Evidence map›Paper›PMID 40661969›Full record

ArticleFrontiers in cellular and infection microbiology2025

Innovo GenMax MTB-RIF/INH: a moderate-complexity automated NAAT for rapid simultaneous detection of

Xichao Ou, Bing Zhao, Huiwen Zheng, Ruida Xing, Qian Sun, Zhonghua Qin, Lixia Zhang, Kai Cui, Yuanyuan Song, Yang Zheng and 4 more

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

14 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.
Bing Zhao *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.
Huiwen Zheng *Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, Laboratory of Respiratory Diseases, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Ruida XingNational 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.
Qian SunMicrobial Inspection Department, Changping District Center for Disease Control and Prevention, Beijing, China.
Zhonghua QinCenter for Accurate Detection of Tuberculosis, Tianjin Haihe Hospital, Tianjin, China.
Lixia ZhangCenter for Accurate Detection of Tuberculosis, Tianjin Haihe Hospital, Tianjin, China.
Kai CuiNational 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.
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.
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.
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.
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.
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

Objective: Given the increase of treatment failure, relapse and acquired resistance observed in isoniazid (INH) resistance, there is an urgent to improve rifampin (RIF) -priority based diagnostic strategies. Therefore, we evaluated the performance of Innovo GenMax MTB-RIF/INH (GenMax), a moderate- complexity automated nucleic acid amplification test (NAAT), for detecting Mycobacterium tuberculosis complex (MTBC) and resistance to RIF and INH. Methods: Analytical sensitivity (limit of detection, LOD) was determined using serial dilutions of Results: GenMax demonstrated a calculated LOD of 8.8 CFU/mL (95% CI: 7.4-11.4) for MTBC, 674.1 CFU/mL (95% CI: 578.8-923.5) for RIF resistance, and 747.3 CFU/mL (95% CI: 613.7-1081.3) for INH resistance. In clinical evaluation, the sensitivity and specificity for MTBC detection were 97.52% (95% CI: 92.38-99.36) and 93.65% (95% CI: 88.91-96.53), respectively. For RIF and INH resistance, sensitivities were 88.46% (95% CI: 68.72-96.97) and 85.19% (95% CI: 65.39-95.14), with specificity of 92.42% (95% CI: 82.50-97.18) and 94.12% (95% CI: 84.86-98.10). Conclusion: Innovo GenMax MTB-RIF/INH is a rapid and automated assay with high sensitivity for MTBC detection, suitable for decentralized settings. While its performance for RIF/INH resistance detection is competitive with existing assays, its sensitivity remains gaps relative to WHO targets. Further optimization, particularly through expanded probe coverage, is needed to bridge this gap and ensure reliable detection in clinical settings.

Indexed as

Antitubercular AgentsDrug Resistance, BacterialIsoniazidMycobacterium tuberculosisNucleic Acid Amplification TechniquesRifampinTuberculosis, Multidrug-ResistantHumansLimit of DetectionMicrobial Sensitivity TestsSensitivity and SpecificitySputumTuberculosisAntitubercular AgentsIsoniazidRifampindrug resistanceisoniazidrapid molecular diagnosisrifampintuberculosis

Identifiers

PMID40661969
PMCPMC12256539

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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.