The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Diagnostic methods and potential portable biosensors for coronavirus disease 2019
Open accessAbstract
Timely detection and diagnosis are urgently needed to guide epidemiological measures, infection control, antiviral treatment, and vaccine research. In this review, biomarkers/indicators for diagnosis of coronavirus disease 2019 (COVID-19) or detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the environment are summarized and discussed. It is concluded that the detection methods targeting antibodies are not suitable for screening of early and asymptomatic cases since most patients had an antibody response at about 10 days after onset of symptoms. However, antibody detection methods can be combined with quantitative real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) to significantly improve the sensitivity and specificity of diagnosis, and boost vaccine research. Fast, sensitive and accurate detection methods targeting antigens need to be developed urgently. Various specimens for diagnosis or detection are compared and analyzed. Among them, deep throat saliva and induced sputum are desired for RT-qPCR test or other early detection technologies. Chest computerized tomography (CT) scan, RT-qPCR, lateral flow immunochromatographic strip (LFICS) for diagnosis of COVID-19 are summarized and compared. Specially, potential electrochemical (EC) biosensor, surface enhanced Raman scattering (SERS)-based biosensor, field-effect transistor (FET)-based biosensor, surface plasmon resonance (SPR)-based biosensor and artificial intelligence (AI) assisted diagnosis of COVID-19 are emphasized. Finally, some commercialized portable detection device, current challenges and future directions are discussed.
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
Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2004973 · Cited by 9,951
Guo (2020) · doi:10.1186/s40779-020-00240-0 · Cited by 4,735
Antibody 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
Ong, S. W. X., Tan, Y. K., Chia, P. Y., Lee, T. H., Ng, O. T., Wong, M. S. Y., & Marimuthu, K. (2020) · JAMA · doi:10.1001/jama.2020.3227 · Cited by 2,140
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020) · Radiology · doi:10.1148/radiol.2020200905 · Cited by 1,927
Hayden, F. G., Treanor, J. J., Fritz, R. S., Lobo, M., Betts, R. F., Miller, M., Kinnersley, N., Mills, R. G., Ward, P., & Straus, S. E. (1999) · JAMA · doi:10.1001/jama.282.13.1240 · Cited by 550
SARS-CoV-2 proteome microarray for global profiling of COVID-19 specific IgG and IgM responses
Jiang, H.-W., Li, Y., Zhang, H.-N., Wang, W., Yang, X., Qi, H., Li, H., Men, D., Zhou, J., & Tao, S.-C. (2020) · Nature Communications · doi:10.1038/s41467-020-17488-8 · Cited by 329
Cited by
Li, Z., Yi, Y., Luo, X., Xiong, N., Liu, Y., Li, S., Sun, R., Wang, Y., Hu, B., Chen, W., et al. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25727 · Cited by 1,771
Metadata
- Authors
- Cui, F., & Zhou, H. S.
- Venue
- Biosensors and Bioelectronics
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 165
- Pages
- 112349
- ISSN
- 0956-5663 (ISSN-L)
- OpenAlex
- W3029841422
- Cited by
- 373
- References
- 7
- FWCI
- 9.08
- 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: Diagnostic methods and potential portable biosensors for coronavirus disease 2019. https://app.cochat.ai/papers/10.1016/j.bios.2020.112349
- The paper
- Cui, F., & Zhou, H. S. (2020). Diagnostic methods and potential portable biosensors for coronavirus disease 2019. Biosensors and Bioelectronics. https://doi.org/10.1016/j.bios.2020.112349
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.
