The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Rapid and Sensitive Detection of anti-SARS-CoV-2 IgG, Using Lanthanide-Doped Nanoparticles-Based Lateral Flow Immunoassay
Open accessAbstract
The outbreak of 2019 coronavirus disease (COVID-19) has been a challenge for hospital laboratories because of the huge number of samples that must be tested for the presence of the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Simple and rapid immunodiagnostic methods are urgently needed to identify positive cases. Here we report the development of a rapid and sensitive lateral flow immunoassay (LFIA) that uses lanthanide-doped polysterene nanoparticles (LNPs) to detect anti-SARV-CoV-2 IgG in human serum. A recombinant nucleocapsid phosphoprotein of SARS-CoV-2 was dispensed onto a nitrocellulose membrane to capture specific IgG. Mouse anti-human IgG antibody was labeled with self-assembled LNPs that served as a fluorescent reporter. A 100-μL aliquot of serum samples (1:1000 dilution) was used for this assay and the whole detection process took 10 min. The results of the validation experiment met the requirements for clinical diagnostic reagents. A value of 0.0666 was defined as the cutoff value by assaying 51 normal samples. We tested 7 samples that were positive by reverse-transcription (RT-)PCR and 12 that were negative but clinically suspicious for the presence of anti-SARS-CoV-2 IgG. One of the negative samples was determined to be SARS-CoV-2 IgG positive, while the results for the other samples were consistent with those obtained by RT-PCR. Thus, this assay can achieve rapid and sensitive detection of anti-SARS-CoV-2 IgG in human serum and allow positive identification in suspicious cases; it can also be useful for monitoring the progression COVID-19 and evaluating patients' response to treatment.
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 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-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
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-citationXie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020) · Radiology · doi:10.1148/radiol.2020200343 · Cited by 1,960
Cited by
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
Jain, S., Nehra, M., Kumar, R., Dilbaghi, N., Hu, T., Kumar, S., Kaushik, A., & Li, C.-Z. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113074 · Cited by 357
Optical biosensors: A decade in review
Singh, A. K., Mittal, S., Das, M., Saharia, A., & Tiwari, M. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.12.040 · Cited by 292
Liu, H., Dai, E., Xiao, R., Zhou, Z., Zhang, M., Bai, Z., Shao, Y., Qi, K., Tu, J., Wang, C., et al. (2021) · Sensors and Actuators B: Chemical · doi:10.1016/j.snb.2020.129196 · Cited by 267
Metadata
- Authors
- Chen, Z., Zhang, Z., Zhai, X., Li, Y., Lin, L., Zhao, H., Bian, L., Li, P., Yu, L., Wu, Y., & Lin, G.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-04-23
- Volume
- 92
- Issue
- 10
- Pages
- 7226-7231
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W3018469982
- Cited by
- 428
- References
- 4
- FWCI
- 11.14
- 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: Rapid and Sensitive Detection of anti-SARS-CoV-2 IgG, Using Lanthanide-Doped Nanoparticles-Based Lateral Flow Immunoassay. https://app.cochat.ai/papers/10.1021/acs.analchem.0c00784
- The paper
- Chen, Z., Zhang, Z., Zhai, X., Li, Y., Lin, L., Zhao, H., Bian, L., Li, P., Yu, L., Wu, Y., & Lin, G. (2020). Rapid and Sensitive Detection of anti-SARS-CoV-2 IgG, Using Lanthanide-Doped Nanoparticles-Based Lateral Flow Immunoassay. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.0c00784
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.