Evidence map›Paper›PMID 39534701›Full record

ArticleFrontiers in cellular and infection microbiology2024

Development and preliminary assessment of the iFIND TBR: all-in- one molecular diagnostic assay for rapid detection of

Xichao Ou, Zexuan Song, Ruida Xing, Bing Zhao, Shaojun Pei, Chong Teng, Lincai Zhang, Qian Sun, Fang Liu, Hui Xia and 7 more

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

17 authors.

Xichao Ou *National 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, China.
Zexuan Song *National 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, China.
Ruida Xing *National 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, 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, China.
Shaojun PeiNational 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, China.
Chong TengDepartment of Tuberculosis, Beijing Dongcheng District Center for Disease Control and Prevention, Beijing, China.
Lincai ZhangInstitute for Tuberculosis Control and Prevention, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China.
Qian SunDepartment of Clinical Laboratory, Tuberculosis Dispensary of Changping District, Beijing, China.
Fang LiuInstitute for Tuberculosis Control and Prevention, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, 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, China.
Yang ZhouNational 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, 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, 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, China.
Zhiguo ZhangDepartment of Clinical Laboratory, Tuberculosis Dispensary of Changping District, Beijing, China.
Shengfen WangNational 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, China.
Richard AnthonyNational Institute for Public Health and the Environment, Bilthoven, Netherlands.
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, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Early and accurate diagnosis of tuberculosis (TB) is crucial for initiating timely treatment and preventing new infections. In this study, we introduced the iFIND TBR assay, an automated all-in-one tuberculosis detection approach that simultaneously detect Methods: The limits of detection (LOD), sensitivity, specificity, and RIF-R rpoB mutation detection of the iFIND TBR were tested on Mycobacterium tuberculosis DNA or sputum samples spiked with known numbers of M.tuberculosis H37Rv. Frozen clinical samples from patients suspected of having TB were also tested. Results: The LOD of the iFIND TBR for MTB detection were 13.34 CFU/ml (95% CI, 11.71-16.47), and for RIF resistance was 109.79CFU/mL (95% CI, 95-138.19). The iFIND TBR assay accurately distinguish MTB strains from non-tuberculous mycobacteria (NTM) without any cross reactivity. Testing on 157 clinical sputum samples, compared with the bacteriologically TB standard, the overall sensitivity and specificity of the iFIND TBR was 100% (95%CI, 94.64, 100) and 85.29% (95% CI, 74.61, 92.72), respectively. When assessing RIF susceptibility, the iFIND TBR achieved a sensitivity of 98.15% (95% CI, 90.11-99.95) and a specificity of 85.71% (95% CI, 67.33-95.97), compared with phenotypic drug susceptibility testing. Discordant RIF susceptibility results were more frequently observed in samples exhibiting heteroresistance. Discussion: These findings demonstrate that iFIND TBR assay performs well in detecting TB and RIF resistance, and shows promise as a point-of-care tool in resource-limited areas.

Indexed as

Drug Resistance, BacterialMolecular Diagnostic TechniquesMycobacterium tuberculosisRifampinSensitivity and SpecificitySputumBacterial ProteinsDNA, BacterialDNA-Directed RNA PolymerasesHumansLimit of DetectionMicrobial Sensitivity TestsMutationTuberculosisTuberculosis, Multidrug-ResistantBacterial ProteinsDNA, BacterialDNA-Directed RNA PolymerasesRifampinrpoB protein, Mycobacterium tuberculosisMycobacterium tuberculosisrapid detectionrifampicin resistancethe iFIND TBRtuberculosis

Identifiers

PMID39534701
PMCPMC11554655

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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.