Safety, immunogenicity, and efficacy of the mRNA vaccine CS-2034 as a heterologous booster versus homologous booster with BBIBP-CorV in adults aged ≥18 years: a randomised, double-blind, phase 2b trial.

BACKGROUND: Heterologous boosting is suggested to be of use in populations who have received inactivated COVID-19 vaccines. We aimed to assess the safety and immunogenicity of a heterologous vaccination with the mRNA vaccine CS-2034 versus the inactivated BBIBP-CorV as a fourth dose, as well as the efficacy against the SARS-CoV-2 omicron (BA.5) variant. METHODS: This trial contains a randomised, double-blind, parallel-controlled study in healthy participants aged 18 years or older (group A) and an open-label cohort in participants 60 years and older (group B), who had received three doses of inactivated whole-virion vaccines at least 6 months before enrolment. Pregnant women and people with major chronic illnesses or a history of allergies were excluded. Eligible participants in group A were stratified by age (18-59 years and ≥60 years) and then randomised by SAS 9.4 in a ratio of 3:1 to receive a dose of the mRNA vaccine (CS-2034, CanSino, Shanghai, China) or inactivated vaccine (BBIBP-CorV, Sinopharm, Beijing, China). Safety and immunogenicity against omicron variants of the fourth dose were evaluated in group A. Participants 60 years and older were involved in group B for safety observations. The primary outcome was geometric mean titres (GMTs) of the neutralising antibodies against omicron and seroconversion rates against BA.5 variant 28 days after the boosting, and incidence of adverse reactions within 28 days. The intention-to-treat group was involved in the safety analysis, while all patients in group A who had blood samples taken before and after the booster were involved in the immunogenicity analysis. This trial was registered at the Chinese Clinical Trial Registry Centre (ChiCTR2200064575). FINDINGS: Between Oct 13, and Nov 22, 2022, 320 participants were enrolled in group A (240 in the CS-2034 group and 80 in the BBIBP-CorV group) and 113 in group B. Adverse reactions after vaccination were more frequent in CS-2034 recipients (158 [44·8%]) than BBIBP-CorV recipients (17 [21·3%], p<0·0001). However, most adverse reactions were mild or moderate, with grade 3 adverse reactions only reported by eight (2%) of 353 participants receiving CS-2034. Heterologous boosting with CS-2034 elicited 14·4-fold (GMT 229·3, 95% CI 202·7-259·4 vs 15·9, 13·1-19·4) higher concentration of neutralising antibodies to SARS-CoV-2 omicron variant BA.5 than did homologous boosting with BBIBP-CorV. The seroconversion rates of SARS-CoV-2-specific neutralising antibody responses were much higher in the mRNA heterologous booster regimen compared with BBIBP-CorV homologous booster regimen (original strain 47 [100%] of 47 vs three [18·8%] of 16; BA.1 45 [95·8%] of 48 vs two [12·5%] 16; and BA.5 233 [98·3%] of 240 vs 15 [18·8%] of 80 by day 28). INTERPRETATION: Both the administration of mRNA vaccine CS-2034 and inactivated vaccine BBIBP-CorV as a fourth dose were well tolerated. Heterologous boosting with mRNA vaccine CS-2034 induced higher immune responses and protection against symptomatic SARS-CoV-2 omicron infections compared with homologous boosting, which could support the emergency use authorisation of CS-2034 in adults. FUNDING: Science and Technology Commission of Shanghai, National Natural Science Foundation of China, Jiangsu Provincial Science Fund for Distinguished Young Scholars, and Jiangsu Provincial Key Project of Science and Technology Plan. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

COVID-19 infections among passengers in an inbound flight to Beijing

Objective To understand the epidemiological characteristics and laboratory test results of COVID-19 infections among passengers in an inbound flight to Beijing, and to provide reference for the management of imported COVID-19 cases. Methods Flight information, centralized quarantine sites, transfer vehicle, laboratory test results, clinical progression and outcome and other information of all passengers in an inbound flight to Beijing on August 6, 2021 were collected and analyzed. Results A total of 15 passengers were COVID-19 positive. They were all tested negative for nucleic acid 48 h before boarding. The earliest positive was on the day of entry, and the longest was on the 13th day of entry, with the median of being the 3rd day after entry. There were inconsistent nucleic acid test results of 8 positive passengers reported by two institutions and the CT values were close to cutoff. The initial serum antibody levels were higher than 100（s/co） in 6 positive passengers. Nobody was infected during transportation and quarantine. Conclusions Nucleic acid testing before boarding the flight should be able to identify majority of positive cases. Accordingly, joint screening strategy such as blood serum antibody test for inbound passengers with suspicious preliminary screening results of COVID-19 should be implemented to determine the infection history of the case.

An ultrasensitive and rapid "sample-to-answer" microsystem for on-site monitoring of SARS-CoV-2 in aerosols using "in situ" tetra-primer recombinase polymerase amplification.

Airborne transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the urgent need for aerosol monitoring of SARS-CoV-2 to prevent sporadic outbreaks of COVID-19. The inadequate sensitivity of conventional methods and the lack of an on-site detection system limited the practical SARS-CoV-2 monitoring of aerosols in public spaces. We have developed a novel SARS-CoV-2-in-aerosol monitoring system (SIAMs) which consists of multiple portable cyclone samplers for collecting aerosols from several venues and a sensitive "sample-to-answer" microsystem employing an integrated cartridge for the analysis of SARS-CoV-2 in aerosols (iCASA) near the sampling site. By seamlessly combining viral RNA extraction based on a chitosan-modified quartz filter and "in situ" tetra-primer recombinase polymerase amplification (tpRPA) into an integrated microfluidic cartridge, iCASA can provide an ultra-high sensitivity of 20 copies/mL, which is nearly one order of magnitude greater than that of the commercial kit, and a short turnaround time of 25 min. By testing various clinical samples of nasopharyngeal swabs, saliva, and exhaled breath condensates obtained from 23 COVID-19 patients, we demonstrate that the positive rate of our system was 3.3 times higher than those of the conventional method. Combining with multiple portable cyclone samplers, we detected 52.2% (12/23) of the aerosol samples, six times higher than that of the commercial kit, collected from the isolation wards of COVID-19 patients, demonstrating the excellent performance of our system for SARS-CoV-2-in-aerosol monitoring. We envision the broad application of our microsystem in aerosol monitoring for fighting the COVID-19 pandemic.

One-Stop Extraction and In Situ RT-qPCR for Ultrasensitive Detection of Highly Diluted SARS-CoV-2 in Large-Volume Samples from Aquatic Environments.

Surveillance of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in aquatic environments attracted attention due to its considerable impacts on human health and ecology, especially in countries with poor sanitation standards. Based on a strategy of one-stop extraction and in situ amplification, we developed an ultrasensitive method that uses a polyacrylamide derivative-modified filter disc (PAD-FD), in which highly diluted RNA can be efficiently concentrated onto the filter disc and directly used for amplification. A newly designed spin column with a cup-like filter base facilitated the non-contact transfer of the affinity filter disc from the column to a PCR tube. The limit of detection of the PAD-FD coupled with RT-qPCR is 10 copies/mL. Using 32 suspected SARS-CoV-2 samples, we demonstrated that the detection rate of our method (62.5%, 20/32) was triple the rate of the commercial kit (18.8%, 6/32). Using a PAD-FD, 56.3% (18/32) and 40.6% (13/32) of the 10-fold-dilution samples with river and tap water, respectively, were detected. Even when diluted 100-fold, 28.1% (9/32) and 37.5% (12/32) were still detected in river and tap water, respectively. We believe that the PAD-FD method offers an accurate testing tool for monitoring viral RNA in aquatic environments, contributing to the forewarning of the SARS-CoV-2 outbreak and the breaking of the transmission chain.

Evolving trend change during the COVID-19 pandemic

Coronavirus disease (COVID-19) has caused unimaginable damage to public health and socio-economic structures worldwide;thus, an epidemiological depiction of the global evolving trends of this disease is necessary. As of March 31, 2022, the number of cases increased gradually over the four waves of the COVID-19 pandemic, indicating the need for continuous countermeasures. The highest total cases per million and total deaths per million were observed in Europe (240,656.542) and South America (2,912.229), despite these developed countries having higher vaccination rates than other continents, such as Africa. In contrast, the lowest of the above two indices were found in undeveloped African countries, which had the lowest number of vaccinations. These data indicate that the COVID-19 pandemic is positively related to the socio-economic development level;meanwhile, the data suggest that the vaccine currently used in these continents cannot completely prevent the spread of COVID-19. Thus, rethinking the feasibility of a single vaccine to control the disease is needed. Although the number of cases in the fourth wave increased exponentially compared to those of the first wave, ~43.1% of deaths were observed during the first wave. This was not only closely linked to multiple factors, including the inadequate preparation for the initial response to the COVID-19 pandemic, the gradual reduction in the severity of additional variants, and the protection conferred by prior infection and/or vaccination, but this also indicated the change in the main driving dynamic in the fourth wave. Moreover, at least 12 variants were observed globally, showing a clear spatiotemporal profile, which provides the best explanation for the presence of the four waves of the pandemic. Furthermore, there was a clear shift in the trend from multiple variants driving the spread of disease in the early stage of the pandemic to a single Omicron lineage predominating in the fourth wave. These data suggest that the Omicron variant has an advantage in transmissibility over other contemporary co-circulating variants, demonstrating that monitoring new variants is key to reducing further spread. We recommend that public health measures, along with vaccination and testing, are continually implemented to stop the COVID-19 pandemic.

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation.

Taking the Chinese city of Xiamen as an example, simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019 (COVID-19) and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures. A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios. The comparison was conducted between simulated and real cases in Xiamen. A web interface with adjustable parameters, including choice of intervention measures, intervention weights, vaccination, and viral variants, was designed for users to run the simulation. The total case number was set as the outcome. The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set. Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model. The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200, which were 25 more days and 36 fewer cases than the real situation, respectively. Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people's livelihood.

Integration of protein context improves protein- based COVID-19 patient stratification (preprint)

Background: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating the four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. Methods: A SWATH-based proteomic data set of 54 sera samples from 40 COVID-19 patients was employed as the training cohort. Results: Machine learning prioritized two complexes, one stoichiometric ratio, five pathways, twelve proteins and five network degrees. A model based on these 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP complex, the stoichiometric ratio of SAA2/ YLPM1, and the network extent of SIRT7 and A2M were highlighted in this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort and an independent SWATH-based proteomic data set from Germany, reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. Conclusion: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Retrospective analysis of influencing factors on the efficacy of mechanical ventilation in severe and critical COVID-19 patients.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has spread widely around the world with strong infectivity, rapid mutation and a high mortality rate. Mechanical ventilation has been included in the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 8) as an important treatment for severe and critical COVID-19 patients, but its clinical efficacy in COVID-19 patients is various. Therefore, it is necessary to study the influencing factors on the efficacy of mechanical ventilation in severe and critical COVID-19 patients. AIM: The aim of this study was to determine the influencing factors on the efficacy of mechanical ventilation in severe and critical COVID-19 patients. METHODS: A total of 27 severe and critical COVID-19 patients were enrolled in this study and treated with mechanical ventilation at the Optical Valley Campus of Hubei Maternal and Child Health Care Hospital (Wuhan, Hubei Province) from February 20, 2020 to April 5, 2020. According to the final treatment outcomes, the patients were divided into the "effective group" and "death group." The clinical data of the two groups, such as the treatment process and final outcome, were retrospectively analyzed in order to determine the specific curative effects on the two groups and the reasons for the differences in such curative effects, as well as to explore the factors related to death. RESULTS: This study enrolled 27 severe and critical COVID-19 patients, including 17 males (63.0%) and 10 females (37.0%). Their ages were 74.41 ± 11.73-years-old, and 19 patients (70.4%) were over 70-years-old. Severe COVID-19 patients over 70-years-old who were treated with mechanical ventilation died in 14 cases (82.4%); thus, this was the peak age. A total of 17 patients died of basic disease, 16 of whom had more than two basic diseases. The basic diseases were hypertension, diabetes, and cardiovascular and cerebrovascular diseases. At the same time, 13 patients (76.5%) died from an abnormal increase in blood glucose. Among them, eight had diabetes before contracting COVID-19 and five had a stress-induced increase in blood glucose after contracting COVID-19. Diabetic ketoacidosis occurred in one case. The use of tocilizumab may be a double-edged sword that carries a certain risk in clinical usage. Among the patients who died, 16 (94.1%) went into septic shock at the end. There were significant differences in the degree of infection, cardiac and renal function, and blood glucose between the death group and effective group. CONCLUSION: Age, blood glucose, cardiac and renal function, and inflammatory reaction are important indicators of poor prognosis for mechanical ventilation in severe and critical COVID-19 patients.

COVID-19 screening using breath-borne volatile organic compounds.

Rapid screening of COVID-19 is key to controlling the pandemic. However, current nucleic acid amplification involves lengthy procedures in addition to the discomfort of taking throat/nasal swabs. Here we describe potential breath-borne volatile organic compound (VOC) biomarkers together with machine learning that can be used for point-of-care screening of COVID-19. Using a commercial gas chromatograph-ion mobility spectrometer, higher levels of propanol were detected in the exhaled breath of COVID-19 patients (N= 74) and non-COVID-19 respiratory infections (RI) (N= 30) than those of non-COVID-19 controls (NC)/health care workers (HCW) (N= 87), and backgrounds (N= 87). In contrast, breath-borne acetone was found to be significantly lower for COVID-19 patients than other subjects. Twelve key endogenous VOC species using supervised machine learning models (support vector machines, gradient boosting machines (GBMs), and Random Forests) were shown to exhibit strong capabilities in discriminating COVID-19 from (HCW + NC) and RI with a precision ranging from 91% to 100%. GBM and Random Forests models can also discriminate RI patients from healthy subjects with a precision of 100%. In addition, the developed models using breath-borne VOCs could also detect a confirmed COVID-19 patient but with a false negative throat swab polymerase chain reaction test. It takes 10 min to allow an entire breath test to finish, including analysis of the 12 key VOC species. The developed technology provides a novel concept for non-invasive rapid point-of-care-test screening for COVID-19 in various scenarios.

Coronavirus Disease 2019 Patients in Earlier Stages Exhaled Millions of Severe Acute Respiratory Syndrome Coronavirus 2 Per Hour.

Coronavirus disease 2019 (COVID-19) patients exhaled millions of severe acute respiratory syndrome coronavirus 2 RNA copies per hour, which plays an important role in COVID-19 transmission. Exhaled breath had a higher positive rate (26.9%, n = 52) than surface (5.4%, n = 242) and air (3.8%, n = 26) samples.

Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart.

BACKGROUND: Considering the widespread of coronavirus disease 2019 (COVID-19) pandemic in the world, it is important to understand the spatiotemporal development of the pandemic. In this study, we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries. METHODS: Using COVID-19 case and death data from February to December 2020 offered by Johns Hopkins University, we generated time-associated balloon charts with multiple epidemiological indicators including crude case fatality rate (CFR), morbidity, mortality and the total number of cases, to compare the progression of the pandemic within a specific period across regions and countries, integrating seven related dimensions together. The area chart is used to supplement the display of the balloon chart in daily new COVID-19 case changes in UN geographic regions over time. Javascript and Vega-Lite were chosen for programming and mapping COVID-19 data in browsers for visualization. RESULTS: From February 1st to December 20th 2020, the COVID-19 pandemic spread across UN subregions in the chronological order. It was first reported in East Asia, and then became noticeable in Europe (South, West and North), North America, East Europe and West Asia, Central and South America, Southern Africa, Caribbean, South Asia, North Africa, Southeast Asia and Oceania, causing several waves of epidemics in different regions. Since October, the balloons of Europe, North America and West Asia have been rising rapidly, reaching a dramatically high morbidity level ranging from 200 to 500/10 000 by December, suggesting an emerging winter wave of COVID-19 which was much bigger than the previous ones. By late December 2020, some European and American countries displayed a leading mortality as high as or over 100/100 000, represented by Belgium, Czechia, Spain, France, Italy, UK, Hungary, Bulgaria, Peru, USA, Argentina, Brazil, Chile and Mexico. The mortality of Iran was the highest in Asia (over 60/100 000), and that of South Africa topped in Africa (40/100 000). In the last 15 days, the CFRs of most countries were at low levels of less than 5%, while Mexico had exceptional high CFR close to 10%. CONCLUSIONS: We creatively used visualization integrating 7-dimensional epidemiologic and spatiotemporal indicators to assess the progression of COVID-19 pandemic in terms of transmissibility and severity. Such methodology allows public health workers and policy makers to understand the epidemics comparatively and flexibly.

Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.

PURPOSE: Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting against COVID-19, effective diagnosis of infected patient is critical for preventing the spread of diseases. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost, and portability gains much attention and becomes very promising. In order to reduce intra- and inter-observer variability, during radiological assessment, computer-aided diagnostic tools have been used in order to supplement medical decision making and subsequent management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologist in the interpretation of the collected data. METHOD: In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. CXR images are enhanced using a local phase-based image enhancement method. The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture. Using ablation studies, we show the effectiveness of the enhanced images in improving the diagnostic accuracy. We provide quantitative evaluation on two datasets and qualitative results for visual inspection. Quantitative evaluation is performed on data consisting of 8851 normal (healthy), 6045 pneumonia, and 3323 COVID-19 CXR scans. RESULTS: In Dataset-1, our model achieves 95.57% average accuracy for a three classes classification, 99% precision, recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained 94.44% average accuracy, and 95% precision, recall, and F1-scores for detection of COVID-19. CONCLUSIONS: Our proposed multi-feature-guided CNN achieves improved results compared to single-feature CNN proving the importance of the local phase-based CXR image enhancement. Future work will involve further evaluation of the proposed method on a larger-size COVID-19 dataset as they become available.

Multi-organ Proteomic Landscape of COVID-19 Autopsies (preprint)

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report an in-depth multi-organ proteomic landscape of COVID-19 patient autopsy samples. By integrative analysis of proteomes of seven organs, namely lung, spleen, liver, heart, kidney, thyroid and testis, we characterized 11,394 proteins, in which 5336 were perturbed in COVID-19 patients compared to controls. Our data showed that CTSL, rather than ACE2, was significantly upregulated in the lung from COVID-19 patients. Dysregulation of protein translation, glucose metabolism, fatty acid metabolism was detected in multiple organs. Our data suggested upon SARS-CoV-2 infection, hyperinflammation might be triggered which in turn induces damage of gas exchange barrier in the lung, leading to hypoxia, angiogenesis, coagulation and fibrosis in the lung, kidney, spleen, liver, heart and thyroid. Evidence for testicular injuries included reduced Leydig cells, suppressed cholesterol biosynthesis and sperm mobility. In summary, this study depicts the multi-organ proteomic landscape of COVID-19 autopsies, and uncovered dysregulated proteins and biological processes, offering novel therapeutic clues. HIGHLIGHTSO_LICharacterization of 5336 regulated proteins out of 11,394 quantified proteins in the lung, spleen, liver, kidney, heart, thyroid and testis autopsies from 19 patients died from COVID-19. C_LIO_LICTSL, rather than ACE2, was significantly upregulated in the lung from COVID-19 patients. C_LIO_LIEvidence for suppression of glucose metabolism in the spleen, liver and kidney; suppression of fatty acid metabolism in the kidney; enhanced fatty acid metabolism in the lung, spleen, liver, heart and thyroid from COVID-19 patients; enhanced protein translation initiation in the lung, liver, renal medulla and thyroid. C_LIO_LITentative model for multi-organ injuries in patients died from COVID-19: SARS-CoV-2 infection triggers hyperinflammatory which in turn induces damage of gas exchange barrier in the lung, leading to hypoxia, angiogenesis, coagulation and fibrosis in the lung, kidney, spleen, liver, heart, kidney and thyroid. C_LIO_LITesticular injuries in COVID-19 patients included reduced Leydig cells, suppressed cholesterol biosynthesis and sperm mobility. C_LI

Proteomics Uncovers Immunosuppression in COVID-19 Patients with Long Disease Course (preprint)

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we present a longitudinal sera proteomic resource for 37 COVID-19 patients over nine weeks, in which 2700 proteins were quantified with high quality. Remarkably, we found that during the first three weeks since disease onset, while clinical symptoms and outcome were indistinguishable, patients with prolonged disease course displayed characteristic immunological responses including enhanced Natural Killer (NK) cell-mediated innate immunity and regulatory T cell-mediated immunosuppression. We further showed that it is possible to predict the length of disease course using machine learning based on blood protein levels during the first three weeks. Validation in an independent cohort achieved an accuracy of 82%. In summary, this study presents a rich serum proteomic resource to understand host responses in COVID-19 patients and identifies characteristic Treg-mediated immunosuppression in LC patients, nominating new therapeutic target and diagnosis strategy.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera (preprint)

Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.Funding: This work is supported by grants from Westlake Special Program for COVID19 (2020), and Tencent foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04). Conflict of Interest: The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc. C.Z., Z.K., Z.K. and S.Q. are employees of DIAN Diagnostics.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera (preprint)

Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.