Evidence map›Paper›PMID 41158782›Full record

ArticleInfection and drug resistance2025

tNGS Reveals Predominant Mixed Viral Infections in a Multicenter Retrospective Study.

Xiaoqi Zhang, Rui Tang, Xiaoyan Li, Lijuan Li

Abstract read
In one paragraph

Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review of the Microbial Spectrum of Mixed Respiratory Fungal Infections.Journal of epidemiology and global health · 2026
    Review
  3. Article
  4. Article
  5. 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.

Xiaoqi Zhang *Department of Pulmonary and Critical Care Medicine, Second People's Hospital of Weifang, Weifang, 261041, People's Republic of China.ORCID 0009-0006-9878-2208
Rui Tang *Department of Pulmonary and Critical Care Medicine, Tangshan Gongren Hospital, Tangshan, 063003, People's Republic of China.
Xiaoyan Li *Department of Pulmonary and Critical Care Medicine, Renqiu Friendship Hospital, Cangzhou, 062550, People's Republic of China.
Lijuan LiDepartment of Pulmonary and Critical Care Medicine, National Centre for Clinical Research on Respiratory Diseases, China-Japan Friendship Hos-Pital, Beijing, 100029, People's Republic of China.ORCID 0000-0001-5984-1603

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Viral respiratory infections can damage epithelial cells and suppress host immunity, thereby predisposing patients to secondary bacterial or fungal infections. Accurate and timely identification of co-pathogens remains a clinical challenge. We aimed to examine whether tNGS can more comprehensively identify viral and mixed infections in comparison to conventional methods. Patients and Methods: We retrospectively analyzed 834 patients tested using targeted next-generation sequencing (tNGS) and 2263 patients tested using conventional methods across multiple centers. Pathogen profiles-including viral, bacterial, fungal, and atypical microorganisms-were compared between the groups. Results: Compared with conventional diagnostic methods, tNGS detected significantly higher proportions of viral co-infections, including fungal pathogens (eg, Conclusion: Targeted next-generation sequencing identified a higher prevalence and broader spectrum of viral co-infections and secondary bacterial/fungal infections compared with conventional methods. These findings highlight the complexity of respiratory infections in the post-COVID-19 era and support the potential of tNGS as a comprehensive diagnostic approach in clinical practice.

Indexed as

Aspergillusmixed viral infectionsMucorpost-COVID-19tNGSvirus

Identifiers

PMID41158782
PMCPMC12558357

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Registered trials

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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.