Evidence map›Paper›PMID 38868072›Full record

ArticleHeliyon2024

Evaluation of genetic correlation with fluoroquinolones resistance in rifampicin-resistant Mycobacterium tuberculosis isolates.

Chong Teng, Ling Li, Dan Su, Hui Li, Bing Zhao, Hui Xia, Hui Teng, Yuanyuan Song, Yang Zheng, Xiaolong Cao and 3 more

Abstract read
In one paragraph

Article in Heliyon, 2024. 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. Article
  3. Fluoroquinolone resistance and mutation profiles inFrontiers in public health · 2026
    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

13 authors.

Chong TengDepartment of Tuberculosis, Beijing Dongcheng District Center for Disease Control and Prevention, Beijing, 100050, China.
Ling LiDepartment of Clinical Laboratory, Ya'an People's Hospital, Sichuan, 625000, China.
Dan SuDepartment of Pathology, Capital Medical University Affiliated Beijing Chest Hospital, Beijing, 101149, China.
Hui LiDepartment of Tuberculosis, Beijing Dongcheng District Center for Disease Control and Prevention, Beijing, 100050, China.
Bing ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Hui XiaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Hui TengCentre of Health Management, Hunan Prevention and Treatment Institute for Occupational Diseases, Hunan, 410007, China.
Yuanyuan SongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Yang ZhengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Xiaolong CaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Huiwen ZhengLaboratory of Respiratory Diseases, Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, Beijing, 100045, China.
Yanlin ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Xichao OuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To detect levofloxacin (LFX) and moxifloxacin (MFX) resistance among rifampicin-resistant tuberculosis (RR-TB) isolates, and predict the resistance level based on specific mutations in Methods: A total of 686 RR-TB isolates were collected from Chinese Drug Resistance Surveillance Program from 2013 to 2020. The minimum inhibitory concentrations (MICs) of 12 anti-TB drugs were acquired using the broth microdilution method, followed by whole genome sequencing (WGS) analysis. Results: Among the 686 RR isolates, the most prevalent resistance was to isoniazid (80.5 %) and ethambutol (28.4 %), followed by LFX (26.1 %) and MFX (21.9 %). The resistance rate of LFX (26.1%-99.4 %) was higher than that of MFX (21.9%-83.3 %) across various drug resistance patterns. Of the 180 fluoroquinolones (FQs) resistant isolates, 168 (93.3 %) had mutations in quinolone-resistant determining regions (QRDRs) with 21 mutation types, and Asp94Gly (32.7 %, 55/168) was the predominant mutation. Isolates with mutations in Asp94Asn and Asp94Gly were associated with high levels of resistance to LFX and MFX. Using broth microdilution method as gold standard, the sensitivities of WGS for LFX and MFX were 93.3 % and 98.0 %, and the specificities were 98.6 % and 95.0 %, respectively. Conclusion: The resistance rate of LFX was higher than that of MFX among various drug resistance patterns in RR-TB isolates. The

Indexed as

FluoroquinolonesMutationMycobacterium tuberculosisResistanceRifampicin-resistant

Identifiers

PMID38868072
PMCPMC11167346

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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.