Longitudinal wastewater surveillance addressed public health priorities during the transition from "dynamic COVID-zero" to "opening up" in China: a population-based study (preprint)

Background Wastewater surveillance provides real-time, cost-effective monitoring of SARS-CoV-2 transmission. We developed the first city-level wastewater warning system in mainland China, located in Shenzhen. Our study aimed to reveal cryptic transmissions under the "dynamic COVID-zero" policy and characterize the dynamics of the infected population and variant prevalence, and then guide the allocation of medical resources during the transition to "opening up" in China. Methods In this population-based study, a total of 1,204 COVID-19 cases were enrolled to evaluate the contribution of Omicron variant-specific faecal shedding rates in wastewater. After that, wastewater samples from up to 334 sites distributed in communities and port areas in two districts of Shenzhen covering 1.74 million people were tested daily to evaluate the sensitivity and specificity of this approach and were validated against daily SARS-CoV-2 screening. After the public health policy was switched to "opening up" in December 7, 2022, we conducted wastewater surveillance at wastewater treatment plants and pump stations covering 3.55 million people to estimate infected populations using model prediction and detect the relative abundance of SARS-CoV-2 lineages using wastewater sequencing. Findings In total, 82.4% of SARS-CoV-2 Omicron cases tested positive for faecal viral RNA within the first four days after the diagnosis, which was far more than the proportion of the ancestral variant. A total of 27,759 wastewater samples were detected from July 26 to November 30 in 2022, showing a sensitivity of 73.8% and a specificity of 99.8%. We further found that wastewater surveillance played roles in providing early warnings and revealing cryptic transmissions in two communities. Based on the above results, we employed a prediction model to monitor the daily number of infected individuals in Shenzhen during the transition to "opening up" in China, with over 80% of the population infected in both Futian District and Nanshan District. Notably, the prediction of the daily number of hospital admission was consistent with the actual number. Further sequencing revealed that the Omicron subvariant BA.5.2.48 accounted for the most abundant SARS-CoV-2 RNA in wastewater, and BF.7.14 and BA.5.2.49 ranked second and third, respectively, which was consistent with the clinical sequencing. Interpretation This study provides a scalable solution for wastewater surveillance of SARS-CoV-2 to provide real-time monitoring of the new variants, infected populations and facilitate the precise prediction of hospital admission. This novel framework could be a One Health system for the surveillance of other infectious and emerging pathogens with faecal shedding and antibiotic resistance genes in the future. Funding Sanming Project of Medicine in Shenzhen, Shenzhen Key Medical Discipline Construction Fund.

Towards a global One Health index: a potential assessment tool for One Health performance.

BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.

Lung remodeling regions in long-term Covid-19 feature basal epithelial cell reprogramming (preprint)

Respiratory viruses, including SARS-CoV-2, can trigger chronic lung disease that persists and even progresses after expected clearance of infectious virus. To gain an understanding of this process, we examined a series of consecutive fatal cases of Covid-19 that came to autopsy at 27-51 d after hospital admission. In each patient, we identify a stereotyped bronchiolar-alveolar pattern of lung remodeling with basal epithelial cell hyperplasia and mucinous differentiation. Remodeling regions also feature macrophage infiltration and apoptosis and a marked depletion of alveolar type 1 and 2 epithelial cells. This entire pattern closely resembles findings from an experimental model of post-viral lung disease that requires basal-epithelial stem cell growth, immune activation, and differentiation. The present results thereby provide evidence of possible basal epithelial cell reprogramming in long-term Covid-19 as well and thereby a pathway for explaining and correcting lung dysfunction in this type of disease.

Trends in secondary inorganic aerosol pollution in China and its responses to emission controls of precursors in wintertime

The Chinese government recently proposed ammonia (NH3) emission reductions (but without a specific national target) as a strategic option to mitigate fine particulate matter (PM2.5) pollution. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m-3 at 74 % of 1498 monitoring sites during 2015–2019. The concentration of PM2.5 and its components were significantly higher (16 %–195 %) on hazy days than on non-hazy days. Compared with mean values of other components, this difference was more significant for the secondary inorganic ions SO42-, NO3-, and NH4+ (average increase 98 %). While sulfate concentrations significantly decreased over this period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50 % NH3 emission reduction for controlling secondary inorganic aerosol (SIA) concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the COVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8 % lower than those in 2017. In addition, the reduction in SIA concentrations in 2017 was greater for 50 % acid gas reductions than for the 50 % NH3 emission reductions. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by emissions of acid gases, while an additional control of NH3 emissions would become more important as reductions of SO2 and NOx emissions progress.

Optimize Data-Driven Multi-Agent Simulation for COVID-19 Transmission (preprint)

Background: Multi-Agent Simulation is an essential technique for exploring complex systems. In researches of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and pour data locality, optimizing the performance is challenging. Results: This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelisation strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and create a thread-safe data-mapping paradigm called hierachical hash table to accelerate hash operations. Conclusions: Our performance results demonstrate capabilities of these methods exhibiting significant improvements of system performance. The case-focused method degrades $\sim 90 \%$ cache references and achieves $\times 4.3$ speedup. Hierachical hash table can further boost computation speed by 47\%. And parallel implementation with 20 threads on CPU achieves $\times 81$ speedup consequently.

Comprehensive analysis of RNA-seq and whole genome sequencing data reveals no evidence for SARS-CoV-2 integrating into host genome (preprint)

SARS-CoV-2, as the causation of severe epidemic of COVID-19, is one kind of positive single-stranded RNA virus with high transmissibility. However, whether or not SARS-CoV-2 can integrate into host genome needs thorough investigation. Here, we performed both RNA sequencing (RNA-seq) and whole genome sequencing on SARS-CoV-2 infected human and monkey cells, and investigated the presence of host-virus chimeric events. Through RNA-seq, we did detect the chimeric host-virus reads in the infected cells. But further analysis using mixed libraries of infected cells and uninfected zebrafish embryos demonstrated that these reads are falsely generated during library construction. In support, whole genome sequencing also didn't identify the existence of chimeric reads in their corresponding regions. Therefore, the evidence for SARS-CoV-2's integration into host genome is lacking.

Re-emergence of COVID-19 in Beijing was triggered by frozen virus: evidence from molecular clock (preprint)

ABSTRACT The COVID-19 outbreak in Xinfadi (XFD) Wholesale market in Beijing, China in June, 2020 caused 368 reported cases within 39 days. Genetic evidences indicated that imported SARS-CoV-2 (belong to the lineage B1.1.29) initiated this outbreak. However, the transmission route of the virus is still unknown. We obtained from public database three SARS-CoV-2 genomes isolated in XFD (XFD genomes) and adopted the leaf-dating method to calculate their expected collection dates using temporal calibrating information from other 241 genomes collected in mainland of China. All three XFD genomes were calculated to have earlier collection dates than the recorded (Bayes factor >1), and hence exhibited a lack of genetic divergence. We additionally combined the XFD genomes with other 225 genomes subsampled from those of the lineage B1.1.29, among which five sequences were also included for control analysis. Two of three XFD genomes were calculated to have earlier collection dates than the recorded (Bayes factor >1), while no control genomes provided such evidence. According to present understanding of SARS-CoV-2, a lack of genetic divergence is most likely due to being frozen. Considering the fact that the XFD outbreak started from a booth of frozen food, we judged that the XFD outbreak was caused by contaminated frozen food. Our results provided molecular evidence for the source of COVID-19 outbreak in Beijing XFD, which highlights new targets for SARS-CoV-2 surveillance for the public health.

Twelve-month specific IgG response to SARS-CoV-2 receptor-binding domain among COVID-19 convalescent plasma donors in Wuhan (preprint)

To investigate the duration of humoral immune response in convalescent coronavirus disease 2019 (COVID-19) patients, we conducted a 12-month longitudinal study through collecting a total of 1,782 plasma samples from 869 convalescent plasma donors in Wuhan, China and tested specific antibody response. The results show that positive rate of IgG antibody against receptor-binding domain of spike protein (RBD-IgG) to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the COVID-19 convalescent plasma donors exceeded 70% for 12 months post diagnosis. RBD-IgG kinetics displayed a gradually downward trend, the titer started to stabilize after 9 months and decreased by 68.1% compared with the 1st month. Moreover, male plasma donors produced more RBD-IgG than female plasma donors and patient age positively correlated with the RBD-IgG titer. A strong positive correlation between RBD-IgG and neutralizing antibody titers was also identified. This study is essential for understanding SARS-CoV-2-induced immune memory to develop vaccine and therapeutics.

DABC-Net for robust pneumonia segmentation and prediction of COVID-19 progression on chest CT scans (preprint)

Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-net for intra-slice spatial information processing, and a recurrent LSTM network to leverage inter-slice context, for automatic volumetric segmentation of lung and pneumonia lesions. We evaluate DABC-Net on more than 10,000 radiologists-labeled CT slices from four different cohorts. Compared to state-of-the-art segmentation tools, DABC-Net is much faster, more robust, and able to estimate segmentation uncertainty. Based only on the first two CT scans within 3 days after admission from 656 longitudinal CT scans, the AUC of our DBAC-Net for disease progression prediction reaches 93%. We release our tool as a GUI for patient-specific prediction of pneumonia progression, to provide clinicians with additional assistance to triage patients at early days after the diagnosis and to optimize the assignment of limited medical resources, which is of particular importance in current critical COVID-19 pandemic.

Reduced Inflammatory Responses to SARS-CoV-2 Infection in Children Presenting to Hospital with COVID-19 in China (preprint)

Background: Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in children is associated with better outcomes than in adults. The inflammatory response to COVID-19 infection in children remains poorly characterised.Methods: We retrospectively analysed the medical records of 127 laboratory-confirmed COVID-19 patients aged 1 month to 16 years from Wuhan and Jingzhou of Hubei Province. Patients presented between January 25th and March 24th 2020. Information on clinical features, laboratory results, plasma cytokines/chemokines and lymphocyte subsets were analysed.Findings: Children admitted to hospital with COVID-19 were more likely to be male (67.7%) and the median age was 7.3 [IQR 4.9] years. All but one patient with severe disease was aged under 2 and the majority (5/7) had significant co-morbidities. Despite 53% having viral pneumonia on CT scanning only 2 patients had low lymphocyte counts and no differences were observed in the levels of plasma proinflammatory cytokines, including interleukin (IL)-2, IL-4, IL-6, tumour necrosis factor (TNF)- , and interferon (IFN)- between patients with mild, moderate or severe disease.Interpretations: We demonstrated that the immune responses of children to COVID-19 infection is significantly different from that seen in adults. Our evidence suggests that SARS-CoV-2 does not trigger a robust inflammatory response or ‘cytokine storm’ in children with COVID-19, and this may underlie the generally better outcomes seen in children with this disease. These data also imply anti-cytokine therapies may not be effective in children with moderate COVID-19.Funding Statement: This study was funded by National Natural Foundation of China (No. 81970653).Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: This study was conducted in accordance with the Declaration of Helsinki and was reviewed and approved by the Medical Ethical Committees (2020-R120). Due to the urgent need to collect data on this emerging infectious disease, the requirement for written informed consent was waived.

Scores based on neutrophil percentage and lactate dehydrogenase with or without oxygen saturation predict the risk of hospital mortality in severe COVID-19 patients (preprint)

Background. Risk scores are urgently needed to assist clinicians in predicting the risk of death in severe patients with SARS-CoV-2 infection in the context of millions of people infected, rapid disease progression, and shortage of medical resources.Method. A total of 139 severe patients with SARS-CoV-2 from China and Iran were included. Using data from China (training dataset, n = 96), prediction models were developed based on logistic regression models, nomogram and risk scoring system for simplification. Leave-one-out cross validation was used for internal validation and data from Iran (test dataset, n = 43) for external validation. Results. The NSL model (Area under the curve (AUC) 0.932) and NL model (AUC 0.903) were developed based on neutrophil percentage (NE), lactate dehydrogenase (LDH) with or without oxygen saturation (SaO2) using the training dataset. Compared with the training dataset, the predictability of NSL model (AUC 0.910) and NL model (AUC 0.871) were similar in the test dataset. The risk scoring systems corresponding to these two models were established for clinical application. The AUCs of the NSL and NL scores were 0.928 and 0.901 in the training dataset, respectively. At the optimal cut-off value of NSL score, the sensitivity was 94% and specificity was 82%. In addition, for NL score, the sensitivity and specificity were 94% and 75%, respectively.Conclusion. NSL and NL score are straightforward means for clinicians to predict the risk of death in severe patients. NL score could be used in selected regions where patients’ SaO2 cannot be tested.

Gastrointestinal symptoms in coronavirus disease 2019: a cross-sectional study in Wuhan, China (preprint)

Background To investigate the clinical symptoms of coronavirus disease 2019 (COVID-19), particularly the prevalence, time of symptom onset, and duration of gastrointestinal (GI) symptoms.Methods This was a cross-sectional study using paper questionnaires. COVID-19 patients in a temporary hospital in Wuhan voluntarily completed surveys collecting data on COVID-19 symptoms and investigation results.Results A total of 212 adults were enrolled in this study, of whom 127 (59.9%) were female, mean age was 48.50 ± 13.15 years. Concerning symptoms, 78.8% (167/212) had fever, and 66% (140/212) had cough. Diarrhoea occurred in 43.8% (93/212) of patients. Nausea and vomiting were also common (20.7%). Fever and cough were frequently the initial symptoms of COVID-19, and they lasted for 5.00 [interquartile range (IQR): 3.00–10.00] days and 10.00 (IQR: 5.00–24.00) days, respectively. Most patients developed nausea and vomiting 2.00 (IQR: 0–9.00) days and diarrhoea 5.00 (IQR: 0.25–11.00) days after the onset of initial symptoms, respectively. There was a median duration of 4.00 (IQR: 2.00–8.75) days with diarrhoea, and 6.00 (IQR: 4.00–10.00) days with nausea and vomiting. The patients with diarrhoea were younger [45.85 ± 13.28 years vs 50.61 ± 12.82 years, P = 0.009] and were more likely to have an abnormal chest CT (95.7% vs 82.4%, P = 0.001) than those without diarrhoea.Conclusions In our cohort of patients, GI symptoms were common in COVID-19, occurred mostly during the middle stage of the disease, and lasted for a short duration. GI symptoms may not be associated with COVID-19 related treatment.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China (preprint)

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. It causes acute respiratory distress syndrome and results in a high mortality rate if pneumonia is involved. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: 'Residing or visiting history in epidemic regions', 'Exposure history to COVID-19 patient', 'Dry cough', 'Fatigue', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood oxygen saturation<=93%', 'Lymphopenia', and 'C-reactive protein (CRP) increased'. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity of the model, we used a cutoff value of 0.09. The sensitivity and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey 'Questionnaire Star' showed that 90.9% of nCapp users in WeChat mini programs were 'satisfied' or 'very satisfied' with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for 'availability and sharing convenience of the App' and 'fast speed of log-in and data entry'. Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission of the disease from asymptomatic patients at the community level.

Mucin-type O-glycosylation Landscapes of SARS-CoV-2 Spike Proteins (preprint)

The densely glycosylated spike (S) proteins that are highly exposed on the surface of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) facilitate viral attachment, entry, and membrane fusion. We have previously reported all the 22 N-glycosites and site-specific N-glycans in the S protein protomer. Herein, we report the comprehensive and precise site-specific O-glycosylation landscapes of SARS-CoV-2 S proteins, which were characterized using high-resolution mass spectrometry. Following digestion using trypsin and trypsin/Glu-C, and de-N-glycosylation using PNGase F, we determined the mucin-type (GalNAc-type) O-glycosylation pattern of S proteins, including unambiguous O-glycosites and the 6 most common O-glycans occupying them, via Byonic identification and manual validation. Finally, 43 O-glycosites were identified in the insect cell-expressed S protein. Most glycosites were modified by non-sialylated O-glycans such as HexNAc(1) and HexNAc(1)Hex(1). In contrast, 30 O-glycosites were identified in the human cell-expressed S protein S1 subunit. Most glycosites were modified by sialylated O-glycans such as HexNAc(1)Hex(1)NeuAc(1) and HexNAc(1)Hex(1)NeuAc(2). Our results are the first to reveal that the SARS-CoV-2 S protein is a mucin-type glycoprotein; clustered O-glycans often occur in the N- and the C-termini of the S protein, and the O-glycosite and O-glycan compositions vary with the host cell type. These site-specific O-glycosylation landscapes of the SARS-CoV-2 S protein are expected to provide novel insights into the viral binding mechanism and present a strategy for the development of vaccines and targeted drugs.

Reduced inflammatory responses to SARS-CoV-2 infection in children presenting to hospital with COVID19 in China (preprint)

Background Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in children is associated with better outcomes than in adults. The inflammatory response to COVID-19 infection in children remains poorly characterised. Methods We retrospectively analysed the medical records of 127 laboratory-confirmed COVID-19 patients aged 1 month to 16 years from Wuhan and Jingzhou of Hubei Province. Patients presented between January 25th and March 24th 2020. Information on clinical features, laboratory results, plasma cytokines/chemokines and lymphocyte subsets were analysed. Findings Children admitted to hospital with COVID-19 were more likely to be male (67.7%) and the median age was 7.3 [IQR 4.9] years. All but one patient with severe disease was aged under 2 and the majority (5/7) had significant co-morbidities. Despite 53% having viral pneumonia on CT scanning only 2 patients had low lymphocyte counts and no differences were observed in the levels of plasma proinflammatory cytokines, including interleukin (IL)-2, IL-4, IL-6, tumour necrosis factor (TNF)-alpha; and interferon (IFN)-gamma; between patients with mild, moderate or severe disease. Interpretations We demonstrated that the immune responses of children to COVID-19 infection is significantly different from that seen in adults. Our evidence suggests that SARS-CoV-2 does not trigger a robust inflammatory response or "cytokine storm" in children with COVID-19, and this may underlie the generally better outcomes seen in children with this disease. These data also imply anti-cytokine therapies may not be effective in children with moderate COVID-19.

Cytokine biomarkers of COVID-19 (preprint)

We used a new strategy to screen cytokines associated with SARS-CoV-2 infection. Cytokines that can classify populations in different states of SARS-CoV-2 infection were first screened in cross-sectional serum samples from 184 subjects by 2 statistical analyses. The resultant cytokines were then analyzed for their interrelationships and fluctuating features in sequential samples from 38 COVID-19 patients. Three cytokines, M-CSF, IL-8 and SCF, which were clustered into 3 different correlation groups and had relatively small fluctuations during SARS-CoV-2 infection, were selected for the construction of a multiclass classification model. This model discriminated healthy individuals and asymptomatic and nonsevere patients with accuracy of 77.4% but was not successful in classifying severe patients. Further searching led to a single cytokine, hepatocyte growth factor (HGF), which classified severe from nonsevere COVID-19 patients with a sensitivity of 84.6% and a specificity of 97.9% under a cutoff value of 1128 pg/ml. The level of this cytokine did not increase in nonsevere patients but was significantly elevated in severe patients. Considering its potent antiinflammatory function, we suggest that HGF might be a new candidate therapy for critical COVID-19. In addition, our new strategy provides not only a rational and effective way to focus on certain cytokine biomarkers for infectious diseases but also a new opportunity to probe the modulation of cytokines in the immune response.

Risk Factors for In-hospital Progression of Ordinary COVID-19 in Wuhan, China: A Retrospective Cohort Study (preprint)

Objective: To describe the clinical characteristics and outcomes of ordinary COVID-19 when admitted, to describe how these patients were treated and risk factors for in-hospital progression.Methods: In this retrospective study, we included 291 adult patients diagnosed as ordinary COVID-19 on admission who had been discharged or had died between Jan 20, 2020 and Mar 16, 2020 from General Hospital of Central Theatre Command (Wuhan, China).Results: Of the 291 patients diagnosed as ordinary COVID-19 when admitted, 65 (22.34%) had been recorded COVID-19 progressing at least once, and 226 (77.66%) had been recorded COVID-19 improving during hospitalization. The median time from admission to disease progressed was 5.0 days (2.0-7.0). Multivariable regression showed increasing odds of in-hospital progression associated with male (odds ratio 2.333, 95% CI 1.135-4.395; P=0.020), preexisting cardiovascular diseases (2.433, 1.044-5.671; P=0.039), and lymphopenia (3.482, 1.783-6.799; P<0.001), elevated IL-6 (2.669, 1.084-6.574; P=0.033), d-dimer (2.829, 1.420-5.636; P=0.003) and lactate dehydrogenase (2.855, 1.458-5.591; P= 0.002) on admission.Conclusions: The potential risk factors of male, preexisting cardiovascular disease, lymphopenia, elevated IL-6, and lactate dehydrogenase, d-dimer could help clinicians to identify in-hospital progression among ordinary COVID-19 at early stage to optimize medical treatment.

Pulmonary radiological change of COVID-19 patients with 99mTc-MDP treatment (preprint)

Background: As increasing cases of COVID-19 around world, urgent need for effective COVID-19-specific therapeutic drugs is necessary; therefore, we conducted a pilot randomized-controlled study to evaluate the efficacy of 99mTc-MDP for COVID-19 therapeutic treatment. Methods: A total of 21 mild patients with COVID-19 were enrolled in this pilot RCT from February 2020 through March 2020, and then were assigned, in a 1:1 ratio, into control (11 patients) and 99mTc-MDP group (10 patients). Patients in the control group received routine treatment and patients assigned to the 99mTc-MDP group received a combination of routine treatment and an administration of 99mTc-MDP injection of 5ml/day. Both of the patients in the control and 99mTc-MDP groups were treated for 7 days with the primary end point of CT-based radiological pulmonary changes during 7-day follow-up. Findings: From baseline to the day 7, 8 (80%) of 10 mild patients in the 99mTc-MDP group had a significant radiological improvement in lung and a decline in inflammatory infiltration, whereas only 1 (9.1%) of 11 patients in the control group had a radiological improvement in lung. None of the patients in the 99mTc-MDP group had disease progression from mild to severe, as well as an inflammatory cytokine storm, and 2 mild patients (18.2%) in the control group developed severe. During days 7 through 14, the number of patients with radiological improvement in the 99mTc-MDP group remained consistent, and only 1 additional case (22%) in the control group were reported. Conclusion: In this randomized pilot study, 99mTc-MDP had an effective inhibitory effect on the inflammatory disease progression for the therapy of COVID-19, and it can accelerate the absorption of pulmonary inflammation in a short period of time during the process of treatment.