Evidence map›Paper›PMID 38716089›Full record

ArticleJournal of ophthalmology2024

Application of Metagenomic Next-Generation Sequencing in the Diagnosis of Infectious Keratitis.

Xin-Yu Pan, Meng Wang, Yi-Dan Xu, Lin-Nong Wang

Abstract read
In one paragraph

Article in Journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Metagenomic next-generation sequencing for comprehensive pathogen detection in intraocular infection.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
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  12. Emerging diagnostic modalities in microbial keratitis: Beyond culture and smear.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society
    Article
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

4 authors.

Xin-Yu PanDepartment of Ophthalmology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province 210006, China.ORCID https://orcid.org/0009-0009-6504-717X
Meng WangDepartment of Ophthalmology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province 210006, China.ORCID https://orcid.org/0009-0002-8597-1887
Yi-Dan XuDepartment of Ophthalmology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province 210006, China.ORCID https://orcid.org/0009-0002-4484-5462
Lin-Nong WangDepartment of Ophthalmology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province 210006, China.ORCID https://orcid.org/0009-0004-6806-0103

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To determine the advantages of next-generation metagenomic sequencing (mNGS) technology in the diagnosis and treatment of infectious keratitis (IK). Methods: A total of 287 patients with IK admitted to the Department of Ophthalmology of Nanjing First Hospital between August 2018 and December 2022 were analyzed retrospectively, and the pathogenic causes, etiological characteristics, detection, treatment methods, and efficacy were summarized. Results: Trauma and foreign matter were the most common causes of IK (144 patients, 50.2%). Of the 287 patients, 228 (79.4%) were diagnosed with a specific etiology, including 110 (48.2%) fungal infections, 44 (19.3%) viral infections, 42 (18.4%) mixed infections, and 30 (13.2%) bacterial infections. Filamentous fungi represented by Conclusion: Trauma and foreign matter are the main causes of IK. The mNGS technology is an efficient and comprehensive detection method for viruses and bacteria, especially for mixed infections.

Identifiers

PMID38716089
PMCPMC11074721

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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.