Advantages and prospects of traditional Chinese medicine in treating COVID-19

Towards the end of 2019, a novel coronavirus pneumonia (coronavirus disease 19, COVID-19) caused by SARS-CoV-2 infection emerged in Wuhan. The SARS-CoV-2 virus quickly spread across the globe, seriously affecting public health and economic development of countries worldwide. Currently, antiviral drugs developed for COVID-19 lack strong clinical trial support and the high mutation rate of the virus causes difficulties in vaccine development, thus a complex and delayed large scale role out of an efficacious vaccine. Traditional Chinese medicine (TCM) has been used for treating various conditions for thousands of years and has a unique systems theory. It can be individualized into specific therapeutic regimens according to the patients' physical condition, clinical symptoms, and other distinguishing factors. In addition, TCM often has different effects at different disease stages, thus contributing to disease prevention, treatment, and rehabilitation. Existing evidence shows that TCM has efficacy in the treatment of COVID-19. The active ingredients of TCM have various pharmacological properties, including antiviral, anti-inflammatory, and immunomodulatory activity, with clinical trials showing that these prescriptions reduce symptoms of COVID-19, promote viral clearance, and ultimately improve survival in infected patients. This article discusses the advantages and mechanisms of TCM in the treatment of COVID-19, hoping to provide a reference platform in the fight against the disease. [ FROM AUTHOR] Copyright of Traditional Medicine Research is the property of TMR Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Seasonal variation and ecological risk assessment of Pharmaceuticals and Personal Care Products (PPCPs) in a typical semi-enclosed bay — The Bohai Bay in northern China

The Bohai Bay as a typical semi-enclosed bay in northern China with poor water exchange capacity and significant coastal urbanization, is greatly influenced by land-based inputs and human activities. As a class of pseudo-persistent organic pollutants, the spatial and temporal distribution of Pharmaceuticals and Personal Care Products (PPCPs) is particularly important to the ecological environment, and it will be imperfect to assess the ecological risk of PPCPs for the lack of systematic investigation of their distribution in different season. 14 typical PPCPs were selected to analyze the spatial and temporal distribution in the Bohai Bay by combining online solid-phase extraction (SPE) and HPLC-MS/MS techniques in this study, and their ecological risks to aquatic organisms were assessed by risk quotients (RQs) and concentration addition (CA) model. It was found that PPCPs widely presented in the Bohai Bay with significant differences of spatial and seasonal distribution. The concentrations of ∑PPCPs were higher in autumn than in summer. The distribution of individual pollutants also showed significant seasonal differences. The high values were mainly distributed in estuaries and near-shore outfalls. Mariculture activities in the northern part of the Bohai Bay made a greater contribution to the input of PPCPs. Caffeine, florfenicol, enrofloxacin and norfloxacin were the main pollutants in the Bohai Bay, with detection frequencies exceeding 80 %. The ecological risk of PPCPs to algae was significantly higher than that to invertebrates and fish. CA model indicated that the potential mixture risk of total PPCPs was not negligible, with 34 % and 88 % of stations having mixture risk in summer and autumn, respectively. The temporary stagnation of productive life caused by Covid-19 weakened the input of PPCPs to the Bohai Bay, reducing the cumulative effects of the pollutants. This study was the first full-coverage investigation of PPCPs in the Bohai Bay for different seasons, providing an important basis for the ecological risk assessment and pollution prevention of PPCPs in the bay.

Detection and Staging of Chronic Obstructive Pulmonary Disease Using a Computed Tomography-based Weakly Supervised Deep Learning Approach (preprint)

BackgroundChronic obstructive pulmonary disease (COPD) remains underdiagnosed globally. The coronavirus disease 2019 pandemic has also severely restricted spirometry, the primary tool used for COPD diagnosis and severity evaluation, due to concerns of virus transmission. Computed tomography (CT)-based deep learning (DL) approaches have been suggested as a cost-effective alternative for COPD identification within smokers. The present study aims to develop weakly supervised DL models that utilize CT image data for the automated detection and staging of spirometry-defined COPD among natural population.MethodsA large, highly heterogenous dataset was established comprising 1393 participants recruited from outpatient, inpatient and physical examination center settings of 4 large public hospitals in China. CT scans, spirometry data, demographic data, and clinical information of each participant were collected for the purpose of model development and evaluation. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants and evaluated using a test set comprised of data from 278 non-overlapping participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients and evaluated using 5-fold cross validation. Spirometry tests were used to diagnose COPD, with stages defined according to the GOLD criteria.ResultsThe attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 on the test set and 0.866 on the LDCT subset acquired from NLST. The model exhibited high generalizability across distinct scanning devices and slice thicknesses, with an AUC above 0.90. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale, with a Cohen’s weighted Kappa of 0.619 for the assessment of GOLD categorization .ConclusionThe proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale, with clinically acceptable performance. As such, this approach may be a powerful novel tool for COPD diagnosis and staging at the population level.

Diagnostic value of the serum levels of high-sensitivity c-reactive protein,wbc, lymghocyte and neutrophil counts in COVID-19

Objective To investigate the diagnostic value of the count of serum hypersensitive C-reactive protein (serum hs-CRP) and white blood cell (WBC), lymphocyte (LY) and neutrophil (NT) in coronavirus disease 2019 (COVID-19). Methods Seventy-two patients of pulmonary infection from June to August 2020 were divided into the experimental group (36 cases) and bacterial group (36 cases) according to the infection pathogen, and 30 healthy people were set as the control group. The serum hs-CRP,WBC,LY and NT were measured and compared among three groups, and the sensitivity, specificity and receiver operating characteristic (ROC) curves of four indexes were calculated and plotted. Results The differences of the serum hs-CRP,WBC,LY and NT among three groups were statistically significant (P<0.01). When the combination of four indicators was used to diagnose the COVID-19, the area under the ROC curve of the combination were 0.994, and their sensitivity (100%) and specificity (94.4%) were the highest. Conclusions The serum hs-CRP,WBC,LY and NT have certain diagnostic value in COVID-19. Moreover, the sensitivity and specificity of the combination of four indicators are higher.

Factors Affecting the Mortality of Patients with COVID-19 Undergoing Surgery and the Safety of Surgical Team: A Systematic Review (preprint)

Background: COVID-19 can complicate the perioperative course to increase postoperative mortality in infected patients, and also is a serious threat to medical staff. However, studies summarizing the impact of COVID-19 on the perioperative mortality of patients and on the safety of surgical team are lacking.Methods: We searched PubMed, Cochrane Library, Embase and a Chinese database with the search terms “COVID-19” or “SARS-CoV-2” and “Surgery” or “Operation” for all published articles on COVID-19 since the outbreak. The search was finalized on May 29th, 2020.Findings: A total of 255 patients from 36 studies were included in our meta-analysis. The mean age of operative patients with COVID-19 was 50.03 years, and 56% were female. A total of 27 patients were deceased, with an overall mortality of 7%. All deceased patients had postoperative complications associated with operation or COVID-19, including respiratory failure/ARDS/short of breath/dyspnea, fever/cough/fatigue or myalgia, cardiopulmonary system, shock/infection, acute kidney injury and severe lymphopenia. While only respiratory failure/ARDS/short of breath/dyspnea after operation was associated with significantly higher mortality (r=0.879, p =0.001), while fever/cough/fatigue or myalgia demonstrated marginally significant association with mortality (r=0.619, p =0.056). 13 of the 36 studies reported medical staff infection and levels of personal protection, and a total of 38 medical staff were infected. Of note, none of the staffs with PPE 3 was infected.Interpretation: COVID-19 patients, in particular whose with severe respiratory complications, may have high postoperative mortality. And, medical staff in close contact with infected patients are suggested to take high level PPE.Funding Statement: This work was supported by Heilongjiang postdoctoral scientific research developmental fund (LBH-Q17127); Youth Elite Training Foundation (JY2015-05) of Harbin Medical University Cancer Hospital, and the National Natural Science Foundation of China (NO. 81970247, and NO. 81670770).Declaration of Interests: None.

Bacteriological Characteristics of COVID-19 Patients Nosocomially Co-infected at a Designated Hospital: A Retrospective Study (preprint)

Background: Bacterial co-infection in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a critical factor that increases the complexity and treatment of coronavirus disease 2019 (COVID-19). Methods: We collected the clinical laboratory data of 1799 patients with confirmed COVID-19 who were admitted to Jinyintan Hospital in Wuhan, China, between January 1 to April 26, 2020. The bacterial co-infection along with disease progression was analyzed. Other inflammatory markers, including C-reactive protein (CRP), white blood cells (WBC), lymphocytes (L), neutrocytes (N), interleukin-6 (IL-6), and procalcitonin (PCT), were assessed to estimate the progression of COVID-19. Results: We found that 191 of the 1799 (10.62%) patients had bacterial co-infection. The most prevalent causative agents for bacterial co-infection were Klebsiella pneumoniae (91 cases, 5.06%) and Acinetobacter baumannii (66 cases, 3.67%). The most patients with bacterial co-infection showed extensive drug-resistance. The outcomes of patients with bacterial co-infection were worse than those of patients without bacterial co-infection.Conclusions: Secondary bacterial pneumonia during virus infection is a major risk factor for high mortality resulting from severe pneumonia caused by COVID-19.

Development of a chiral HPLC method for the separation and quantification of hydroxychloroquine enantiomers (preprint)

Hydroxychloroquine (2-[[4-[(7-Chloroquinolin-4-yl) amino]pentyl](ethyl) amino]-ethanol, HCQ), an effective anti-malarial drug, has been applied in the clinics for potential treatment of severe coronavirus disease 2019 (COVID-19). Although the clinical benefits of HCQ require extensive clinical data to confirm, the existence of a chiral center in the molecule to possess two optical isomers suggests that there might be an enantiomeric difference on the treatment of COVID-19. Due to poor resolution and the inability of quantification by previously reported methods for the analysis of HCQ enantiomers, it is necessary to develop an analytical method to achieve baseline separation for quantitative and accurate determination of the enantiomeric purity in order to compare the efficacy and toxicity profiles of different enantiomer. In this study, we developed and validated an accurate and reproducible normal phase chiral HPLC method for the analysis of two enantiomers of HCQ, and the method was further evaluated with biological samples. With this newly developed method, the relative standard deviation of all analytes was lower than 5%, and the limit of quantification was 0.27 μg/ml, 0.34 μg/ml and 0.20 μg/ml for racemate, R- and S-enantiomer, respectively. The present method provides an essential analytical tool for preclinical and clinical evaluation of HCQ enantiomers for potential treatment of COVID-19.

Risk Factors Related to Weight Gain for Chines During Home Confinement in COVID-19 Pandemic: An Observational Retrospective Study (preprint)

Objective: The observational study was intended to explore the weight changes and risk factors of weight gain during the self-quarantine and find available methods to lose weight. Method: This was an online retrospective observational study investigating the weight changes before and after home confinement. A total of 530 participants completed the online questionnaire. diet, sleep, self-reported depression, disease history and exercise information possibly relating to weight changes were incorporated into the questionnaire. The differences among four groups (underweight, normal weight, overweight and obesity) in BMI change and weight change were compared, and the risk factors of weight gain was also analyzed by multiple linear regression analysis. Result: Participants were mostly between 21-50 years old, getting an average weight change of 0.82±3.31kg, and an average BMI change of 0.35 [-0.37, 1.00]. 43.77% of them gained weight by 2.99±2.29kg averagely. People with normal weight were easier to gain weight than obese group (p=0.001). There were differences in food intake (p<0.001), eating habits(p<0.001), taste preference (p=0.047), daily exercise step change(p=0.007), exercise (p=0.02) between non-weight gain group and weight gain group. The multiple linear regression revealed that weight gains were associated with sex (p=0.002), food intake (p=0.004), current daily exercise step (p=0.009) and self-reported depression (p=0.002) and weight loss was related to food intake (p=0.004) and pre-BMI (p=0.001). Conclusion: Eating irregularly, increasing food intake, self-reported depression and decreased daily steps were risk factors of weight gain, yet weight loss was related to decreased food intake and pre-BMI.

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

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

Risk Prediction for Poor Outcome and Death in Hospital In-Patients with COVID-19: Derivation in Wuhan, China and External Validation in London, UK (preprint)

Background: Accurate risk prediction of clinical outcome would usefully inform clinical decisions and intervention targeting in COVID-19. The aim of this study was to derive and validate risk prediction models for poor outcome and death in adult inpatients with COVID-19. Methods: Model derivation using data from Wuhan, China used logistic regression with death and poor outcome (death or severe disease) as outcomes. Predictors were demographic, comorbidity, symptom and laboratory test variables. The best performing models were externally validated in data from London, UK. Findings: 4.3% of the derivation cohort (n=775) died and 9.7% had a poor outcome, compared to 34.1% and 42.9% of the validation cohort (n=226). In derivation, prediction models based on age, sex, neutrophil count, lymphocyte count, platelet count, C-reactive protein and creatinine had excellent discrimination (death c-index=0.91, poor outcome c-index=0.88), with good-to-excellent calibration. Using two cut-offs to define low, high and very-high risk groups, derivation patients were stratified in groups with observed death rates of 0.34%, 15.0% and 28.3% and poor outcome rates 0.63%, 8.9% and 58.5%. External validation discrimination was good (c-index death=0.74, poor outcome=0.72) as was calibration. However, observed rates of death were 16.5%, 42.9% and 58.4% and poor outcome 26.3%, 28.4% and 64.8% in predicted low, high and very-high risk groups. Interpretation: Our prediction model using demography and routinely-available laboratory tests performed very well in internal validation in the lower-risk derivation population, but less well in the much higher-risk external validation population. Further external validation is needed. Collaboration to create larger derivation datasets, and to rapidly externally validate all proposed prediction models in a range of populations is needed, before routine implementation of any risk prediction tool in clinical care. Funding Statement: HW and HZ are supported by Medical Research Council and Health Data Research UK Grant (MR/S004149/1), Industrial Strategy Challenge Grant (MC_PC_18029) and Wellcome Institutional Translation Partnership Award (PIII054). RD is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 (https://www.hdruk.ac.uk). KD is supported by LifeArc STOPCOVID award. This work uses data provided by patients and collected by the NHS as part of their care and support. XW is supported by National Natural Science Foundation of China (grant number:81700006). QL is supported by National Key R&D Program (2018YFC1313700), National Natural Science Foundation of China (grant number: 81870064) and the “Gaoyuan” project of Pudong Health and Family Planning Commission (PWYgy2018-06).Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The derivation study was approved by the Research Ethics Committee of Shanghai Dongfang Hospital and Taikang Tongji Hospital. The external validation study operated under London South East Research Ethics Committee (reference 18/LO/2048) approval granted to the King’s Electronic Records Research Interface (KERRI).

Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK (preprint)

Background Accurate risk prediction of clinical outcome would usefully inform clinical decisions and intervention targeting in COVID-19. The aim of this study was to derive and validate risk prediction models for poor outcome and death in adult inpatients with COVID-19. Methods Model derivation using data from Wuhan, China used logistic regression with death and poor outcome (death or severe disease) as outcomes. Predictors were demographic, comorbidity, symptom and laboratory test variables. The best performing models were externally validated in data from London, UK. Findings 4.3% of the derivation cohort (n=775) died and 9.7% had a poor outcome, compared to 34.1% and 42.9% of the validation cohort (n=226). In derivation, prediction models based on age, sex, neutrophil count, lymphocyte count, platelet count, C-reactive protein and creatinine had excellent discrimination (death c-index=0.91, poor outcome c-index=0.88), with good-to-excellent calibration. Using two cut-offs to define low, high and very-high risk groups, derivation patients were stratified in groups with observed death rates of 0.34%, 15.0% and 28.3% and poor outcome rates 0.63%, 8.9% and 58.5%. External validation discrimination was good (c-index death=0.74, poor outcome=0.72) as was calibration. However, observed rates of death were 16.5%, 42.9% and 58.4% and poor outcome 26.3%, 28.4% and 64.8% in predicted low, high and very-high risk groups. Interpretation Our prediction model using demography and routinely-available laboratory tests performed very well in internal validation in the lower-risk derivation population, but less well in the much higher-risk external validation population. Further external validation is needed. Collaboration to create larger derivation datasets, and to rapidly externally validate all proposed prediction models in a range of populations is needed, before routine implementation of any risk prediction tool in clinical care.

Performance verification of detecting COVID-19 specific antibody by using four chemiluminescence immunoassay systems (preprint)

BackgroundThe purpose of current study is to evaluate the analytical performance of seven kits for detecting IgM/IgG antibody of corona virus (2019-nCoV) by using four chemiluminescence immunoassay systems. Methods50 patients diagnosed with 2019-nCoV infection and 130 controls without corona virus infection from the General Hospital of Chongqing were enrolled in current retrospective study. Four chemiluminescence immunoassay systems including seven IgM/IgG antibody detection Kits for 2019-nCoV (A_IgM, A_IgG, B_IgM, B_IgG, C_IgM, C_IgG, D_Ab) were employed to detecting antibody concentration. Chi-square test, receiver operating characteristic (ROC) curve and Youdens index were demonstrated to verify the cutoff value of each detection system. ResultsThe repeatability verification results of the A, B, C, and D system are all qualified. D-Ab performances best (92% sensitivity and 99.23% specificity), and B_IgM worse than other systems. Except for the system of A_IgM and C_IgG, the optimal diagnostic thresholds and cutoff value of other kits from recommendations are inconsistent with each other. B_IgM got the worst AUC and C_IgG had the best diagnostic accuracy. More importantly, B_IgG system have the highest false positive rate for testing patients with AIDS, tumor and pregnant. A_IgM system test showed highest false positive rates among elder over 90 years old. ConclusionsSystems for CoVID-2019 IgM/IgG antibody test performance difference. Serum diagnosis kit of D-Ab is the most reliable detecting system for 2019-nCoV antibody, which can be used as an alternative method for nucleic acid testing.

Clinical Characteristics and Short-Term Outcomes of Severe Patients with COVID-19 in Wuhan, China (preprint)

ObjectiveA novel pneumonia (COVID-19) which is sweeping the globe was started in December, 2019, in Wuhan, China. Most deaths occurred in severe and critically cases, but information on prognostic risk factors for severe ill patients is incomplete. Further research is urgently needed to guide clinicians, so we prospectively evaluate the clinical outcomes of 114 severe ill patients with COVID-19 for short-term in the Union Hospital in Wuhan, China. MethodsIn this single-centered, prospective and observational study, we enrolled 114 severe ill patients with confirmed COVID-19 from Jan 23, 2020 to February 22, 2020. Epidemiological, demographic and laboratory information were collected at baseline, data on treatment and outcome were collected until the day of death or discharge or for the first 28 days after severe ill diagnosis, whichever was shorter. Univariate and multivariate Cox proportional hazard models were used to determine hazard ratios (HRs) and 95% confidence intervals (CIs) of poor outcome. ResultsAmong enrolled 114 patients, 94 (82.5%) had good outcome while 20 (17.5%) had poor outcome. No significant differences were showed in age, gender and the prevalence of coexisting disorders between outcome groups. Results of multivariate Cox analyses indicated that higher levels of oxygen saturation (HR, 0.123; 95% CI, 0.041-0.369), albumin (HR, 0.060; 95% CI, 0.008-0.460) and arterial partial pressure of oxygen (HR, 0.321; 95% CI, 0.106-0.973) were associated with decreased risk of developing poor outcome within 28 days. In the other hand, higher levels of leucocytes (HR, 5.575; 95% CI, 2.080-14.943), neutrophils (HR, 2.566; 95% CI, 1.022-6.443), total bilirubin (HR, 6.171; 95% CI, 2.458- 15.496), globulin (HR, 2.526; 95% CI, 1.027-6.211), blood urea nitrogen (HR, 5.640; 95% CI, 2.193-14.509), creatine kinase-MB (HR, 3.032; 95% CI, 1.203-7.644), lactate dehydrogenase (HR, 4.607; 95% CI, 1.057-20.090), hypersensitive cardiac troponin I (HR, 5.023; 95% CI, 1.921-13.136), lactate concentration (HR,15.721; 95% CI, 2.099-117.777), Interleukin-10 (HR, 3.551; 95% CI, 1.280-9.857) and C-reactive protein (HR, 5.275; 95% CI, 1.517-18.344) were associated with increased risk of poor outcome development. We also found that traditional Chinese medicine can significantly improve the patients condition, which is conducive to the transformation from severe to mild. ConclusionIn summary, we firstly reported this single-centered, prospective and observational study for short-term outcome in severe patients with COVID-19. We found that cytokine storm and uncontrolled inflammation responses, liver, kidney, cardiac dysfunction may play important roles in final outcome of severe ill patients with COVID-19. Our study will provide clinicians to be benefit to rapidly estimate the likelihood risk of short-term poor outcome for severe patients.

Effect of ambient air pollutants and meteorological factors on COVID-19 transmission (preprint)

The authors have withdrawn this preprint due to author disagreement.

A study on mental health status among the staff in a designated hospital for COVID- 19 (preprint)

Background: We investigate the mental health status of all the staff members who worked for the designated hospital during the initial stage of COVID-19, so as to understand the severity of mental health problems, and analyze the risk factors.Methods: Through the patients health questionnaire-9(PHQ-9) and panic disorder severity scales(PDSS), we surveyed the status of depression and panic disorder of the staff who participated in the prevention and treatment of COVID-19 in designated hospital in the early stage of epidemic. The data is described by the number of cases (percentage), median and interquartile range. The chi square test was used for categorical variables and the rank sum test was used for continuous variables. The risk factors of severe depression or panic disorder were analyzed by binary logistic regression test.Results: Totally 702 questionnaires were sent out and 694(98.9%) was received and qualified, the median score of PHQ-9 among all the staff was 1 (IQR,0-4), 143(20.6%) of them had depression, 39 (5.6%) had serious depression; the median score of PDSS was 2 (IQR,0-5), 81 (11.7%) of them had panic disorder and 47(6.7%) of them had severe panic disorder; Among the people in different work lines, the first-line staff scored the highest: PHQ-9 score was 4 (0-8); PDSS score was 4 (1-9), which were significantly higher than the second-line and third-line staff (P<0.001). Multivariate logistic regression analysis showed that the adjusted risk of severe depression in first-line staff was 6.63 fold(P < 0.001); the risk of severe panic disorder was 2.62 fold (P=0.003) higher than that of non-first line group.Conclusions: Many staff in the designated hospital for COVID-19 have mental health problems. Among them, first-line workers are a high-risk group with severe depression and panic disorder, and further psychological intervention is needed for them.

Antibody responses to SARS-CoV-2 in COVID-19 patients: the perspective application of serological tests in clinical practice (preprint)

Background We aim to investigate the profile of acute antibody response in COVID-19 patients, and provide proposals for the usage of antibody test in clinical practice. Methods A multi-center cross-section study (285 patients) and a single-center follow-up study (63 patients) were performed to investigate the feature of acute antibody response to SARS-CoV-2. A cohort of 52 COVID-19 suspects and 64 close contacts were enrolled to evaluate the potentiality of the antibody test. Results The positive rate for IgG reached 100% around 20 days after symptoms onset. The median day of serocon-version for both lgG and IgM was 13 days after symptoms onset. Seroconversion of IgM occurred at the same time, or earlier, or later than that of IgG. IgG levels in 100% patients (19/19) entered a platform within 6 days after seroconversion. The criteria of IgG seroconversion and [≥] 4-fold increase in the IgG titers in sequential samples together diagnosed 82.9% (34/41) of the patients. Antibody test aided to confirm 4 patients with COVID-19 from 52 suspects who failed to be confirmed by RT-PCR and 7 patients from 148 close contacts with negative RT-PCR. Conclusion IgM and IgG should be detected simultaneously at the early phase of infection. The serological diagnosis criterion of seroconversion or [≥] 4-fold increase in the IgG titer is suitable for a majority of COVID-19 patients. Serologic test is helpful for the diagnosis of SARS-CoV-2 infection in suspects and close contacts.