Evidence map›Paper›PMID 40978729›Full record

ArticleFrontiers in medicine2025

Microbial profile and antimicrobial resistance patterns of ocular infection pre/during and post the COVID-19 pandemic.

Min Du, Ying Zhang, Tian Xin, Yan Liu, Shumei Zhang

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Min DuDepartment of Hospital Infection Management, Jinan Second People's Hospital, Jinan, China.
Ying ZhangDepartment of Clinical Laboratory, Shandong Second Provincial General Hospital, Jinan, China.
Tian XinDepartment of Operating Room, Jinan Second People's Hospital, Jinan, China.
Yan LiuDepartment of Ophthalmology, Jinan Second People's Hospital, Jinan, China.
Shumei ZhangDepartment of Ophthalmology, Jinan Second People's Hospital, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This research investigated the distribution characteristics and antimicrobial resistance patterns of pathogenic microorganisms in patients with ocular infection before/during and post the coronavirus disease 2019 (COVID-19) pandemic. Methods: This retrospective study, conducted at the Second People's Hospital of Jinan, Shandong, China, analyzed the microorganism cultures of specimens (eye secretions, anterior chamber fluid, and vitreous body) obtained from patients with ocular infection (including ocular trauma, endophthalmitis, keratitis, conjunctivitis, dacryocystitis, and blepharitis) pre/during (from May 2019 to January 2023, A group) and after (from February 2023 to November 2024, B group) the COVID-19 pandemic. The microorganism species was analyzed using a microbial identification instrument, and antimicrobial susceptibility testing was carried out using Kirby-Bauer (K-B) disk diffusion method. Results: 465 and 319 strains of pathogenic microorganisms were obtained from specimens in A and B groups, respectively. The isolates of Conclusion: Changes in resistance patterns were observed after the COVID-19 pandemic, which might be influenced by relaxation of infection prevention and control (IPC) measures. These alterations might be also attributed to other factors, such us changes over time or the use of antibiotics. And further investigation was required to establish causality.

Indexed as

antimicrobial resistanceCOVID-19infection incidenceocular infectiontype I incision

Identifiers

PMID40978729
PMCPMC12443767

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.