Evidence map›Paper›PMID 39226681›Full record

ArticleEBioMedicine2024

Enhancing lower respiratory tract infection diagnosis: implementation and clinical assessment of multiplex PCR-based and hybrid capture-based targeted next-generation sequencing.

Yuyao Yin, Pengyuan Zhu, Yifan Guo, Yingzhen Li, Hongbin Chen, Jun Liu, Lingxiao Sun, Shuai Ma, Chaohui Hu, Hui Wang

Abstract read
In one paragraph

Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.

0numbers the graph read from it
0cells of the map it votes in
103citing 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

103 citing papers in PubMed.

  1. Trial
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  5. Rapid diagnosis ofExperimental and therapeutic medicine · 2026
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  12. Application of targeted next-generation sequencing in children with community-acquired pneumonia.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
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  18. Association between normalizedTranslational pediatrics · 2026
    Article
  19. Article
  20. Article

43 more citing papers are in PubMed but not listed here.

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

10 authors.

Yuyao YinDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Pengyuan ZhuGuangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, China.
Yifan GuoDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Yingzhen LiGuangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, China.
Hongbin ChenDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Jun LiuGuangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, China.
Lingxiao SunDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Shuai MaDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
Chaohui HuGuangzhou KingCreate Biotechnology Co., Ltd., Guangzhou, China. Electronic address: huzh@kingcreate.com.cn.
Hui WangDepartment of Clinical Laboratory, Peking University People's Hospital, Beijing, China. Electronic address: wanghui@pkuph.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundShotgun metagenomic next-generation sequencing (mNGS) is widely used to detect pathogens in bronchoalveolar lavage fluid (BALF). However, mNGS is complex and expensive. This study explored the feasibility of targeted next-generation sequencing (tNGS) in distinguishing lower respiratory tract infections in clinical practice.

methodsWe used 229 retrospective BALF samples to establish thresholds and diagnostic values in a prospective cohort of 251 patients. After target pathogen selection, primer and probe design, optimization experiments, and bioinformatics analysis, multiplex PCR-based tNGS (mp-tNGS) and hybrid capture-based tNGS (hc-tNGS), targeting 198 and 3060 pathogens (DNA and RNA co-detection workflow) were established and performed.

findingsmp-tNGS and hc-tNGS took 10.3 and 16 h, respectively, with low sequencing data sizes of 0.1 M and 1 M reads, and test costs reduced to a quarter and half of mNGS. The LoDs of mp-tNGS and hc-tNGS were 50-450 CFU/mL. mp-tNGS and hc-tNGS were highly accurate, with 86.5% and 87.3% (vs. 85.5% for mNGS) sensitivities and 90.0% and 88.0% (vs. 92.1% for mNGS) specificities. tNGS detection rates for casual pathogens were 84.3% and 89.5% (vs. 88.5% for mNGS), significantly higher than conventional microbiological tests (P < 0.001). In seven samples, tNGS detected Pneumocystis jirovecii, a fungus not detected by mNGS. Whereas mNGS detected six samples with filamentous fungi (Rhizopus oryzae, Aureobasidium pullulans, Aspergillus niger complex, etc.) which missed by tNGS. The anaerobic bacteria as pathogen in eight samples was failed to detect by mp-tNGS.

interpretationtNGS may offer a new, broad-spectrum, rapid, accurate and cost-effective approach to diagnosing respiratory infections.

fundingNational Natural Science Foundation of China (81625014 and 82202535).

Indexed as

Bronchoalveolar Lavage FluidHigh-Throughput Nucleotide SequencingMultiplex Polymerase Chain ReactionRespiratory Tract InfectionsAdolescentAdultAgedAged, 80 and overComputational BiologyFemaleHumansMaleMetagenomicsMiddle AgedRetrospective StudiesSensitivity and SpecificityBronchoalveolar lavage fluidMetagenomic next-generation sequencingPathogenPneumoniaTargeted next-generation sequencing

Identifiers

PMID39226681
PMCPMC11403251

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.