Evidence map›Paper›PMID 42682482›Full record

ArticleFrontiers in cellular and infection microbiology2026

Clinical utility of targeted next-generation sequencing for rapid detection, species differentiation, drug resistance profiling, and prognostic evaluation in

Li Shi, Mingde Ni, Fan Yang, Wei Shi, Xiuwu Liu, Chen Ni, Fang He, Yunhui Huang, Xiaoyun Yang, Yi Pei

Abstract readMulticenter Study
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2026. 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

10 authors.

Li Shi *Department of Tuberculosis, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China.
Mingde Ni *Linyi People's Hospital, Linyi, Shandong, China.
Fan Yang *Dinfectome Inc., Nanjing, Jiangsu, China.
Wei ShiSchool of Information Engineering, Changsha Medical University, Changsha, Hunan, China.
Xiuwu LiuLinyi People's Hospital, Linyi, Shandong, China.
Chen NiThe Second Clinical Medical College of Guangxi Medical University, Nanning, Guangxi, China.
Fang HeDepartment of Tuberculosis, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China.
Yunhui HuangDepartment of Tuberculosis, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China.
Xiaoyun YangDepartment of Tuberculosis, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China.
Yi PeiDepartment of Tuberculosis, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tuberculosis (TB) remains a global health threat, which is complicated by challenges in distinguishing TB from nontuberculous mycobacteria (NTM) infections and drug resistance. Conventional diagnostics (including culture, Xpert MTB/RIF, AFB smear, T-SPOT.TB, etc.) are limited by inadequate differentiation of TB and NTM, incomplete resistance profiling, and prolonged turnaround times. Methods: This retrospective multicenter study enrolled 130 patients with suspected mycobacterial infection between August 2023 and June 2024 at two tertiary hospitals in China. Patients were classified as TB (n=91), NTM (n=18), Other (n=17) and Mixed infection (n=4) based on a composite reference standard (final clinical diagnosis) established independently of targeted next-generation sequencing (tNGS) results. Performance of tNGS versus conventional methods was evaluated across different sample types. Diagnostic performance, drug-resistance detection, therapeutic impact and clinical outcomes were analyzed. Results: The overall agreement of tNGS detection with clinical diagnoses of pathogens was 80.9% in sputum and 71.3% in bronchoalveolar lavage fluid (BALF), exceeding conventional methods (which remained below 55%). Critically, tNGS effectively distinguished TB from NTM, with diagnostic sensitivity of 84.0% (95% CI, 75.0%-90.8%) for TB and 90.5% (95% CI, 69.6%-98.8%) for NTM, and specificity of 100.0% (95% CI, 90.3%-100.0%) for TB and 99.1% (95% CI, 95.0%-100.0%) for NTM. tNGS simultaneously detected resistance-associated variants relevant to 11 antimycobacterial drugs within approximately 24 hours. Treatment regimens for 52 patients (41.3%) were adjusted following tNGS reporting. Of these, 82.7% showed subsequent clinical improvement, however, these outcome data remain descriptive and do not establish causality. Conclusions: tNGS enables rapid, accurate, and comprehensive diagnosis of mycobacterial infections, reliably differentiates TB from NTM, and provides drug-resistance profiles to inform therapy, with the potential to assist in optimizing clinical management of TB.

Indexed as

Drug Resistance, BacterialHigh-Throughput Nucleotide SequencingMycobacterium tuberculosisTuberculosisAntitubercular AgentsChinaFemaleHumansMicrobial Sensitivity TestsMolecular Diagnostic TechniquesMycobacterium Infections, NontuberculousPrognosisRetrospective StudiesSensitivity and SpecificityAntitubercular Agentsclinical outcomesdrug resistancenontuberculous mycobacteriarapid diagnosistargeted next-generation sequencingtreatment adjustmenttuberculosis

Identifiers

PMID42682482
PMCPMC13529550

What Socratic holds

Textmetadata
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 Socratic graph.