Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,105

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Evaluation of coronavirus in tears and conjunctival secretions of patients with SARS‐CoV‐2 infection

Open access
Xia, Jianhua, Tong, Jianping, Liu, Mengyun, Shen, Ye, Guo, Dongyu·2020·Journal of Medical Virology

Abstract

Objective: This study aimed to assess the presence of novel coronavirus in tears and conjunctival secretions of SARS–CoV‐2‐infected patients. Methods: A prospective interventional case series study was performed, and 30 confirmed novel coronavirus pneumonia (NCP) patients were selected at the First Affiliated Hospital of Zhejiang University from 26 January 2020 to 9 February 2020. At an interval of 2 to 3 days, tear and conjunctival secretions were collected twice with disposable sampling swabs for reverse‐transcription polymerase chain reaction (RT‐PCR) assay. Results: Twenty‐one common‐type and nine severe‐type NCP patients were enrolled. Two samples of tear and conjunctival secretions were obtained from the only one patient with conjunctivitis yielded positive RT‐PCR results. Fifty‐eight samples from other patents were all negative. Conclusion: We speculate that SARS‐CoV‐2 may be detected in the tears and conjunctival secretions in NCP patients with conjunctivitis.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/jmv.25725

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 2 references

Cited by

See all 1,105

Metadata

Authors
Xia, J., Tong, J., Liu, M., Shen, Y., & Guo, D.
Venue
Journal of Medical Virology
Publisher
Wiley
Published
2020-03-12
Volume
92
Issue
6
Pages
589-594
ISSN
  • 0146-6615 (ISSN-L)
  • 1096-9071
OpenAlex
W3007568630
Cited by
1,105
References
2
FWCI
179.37
Type
journal article
Language
en
Open access
green

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: Evaluation of coronavirus in tears and conjunctival secretions of patients with SARS‐CoV‐2 infection. https://app.cochat.ai/papers/10.1002/jmv.25725
The paper
Xia, J., Tong, J., Liu, M., Shen, Y., & Guo, D. (2020). Evaluation of coronavirus in tears and conjunctival secretions of patients with SARS‐CoV‐2 infection. Journal of Medical Virology. https://doi.org/10.1002/jmv.25725

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.