The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Antibody Detection and Dynamic Characteristics in Patients With Coronavirus Disease 2019
Open accessAbstract
Background: The coronavirus disease 2019 (COVID-19), caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been rapidly spreading nationwide and abroad. A serologic test to identify antibody dynamics and response to SARS-CoV-2 was developed. Methods: The antibodies against SARS-CoV-2 were detected by an enzyme-linked immunosorbent assay based on the recombinant nucleocapsid protein of SARS-CoV-2 in patients with confirmed or suspected COVID-19 at 3–40 days after symptom onset. The gold standard for COVID-19 diagnosis was nucleic acid testing for SARS-CoV-2 by real-time reverse-transcription polymerase chain reaction (rRT-PCR). The serodiagnostic power of the specific immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies against SARS-CoV-2 was investigated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and consistency rate. Results: The seroconversion of specific IgM and IgG antibodies were observed as early as the fourth day after symptom onset. In the patients with confirmed COVID-19, sensitivity, specificity, PPV, NPV, and consistency rate of IgM were 77.3% (51/66), 100%, 100%, 80.0%, and 88.1%, respectively, and those of IgG were 83.3% (55/66), 95.0%, 94.8%, 83.8%, and 88.9%. In patients with suspected COVID-19, sensitivity, specificity, PPV, NPV, and consistency rate of IgM were 87.5% (21/24), 100%, 100%, 95.2%, and 96.4%, respectively, and those of IgG were 70.8% (17/24), 96.6%, 85.0%, 89.1%, and 88.1%. Both antibodies performed well in serodiagnosis for COVID-19 and rely on great specificity. Conclusions: The antibodies against SARS-CoV-2 can be detected in the middle and later stages of the illness. Antibody detection may play an important role in the diagnosis of COVID-19 as a complementary approach to viral nucleic acid assays.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citationA novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Self-citation
Cited by
Neurological associations of COVID-19
Ellul, M. A., Benjamin, L., Singh, B., Lant, S., Michael, B. D., Easton, A., Kneen, R., Defres, S., Sejvar, J., & Solomon, T. (2020) · The Lancet Neurology · doi:10.1016/s1474-4422(20)30221-0 · Cited by 2,156
Single-cell landscape of immunological responses in patients with COVID-19
Zhang, J.-Y., Wang, X.-M., Xing, X., Xu, Z., Zhang, C., Song, J.-W., Fan, X., Xia, P., Fu, J.-L., Wang, S.-Y., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0762-x · Cited by 642
Self-citationCOVID-19 vaccine-readiness for anti-CD20-depleting therapy in autoimmune diseases
Baker, D., Roberts, C. A. K., Pryce, G., Kang, A. S., Marta, M., Reyes, S., Schmierer, K., Giovannoni, G., & Amor, S. (2020) · Clinical and Experimental Immunology · doi:10.1111/cei.13495 · Cited by 192
Reorganisation of Faecal Microbiota Transplant Services During the COVID-19 Pandemic
Ianiro, G., Mullish, B. H., Kelly, C. R., Kassam, Z., Kuijper, E. J., Ng, S. C., Iqbal, T. H., Allegretti, J. R., Bibbò, S., Sokol, H., et al. (2020) · Gut · doi:10.1136/gutjnl-2020-321829 · Cited by 147
Pathela, P., Crawley, A., Weiss, D., Maldin, B., Cornell, J., Purdin, J., Schumacher, P. K., Marovich, S., Li, J., Daskalakis, D., et al. (2021) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiab200 · Cited by 52
Metadata
- Authors
- Xiang, F., Wang, X., He, X., Peng, Z., Yang, B., Zhang, J., Zhou, Q., Ye, H., Ma, Y., Li, H., Wei, X., Cai, P., & Ma, W.-L.
- Venue
- Clinical Infectious Diseases
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-04-19
- Volume
- 71
- Issue
- 8
- Pages
- 1930-1934
- ISSN
- 1058-4838 (ISSN-L)
- 1537-6591
- OpenAlex
- W3016611530
- Cited by
- 535
- References
- 2
- FWCI
- 15.36
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Antibody Detection and Dynamic Characteristics in Patients With Coronavirus Disease 2019. https://app.cochat.ai/papers/10.1093/cid/ciaa461
- The paper
- Xiang, F., Wang, X., He, X., Peng, Z., Yang, B., Zhang, J., Zhou, Q., Ye, H., Ma, Y., Li, H., Wei, X., Cai, P., & Ma, W.-L. (2020). Antibody Detection and Dynamic Characteristics in Patients With Coronavirus Disease 2019. Clinical Infectious Diseases. https://doi.org/10.1093/cid/ciaa461
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.