Sex disparities of the effect of the COVID-19 pandemic on mortality among patients living with tuberculosis in the United States (preprint)

Background： We aimed to determine the trend of tuberculosis (TB)-related deaths during the pandemic, with focus on the impact of the epidemic on mortality in males and females. Methods： Using data from the Centers for Disease Control and Prevention and the U.S. Census Bureau, TB-related mortality data of decedents aged ≥ 25 years from 2006-2021 were analyzed. Excess TB-related deaths were estimated by determining the difference between observed and projected mortality rates during the pandemic. The mortality trends were then quantified with Joinpoint regression analysis. Results： A total of 18,626 TB-related deaths were documented among adults aged 25 years and older from 2006-2021. A downward trend was noted in TB-related mortality rates before the pandemic, followed by an increase during the pandemic. TB-related age-standardized mortality rates (ASMRs) were 0.51 in 2020 and 0.52 in 2021, corresponding to an excess mortality of 10.22% and 9.19%, respectively. Increased TB-related mortality was observed across all age and sex subgroups, but female with TB demonstrated a higher relative increase in mortality (26.33% vs. 2.17% in 2020; 21.48% vs.3.23% in 2021) during the pandemic when compared to male. Furthermore, female with TB and aged 45-64 years old showed a surge in mortality, with an annual percent change (APC) of -2.2% pre-pandemic to 22.8% (95% CI: -1.7% to 68.7%) during the pandemic, corresponding to excess mortalities of 62.165% and 99.16% in 2020 and 2021, respectively; these excess mortality rates were higher than those observed in the overall female population ages 45-64 years in 2020 (17.53%) and 2021 (33.79%). Conclusions： The steady decline in TB-related mortality in the United States has been reversed by COVID-19. Female patients with TB were disproportionately affected by the pandemic, largely owing to care gaps and health disparities experienced by this population.

Oncological big data platforms for promoting digital competencies and professionalism in Chinese medical students: a cross-sectional study.

OBJECTIVES: Advancements in big data technology are reshaping the healthcare system in China. This study aims to explore the role of medical big data in promoting digital competencies and professionalism among Chinese medical students. DESIGN, SETTING AND PARTICIPANTS: This study was conducted among 274 medical students who attended a workshop on medical big data conducted on 8 July 2021 in Tongji Hospital. The workshop was based on the first nationwide multifunction gynecologic oncology medical big data platform in China, at the National Union of Real-World Gynecologic Oncology Research & Patient Management Platform (NUWA platform). OUTCOME MEASURES: Data on knowledge, attitudes towards big data technology and professionalism were collected before and after the workshop. We have measured the four skill categories: doctor‒patient relationship skills, reflective skills, time management and interprofessional relationship skills using the Professionalism Mini-Evaluation Exercise (P-MEX) as a reflection for professionalism. RESULTS: A total of 274 students participated in this workshop and completed all the surveys. Before the workshop, only 27% of them knew the detailed content of medical big data platforms, and 64% knew the potential application of medical big data. The majority of the students believed that big data technology is practical in their clinical practice (77%), medical education (85%) and scientific research (82%). Over 80% of the participants showed positive attitudes toward big data platforms. They also exhibited sufficient professionalism before the workshop. Meanwhile, the workshop significantly promoted students' knowledge of medical big data (p<0.05), and led to more positive attitudes towards big data platforms and higher levels of professionalism. CONCLUSIONS: Chinese medical students have primitive acquaintance and positive attitudes toward big data technology. The NUWA platform-based workshop may potentially promote their understanding of big data and enhance professionalism, according to the self-measured P-MEX scale.

Clinical Characteristics of Omicron SARS-CoV-2 Variant Infection After Non-mRNA-Based Vaccination in China.

Introduction: To date, little is known about the real-world protective role of Chinese inactivated and recombinant coronavirus disease 2019 (COVID-19) vaccines under the background of the long-term "Dynamic Zero COVID-19 Case" (i.e., no infection source) in China, especially when facing the widespread Omicron severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant infection. Methods: In this prospective, single-center cohort study, the clinical characteristics of post-vaccination Omicron SARS-CoV-2 variant infection were investigated in the initial largest outbreak of Omicron SARS-CoV-2 variant infection that occurred between the 8 January, 2022 and 29 January, 2022 in Anyang City, Henan Province, China. The primary endpoints were the rates of severe and critical diseases or death. The secondary endpoints were the SARS-CoV-2 shedding duration and length of hospitalization. Results: A total of 380 post-vaccination patients infected with the Omicron SARS-CoV-2 variant were enrolled. The median age was 18 (interquartile range [IQR] 17-35) years, 219 (57.6%) cases were female, and 247 (65.0%) cases were students. Before confirmation of Omicron SARS-CoV-2 variant infection, patients had 3 (IQR 2-4) days of dry cough (40.3%), nasal congestion (26.3%), and sore throat (26.3%). On admission, 294 (77.4%) cases had normal chest computerized tomography (CT) imaging. Additionally, only 5 (1.3%), 30 (7.9%), 4 (4/342, 1.2%), and 7 (7/379, 0.2%) patients had lymphocyte counts <800 per mm3, C-reactive protein levels >10 mg/L, lactate dehydrogenase levels ≥250 U/L, and D-dimer levels ≥0.5 mg/L on admission, respectively. During hospitalization, 308 (81.1%) and 72 (18.9%) were identified as mild and moderate cases, respectively, and no one progressed to severe and critical types, with a SARS-CoV-2 shedding period and length of hospital stay of 17 (IQR 12-22) and 19 (IQR 15-24) days, respectively. Conclusion: The current study found that approximately 80% of individuals infected with the Omicron SARS-CoV-2 variant were mild, approximately 20% of patients were moderate, and no severe, critical, or fatal cases were identified in a prospective cohort including 380 participants vaccinated with non-mRNA-based vaccines. Discussion: This study supports the consideration of policy adjustments and changes to prevent and control the Omicron-predominant COVID-19 in China and other regions with high SARS-CoV-2 vaccination rates.

Case report: subacute thyroiditis after receiving SARS-CoV-2 vaccine, maybe not only adjuvants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induced the new coronavirus disease 2019 (COVID-19) pandemic worldwide. SARS-CoV-2 vaccines are designed to control the transmission of the disease. However, post-vaccination subacute thyroiditis (SAT) also appears with increase vaccination rate. Three cases of SAT after SARS-CoV-2 vaccines are described in this study. We have reported the patients’ clinical symptoms, laboratory tests, and thyroid imaging. Tests for COVID-19 were all negative, and the patients did not report thyroid-related diseases, autoimmune diseases, or preceding upper respiratory system infections in their medical history. Three female patients showed neck pain on physical examination. The laboratory test results and imaging findings were consistent with the diagnostic criteria of SAT. The patients were carried out a standardized treatment according to their symptoms, and we closely followed up their response to the treatment. Clinicians must be aware of the possibility of SAT after receiving the vaccines and make timely therapy.

The Epidemiological, Clinical Features and Outcomes of Imported Chinese COVID-19 Patients Following Inactivated Vaccines Injection.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination had been demonstrated as an effective way to reduce the risk of coronavirus disease 2019 (COVID-19), and only a few vaccines suffered from SARS-CoV-2 infection. However, limited data concerning the clinical features of these vaccines infected with SARS-CoV-2 can be identified. Methods: We retrospectively collected and analyzed epidemiological and clinical characteristics data of the imported COVID-19 cases who received Chinese inactivated vaccines abroad. Data were extracted from electronic medical records from a designated hospital in the Shaanxi Province of China between March 22 and May 17, 2021. Results: Totally, 46 confirmed SARS-CoV-2 infection patients were enrolled. The mean age was 40.5 years (range 20-61), 41 (89.1%) are male. Eighteen (39.1%) patients were from Pakistan. Fourteen (30.4%) patients had at least one comorbidity. Forty (87.0%) and 6 cases were fully vaccinated and partly vaccinated. The time interval between vaccination and infection was 88 days (IQR, 33-123), 31 (67.4%) and 15 (32.6%) were asymptomatic and symptomatic cases, respectively. Fever (3/46, 6.5%) was the most common symptom; however, none had a body temperature higher than 38.0°C, and no severe case was observed. Notably, the rate of SARS-CoV-2 shedding discontinuation at 7 days after hospitalization in asymptomatic cases was higher than symptomatic one (93.5% vs 40%, P < 0.0001). Conclusion: Individuals who received Chinese inactivated vaccines abroad remain to have the probability of being infected with SARS-CoV-2, but all the vaccines infected with SARS-CoV-2 were asymptomatic or had mild symptoms with favorable clinical outcomes.

Clinical Characteristics of Omicron SARS-CoV-2 Variant Infection After Non-mRNA-Based Vaccination in China

Introduction To date, little is known about the real-world protective role of Chinese inactivated and recombinant coronavirus disease 2019 (COVID-19) vaccines under the background of the long-term “Dynamic Zero COVID-19 Case” (i.e., no infection source) in China, especially when facing the widespread Omicron severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant infection. Methods In this prospective, single-center cohort study, the clinical characteristics of post-vaccination Omicron SARS-CoV-2 variant infection were investigated in the initial largest outbreak of Omicron SARS-CoV-2 variant infection that occurred between the 8 January, 2022 and 29 January, 2022 in Anyang City, Henan Province, China. The primary endpoints were the rates of severe and critical diseases or death. The secondary endpoints were the SARS-CoV-2 shedding duration and length of hospitalization. Results A total of 380 post-vaccination patients infected with the Omicron SARS-CoV-2 variant were enrolled. The median age was 18 (interquartile range [IQR] 17–35) years, 219 (57.6%) cases were female, and 247 (65.0%) cases were students. Before confirmation of Omicron SARS-CoV-2 variant infection, patients had 3 (IQR 2–4) days of dry cough (40.3%), nasal congestion (26.3%), and sore throat (26.3%). On admission, 294 (77.4%) cases had normal chest computerized tomography (CT) imaging. Additionally, only 5 (1.3%), 30 (7.9%), 4 (4/342, 1.2%), and 7 (7/379, 0.2%) patients had lymphocyte counts <800 per mm3, C-reactive protein levels >10 mg/L, lactate dehydrogenase levels ≥250 U/L, and D-dimer levels ≥0.5 mg/L on admission, respectively. During hospitalization, 308 (81.1%) and 72 (18.9%) were identified as mild and moderate cases, respectively, and no one progressed to severe and critical types, with a SARS-CoV-2 shedding period and length of hospital stay of 17 (IQR 12–22) and 19 (IQR 15–24) days, respectively. Conclusion The current study found that approximately 80% of individuals infected with the Omicron SARS-CoV-2 variant were mild, approximately 20% of patients were moderate, and no severe, critical, or fatal cases were identified in a prospective cohort including 380 participants vaccinated with non-mRNA-based vaccines. Discussion This study supports the consideration of policy adjustments and changes to prevent and control the Omicron-predominant COVID-19 in China and other regions with high SARS-CoV-2 vaccination rates.

Safety and immunogenicity of COVID-19 vaccination in patients with hepatocellular carcinoma (CHESS-NMCID 2101): A multicenter prospective study.

Data on safety and immunogenicity of coronavirus disease 2019 (COVID-19) vaccinations in hepatocellular carcinoma (HCC) patients are limited. In this multicenter prospective study, HCC patients received two doses of inactivated whole-virion COVID-19 vaccines. The safety and neutralizing antibody were monitored. Totally, 74 patients were enrolled from 10 centers in China, and 37 (50.0%), 25 (33.8%), and 12 (16.2%) received the CoronaVac, BBIBP-CorV, and WIBP-CorV, respectively. The vaccines were well tolerated, where pain at the injection site (6.8% [5/74]) and anorexia (2.7% [2/74]) were the most frequent local and systemic adverse events. The median level of neutralizing antibody was 13.5 (interquartile range [IQR]: 6.9-23.2) AU/ml at 45 (IQR: 19-72) days after the second dose of vaccinations, and 60.8% (45/74) of patients had positive neutralizing antibody. Additionally, lower γ-glutamyl transpeptidase level was related to positive neutralizing antibody (odds ratio = 1.022 [1.003-1.049], p = 0.049). In conclusion, this study found that inactivated COVID-19 vaccinations are safe and the immunogenicity is acceptable or hyporesponsive in patients with HCC. Given that the potential benefits may outweigh the risks and the continuing emergences of novel severe acute respiratory syndrome coronavirus 2 variants, we suggest HCC patients to be vaccinated against COVID-19. Future validation studies are warranted.

Dynamic changes in liver function parameters in patients with coronavirus disease 2019: a multicentre, retrospective study.

BACKGROUND: Liver injuries have been reported in patients with coronavirus disease 2019 (COVID-19). This study aimed to investigate the clinical role played by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: In this multicentre, retrospective study, the parameters of liver function tests in COVID-19 inpatients were compared between various time-points in reference to SARS-CoV-2 shedding, and 3 to 7 days before the first detection of viral shedding was regarded as the reference baseline. RESULTS: In total, 70 COVID-19 inpatients were enrolled. Twenty-two (31.4%) patients had a self-medication history after illness. At baseline, 10 (14.3%), 7 (10%), 9 (12.9%), 2 (2.9%), 15 (21.4%), and 4 (5.7%) patients already had abnormal alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), albumin, and total bilirubin (TBIL) values, respectively. ALT and AST abnormal rates and levels did not show any significant dynamic changes during the full period of viral shedding (all p > 0.05). The GGT abnormal rate (p = 0.008) and level (p = 0.033) significantly increased on day 10 of viral shedding. Meanwhile, no simultaneous significant increases in abnormal ALP rates and levels were observed. TBIL abnormal rates and levels significantly increased on days 1 and 5 of viral shedding (all p < 0.05). Albumin abnormal decrease rates increased, and levels decreased consistently from baseline to SARS-CoV-2 clearance day (all p < 0.05). Thirteen (18.6%) patients had chronic liver disease, two of whom died. The ALT and AST abnormal rates and levels did not increase in patients with chronic liver disease during SARS-CoV-2 shedding. CONCLUSIONS: SARS-CoV-2 does not directly lead to elevations in ALT and AST but may result in elevations in GGT and TBIL; albumin decreased extraordinarily even when SARS-CoV-2 shedding ended.

COVID-ONE-humoral immune: The One-stop Database for COVID-19-specific Antibody Responses and Clinical Parameters (preprint)

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, varies with regard to symptoms and mortality rates among populations. Humoral immunity plays critical roles in SARS-CoV-2 infection and recovery from COVID-19. However, differences in immune responses and clinical features among COVID-19 patients remain largely unknown. Here, we report a database for COVID-19-specific IgG/IgM immune responses and clinical parameters (COVID-ONE humoral immune). COVID-ONE humoral immunity is based on a dataset that contains the IgG/IgM responses to 21 of 28 known SARS-CoV-2 proteins and 197 spike protein peptides against 2,360 COVID-19 samples collected from 783 patients. In addition, 96 clinical parameters for the 2,360 samples and information for the 783 patients are integrated into the database. Furthermore, COVID-ONE humoral immune provides a dashboard for defining samples and a one-click analysis pipeline for a single group or paired groups. A set of samples of interest is easily defined by adjusting the scale bars of a variety of parameters. After the "START" button is clicked, one can readily obtain a comprehensive analysis report for further interpretation. COVID-ONE-humoral immune is freely available at www.COVID-ONE.cn.

COVID-ONE-humoral immune: The One-stop Database for COVID-19-specific Antibody Responses and Clinical Parameters (preprint)

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, varies with regard to symptoms and mortality rates among populations. Humoral immunity plays critical roles in SARS-CoV-2 infection and recovery from COVID-19. However, differences in immune responses and clinical features among COVID-19 patients remain largely unknown. Here, we report a database for COVID-19-specific IgG/IgM immune responses and clinical parameters (COVID-ONE humoral immune). COVID-ONE humoral immunity is based on a dataset that contains the IgG/IgM responses to 21 of 28 known SARS-CoV-2 proteins and 197 spike protein peptides against 2,360 COVID-19 samples collected from 783 patients. In addition, 96 clinical parameters for the 2,360 samples and information for the 783 patients are integrated into the database. Furthermore, COVID-ONE humoral immune provides a dashboard for defining samples and a one-click analysis pipeline for a single group or paired groups. A set of samples of interest is easily defined by adjusting the scale bars of a variety of parameters. After the START button is clicked, one can readily obtain a comprehensive analysis report for further interpretation. COVID-ONE-humoral immune is freely available at www.COVID-ONE.cn.

Clinical Features of Imported Chinese COVID-19 Patients Following Chinese Inactivated Vaccines Injection Abroad (preprint)

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination has been demonstrated as an effective way to reduce the risk of coronavirus disease 2019 (COVID-19), and only a few vaccinees suffered from SARS-CoV-2 infection. However, limited data concerning the clinical features of these vaccinees infected with SARS-CoV-2 can be identified. Methods We retrospectively collected and analyzed epidemiological and clinical characteristics data of the imported COVID-19 cases who received Chinese inactivated vaccines abroad. Data were extracted from electronic medical records from a designated hospital in the Shaanxi Province of China between March 22 and May 17, 2021. Results Totally, 46 confirmed SARS-CoV-2 infection patients were enrolled. The mean age was 40.5 years (range 20-61), 41 (89.1%) are male. Eighteen (39.1%) patients were from Pakistan. Fourteen (30.4%) patients had at least one comorbidity. Forty (87.0%) and 6 cases were fully vaccinated and partly vaccinated. The time interval between vaccination and infection was 88 days (IQR, 33-123), 31 (67.4%) and 15 (32.6%) were asymptomatic and symptomatic cases, respectively. Fever (3/46, 6.5%) was the most common symptom; however, none had a body temperature higher than 38.0℃, and no severe case was observed. Notably, the rate of SARS-CoV-2 shedding discontinuation at 7 days after hospitalization in asymptomatic cases was higher than symptomatic one (93.5% vs 40%, P < 0.0001). Conclusions Individuals who received Chinese inactivated vaccines abroad remain have the probability to be infected with SARS-CoV-2, but all the vaccinees infected with SARS-CoV-2 were asymptomatic or had mild symptoms with favorable clinical outcomes.

Genomic Epidemiology of SARS-CoV-2 From Mainland China With Newly Obtained Genomes From Henan Province.

Even though the COVID-19 epidemic in China has been successfully put under control within a few months, it is still very important to infer the origin time and genetic diversity from the perspective of the whole genome sequence of its agent, SARS-CoV-2. Yet, the sequence of the entire virus genome from China in the current public database is very unevenly distributed with reference to time and place of collection. In particular, only one sequence was obtained in Henan province, adjacent to China's worst-case province, Hubei Province. Herein, we used high-throughput sequencing techniques to get 19 whole-genome sequences of SARS-CoV-2 from 18 severe patients admitted to the First Affiliated Hospital of Zhengzhou University, a provincial designated hospital for the treatment of severe COVID-19 cases in Henan province. The demographic, baseline, and clinical characteristics of these patients were described. To investigate the molecular epidemiology of SARS-CoV-2 of the current COVID-19 outbreak in China, 729 genome sequences (including 19 sequences from this study) sampled from Mainland China were analyzed with state-of-the-art comprehensive methods, including likelihood-mapping, split network, ML phylogenetic, and Bayesian time-scaled phylogenetic analyses. We estimated that the evolutionary rate and the time to the most recent common ancestor (TMRCA) of SARS-CoV-2 from Mainland China were 9.25 × 10-4 substitutions per site per year (95% BCI: 6.75 × 10-4 to 1.28 × 10-3) and October 1, 2019 (95% BCI: August 22, 2019 to November 6, 2019), respectively. Our results contribute to studying the molecular epidemiology and genetic diversity of SARS-CoV-2 over time in Mainland China.

SARS-CoV-2 Antibody Signatures for Predicting the Outcome of COVID-19 (preprint)

Background: The COIVD-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies remain elevated with disease progression and severity in COIVD-19 patients. We assumed that antibodies may serve as biomarkers for predicting disease outcome.Method: By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgM/IgG responses against 20 SARS-CoV-2 proteins in 1,034 hospitalized COVID-19 patients on admission, who were followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality.Results: We found that high level of IgM against ORF7b at the time of hospitalization is an independent predictor of patient survival ( p  trend = 0.002), while levels of IgG responses to 6 non-structural proteins and 1 accessory protein, i. e., NSP4, NSP7, NSP9, NSP10, RdRp (NSP12), NSP14, and ORF3b, possess significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory markers for disease severity (all with p trend < 0.05). Spline regression analysis indicated that the correlation between ORF7b IgM, NSP9 IgG, and NSP10 IgG and the risk of COVID-19 mortality shows linear ( p = 0.0013, 0.0073 and 0.0003, respectively). Their AUCs for predictions, determined by computational cross-validations (validation1), were 0.74 (cut-off = 7.59), 0.66 (cut-off = 9.13), and 0.68 (cut-off = 6.29), respectively. Further validations were conducted in the second and third serial samples of these cases (validation2A, n = 633, validation2B, n = 382), with high accuracy of prediction for outcome.Conclusion: These findings have important implications for improving clinical management, and especially for developing medical interventions and vaccines.Funding Statement: This work was supported by grants from the Fundamental Research Funds for the Central Universities (HUST COVID-19 Rapid Response Call No. 2020kfyXGYJ040) and Wuhan Bureau of Science and Technology (No. 2020020601012218).Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval Statement: The study was approved by the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (IRB ID:TJ-C20200128).

Antibody Landscape Against SARS-CoV-2 Proteome Revealed Significant Differences between Non-Structural/ Accessory Proteins and Structural Proteins (preprint)

The immunogenicity of SARS-CoV-2 proteome is largely unknown, especially for non-structural proteins and accessory proteins. Here we collected 2,360 COVID-19 sera and 601 control sera. We analyzed these sera on a protein microarray with 20 proteins of SARS-CoV-2, built an antibody response landscape for IgG and IgM. We found that non-structural proteins and accessory proteins NSP1, NSP7, NSP8, RdRp, ORF3b and ORF9b elicit prevalent IgG responses. The IgG patterns and dynamic of non-structural/ accessory proteins are different from that of S and N protein. The IgG responses against these 6 proteins are associated with disease severity and clinical outcome and declined sharply about 20 days after symptom onset. In non-survivors, sharp decrease of IgG antibodies against S1 and N protein before death was observed. The global antibody responses to non-structural/ accessory proteins revealed here may facilitate deeper understanding of SARS-CoV-2 immunology.Funding: This work was partially supported by the National Key Research and Development Program of China Grant (No.2016YFA0500600), National Natural Science Foundation of China (No. 31970130, 31600672, 31670831, 31370813, 31900112 and 21907065).Conflict of Interest: The authors declare no competing interests.Ethical Approval: The study was approved by the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (ITJ-C20200128). Written informed consent was obtained from all participants enrolled in this study.

Antibody landscape against SARS-CoV-2 proteome revealed significant differences between non-structural/ accessory proteins and structural proteins (preprint)

The immunogenicity of SARS-CoV-2 proteome is largely unknown, especially for non-structural proteins and accessory proteins. Here we collected 2,360 COVID-19 sera and 601 control sera. We analyzed these sera on a protein microarray with 20 proteins of SARS-CoV-2, built an antibody response landscape for IgG and IgM. We found that non-structural proteins and accessory proteins NSP1, NSP7, NSP8, RdRp, ORF3b and ORF9b elicit prevalent IgG responses. The IgG patterns and dynamic of non-structural/ accessory proteins are different from that of S and N protein. The IgG responses against these 6 proteins are associated with disease severity and clinical outcome and declined sharply about 20 days after symptom onset. In non-survivors, sharp decrease of IgG antibodies against S1 and N protein before death was observed. The global antibody responses to non-structural/ accessory proteins revealed here may facilitate deeper understanding of SARS-CoV-2 immunology. HighlightsO_LIAn antibody response landscape against SARS-CoV-2 proteome was constructed C_LIO_LINon-structural/accessory proteins elicit prevalent antibody responses but likely through a different mechanism to that of structural proteins C_LIO_LIIgG antibodies against non-structural/accessory proteins are more associated with disease severity and clinical outcome C_LIO_LIFor non-survivors, the levels of IgG antibodies against S1 and N decline significantly before death C_LI

SARS-CoV-2 antibody signatures for predicting the outcome of COVID-19 (preprint)

The COIVD-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies remain elevated with disease progression and severity in COIVD-19 patients. We assumed that antibodies may serve as biomarkers for predicting disease outcome. By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgM/ IgG responses against 20 SARS-CoV-2 proteins in 1,034 hospitalized COVID-19 patients on admission, who were followed till 66 days. The microarray results were correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality. We found that high level of IgM against ORF7b at the time of hospitalization is an independent predictor of patient survival (p trend = 0.002), while levels of IgG responses to 6 non-structural proteins and 1 accessory protein, i. e., NSP4, NSP7, NSP9, NSP10, RdRp (NSP12), NSP14, and ORF3b, possess significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory markers for disease severity (all with p trend < 0.05). Spline regression analysis indicated that the correlation between ORF7b IgM, NSP9 IgG, and NSP10 IgG and risk of COVID-19 mortality is linear (p = 0.0013, 0.0073 and 0.0003, respectively). Their AUCs for predictions, determined by computational cross-validations (validation1), were 0.74 (cut-off = 7.59), 0.66 (cut-off = 9.13), and 0.68 (cut-off = 6.29), respectively. Further validations were conducted in the second and third serial samples of these cases (validation2A, n = 633, validation2B, n = 382), with high accuracy of prediction for outcome. These findings have important implications for improving clinical management, and especially for developing medical interventions and vaccines.

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Systematic evaluation of SARS-CoV-2 spike protein derived peptides for diagnosis of COVID-19 patients (preprint)

Serological test plays an essential role in monitoring and combating COVID-19 pandemic. Recombinant spike protein (S protein), especially S1 protein is one of the major reagents for serological tests. However, the high cost in production of S protein, and the possible cross-reactivity with other human coronaviruses poses unneglectable challenges. Taking advantage of a peptide microarray of full spike protein coverage, we analyzed 2,434 sera from 858 COVID-19 patients, sera from 63 asymptomatic patients and 610 controls collected from multiple clinical centers. Based on the results of the peptide microarray, we identified several S protein derived 12-mer peptides that have high diagnosis performance. Particularly, for monitoring IgG response, one peptide (aa 1148-1159 or S2-78) has a comparable sensitivity (95.5%, 95% CI 93.7-96.9%) and specificity (96.7%, 95% CI 94.8-98.0%) to that of S1 protein for detection of both COVID-19 patients and asymptomatic infections. Furthermore, the performance of S2-78 IgG for diagnosis was successfully validated by ELISA with an independent sample cohort. By combining S2-78/ S1 with other peptides, a two-step strategy was proposed to ensure both the sensitivity and specificity of S protein based serological assay. The peptide/s identified in this study could be applied independently or in combination with S1 protein for accurate, affordable, and accessible COVID-19 diagnosis.