Quantitative detection of SARS-CoV-2 antigen: an effective approach for evaluating the infectivity of COVID- 19 convalescent patients

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Methods Analytical performances of the MAG-CLIA were evaluated, including precision, limit of quantitation (LoQ), linearity and specificity. Nasopharyngeal specimens from 232 patients who were SARS-CoV-2 RT-qPCR positive and from 477 healthy donors were included to evaluate the diagnostic performance. The performance of the Wondfo antigen-detecting lateral flow test (LFT) was evaluated in parallel. The longitudinal studies were performed by monitoring antigen concentrations alongside with RT-qPCR results in 14 COVID-19 participants for up to 22 days. The critical antigen concentration in determining virus infectivity was evaluated at the reference cycle threshold (Ct) of 35. Results COVID-19 patients were well-identified using an optimal threshold of 0.64 pg/mL antigen concentration, with sensitivity and specificity of 95.7% (95% CI: 92.2%-97.9%) and 98.3% (95% CI: 96.7%-99.3%), respectively, while the Wondfo LFT exhibited those of 34.9% (95% CI: 28.8%-41.4%) and 100% (95% CI: 99.23%-100%), respectively. Close dynamic consistence was observed between SARS-CoV-2 Ag and viral load time series in the longitudinal studies. The critical value of 8.82 pg/mL antigen showed adequate sensitivity and specificity in evaluating the infectivity of convalescent patients. Conclusions The MAG-CLIA SARS-CoV-2 Ag detection is an effective and alternative approach for rapid diagnosis and enables us to evaluate the infectivity of convalescent patients. SARS-CoV-2 antigen detection chemiluminescent immunoassay COVID-19 diagnosis COVID-19 monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally since the end of 2019. The emergence of SARS-CoV-2 extensively impacted healthcare worldwide, with over 500 million confirmed cases and nearly 6.5 million deaths [ 1 ]. To date, the pandemic has not yet been under complete control due to the swift evolution of SARS-CoV-2. Omicron subvariant BA.2, one of the variant of concerns (VOCs), has now become dominant in many regions of the world, including this epidemic in Shanghai since late February, 2022 [ 2 ]. It has been demonstrated that Omicron BA.2 has an increased transmissibility and immune escape capability when compared to other variants [ 3 , 4 ]. Therefore, valid markers that can be used for rapid and accurate diagnosis and monitoring of SARS-CoV-2 infection have become increasingly essential to healthcare strategies for effective COVID-19 management [ 5 – 7 ]. The real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) is currently the gold standard and the most routinely used diagnostic testing method for COVID-19 due to its specificity and adequate sensitivity [ 8 ], but a long turnaround time and high cost limit its utility. While this nucleic acid-based method is sensitive enough to detect SARS-CoV-2 qualitatively, persistent nucleic acid positivity after symptom relief and disease recovery makes it challenging to determine the correct level of infection control measures during patient care [ 9 ]. Antigen-detecting lateral flow tests (LFTs) have been developed and introduced by the World Health Organization (WHO) to achieve high coverage and quick turnaround of testing [ 10 ]. LFTs offer rapid results at low costs [ 11 ], yet the performance of such tests is controversial. Studies suggest that the sensitivities for antigen-detecting LFTs are low, especially when the tests are applied to asymptomatic individuals [ 11 , 12 ]. The ultra-sensitive chemiluminescent immunoassay (CLIA), which is quality-controlled and possessed of relative quick turnaround time in detecting SARS-CoV-2, is also one of the alternative approaches to RT-qPCR [ 13 ]. This study evaluated the MAGLUMI® SARS-CoV-2 Ag assay in comparison to RT-qPCR and LFT, in 232 individual COVID-19 patients with a variety of comorbidities during the peak of COVID-19 Shanghai epidemics (April 1st - May 31st, 2022) in a tertiary hospital in Shanghai. We estimated the diagnostic performance of the MAGLUMI® SARS-CoV-2 Ag assay in rapid COVID-19 screening and innovatively investigated the antigen concentration to evaluate the infectivity of convalescent patients. Materials And Methods MAGLUMI ® SARS-CoV-2 Ag assay Automated quantitative detection of the SARS-CoV-2 nucleocapsid antigen was performed using MAGLUMI ® SARS-CoV-2 Ag chemiluminescent immunoassay (hereafter referred to as MAG-CLIA) on a MAGLUMI X8 analyzer. This method has been specifically developed for detecting and quantifying the SARS-CoV-2 nucleocapsid antigen in human nasopharyngeal and oropharyngeal swabs. In brief, pretreated nasopharyngeal or oropharyngeal swab samples, magnetic microbeads coated with anti-SARS-CoV-2 nucleocapsid protein monoclonal antibody, and N-(4-aminobutyl)-N-ethylisoluminol (ABEI) labeled with another anti-SARS-CoV-2 nucleocapsid protein monoclonal antibody are mixed and incubated thoroughly to form sandwich complexes. After precipitation in a magnetic field, the solid phase is washed and subsequently initiated for a chemiluminescent reaction. The light signal is measured by the specific instrument as relative light units (RLUs). The signal is proportional to the concentration of SARS-CoV-2 nucleocapsid protein present in the test sample. Analytical performance studies The analytical performance of the MAG-CLIA was evaluated, including precision, limit of quantitation, linearity and analytical specificity. For detailed materials, please refer to the supplementary data. RT-qPCR and Antigen-detecting lateral flow test (LFT) SARS-CoV-2 nucleic acid tests were performed on a MA-6000 Real-Time Quantitative Thermal Cycler (Sansure Biotech Inc.) using the COVID-19 Coronavirus Real Time PCR Kit (Bioperfectus Technologies Co., Ltd.). Viral loads in respiratory specimens were estimated from cycle threshold (Ct) values. Specimens were judged positive when the measured Ct value for either ORF1 gene or N gene was 40 or less. The Wondfo 2019-nCoV Antigen Test kit (Wondfo Biotech Co., Ltd.) was used as LFT in this study. Study Design and Sample Collection This study was conducted from April 1 st to May 31 st 2022 at the Department of Laboratory Medicine of Huashan Hospital affiliated to Fudan University, Shanghai, China. The protocol of the current study was approved by the Huashan Hospital Institutional Review Board (HIRB) (NO. 2022-571). Nasopharyngeal samples from 232 COVID-19 patients (median age of 71 years with interquartile range (IQR) from 59 to 83) and from 477 healthy donors (median age of 46 years with IQR from 33 to 56) were included. Among 232 COVID-19 patients, 48 were asymptomatic, 145 mild, 32 moderate and 7 severe (Table 1). There were 136 patients with comorbidity(ies), mainly including hypertension, diabetes, nephropathy, coronary heart disease, and cerebral infarction. The same specimen was used for RT-qPCR, and MAG-CLIA SARS-CoV-2 Ag assessment. All tests were performed within a maximum of 2 hours after sample collection. The LFTs were conducted at bedside immediately. In longitudinal studies, we monitored the antigen concentrations, in parallel with RT-qPCR results, in 14 patients for up to 22 days. The study initiated from the first nucleic acid positive test and terminated after the test showed negative. In addition, the efficiency of antigen concentration in determining virus infectivity was evaluated by using Ct value 35 as a reference for virus infectivity. Statistical analyses Data analyses were performed using GraphPad Prism (version 9.00, GraphPad Software, La Jolla, CA, USA) and MedCalc statistical software (version 18.2.1, MedCalc Software Ltd., Ostend, Belgium). GraphPad Prism was used for regression analyses and for plotting the diagnostic performance of CLIA and LFT. Receiver operating characteristic (ROC) curve analyses were performed in MedCalc. The ROC curve was created by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. Youden index was used to estimate the best thresholds. Statistical significance of the difference between groups was performed using the Kruskal-Wallis test. The difference is statistically significant when P < 0.05. Results Analytical performance of MAGLUMI ® SARS-CoV-2 Ag tests The default unit for the analyte detected by the MAGLUMI ® SARS-CoV-2 Ag tests was the manufacturer’s arbitrary unit (AU)/mL. A linear relationship between AU/mL and pg/mL was found (R 2 =0.9976) , and the conversion factor to obtain pg/mL is 1.592 (Figure 1A). The total imprecision of the CLIA quantitative assay was less than 8% (Supplemental Table 1), with the limit of quantitation (LoQ) at 0.399 pg/mL (Figure 1B) and linear range from 0.4 to 3184.00 pg/mL (Figure 1C). No interference was detected in 48 commercial virus forms and 10 common clinical virus specimens (Figure 1D). There is a remarkable linear correlation between the antigen concentration versus the infectious dose for five different SARS-CoV-2 VOCs, respectively (Figure 1E). For detailed results, please refer to the supplementary data. Characteristics of the subjects Nasopharyngeal samples from 709 individuals were included in this study. Table 1 reports the demographic characteristics of the study subjects. The majority of the patients had relatively high Ct values (32.76% for 30-35 Ct and 34.48% for 35-40 Ct) corresponding to low viral loads, while 35 of 232 (15.09%) patients had high viral loads with Ct < 25. The median antigen concentration in patients was 16.64 pg/mL (IQR 3.28-285.37) and in healthy individuals was negligible (less than 0.1pg/mL). Overall, the SARS-CoV-2 antigen levels of patients and that of healthy subjects differed significantly ( P < 0.001). Table 1. Demographic, SARS-CoV-2 viral loads a , antigen concentrations and time from –first positive test for COVID-19 patients and healthy individuals. Covariate b COVID-19 patients (n=232) Healthy individuals (n=477) P value Age (years) 71 (59-83) 69 (56-79) 0.378 Gender (female) 116 (50%) 233 (48.85%) 0.200 Antigen concentration c 16.64 (3.28-285.37) < 0.1 40 <0.0001 Ct < 25 35 (15.09%) 0 <0.0001 25≤Ct< 30 39 (16.81%) 0 <0.0001 30≤Ct<35 76 (32.76%) 0 <0.0001 35≤Ct<40 80 (34.48%) 0 <0.0001 Ct ≥ 40 2 (0.86%) 477 (100%) <0.0001 Days from first positive test 8 (5-12) 0 <0.0001 Symptom-free 48 (20.69%) NA d NA d Mild symptom 145 (62.5%) NA d NA d Moderate symptom 32 (13.79%) NA d NA d Severe symptom 7 (3.02%) NA d NA d with comorbidity(ies) 136 (58.62%) NA d NA d without comorbidity 96 (41.38%) NA d NA d a. SARS-CoV-2 viral loads were estimated via cycle threshold (Ct) of ORF1 gene. b. Continuous variables reported as median (interquatile range) and categorical variables reported as N (percentage). c. Antigen concentration is shown in pg/mL. d. NA, not applicable. Diagnostic performance A receiver operating characteristic (ROC) curve was plotted to determine the optimal cutoff value of the SARS-CoV-2 antigen, which allows the distinction of SARS-CoV-2 infection from healthy status (Figure 2A). By comparing the antigen results between SARS-CoV-2 positive patients (Ct value ≤ 40) and healthy individuals, results provided an area under the ROC curve (AUC) of 0.987, with 95% confidence interval (CI) ranging from 0.976 to 0.994. Based on the Youden index calculation, the sensitivity and the specificity of the test reached 95.7% (95% CI: 92.2%-97.9%) and 98.3% (95% CI: 96.7%-99.3%) respectively, when a cutoff of 0.640 pg/mL was used. The diagnostic performance of the assay was next evaluated by stratifying the results according to Ct values of RT-qPCR (Table 2). The performance of the Wondfo 2019-nCoV Antigen Test (an LFT) was evaluated in parallel (Figure 2B, 2C). Compared with RT-qPCR, the MAG-CLIA SARS-CoV-2 Ag test was positive and showed 100% concordance for samples with Ct < 33. For samples with Ct ranging from 33 to 40, the sensitivity of the test remained over 95% concordance. Contrarily, the sensitivity of the LFT decreased gradually and remarkably when Ct values increased. The LFT showed 87.8% sensitivity in detecting samples with Ct < 30. The sensitivity dropped to 66.4% for samples with Ct < 33, and to only 34.9% for samples with Ct ≤ 40. Table 2. The sensitivity of two antigen detection assay according to the Ct values from RT-qPCR. Ct value n (total) MAGLUMI ® SARS-CoV-2 Ag assay Wondfo 2019-nCoV Antigen Test N (> 0.64 pg/mL ） n (≤ 0.64 pg/mL ） accumulated sensitivity (%) n (positive) n (negative) accumulated sensitivity (%) < 30 74 74 0 100.0% 65 9 87.8% < 31 89 89 0 100.0% 70 19 78.7% < 32 101 101 0 100.0% 72 29 71.3% < 33 113 113 0 100.0% 75 38 66.4% < 34 131 130 1 99.2% 77 54 58.8% < 35 150 147 3 98.0% 78 72 52.0% < 36 173 169 4 97.7% 79 94 45.7% < 37 186 179 7 96.2% 79 107 42.5% < 38 206 197 9 95.6% 79 127 38.3% < 39 223 213 10 95.5% 81 142 36.3% ≤ 40 232 222 10 95.7% 81 151 34.9% Correlation between SARS-CoV-2 antigen and Ct values MAG-CLIA targets for the N-terminal domain of N protein in SARS-CoV-2, which wraps coronavirus RNA through noncovalent bonds to form the nucleocapsid. This should result in a significant positive correlation between viral load and antigen concentration. Therefore, we examined correlations between SARS-CoV-2 antigen concentration and viral load determined by Ct values of RT-qPCR. Indeed, a significant ( P < 0.0001) linear inverse relationship between the Ct values and log 10 antigen levels was observed (correlation coefficient R 2 = 0.747; Figure 3A). It is also notable that individual variations were remarkable. Individual time lines To explore the dynamic performance of MAG-CLIA SARS-CoV-2 Ag in the viral load time courses, and to explore the influence of comorbidities on the time kinetics, we performed longitudinal studies in 14 COVID-19 positive participants (Figure 4). Among the 14 participants, 2 of them had bacterial infection (patient 9, 13), 2 with diabetes (patient 7, 10), 3 hypertension (patient 2, 4, 5), 3 nephropathy (patient 6, 8, 14), 1 fungal infection (patient 3) and the other 3 (patient 1, 11, 12) without any underlying disease. Figure 4 shows modelled SARS-CoV-2 viral RNA (as seen by Ct values of ORF1 gene) trajectories together with the viral antigen measured for individuals. As the antigen concentration decreased, a decrease of SARS-CoV-2 viral loads was observed over time, as indicated by the increase of Ct values. This trend was observed for the majority of the participants. Moreover, as exemplified in patients 6, 8, 13 and 14, the fluctuation of SARS-CoV-2 Ag was found to be closely and dynamically consistent with that of the viral nucleic acid loads in the time courses. Notably, the underlying comorbidities remarkably prolonged the time for both viral nucleic acid loads and antigen concentrations to return to negative. Evaluation of COVID-19 transmission with CLIA quantification Recent studies observed a strong correlation between SARS-CoV-2 viral loads and transmission [14], and reported no cases of COVID-19 transmission with SARS-CoV-2 viral RNA loads <4 log 10 copies/mL [15]. Given that, Log 10 viral load was then estimated from the Ct value using the empirical formula 14.543-(Ct*0.3018) for the MA-6000 Real-Time Quantitative Thermal Cycler system, and the viral load of 4 log 10 copies/mL was verified to correspond to the 35 Ct value of the current system. This was in accordance with previous viral culture studies which showed that SARS-CoV-2 is no longer contagious for samples with Ct values ≥ 35 [16,17]. The antigen concentration corresponding to 35 cycles was then calculated to be 8.71 pg/mL using the inverse linear regression model shown in Figure 3A. In addition, we also observed a consistent average antigen concentration of 8.82 pg/mL in nasopharyngeal swab samples of another 50 COVID-19 patients on the day when RT-qPCR Ct values first returned to above 35. Thus, we hypothesized that 8.82 pg/mL might be a promising antigen concentration in differentiating contagious patients from the recovering. To further assess the diagnostic ability of the ultra-sensitive SARS-CoV-2 antigen test with the concentration of 8.82 pg/mL in differentiating contagious patients from the recovering, an ROC curve was then plotted by classifying results into Ct values less than 35 and Ct values over 35 (and including 35) (Figure 3B). The ROC curve in identifying infectious patients showed an AUC of 0.921 (95% CI: 0.890-0.946). When 8.82 pg/mL was selected as the critical value, the sensitivity and the specificity of the test were 84.5% (95% CI: 78.2%-89.5%) and 85.0% (95% CI: 79.6%-89.4%), respectively. To explore the time nodes of detection, we tracked the RT-qPCR results from over 1,000 COVID-19 cases between April 1 st and 10 th May 2022 in our laboratory to describe the kinetic evidence of the SARS-CoV-2 Omicron subvariant BA.2 in Shanghai, China (Figure 3C). Overall, the viral load in nasopharynx reduced over time, as seen by the increase of Ct values. The Ct value reached 35 after an average of 9 days since the first observation of positive RT-qPCR test. Furthermore, SARS-CoV-2 was no longer detected in nasopharynx (Ct > 40) after 12 days. Discussion The rapid variation and spread of SARS-CoV-2 has brought challenges to healthcare systems worldwide. There is an urgent need to identify appropriate strategies towards the detection and surveillance of SARS-CoV-2 to reduce community transmission. In the present study, we evaluated a CLIA-based SARS-CoV-2 antigen detection assay, the MAGLUMI® SARS-CoV-2 Ag test. Overall, our results show that the assay has excellent precision characteristics (total imprecision < 8%), wide linear range (0.796–63680 pg/mL when the maximum of 20-fold dilution is used) and high analytical specificity, with a short laboratory turnaround time (35 min). These allow the rapid and precise quantitative analysis of SARS-CoV-2 antigen in nasopharyngeal specimens. RT-qPCR is currently the most routinely used diagnostic testing method for COVID-19. However, the long turnaround time of RT-qPCR leads to diagnostic delay. Moreover, Ct values obtained by RT-qPCR are inversely related to the relative viral RNA levels. They are not standardized to give quantitation of viral concentration and across RT-PCR platforms [ 18 ]. Antigen-based LFTs, which have been developed recently, can generate results within 20 min and outside of a laboratory. Studies have shown that the sensitivity of LFTs varies massively, ranging from 0 to 96% with an average of 56% [ 18 , 19 ]. Our study also demonstrates that one of the LFTs, the Wondfo 2019-nCoV Antigen Test, has sensitivity of 35.2%-87.8% based on different RT-qPCR Ct values, which is in agreement with a recent study [ 20 ]. Compared to the LFT assay, we have demonstrated the MAGLUMI® SARS-CoV-2 Ag assay to be a better tool for antigen detection. A good correlation (R 2 = 0.747, P < 0.0001) was found between SARS-CoV-2 antigen concentrations and Ct values, which is similar to the findings of multiple literature reports [ 21 – 23 ]. The sensitivity of the assay (cutoff value at 0.64 pg/mL) is 100% till Ct values of 33, and it still remains at a high sensitivity of 95.7% with the corresponding Ct values ≤ 40. Additionally, it shows excellent screening performance among the present registered CLIA systems [ 24 ]. Furthermore, the MAGLUMI® SARS-CoV-2 Ag test shortens turnaround time, minimizes the chance of missing positive cases and has a similar diagnostic window of SARS-CoV-2 infection as RT-qPCR. The data, therefore, imply that the MAG-CLIA SARS-CoV-2 Ag test may be used as a routine high-throughput test for rapid screening and diagnosis of COVID-19. The SARS-CoV-2 Ag was shown to be closely and dynamically consistent with the viral load time series in the longitudinal studies. In addition, some comorbidities remarkably prolonged the time for both viral nucleic acid loads and antigen concentrations to return to negative. These provide evidence that MAG-CLIA SARS-CoV-2 Ag assay could be a good surrogate of molecular testing for monitoring COVID-19 patients in regardless of underlying diseases. Some recent studies have reported little chance of COVID-19 transmission with SARS-CoV-2 viral RNA loads < 4 log 10 copies/mL [ 25 , 26 ], and some viral culture studies have shown that SARS-CoV-2 is no longer contagious for samples with Ct values ≥ 35 [ 16 , 17 ]. Moreover, it is suggested by the National Health Commission of the PRC in the Diagnosis and Treatment Protocol for COVID-19 (Trial Version 9) that isolation can be discontinued when a Ct value ≥ 35 is observed. Meanwhile, the 35 Ct value of the current system is verified to correspond to a viral load of 4 log 10 copies/mL. Therefore, patients with Ct values below 35 in this study are assumed to be infectious. By using the ROC analysis, we find that the antigen concentration of 8.82 pg/mL, which is related to the 35 Ct values of RT-qPCR, can be used to determine the contagiousness of SARS-CoV-2 with sensitivity and specificity of 84.5% and 85.0% respectively. Given that the majority of the COVID-19 cases obtain a Ct value ≥ 35 after 9 days since the first positive RT-qPCR result (Fig. 3 C), we recommend to monitor antigen levels during the treatment, and determine isolation strategies on day 9 via the SARS-CoV-2 antigen concentration. Furthermore, Pekosz et al have demonstrated that an antigen qualitative test has a higher positive predictive value than RT-qPCR when compared to viral contagiousness determined by viral culture [ 27 ]. Lai and Lam also suggested that Ct values from RT-qPCR are not standardized to give quantitation of viral concentration due to huge deviations between different platforms [ 18 ]. These provide evidence that CLIA SARS-CoV-2 antigen, which is traceable to international units, has an advantage in determining the risk of transmissibility over RT-qPCR. Due to the poor culturability of most clinical specimens, we did not perform viral culture to assess the infectivity of antigen positive samples. However, it is reasonable to believe that the value of the quantitative MAGLUMI® SARS-CoV-2 Ag (CLIA) test may be much higher than that of a nucleic acid test in judging the infectivity of the patients. Thus, the antigen concentration of 8.82 pg/mL could be a recommended threshold for judging the infectivity of the COVID-19 patients. Nevertheless, individual variations lead to inconsistencies between nucleic acids and antigens. This was exemplified in an immunocompromised patient (patient 3 in Fig. 4 ). This 59-year-old male participant had been diagnosed with cryptococcal meningitis in May 2021, and since then has been receiving fluconazole antifungal therapy. He was found to be SARS-CoV-2 positive on April 26, 2022. We have closely monitored the Ct values and the antigen concentrations in his nasopharyngeal samples from his admission (April 28th ) until the nucleic acids were shown negative (May 20th ). On the 15th day since positive, his antigen concentration dropped down to the baseline (< 8.82 pg/mL), while the Ct value remained below 30. As such, even in the acute infection window, there might be no good correlation between antigenic dynamics and nucleic acid dynamics among these immunocompromised individuals. At this time, which criteria should be selected to assess infectivity remains controversial and needs to be further studied. In conclusion, in most cases, the MAGLUMI® SARS-CoV-2 Ag (CLIA) test correlates well with the Ct values determined by RT-qPCR and has shown very good diagnostic efficiency in the distinction of SARS-CoV-2 infection. Given that it facilitates low cost, scalable and rapid diagnosis, we suggest that this laboratory-based SARS-CoV-2 antigen test can be used as an important screening tool in fever outpatient services and in low- and middle-income countries. Furthermore, the change of SARS-CoV-2 antigen concentrations is associated with viral activity, providing good risk assessment of viral contagiousness. Thus, closely monitoring the antigen concentration could be a preferable approach for monitoring the disease and directing clinical isolation strategies. Declarations Human subjects/informed consent statement All procedures were in accordance with the Helsinki Declaration. The protocol of the current study was approved by the Huashan Hospital Institutional Review Board (HIRB) (NO. 2022-571). Informed written consent was obtained from all enrolled patients. A cknowledgments This study was supported by Shanghai Municipal Science and technology major projects (grant no. HS2021SHZX001), three year action plan for the construction of Shanghai public health system (2020-2022) (grant no. GWV-10.1-XK4), and the project of Huashan Hospital North, Fudan University (grant no. HSBY2019020). Disclosure We declare that none of the work contained in this manuscript has been published in any language or is currently under consideration at any other journal, and there are no conflicts of interests to declare. All authors have contributed to, read, and approved this submitted manuscript in its current form. References Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20:533–534. https://doi.org/10.1016/S1473-3099(20)30120-1 . Zhang X, Zhang W, Chen S. Shanghai's life-saving efforts against the current omicron wave of the COVID-19 pandemic. Lancet. 2022;399:2011–2012. https://doi.org/10.1016/S0140-6736(22)00838-8 . Lyngse FP, Kirkeby CT, Denwood M, Christiansen LE, Mølbak K, Møller CH, et al. 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Favresse J, Gillot C, Oliveira M, Cadrobbi J, Elsen M, Eucher C, et al. Head-to-head comparison of rapid and automated antigen detection tests for the diagnosis of SARS-CoV-2 infection. J Clin Med. 2021;10:265. https://doi.org/10.3390/jcm10020265 . Lippi G, Henry BM, Adeli K, Plebani M. Fujirebio Lumipulse SARS-CoV-2 antigen immunoassay: pooled analysis of diagnostic accuracy. Diagnosis (Berl). 2022;9:149–156. https://doi.org/10.1515/dx-2022-0021 . Jones TC, Biele G, Mühlemann B, Veith T, Schneider J, Beheim-Schwarzbach J, et al. Estimating infectiousness throughout SARS-CoV-2 infection course. Science. 2021;373:eabi5273. https://doi.org/10.1126/science.abi5273 . Perera RAPM, Tso E, Tsang OTY, Tsang DNC, Fung K, Leung YWY, et al. SARS-CoV-2 Virus Culture and Subgenomic RNA for Respiratory Specimens from Patients with Mild Coronavirus Disease. Emerg Infect Dis. 2020:2701–2704. https://doi.org/10.3201/eid2611.203219 . Pekosz A, Parvu V, Li M, Andrews JC, Manabe YC, Kodsi S, et al. Antigen-based testing but not real-time polymerase chain reaction correlates with severe acute respiratory syndrome coronavirus 2 viral culture. Clin Infect Dis. 2021;73:e2861-e2866. https://doi.org/10.1093/cid/ciaa1706 . Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1795842","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":116535914,"identity":"5572e4ab-da54-4bfe-8736-4eb45ecb77a2","order_by":0,"name":"Di Wang","email":"","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wang","suffix":""},{"id":116535915,"identity":"eed44e88-d832-482c-9938-8f5453411bb3","order_by":1,"name":"Hailong Lu","email":"","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Lu","suffix":""},{"id":116535916,"identity":"7702cfd0-67b3-44ef-98c9-5207b000be2e","order_by":2,"name":"Yaju Li","email":"","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yaju","middleName":"","lastName":"Li","suffix":""},{"id":116535917,"identity":"cf3b2d91-812d-4308-9a91-19d02c3de49d","order_by":3,"name":"Jiazhen Shen","email":"","orcid":"","institution":"Shenzhen New Industries Biomedical Engineering Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jiazhen","middleName":"","lastName":"Shen","suffix":""},{"id":116535918,"identity":"766078b8-8ad1-4381-897a-6b812e97ff21","order_by":4,"name":"Guangjie Jiang","email":"","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Guangjie","middleName":"","lastName":"Jiang","suffix":""},{"id":116535919,"identity":"b87e6993-2f45-4354-90a7-69757f245d86","order_by":5,"name":"Jin Xiang","email":"","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Xiang","suffix":""},{"id":116535920,"identity":"f9e8d03b-8e97-44f9-a43e-7c60b0945ead","order_by":6,"name":"Huanhuan Qin","email":"","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Huanhuan","middleName":"","lastName":"Qin","suffix":""},{"id":116535922,"identity":"643d9b95-18f3-46bc-8f78-e8595abca261","order_by":7,"name":"Ming Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYNACAwYGfvbmAwyMDaRokew5lkCKFpCuGzkGxGmRj0h+9uhGwR27mT1nvkn83GEjx8B++OgGfFoMb6SZG+cYPEvuZ+/dJtl7Js2YgSct7QZeLTMSzKRzDA4nS/ac3SbB23Y4sUGCx4yAlvRvYC1AvzyT/EuMFnmJHLAtdkAtbNJE2WLA86YMpCUBGMjG1rJtacZshPwi356+TTrnz2F7YFQ+vPm2zUaOn/3wMfy2HIDQiQ0MDCwSIBYbPuVgWxogtD0QM38gpHoUjIJRMApGJgAAdSBOdByh9W4AAAAASUVORK5CYII=","orcid":"","institution":"Huashan Hospital, Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2022-06-26 05:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1795842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1795842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23469781,"identity":"0a359fbf-f948-4fd8-809c-02616accad82","added_by":"auto","created_at":"2022-07-05 17:05:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":571287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalytical performance of MAGLUMI\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e®\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;SARS-CoV-2 Ag (CLIA) tests. (A) \u003c/strong\u003eUnit conversion from arbitrary unit (AU)/mL to pg/mL.\u003cstrong\u003e \u003c/strong\u003eDashed lines indicate the 95% confidence interval (CI). \u003cstrong\u003e(B) \u003c/strong\u003eDetermination of limit of quantitation (LoQ).\u003cstrong\u003e \u003c/strong\u003eThe LoQ was the concentration corresponding to the 20% coefficient of variation (CV).\u003cstrong\u003e (C) \u003c/strong\u003eLinear range validation. Dashed lines indicate the 95% CI. Error bars represent standard deviations. \u003cstrong\u003e(D)\u003c/strong\u003e Box plot comparing relative light unit (RLU) detected in different groups. Healthy controls, nasopharyngeal samples from healthy donors. Spiked-in samples, nasopharyngeal samples from healthy donors spiked with different virus forms. Clinical samples, respiratory specimens that are negative for SARS-CoV-2 but positive for other viruses. Ns, not significant. \u003cstrong\u003e(E) \u003c/strong\u003eThe linear relationship between the infectious dose of 5 common SARS-CoV-2 variants of concerns (VOCs) and antigen concentrations. \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1795842/v1/35b3121e076bd2a7cb8772af.jpg"},{"id":23469780,"identity":"506e6710-b074-45cf-8288-8186225cd3b2","added_by":"auto","created_at":"2022-07-05 17:05:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":477158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical evaluation of antigen detection in the diagnosis of COVID-19. (A) \u003c/strong\u003eReceiver operating characteristic (ROC) curve evaluating the distinction of SARS-CoV-2 infection from healthy state. PPV, positive predictive value. NPV, negative predictive value. Red triangle points to the cutoff value determined by ROC curve. \u003cstrong\u003e(B) \u003c/strong\u003eScatter plot indicating positivity (grey dot) and negativity (red dot) of the MAGLUMI\u003csup\u003e®\u003c/sup\u003e\u0026nbsp;SARS-CoV-2 Ag (MAG-CLIA) tests with the cutoff of 0.64 pg/mL and the Wondfo antigen lateral flow test (Wondfo LFT) in association with RT-qPCR Ct-values. \u003cstrong\u003e(C) \u003c/strong\u003eThe diagnostic sensitivity of CLIA (red) and LFT (black) at different Ct values. Shadowed areas represent the 95% confidence interval (CI).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1795842/v1/a9c63d8d6eb142930c3b2c1a.jpg"},{"id":23469784,"identity":"3cb43719-abeb-4ef8-a13a-7d548a5bd238","added_by":"auto","created_at":"2022-07-05 17:05:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":548850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUltra-sensitive antigen detecting assay for the evaluation of ending isolation. (A) \u003c/strong\u003eRelationship between log10 antigen concentration (pg/mL) and cycle threshold (Ct) value. Black line indicates the regression of antigen versus RNA levels, with dashed lines representing the 95% confidence interval (CI). \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001. \u003cstrong\u003e(B)\u003c/strong\u003e Receiver operating characteristic (ROC) curve differentiating the contagious patients from the recovering patients. PPV, positive predictive value. NPV, negative predictive value. Red triangle points to the critical value determined as the average concentrations of SARS-CoV-2 antigen corresponding to the Rt-qPCR Ct values of 35. \u003cstrong\u003e(C) \u003c/strong\u003eThe kinetic evidence of the Omicron subvariant BA.2 since the first positive RT-qPCR tests. The median Ct values (blue line) from patients tested on each day is plotted against days since first positive RT-qPCR was detected. 95% CI is shadowed in grey.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1795842/v1/a3419f6afbd75f136d548879.jpg"},{"id":23470680,"identity":"7bd30d14-e07c-4925-9e2f-53bba61790e2","added_by":"auto","created_at":"2022-07-05 17:10:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1750748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe longitudinal data of 14 individual COVID-19 positive patients.\u003c/strong\u003e The log\u003csub\u003e10\u003c/sub\u003e antigen concentrations and Ct values are plotted against days since first positive RT-qPCR. The time kinetics for antigen are shown in red, and for viral loads (indicated in Ct values of RT-qPCR) in blue.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1795842/v1/f7802166ce6a0c2509a63ad4.jpg"},{"id":23470681,"identity":"cf376c06-a9c9-4eaf-97c5-dd6631181f3b","added_by":"auto","created_at":"2022-07-05 17:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":858356,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1795842/v1/8a4d3be6-bf01-4ce6-b742-e90880b0cc22.pdf"},{"id":23470679,"identity":"167b4896-db85-4bbb-9181-385c046c4cc3","added_by":"auto","created_at":"2022-07-05 17:10:20","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":32786,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-1795842/v1/ec92bab1b15346c0ec35243b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative detection of SARS-CoV-2 antigen: an effective approach for evaluating the infectivity of COVID- 19 convalescent patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally since the end of 2019. The emergence of SARS-CoV-2 extensively impacted healthcare worldwide, with over 500\u0026nbsp;million confirmed cases and nearly 6.5\u0026nbsp;million deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To date, the pandemic has not yet been under complete control due to the swift evolution of SARS-CoV-2. Omicron subvariant BA.2, one of the variant of concerns (VOCs), has now become dominant in many regions of the world, including this epidemic in Shanghai since late February, 2022 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It has been demonstrated that Omicron BA.2 has an increased transmissibility and immune escape capability when compared to other variants [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, valid markers that can be used for rapid and accurate diagnosis and monitoring of SARS-CoV-2 infection have become increasingly essential to healthcare strategies for effective COVID-19 management [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) is currently the gold standard and the most routinely used diagnostic testing method for COVID-19 due to its specificity and adequate sensitivity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but a long turnaround time and high cost limit its utility. While this nucleic acid-based method is sensitive enough to detect SARS-CoV-2 qualitatively, persistent nucleic acid positivity after symptom relief and disease recovery makes it challenging to determine the correct level of infection control measures during patient care [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Antigen-detecting lateral flow tests (LFTs) have been developed and introduced by the World Health Organization (WHO) to achieve high coverage and quick turnaround of testing [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. LFTs offer rapid results at low costs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], yet the performance of such tests is controversial. Studies suggest that the sensitivities for antigen-detecting LFTs are low, especially when the tests are applied to asymptomatic individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The ultra-sensitive chemiluminescent immunoassay (CLIA), which is quality-controlled and possessed of relative quick turnaround time in detecting SARS-CoV-2, is also one of the alternative approaches to RT-qPCR [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study evaluated the MAGLUMI\u0026reg; SARS-CoV-2 Ag assay in comparison to RT-qPCR and LFT, in 232 individual COVID-19 patients with a variety of comorbidities during the peak of COVID-19 Shanghai epidemics (April 1st - May 31st, 2022) in a tertiary hospital in Shanghai. We estimated the diagnostic performance of the MAGLUMI\u0026reg; SARS-CoV-2 Ag assay in rapid COVID-19 screening and innovatively investigated the antigen concentration to evaluate the infectivity of convalescent patients.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eMAGLUMI\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026reg;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SARS-CoV-2 Ag assay\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAutomated quantitative detection of the SARS-CoV-2 nucleocapsid antigen was performed using MAGLUMI\u003csup\u003e\u0026reg;\u003c/sup\u003e SARS-CoV-2 Ag chemiluminescent immunoassay (hereafter referred to as MAG-CLIA) on a MAGLUMI X8 analyzer. This method has been specifically developed for detecting and quantifying the SARS-CoV-2 nucleocapsid antigen in human nasopharyngeal and oropharyngeal swabs. In brief, pretreated nasopharyngeal or oropharyngeal swab samples, magnetic microbeads coated with anti-SARS-CoV-2 nucleocapsid protein monoclonal antibody, and N-(4-aminobutyl)-N-ethylisoluminol (ABEI) labeled with another anti-SARS-CoV-2 nucleocapsid protein monoclonal antibody are mixed and incubated thoroughly to form sandwich complexes. After precipitation in a magnetic field, the solid phase is washed and subsequently initiated for a chemiluminescent reaction. The light signal is measured by the specific instrument as relative light units (RLUs). The signal is proportional to the concentration of SARS-CoV-2 nucleocapsid protein present in the test sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical performance studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical performance of the\u0026nbsp;MAG-CLIA\u0026nbsp;was evaluated, including precision, limit of quantitation, linearity and analytical specificity. For detailed materials, please refer to the supplementary data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRT-qPCR and Antigen-detecting lateral flow test (LFT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2 nucleic acid tests were performed on a MA-6000 Real-Time Quantitative Thermal Cycler (Sansure Biotech Inc.) using the COVID-19 Coronavirus Real Time PCR Kit (Bioperfectus Technologies Co., Ltd.). Viral loads in respiratory specimens were estimated from cycle threshold (Ct) values.\u0026nbsp;Specimens were judged positive when the measured Ct value for either ORF1 gene or N gene was 40 or less. The Wondfo 2019-nCoV Antigen Test kit (Wondfo Biotech Co., Ltd.) was used as LFT in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design and Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted from April 1\u003csup\u003est\u003c/sup\u003e to May 31\u003csup\u003est\u003c/sup\u003e 2022 at the\u0026nbsp;Department of Laboratory\u0026nbsp;Medicine of Huashan Hospital affiliated to Fudan University, Shanghai, China.\u0026nbsp;The protocol of the current study was approved by the Huashan Hospital Institutional Review Board (HIRB) (NO. 2022-571).\u0026nbsp;Nasopharyngeal samples from 232 COVID-19 patients\u0026nbsp;(median age of 71 years with interquartile range (IQR) from 59 to 83) and from 477 healthy donors (median age of 46 years with IQR from 33 to 56) were included. Among 232 COVID-19 patients, 48 were asymptomatic, 145 mild, 32 moderate and 7 severe (Table 1).\u0026nbsp;There were 136 patients with comorbidity(ies), mainly including hypertension, diabetes, nephropathy, coronary heart disease, and cerebral infarction. The same specimen was used for RT-qPCR, and\u0026nbsp;MAG-CLIA\u0026nbsp;SARS-CoV-2 Ag assessment. All tests were performed within a maximum of 2 hours after sample collection. The LFTs were conducted at bedside immediately.\u0026nbsp;In longitudinal studies, we monitored the antigen concentrations, in parallel with RT-qPCR results, in 14 patients for up to 22 days. The study initiated from the first nucleic acid positive test and terminated after the test showed negative. In addition, the efficiency of antigen concentration in determining virus infectivity was evaluated by using Ct value 35 as a reference for virus infectivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analyses were performed using GraphPad Prism (version 9.00, GraphPad Software, La Jolla, CA, USA) and MedCalc statistical software (version 18.2.1, MedCalc Software Ltd., Ostend, Belgium). GraphPad Prism was used for regression analyses and for plotting the diagnostic performance of CLIA and LFT. Receiver operating characteristic (ROC) curve analyses were performed in MedCalc. The ROC curve was created by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. Youden index was used to estimate the best thresholds. Statistical significance of the difference between groups was performed using the Kruskal-Wallis test. The difference is statistically significant when \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAnalytical performance of MAGLUMI\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026reg;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SARS-CoV-2 Ag tests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe default unit for the analyte detected by the MAGLUMI\u003csup\u003e\u0026reg;\u003c/sup\u003e SARS-CoV-2 Ag tests was the manufacturer\u0026rsquo;s arbitrary unit (AU)/mL. A linear relationship between AU/mL and pg/mL was found (R\u003csup\u003e2\u003c/sup\u003e=0.9976) , and the conversion factor to obtain pg/mL is 1.592 (Figure 1A). The total imprecision of the CLIA quantitative assay was less than 8% (Supplemental Table 1), with the limit of quantitation (LoQ) at 0.399 pg/mL (Figure 1B) and linear range from 0.4 to 3184.00 pg/mL (Figure 1C). No interference was detected in 48 commercial virus forms and 10 common clinical virus specimens (Figure 1D). There is a remarkable linear correlation between the antigen concentration versus the infectious dose for five different SARS-CoV-2 VOCs, respectively (Figure 1E). For detailed results, please refer to the supplementary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of the subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNasopharyngeal samples from 709 individuals were included in this study. Table 1 reports the demographic characteristics of the study subjects. The majority of the patients had relatively high Ct values (32.76% for 30-35 Ct and 34.48% for 35-40 Ct) corresponding to low viral loads, while 35 of 232 (15.09%) patients had high viral loads with Ct \u0026lt; 25. The median antigen concentration in patients was 16.64 pg/mL (IQR 3.28-285.37) and in healthy individuals was negligible (less than 0.1pg/mL). Overall, the SARS-CoV-2 antigen levels of patients and that of healthy subjects differed significantly (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eDemographic, SARS-CoV-2 viral loads\u003csup\u003ea\u003c/sup\u003e, antigen concentrations and time from \u0026ndash;first positive test for COVID-19 patients and healthy individuals.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariate\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 patients (n=232)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy individuals (n=477)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e71 (59-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e69 (56-79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eGender (female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e116 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e233 (48.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eAntigen concentration\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e16.64 (3.28-285.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e\u0026lt; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eCt value for ORF1 gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e33.015 (28.19-36.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e\u0026gt; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eCt \u0026lt; 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e35 (15.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003e25\u0026le;Ct\u0026lt; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e39 (16.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003e30\u0026le;Ct\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e76 (32.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003e35\u0026le;Ct\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e80 (34.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eCt \u0026ge; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e2 (0.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e477 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eDays from first positive\u003c/p\u003e\n \u003cp\u003etest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e8 (5-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eSymptom-free\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e48 (20.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eMild symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e145 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eModerate symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e32 (13.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003eSevere symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e7 (3.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003ewith comorbidity(ies)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e136 (58.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.01023890784983%\"\u003e\n \u003cp\u003ewithout comorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003e96 (41.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.474402730375427%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.040955631399317%\"\u003e\n \u003cp\u003eNA\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e SARS-CoV-2 viral loads were estimated via cycle threshold (Ct) of ORF1 gene.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003e Continuous variables reported as median (interquatile range) and categorical variables reported as N (percentage).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec.\u003c/strong\u003e Antigen concentration is shown in pg/mL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed.\u003c/strong\u003e NA, not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA receiver operating characteristic (ROC) curve was plotted to determine the optimal cutoff value of the SARS-CoV-2 antigen, which allows the distinction of SARS-CoV-2 infection from healthy status (Figure 2A). By comparing the antigen results between SARS-CoV-2 positive patients (Ct value\u0026nbsp;\u0026le;\u0026nbsp;40) and healthy individuals, results provided an area under the ROC curve (AUC) of 0.987, with 95% confidence interval (CI) ranging from 0.976 to 0.994. Based on the Youden index calculation,\u0026nbsp;the sensitivity and the specificity of the test reached 95.7% (95% CI: 92.2%-97.9%) and 98.3% (95% CI: 96.7%-99.3%) respectively, when a cutoff of 0.640 pg/mL was used.\u003c/p\u003e\n\u003cp\u003eThe diagnostic performance of the assay was next evaluated by stratifying the results according to Ct values of RT-qPCR (Table 2). The performance of the Wondfo 2019-nCoV Antigen Test (an LFT) was evaluated in parallel (Figure 2B, 2C). Compared with RT-qPCR, the MAG-CLIA SARS-CoV-2 Ag test was positive and showed 100% concordance for samples with Ct \u0026lt; 33. For samples with Ct ranging from 33 to 40, the sensitivity of the test remained over 95% concordance. Contrarily, the sensitivity of the LFT decreased gradually and remarkably when Ct values increased. The LFT showed 87.8% sensitivity in detecting samples with Ct \u0026lt; 30. The sensitivity dropped to 66.4% for samples with Ct \u0026lt; 33, and to only 34.9% for samples with Ct \u0026le; 40.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The sensitivity of two antigen detection assay according to the Ct values from RT-qPCR.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"10.259179265658748%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCt value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"9.1792656587473%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(total)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"42.33261339092873%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAGLUMI\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026reg;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SARS-CoV-2 Ag assay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"38.22894168466523%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWondfo 2019-nCoV Antigen Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.71812080536913%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026gt; 0.64 pg/mL\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.71812080536913%\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026le; 0.64 pg/mL\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.046979865771814%\"\u003e\n \u003cp\u003e\u003cstrong\u003eaccumulated sensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.838926174496644%\"\u003e\n \u003cp\u003e\u003cstrong\u003en (positive)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.838926174496644%\"\u003e\n \u003cp\u003e\u003cstrong\u003en (negative)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.838926174496644%\"\u003e\n \u003cp\u003e\u003cstrong\u003eaccumulated sensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e87.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e78.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e71.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e66.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e99.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e58.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e98.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e52.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e97.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e45.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e96.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e42.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e95.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e38.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026lt; 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e95.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e36.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.27027027027027%\"\u003e\n \u003cp\u003e\u0026le; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.18918918918919%\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.27027027027027%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.72972972972973%\"\u003e\n \u003cp\u003e95.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.756756756756756%\"\u003e\n \u003cp\u003e34.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between SARS-CoV-2 antigen and Ct values\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMAG-CLIA targets for the N-terminal domain of N protein in SARS-CoV-2, which wraps coronavirus RNA through noncovalent bonds to form the nucleocapsid. This should result in a significant positive correlation between viral load and antigen concentration. Therefore, we examined correlations between SARS-CoV-2 antigen concentration and viral load determined by Ct values of RT-qPCR. Indeed, a significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001) linear inverse relationship between the Ct values and log\u003csub\u003e10\u003c/sub\u003e antigen levels was observed (correlation coefficient R\u003csup\u003e2\u003c/sup\u003e = 0.747; Figure 3A). It is also notable that individual variations were remarkable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual time lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the dynamic performance of MAG-CLIA SARS-CoV-2 Ag in the viral load time courses, and to explore the influence of comorbidities on the time kinetics, we performed longitudinal studies in 14 COVID-19 positive participants (Figure 4). Among the 14 participants, 2 of them had bacterial infection (patient 9, 13), 2 with diabetes (patient 7, 10), 3 hypertension (patient 2, 4, 5), 3 nephropathy (patient 6, 8, 14), 1 fungal infection (patient 3) and the other 3 (patient 1, 11, 12) without any underlying disease. Figure 4 shows modelled SARS-CoV-2 viral RNA (as seen by Ct values of ORF1 gene) trajectories together with the viral antigen measured for individuals. As the antigen concentration decreased, a decrease of SARS-CoV-2 viral loads was observed over time, as indicated by the increase of Ct values. This trend was observed for the majority of the participants. Moreover, as exemplified in patients 6, 8, 13 and 14, the fluctuation of SARS-CoV-2 Ag was found to be closely and dynamically consistent with that of the viral nucleic acid loads in the time courses. Notably, the underlying comorbidities remarkably prolonged the time for both viral nucleic acid loads and antigen concentrations to return to negative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCOVID-19\u0026nbsp;transmission\u0026nbsp;with CLIA quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent studies observed a strong correlation between SARS-CoV-2 viral loads and transmission [14], and reported no cases of COVID-19 transmission with SARS-CoV-2 viral RNA loads \u0026lt;4 log\u003csub\u003e10\u003c/sub\u003e copies/mL [15]. Given that, Log\u003csub\u003e10\u003c/sub\u003e viral load was then estimated from the Ct value using the empirical formula 14.543-(Ct*0.3018) for the MA-6000 Real-Time Quantitative Thermal Cycler system, and the viral load of 4 log\u003csub\u003e10\u003c/sub\u003e copies/mL was verified to correspond to the 35 Ct value of the current system. This was in accordance with previous viral culture studies which showed that SARS-CoV-2 is no longer contagious for samples with Ct values \u0026ge; 35 [16,17]. The antigen concentration corresponding to 35 cycles was then calculated to be 8.71 pg/mL using the inverse linear regression model shown in Figure 3A. In addition, we also observed a consistent average antigen concentration of 8.82 pg/mL in nasopharyngeal swab samples of another 50 COVID-19 patients on the day when RT-qPCR Ct values first returned to above 35. Thus, we hypothesized that 8.82 pg/mL might be a promising antigen concentration in differentiating contagious patients from the recovering.\u003c/p\u003e\n\u003cp\u003eTo further assess the diagnostic ability of the ultra-sensitive SARS-CoV-2 antigen test with the concentration of 8.82 pg/mL in differentiating contagious patients from the recovering, an ROC curve was then plotted by classifying results into Ct values less than 35 and Ct values over 35 (and including 35) (Figure 3B). The ROC curve in identifying infectious patients showed an AUC of 0.921 (95% CI: 0.890-0.946). When 8.82 pg/mL was selected as the critical value, the\u0026nbsp;sensitivity and the specificity of the test were 84.5% (95% CI: 78.2%-89.5%) and 85.0% (95% CI: 79.6%-89.4%), respectively.\u003c/p\u003e\n\u003cp\u003eTo explore the time nodes of detection, we tracked the RT-qPCR results from over 1,000 COVID-19 cases between April 1\u003csup\u003est\u003c/sup\u003e and 10\u003csup\u003eth\u003c/sup\u003e May 2022 in our laboratory to describe the kinetic evidence of the SARS-CoV-2 Omicron subvariant BA.2 in Shanghai, China (Figure 3C). Overall, the viral load in nasopharynx reduced over time, as seen by the increase of Ct values. The Ct value reached 35 after an average of 9 days since the first observation of positive RT-qPCR test. Furthermore, SARS-CoV-2 was no longer detected in nasopharynx (Ct \u0026gt; 40) after 12 days.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe rapid variation and spread of SARS-CoV-2 has brought challenges to healthcare systems worldwide. There is an urgent need to identify appropriate strategies towards the detection and surveillance of SARS-CoV-2 to reduce community transmission.\u003c/p\u003e \u003cp\u003eIn the present study, we evaluated a CLIA-based SARS-CoV-2 antigen detection assay, the MAGLUMI\u0026reg; SARS-CoV-2 Ag test. Overall, our results show that the assay has excellent precision characteristics (total imprecision\u0026thinsp;\u0026lt;\u0026thinsp;8%), wide linear range (0.796\u0026ndash;63680 pg/mL when the maximum of 20-fold dilution is used) and high analytical specificity, with a short laboratory turnaround time (35 min). These allow the rapid and precise quantitative analysis of SARS-CoV-2 antigen in nasopharyngeal specimens.\u003c/p\u003e \u003cp\u003eRT-qPCR is currently the most routinely used diagnostic testing method for COVID-19. However, the long turnaround time of RT-qPCR leads to diagnostic delay. Moreover, Ct values obtained by RT-qPCR are inversely related to the relative viral RNA levels. They are not standardized to give quantitation of viral concentration and across RT-PCR platforms [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Antigen-based LFTs, which have been developed recently, can generate results within 20 min and outside of a laboratory. Studies have shown that the sensitivity of LFTs varies massively, ranging from 0 to 96% with an average of 56% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study also demonstrates that one of the LFTs, the Wondfo 2019-nCoV Antigen Test, has sensitivity of 35.2%-87.8% based on different RT-qPCR Ct values, which is in agreement with a recent study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCompared to the LFT assay, we have demonstrated the MAGLUMI\u0026reg; SARS-CoV-2 Ag assay to be a better tool for antigen detection. A good correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.747, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was found between SARS-CoV-2 antigen concentrations and Ct values, which is similar to the findings of multiple literature reports [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The sensitivity of the assay (cutoff value at 0.64 pg/mL) is 100% till Ct values of 33, and it still remains at a high sensitivity of 95.7% with the corresponding Ct values\u0026thinsp;\u0026le;\u0026thinsp;40. Additionally, it shows excellent screening performance among the present registered CLIA systems [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, the MAGLUMI\u0026reg; SARS-CoV-2 Ag test shortens turnaround time, minimizes the chance of missing positive cases and has a similar diagnostic window of SARS-CoV-2 infection as RT-qPCR. The data, therefore, imply that the MAG-CLIA SARS-CoV-2 Ag test may be used as a routine high-throughput test for rapid screening and diagnosis of COVID-19.\u003c/p\u003e \u003cp\u003eThe SARS-CoV-2 Ag was shown to be closely and dynamically consistent with the viral load time series in the longitudinal studies. In addition, some comorbidities remarkably prolonged the time for both viral nucleic acid loads and antigen concentrations to return to negative. These provide evidence that MAG-CLIA SARS-CoV-2 Ag assay could be a good surrogate of molecular testing for monitoring COVID-19 patients in regardless of underlying diseases.\u003c/p\u003e \u003cp\u003eSome recent studies have reported little chance of COVID-19 transmission with SARS-CoV-2 viral RNA loads\u0026thinsp;\u0026lt;\u0026thinsp;4 log\u003csub\u003e10\u003c/sub\u003e copies/mL [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and some viral culture studies have shown that SARS-CoV-2 is no longer contagious for samples with Ct values\u0026thinsp;\u0026ge;\u0026thinsp;35 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, it is suggested by the National Health Commission of the PRC in the Diagnosis and Treatment Protocol for COVID-19 (Trial Version 9) that isolation can be discontinued when a Ct value\u0026thinsp;\u0026ge;\u0026thinsp;35 is observed. Meanwhile, the 35 Ct value of the current system is verified to correspond to a viral load of 4 log\u003csub\u003e10\u003c/sub\u003e copies/mL. Therefore, patients with Ct values below 35 in this study are assumed to be infectious. By using the ROC analysis, we find that the antigen concentration of 8.82 pg/mL, which is related to the 35 Ct values of RT-qPCR, can be used to determine the contagiousness of SARS-CoV-2 with sensitivity and specificity of 84.5% and 85.0% respectively. Given that the majority of the COVID-19 cases obtain a Ct value\u0026thinsp;\u0026ge;\u0026thinsp;35 after 9 days since the first positive RT-qPCR result (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), we recommend to monitor antigen levels during the treatment, and determine isolation strategies on day 9 via the SARS-CoV-2 antigen concentration.\u003c/p\u003e \u003cp\u003eFurthermore, Pekosz et al have demonstrated that an antigen qualitative test has a higher positive predictive value than RT-qPCR when compared to viral contagiousness determined by viral culture [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Lai and Lam also suggested that Ct values from RT-qPCR are not standardized to give quantitation of viral concentration due to huge deviations between different platforms [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These provide evidence that CLIA SARS-CoV-2 antigen, which is traceable to international units, has an advantage in determining the risk of transmissibility over RT-qPCR. Due to the poor culturability of most clinical specimens, we did not perform viral culture to assess the infectivity of antigen positive samples. However, it is reasonable to believe that the value of the quantitative MAGLUMI\u0026reg; SARS-CoV-2 Ag (CLIA) test may be much higher than that of a nucleic acid test in judging the infectivity of the patients. Thus, the antigen concentration of 8.82 pg/mL could be a recommended threshold for judging the infectivity of the COVID-19 patients.\u003c/p\u003e \u003cp\u003eNevertheless, individual variations lead to inconsistencies between nucleic acids and antigens. This was exemplified in an immunocompromised patient (patient 3 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This 59-year-old male participant had been diagnosed with cryptococcal meningitis in May 2021, and since then has been receiving fluconazole antifungal therapy. He was found to be SARS-CoV-2 positive on April 26, 2022. We have closely monitored the Ct values and the antigen concentrations in his nasopharyngeal samples from his admission (April 28th ) until the nucleic acids were shown negative (May 20th ). On the 15th day since positive, his antigen concentration dropped down to the baseline (\u0026lt;\u0026thinsp;8.82 pg/mL), while the Ct value remained below 30. As such, even in the acute infection window, there might be no good correlation between antigenic dynamics and nucleic acid dynamics among these immunocompromised individuals. At this time, which criteria should be selected to assess infectivity remains controversial and needs to be further studied.\u003c/p\u003e \u003cp\u003eIn conclusion, in most cases, the MAGLUMI\u0026reg; SARS-CoV-2 Ag (CLIA) test correlates well with the Ct values determined by RT-qPCR and has shown very good diagnostic efficiency in the distinction of SARS-CoV-2 infection. Given that it facilitates low cost, scalable and rapid diagnosis, we suggest that this laboratory-based SARS-CoV-2 antigen test can be used as an important screening tool in fever outpatient services and in low- and middle-income countries. Furthermore, the change of SARS-CoV-2 antigen concentrations is associated with viral activity, providing good risk assessment of viral contagiousness. Thus, closely monitoring the antigen concentration could be a preferable approach for monitoring the disease and directing clinical isolation strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman subjects/informed consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were in accordance with the Helsinki Declaration. The protocol of the current study was approved by the Huashan Hospital Institutional Review Board (HIRB) (NO. 2022-571). Informed written consent was obtained from all enrolled patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ecknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Shanghai Municipal Science and technology major projects (grant no. HS2021SHZX001), three year action plan for the construction of Shanghai public health system (2020-2022) (grant no. GWV-10.1-XK4), and the project of Huashan Hospital North, Fudan University (grant no. HSBY2019020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that none of the work contained in this manuscript has been published in any language or is currently under consideration at any other journal, and there are no conflicts of interests to declare. All authors have contributed to, read, and approved this submitted manuscript in its current form.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. 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Clin Infect Dis. 2021;73:e2861-e2866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cid/ciaa1706\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciaa1706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, antigen detection, chemiluminescent immunoassay, COVID-19 diagnosis, COVID-19 monitoring","lastPublishedDoi":"10.21203/rs.3.rs-1795842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1795842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis study evaluated the quantitative detection of SARS-CoV-2 antigen by the MAGLUMI chemiluminescent immunoassay (MAG-CLIA) in COVID-19 patients during the peak of COVID-19 Shanghai epidemics in a tertiary hospital in Shanghai.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAnalytical performances of the MAG-CLIA were evaluated, including precision, limit of quantitation (LoQ), linearity and specificity. Nasopharyngeal specimens from 232 patients who were SARS-CoV-2 RT-qPCR positive and from 477 healthy donors were included to evaluate the diagnostic performance. The performance of the Wondfo antigen-detecting lateral flow test (LFT) was evaluated in parallel. The longitudinal studies were performed by monitoring antigen concentrations alongside with RT-qPCR results in 14 COVID-19 participants for up to 22 days. The critical antigen concentration in determining virus infectivity was evaluated at the reference cycle threshold (Ct) of 35.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eCOVID-19 patients were well-identified using an optimal threshold of 0.64 pg/mL antigen concentration, with sensitivity and specificity of 95.7% (95% CI: 92.2%-97.9%) and 98.3% (95% CI: 96.7%-99.3%), respectively, while the Wondfo LFT exhibited those of 34.9% (95% CI: 28.8%-41.4%) and 100% (95% CI: 99.23%-100%), respectively. Close dynamic consistence was observed between SARS-CoV-2 Ag and viral load time series in the longitudinal studies. The critical value of 8.82 pg/mL antigen showed adequate sensitivity and specificity in evaluating the infectivity of convalescent patients.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe MAG-CLIA SARS-CoV-2 Ag detection is an effective and alternative approach for rapid diagnosis and enables us to evaluate the infectivity of convalescent patients.\u003c/p\u003e","manuscriptTitle":"Quantitative detection of SARS-CoV-2 antigen: an effective approach for evaluating the infectivity of COVID- 19 convalescent patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-05 17:05:18","doi":"10.21203/rs.3.rs-1795842/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1713508a-83c2-4783-a35d-cf38c293f40f","owner":[],"postedDate":"July 5th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-05T17:05:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-05 17:05:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1795842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1795842","identity":"rs-1795842","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}