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Kinetics of SARS-CoV-2 specific IgM and IgG responses in COVID-19 patients
Open accessAbstract
The emerging COVID-19 caused by SARS-CoV-2 infection poses severe challenges to global public health. Serum antibody testing is becoming one of the critical methods for the diagnosis of COVID-19 patients. We investigated IgM and IgG responses against SARS-CoV-2 nucleocapsid (N) and spike (S) protein after symptom onset in the intensive care unit (ICU) and non-ICU patients. 130 blood samples from 38 COVID-19 patients were collected. The levels of IgM and IgG specific to N and S protein were detected by ELISA. A series of blood samples were collected along the disease course from the same patient, including 11 ICU patients and 27 non-ICU patients for longitudinal analysis. N and S specific IgM and IgG (N-IgM, N-IgG, S-IgM, S-IgG) in non-ICU patients increased after symptom onset. N-IgM and S-IgM in some non-ICU patients reached a peak in the second week, while N-IgG and S-IgG continued to increase in the third week. The combined detection of N and S specific IgM and IgG could identify up to 75% of SARS-CoV-2 infected patients in the first week. S-IgG was significantly higher in non-ICU patients than in ICU patients in the third week. In contrast, N-IgG was significantly higher in ICU patients than in non-ICU patients. The increase of S-IgG positively correlated with the decrease of C-reactive protein (CRP) in non-ICU patients. N and S specific IgM and IgG increased gradually after symptom onset and can be used for detection of SARS-CoV-2 infection. Analysis of the dynamics of S-IgG may help to predict prognosis.
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Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Self-citationChen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897
Pathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Virological assessment of hospitalized patients with COVID-2019
Wölfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Müller, M. A., Niemeyer, D., Jones, T. C., Vollmar, P., Rothe, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2196-x · Cited by 6,661
Structure, Function, and Evolution of Coronavirus Spike Proteins
Li, F. (2016) · Annual Review of Virology · doi:10.1146/annurev-virology-110615-042301 · Cited by 2,740
Self-citationAntibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019
Zhao, J., Yuan, Q., Wang, H., Liu, W., Liao, X., Su, Y., Wang, X., Yuan, J., Li, T., Li, J., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa344 · Cited by 2,607
Coronavirus Spike Proteins in Viral Entry and Pathogenesis
Gallagher, T. M., & Buchmeier, M. J. (2001) · Virology · doi:10.1006/viro.2000.0757 · Cited by 658
The role of interleukin‐6 in monitoring severe case of coronavirus disease 2019
Liu, T., Zhang, J., Yang, Y., Ma, H., Li, Z., Zhang, J., Cheng, J., Zhang, X., Zhao, Y., Xia, Z., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012421 · Cited by 498
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Tan, A. T., Linster, M., Tan, C. W., Le Bert, N., Chia, W. N., Kunasegaran, K., Zhuang, Y., Tham, C. Y. L., Chia, A., Smith, G. J. D., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108728 · Cited by 740
Self-citationCarter, M. J., Fish, M., Jennings, A., Doores, K. J., Wellman, P., Seow, J., Acors, S., Graham, C., Timms, E., Kenny, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1054-6 · Cited by 419
La Marca, A., Capuzzo, M., Paglia, T., Roli, L., Trenti, T., & Nelson, S. M. (2020) · Reproductive BioMedicine Online · doi:10.1016/j.rbmo.2020.06.001 · Cited by 389
Bai, Z., Cao, Y., Liu, W., & Li, J. (2021) · Viruses · doi:10.3390/v13061115 · Cited by 348
Metadata
- Authors
- Sun, B., Feng, Y., Mo, X., Zheng, P., Wang, Q., Li, P., Peng, P., Liu, X., Chen, Z., Huang, H., Zhang, F., Luo, W., Niu, X., Hu, P., Wang, L., Peng, H., Huang, Z., Feng, L., Li, F., Zhang, F., Li, F., Zhong, N., & Chen, L.
- Venue
- Emerging Microbes & Infections
- Publisher
- Informa UK Limited
- Published
- 2020-01
- Volume
- 9
- Issue
- 1
- Pages
- 940-948
- ISSN
- 2222-1751 (ISSN-L)
- OpenAlex
- W3023770076
- Cited by
- 473
- References
- 10
- FWCI
- 12.77
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sun, B., Feng, Y., Mo, X., Zheng, P., Wang, Q., Li, P., Peng, P., Liu, X., Chen, Z., Huang, H., Zhang, F., Luo, W., Niu, X., Hu, P., Wang, L., Peng, H., Huang, Z., Feng, L., Li, F., . . . Chen, L. (2020). Kinetics of SARS-CoV-2 specific IgM and IgG responses in COVID-19 patients. Emerging Microbes & Infections. https://doi.org/10.1080/22221751.2020.1762515
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