The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
C-reactive protein levels in the early stage of COVID-19
Open accessAbstract
BACKGROUND: COVID-19 is a new infectious disease, for which there is currently no treatment. It is therefore necessary to explore biomarkers to determine the extent of lung lesions and disease severity. OBJECTIVE: We aimed to assess the usefulness of CRP levels in the early stage of COVID-19 and to correlate them with lung lesions and severe presentation. METHODS: Confirmed cases of COVID-19 were selected at the Fever Unit in two regions of Guizhou, China. On admission CRP levels were collected, and the diameter of the largest lung lesion was measured in the most severe lung lesion by lung CT scan. Differences in the diameter and CRP levels were compared in the following groups of patients: mild group, moderate group, severe group, and critical group. RESULT: CRP levels and the diameter of the largest lung lesion in the moderate group were higher than those in the mild group (Mann-Whitney test=-2.647, -2.171, P˂0.05), those in the severe group were higher than those in the moderate group (Mann-Whitney test=0.693, -2.177, P˂0.05), and those in the critical group were higher than those in the severe group (Mann-Whitney test=-0.068, -1.549, P˂0.05). The difference was statistically significant. CRP levels were positively correlated with the diameter of lung lesion and severe presentation (correlation coefficient=0.873, 0.734, P˂0.001). CONCLUSION: In the early stage of COVID-19 CRP levels were positively correlated with lung lesions and could reflect disease severity.
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
Chen, 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
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
A novel coronavirus outbreak of global health concern
Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30185-9 · Cited by 7,700
Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19)
Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., Zheng, D., Wang, J., Hesketh, R. L., Yang, L., et al. (2020) · Radiology · doi:10.1148/radiol.2020200370 · Cited by 2,856
CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., et al. (2020) · Radiology · doi:10.1148/radiol.2020200230 · Cited by 2,751
Xu, X., Chen, P., Wang, J., Feng, J., Zhou, H., Li, X., Zhong, W., & Hao, P. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1637-5 · Cited by 2,205
Therapeutic options for the 2019 novel coronavirus (2019-nCoV)
Li, G., & De Clercq, E. (2020) · Nature Reviews Drug Discovery · doi:10.1038/d41573-020-00016-0 · Cited by 1,729
The novel Chinese coronavirus (2019-nCoV) infections: Challenges for fighting the storm.
Bassetti, M., Vena, A., & Giacobbe, D. R. (2020) · European Journal of Clinical Investigation · doi:10.1111/eci.13209 · Cited by 423
Kruse, R. L. (2020) · F1000Research · doi:10.12688/f1000research.22211.1 · Cited by 311
Lin, C., Ding, Y., Xie, B., Sun, Z., Li, X., Chen, Z., & Niu, M. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.02.008 · Cited by 148
Cited by
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
COVID-19-neutralizing antibodies predict disease severity and survival
Garcia-Beltran, W. F., Lam, E. C., Astudillo, M. G., Yang, D., Miller, T. E., Feldman, J., Hauser, B. M., Caradonna, T. M., Clayton, K. L., Nitido, A. D., et al. (2021) · Cell · doi:10.1016/j.cell.2020.12.015 · Cited by 722
The role of biomarkers in diagnosis of COVID-19 – A systematic review
Kermali, M., Khalsa, R. K., Pillai, K., Ismail, Z., & Harky, A. (2020) · Life Sciences · doi:10.1016/j.lfs.2020.117788 · Cited by 687
Doglietto, F., Vezzoli, M., Gheza, F., Lussardi, G. L., Domenicucci, M., Vecchiarelli, L., Zanin, L., Saraceno, G., Signorini, L., Panciani, P. P., et al. (2020) · JAMA Surgery · doi:10.1001/jamasurg.2020.2713 · Cited by 356
Emerging Telemedicine Tools for Remote COVID-19 Diagnosis, Monitoring, and Management
Lukas, H., Xu, C., Yu, Y., & Gao, W. (2020) · ACS Nano · doi:10.1021/acsnano.0c08494 · Cited by 265
Metadata
- Authors
- Wang, L.
- Venue
- Médecine et Maladies Infectieuses
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 50
- Issue
- 4
- Pages
- 332-334
- ISSN
- 0399-077X (ISSN-L)
- OpenAlex
- W3014231150
- Cited by
- 686
- References
- 17
- FWCI
- 18.86
- Type
- journal article
- Language
- fr
- 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: C-reactive protein levels in the early stage of COVID-19. https://app.cochat.ai/papers/10.1016/j.medmal.2020.03.007
- The paper
- Wang, L. (2020). C-reactive protein levels in the early stage of COVID-19. Médecine et Maladies Infectieuses. https://doi.org/10.1016/j.medmal.2020.03.007
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.