The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Peptide-Based Magnetic Chemiluminescence Enzyme Immunoassay for Serological Diagnosis of Coronavirus Disease 2019
Open accessAbstract
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel β-coronavirus, causes severe pneumonia and has spread throughout the globe rapidly. The disease associated with SARS-CoV-2 infection is named coronavirus disease 2019 (COVID-19). To date, real-time reverse-transcription polymerase chain reaction (RT-PCR) is the only test able to confirm this infection. However, the accuracy of RT-PCR depends on several factors; variations in these factors might significantly lower the sensitivity of detection. Methods: In this study, we developed a peptide-based luminescent immunoassay that detected immunoglobulin (Ig)G and IgM. The assay cutoff value was determined by evaluating the sera from healthy and infected patients for pathogens other than SARS-CoV-2. Results: To evaluate assay performance, we detected IgG and IgM in the sera from confirmed patients. The positive rate of IgG and IgM was 71.4% and 57.2%, respectively. Conclusions: Therefore, combining our immunoassay with real-time RT-PCR might enhance the diagnostic accuracy of COVID-19.
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
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
Self-citationAntibody responses to SARS-CoV-2 in patients with COVID-19
Long, Q.-X., Liu, B.-Z., Deng, H.-J., Wu, G.-C., Deng, K., Chen, Y.-K., Liao, P., Qiu, J.-F., Lin, Y., Cai, X.-F., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0897-1 · Cited by 2,876
Self-citation
Cited by
Diagnosing COVID-19: The Disease and Tools for Detection
Udugama, B., Kadhiresan, P., Kozlowski, H. N., Malekjahani, A., Osborne, M., Li, V. Y. C., Chen, H., Mubareka, S., Gubbay, J. B., & Chan, W. C. W. (2020) · ACS Nano · doi:10.1021/acsnano.0c02624 · Cited by 1,754
Immunology of COVID-19: Current State of the Science
Vabret, N., Britton, G. J., Gruber, C., Hegde, S., Kim, J., Kuksin, M., Levantovsky, R., Malle, L., Moreira, A., Park, M. D., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.05.002 · Cited by 1,623
Assay Techniques and Test Development for COVID-19 Diagnosis
Carter, L. J., Garner, L. V., Smoot, J. W., Li, Y., Zhou, Q., Saveson, C. J., Sasso, J. M., Gregg, A. C., Soares, D. J., Beskid, T. R., et al. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00501 · Cited by 985
Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis
Lisboa Bastos, M., Tavaziva, G., Abidi, S. K., Campbell, J. R., Haraoui, L.-P., Johnston, J. C., Lan, Z., Law, S., MacLean, E., Trajman, A., et al. (2020) · BMJ · doi:10.1136/bmj.m2516 · Cited by 832
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
Metadata
- Authors
- Cai, X.-F., Chen, J., li Hu, J., Long, Q.-X., Deng, H.-J., Liu, P., Fan, K., Liao, P., Liu, B.-Z., Wu, G.-C., Chen, Y.-K., Li, Z.-J., Wang, K., Zhang, X.-L., Tian, W.-G., Xiang, J.-L., Du, H.-X., Wang, J., Hu, Y., Tang, N., Lin, Y., Ren, J.-H., Huang, L.-Y., Wei, J., Gan, C.-Y., Chen, Y.-M., Gao, Q.-Z., Chen, A.-M., He, C.-L., Wang, D.-X., Hu, P., Zhou, F.-C., Huang, A.-L., & Wang, D.-Q.
- Venue
- The Journal of Infectious Diseases
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-08
- Volume
- 222
- Issue
- 2
- Pages
- 189-193
- ISSN
- 0022-1899 (ISSN-L)
- 1537-6613
- OpenAlex
- W3021186559
- Cited by
- 186
- References
- 2
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: A Peptide-Based Magnetic Chemiluminescence Enzyme Immunoassay for Serological Diagnosis of Coronavirus Disease 2019. https://app.cochat.ai/papers/10.1093/infdis/jiaa243
- The paper
- Cai, X.-F., Chen, J., li Hu, J., Long, Q.-X., Deng, H.-J., Liu, P., Fan, K., Liao, P., Liu, B.-Z., Wu, G.-C., Chen, Y.-K., Li, Z.-J., Wang, K., Zhang, X.-L., Tian, W.-G., Xiang, J.-L., Du, H.-X., Wang, J., Hu, Y., . . . Wang, D.-Q. (2020). A Peptide-Based Magnetic Chemiluminescence Enzyme Immunoassay for Serological Diagnosis of Coronavirus Disease 2019. The Journal of Infectious Diseases. https://doi.org/10.1093/infdis/jiaa243
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.
