The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Correlation analysis between disease severity and inflammation-related parameters in patients with COVID-19: a retrospective study
Open accessPublished version of a preprint
Abstract
Background: COVID-19 is highly contagious, and the crude mortality rate could reach 49% in critical patients. Inflammation concerns on disease progression. This study analyzed blood inflammation indicators among mild, severe and critical patients, helping to identify severe or critical patients early. Methods: In this cross-sectional study, 100 patients were included and divided into mild, severe or critical groups according to disease condition. Correlation of peripheral blood inflammation-related indicators with disease criticality was analyzed. Cut-off values for critically ill patients were speculated through the ROC curve. Results: Significantly, disease severity was associated with age ( R = -0.564, P < 0.001), interleukin-2 receptor (IL2R) ( R = -0.534, P < 0.001), interleukin-6 (IL-6) ( R = -0.535, P < 0.001), interleukin-8 (IL-8) ( R = -0.308, P < 0.001), interleukin-10 (IL-10) ( R = -0.422, P < 0.001), tumor necrosis factor α (TNFα) ( R = -0.322, P < 0.001), C-reactive protein (CRP) ( R = -0.604, P < 0.001), ferroprotein ( R = -0.508, P < 0.001), procalcitonin ( R = -0.650, P < 0.001), white cell counts (WBC) ( R = -0.54, P < 0.001), lymphocyte counts (LC) ( R = 0.56, P < 0.001), neutrophil count (NC) ( R = -0.585, P < 0.001) and eosinophil counts (EC) ( R = 0.299, P < 0.001). With IL2R > 793.5 U/mL or CRP > 30.7 ng/mL, the progress of COVID-19 to critical stage should be closely observed and possibly prevented. Conclusions: Inflammation is closely related to severity of COVID-19, and IL-6 and TNFα might be promising therapeutic targets.
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
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
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
Self-citationLa certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30251-8 · Cited by 11,907
Self-citationA new coronavirus associated with human respiratory disease in China
Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2008-3 · Cited by 11,688
Self-citationPathological 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
(2020) · PubMed · doi:10.3760/cma.j.issn.0254-6450.2020.02.003 · Cited by 3,323
Xu, X.-W., Wu, X.-X., Jiang, X.-G., Xu, K.-J., Ying, L.-J., Ma, C.-L., Li, S.-B., Wang, H.-Y., Zhang, S., Gao, H.-N., et al. (2020) · BMJ · doi:10.1136/bmj.m606 · Cited by 2,046
Liu, J., Li, S., Liu, J., Liang, B., Wang, X., Wang, H., Li, W., Tong, Q., Yi, J., Zhao, L., et al. (2020) · eBioMedicine · doi:10.1016/j.ebiom.2020.102763 · Cited by 1,702
Cited by
Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages
Merad, M., & Martin, J. C. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0331-4 · Cited by 2,472
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
SARS-CoV-2–triggered neutrophil extracellular traps mediate COVID-19 pathology
Veras, F. P., Pontelli, M. C., Silva, C. M., Toller-Kawahisa, J. E., de Lima, M., Nascimento, D. C., Schneider, A. H., Caetité, D., Tavares, L. A., Paiva, I. M., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20201129 · Cited by 933
The hypercoagulable state in COVID-19: Incidence, pathophysiology, and management
Abou-Ismail, M. Y., Diamond, A., Kapoor, S., Arafah, Y., & Nayak, L. (2020) · Thrombosis Research · doi:10.1016/j.thromres.2020.06.029 · Cited by 694
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
Metadata
- Authors
- Gong, J., Dong, H., Xia, Q.-S., Huang, Z.-Y., Wang, D.-K., Zhao, Y., Liu, W.-H., Tu, S.-H., Zhang, M.-M., Wang, Q., & Lu, F.-E.
- Venue
- BMC Infectious Diseases
- Published
- 2020-12
- Volume
- 20
- Issue
- 1
- Pages
- 963-963
- ISSN
- 1471-2334 (ISSN-L)
- OpenAlex
- W3117854652
- Cited by
- 435
- References
- 12
- FWCI
- 11.89
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0/ (tdm)
- creativecommons.org/licenses/by/4.0/ (vor, 20 d)
- Funders
- National Natural Science Foundation of China (81904158)
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: Correlation analysis between disease severity and inflammation-related parameters in patients with COVID-19: a retrospective study. https://app.cochat.ai/papers/10.1186/s12879-020-05681-5
- The paper
- Gong, J., Dong, H., Xia, Q.-S., Huang, Z.-Y., Wang, D.-K., Zhao, Y., Liu, W.-H., Tu, S.-H., Zhang, M.-M., Wang, Q., & Lu, F.-E. (2020). Correlation analysis between disease severity and inflammation-related parameters in patients with COVID-19: a retrospective study. BMC Infectious Diseases. https://doi.org/10.1186/s12879-020-05681-5
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.
