Evidence map›Paper›PMID 40128686›Full record

ArticleBMC microbiology2025

Comparative analysis of metagenomic next-generation sequencing for pathogenic identification in clinical body fluid samples.

Ning Sun, Jiaxun Zhang, Wentao Guo, Jin Cao, Yong Chen, Deyu Gao, Xinyi Xia

Abstract readComparative Study
In one paragraph

Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Laboratory diagnosis of brucellosis: evolving synergy between serological testing and next-generation sequencing.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Review
  4. Article
  5. Review
  6. Observational
  7. Article
  8. Editorial: Targeted metagenomics in pathogen detection.Frontiers in cellular and infection microbiology · 2025
    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

7 authors.

Ning Sun *Department of Clinical Laboratory, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China. steamsn@163.com.
Jiaxun Zhang *Department of Clinical Laboratory, Anhui Provincial Chest Hospital (Provincial Institute of Tuberculosis Control), Hefei, Anhui, China.
Wentao GuoDepartment of Microbiology and Immunology, College of Basic Medicine, Guangdong Medical University, Dongguan, China.
Jin CaoDepartment of Clinical Laboratory, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Yong ChenDepartment of Clinical Laboratory, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Deyu GaoDepartment of Clinical Laboratory, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Xinyi XiaDepartment of Clinical Laboratory, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China. xinyixia@nju.edu.cn.

Funding

Health Technology Development Special Foundation of Nanjing City YKK18216National Natural Science Foundation of China 81601857Suqian Sci&Tech Program SY202214
6 · The paper itself

Abstract

objectivesThis study aims to evaluate and compare the effectiveness of metagenomic next-generation sequencing (mNGS) in identifying pathogens from clinical body fluid samples, with a specific focus on the application of microbial cell-free DNA (cfDNA) mNGS.

methodsA total of 125 clinical body fluid samples were collected. All samples underwent mNGS targeting whole-cell DNA (wcDNA), with 30 samples also analyzed for cfDNA mNGS and 41 subjected to 16S rRNA NGS for comparative analysis. Patient clinical data, including culture results, were obtained from electronic medical records.

resultsIn comparison to cfDNA mNGS, the mean proportion of host DNA in wcDNA mNGS was 84%, significantly lower than the 95% observed in cfDNA mNGS (p < 0.05). Using culture results as a reference, concordance rates were 63.33% (19/30) for wcDNA mNGS and 46.67% (14/30) for cfDNA mNGS. Additionally, wcDNA mNGS showed greater consistency in bacterial detection with culture results, achieving a rate of 70.7% (29/41) compared to 58.54% (24/41) for 16S rRNA NGS. The sensitivity and specificity of wcDNA mNGS for pathogen detection in body fluid samples were 74.07% and 56.34%, respectively, when compared to culture results.

conclusionWhole-cell DNA mNGS demonstrates significantly higher sensitivity for pathogen detection and identification compared to both cfDNA mNGS and 16S rRNA NGS in clinical body fluid samples, particularly those associated with abdominal infections. However, the compromised specificity of wcDNA mNGS highlights the necessity for careful interpretation in clinical practice.

Indexed as

BacteriaBody FluidsHigh-Throughput Nucleotide SequencingMetagenomicsAdultAgedCell-Free Nucleic AcidsDNA, BacterialFemaleHumansMaleMiddle AgedRNA, Ribosomal, 16SSensitivity and SpecificityCell-Free Nucleic AcidsDNA, BacterialRNA, Ribosomal, 16SClinical body fluid sampleMetagenomic next-generation sequencingMicrobial cell-free DNAPathogen identificationWhole-cell DNA

Identifiers

PMID40128686
PMCPMC11934473

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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.