0.672–0.932, P = 0.005) and creatine kinase (CK) (OR 1.006, 95%CI 1.000-1.012, P = 0.038) were independent risk factors associated with death, with a sensitivity of 90.91% and 44.44% and a specificity of 70.75% and 94.05%, respectively.Conclusions In patients with Covid-19, corticosteroids treatment is associated with increased mortality and reduced rate of virus clearance.

COVID-19 Asymptomatic Infection Estimation (preprint)

Background: Mounting evidence suggests that there is an undetected pool of COVID-19 asymptomatic but infectious cases. Estimating the number of asymptomatic infections has been crucial to understand the virus and contain its spread, which is, however, hard to be accurately counted. Methods: We propose an approach of machine learning based fine-grained simulator (MLSim), which integrates multiple practical factors including disease progress in the incubation period, cross-region population movement, undetected asymptomatic patients, and prevention and containment strength. The interactions among these factors are modeled by virtual transmission dynamics with several undetermined parameters, which are determined from epidemic data by machine learning techniques. When MLSim learns to match the real data closely, it also models the number of asymptomatic patients. MLSim is learned from the open Chinese global epidemic data. Findings: MLSim showed better forecast accuracy than the SEIR and LSTM-based prediction models. The MLSim learned from the data of China's mainland reveals that there could have been 150,408 (142,178-157,417) asymptomatic and had self-healed patients, which is 65% (64% - 65%) of the inferred total infections including undetected ones. The numbers of asymptomatic but infectious patients on April 15, 2020, were inferred as, Italy: 41,387 (29,037 - 57,151), Germany: 21,118 (11,484 - 41,646), USA: 354,657 (277,641 - 495,128), France: 40,379 (10,807 - 186,878), and UK: 144,424 (127,215 - 171,930). To control the virus transmission, the containment measures taken by the government were crucial. The learned MLSim also reveals that if the date of containment measures in China's mainland was postponed for 1, 3, 5, and 7 days later than Jan. 23, there would be 109,039 (129%), 183,930 (218%), 313,342 (371%), 537,555 (637%) confirmed cases on June 12. Conclusions: Machine learning based fine-grained simulators can better model the complex real-world disease transmission process, and thus can help decision-making of balanced containment measures. The simulator also revealed the potential great number of undetected asymptomatic infections, which poses a great risk to the virus containment.

Lymphopenia acted as an adverse factor for severity in patients with COVID-19: a single-centered, retrospective study (preprint)

Purpose: The outbreak of SARS-CoV-2 began in December and rapidly caused a pandemic. To investigate the significance of lymphopenia for the severity of the disease, this study was performed.Methods: 115 patients confirmed COVID-19 from a tertiary hospital in Changsha, China were enrolled. The clinical, laboratory, treatment and outcome data were collected and compared between patients with lymphopenia or not.Results: The median age was 42 years (1-75). 54 patients (47.0%) of the patients had lymphopenia on admission. In the group of lymphopenia, more patients had hypertension (30.8% vs 10.0%, P=0.006) and coronary heart disease (3.6% vs 0%, P=0.029) and more patients with leucopenia (48.1% vs 14.8%, P<0.001) and eosinophilia (92.6% vs 54.1%, P<0.001) were observed. Lymphopenia was also correlated with severity grades of pneumonia (P<0.001) and C-reactive protein (CRP) level (P=0.0014). Lymphopenia was associated with a prolonged duration of hospitalization (17.0 days vs 14.0 days, P=0.002). Moreover, the recovery of lymphocyte appeared the earliest before CRP and chest radiographs in severe cases, suggesting its predictive value for disease improvement. Conclusion: Our results showed the clinical significance of lymphopenia for predicting the severity of COVID-19 and the recovery of the disease, emphasizing the need to monitor the lymphocyte count dynamically.

Potential Factors for Prediction of Disease Severity of COVID-19 Patients (preprint)

ObjectiveCoronavirus disease 2019 (COVID-19) is an escalating global epidemic caused by SARS-CoV-2, with a high mortality in critical patients. Effective indicators for predicting disease severity in SARS-CoV-2 infected patients are urgently needed. MethodsIn this study, 43 COVID-19 patients admitted in Chongqing Public Health Medical Center were involved. Demographic data, clinical features, and laboratory examinations were obtained through electronic medical records. Peripheral blood specimens were collected from COVID-19 patients and examined for lymphocyte subsets and cytokine profiles by flow cytometry. Potential contributing factors for prediction of disease severity were further analyzed. ResultsA total of 43 COVID-19 patients were included in this study, including 29 mild patients and 14 sever patients. Severe patients were significantly older (61.9{+/-}9.4 vs 44.4{+/-}15.9) and had higher incidence in co-infection with bacteria compared to mild group (85.7%vs27.6%). Significantly more severe patients had the clinical symptoms of anhelation (78.6%) and asthma (71.4%). For laboratory examination, 57.1% severe cases showed significant reduction in lymphocyte count. The levels of Interluekin-6 (IL6), IL10, erythrocyte sedimentation rate (ESR) and D-Dimer (D-D) were significantly higher in severe patients than mild patients, while the level of albumin (ALB) was remarkably lower in severe patients. Further analysis demonstrated that ESR, D-D, age, ALB and IL6 were the major contributing factors for distinguishing severe patients from mild patients. Moreover, ESR was identified as the most powerful factor to predict disease progression of COVID-19 patients. ConclusionAge and the levels of ESR, D-D, ALB and IL6 are closely related to the disease severity of COVID-19 patients. ESR can be used as a valuable indicator for distinguishing severe COVID-19 patients in early stage, so as to increase the survival of severe patients.