Evidence map›Paper›PMID 41790160›Full record

ArticleWorld journal of microbiology & biotechnology2026

Simultaneous detection of Mycobacterium tuberculosis complex and rifampin/isoniazid resistance with sanity 2.0 MTBC-MDR: a moderate-complexity automated NAAT.

Xichao Ou, Bing Zhao, Hui Xia, Xundi Bao, Dongfang Xu, Qi Ma, Ye Xu, Jingjing Li, Wenzhi Shi, Feina Li and 4 more

Abstract read
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In one paragraph

Article in World journal of microbiology & biotechnology, 2026. 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

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

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No citing paper in PubMed yet.

4 · The record

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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 (Chinese Academy of Preventive Medicine), Beijing, 102206, 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 (Chinese Academy of Preventive Medicine), Beijing, 102206, China.
Hui Xia *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 (Chinese Academy of Preventive Medicine), Beijing, 102206, China.
Xundi BaoAnhui Provincial Center for Disease Control and Prevention, Tuberculosis Prevention and Control Institute, Hefei, China.
Dongfang XuAnhui Provincial Center for Disease Control and Prevention, Tuberculosis Prevention and Control Institute, Hefei, China.
Qi MaBeijing 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 Children's Hospital, Beijing Pediatric Research Institute, Capital Medical University, National Center for Children's Health, Beijing, China.
Ye XuSchool of Life Sciences, Xiamen Key Laboratory of Multi-Target and Automated Genetic Testing, Xiamen University, Xiamen, China.
Jingjing LiBeijing 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 Children's Hospital, Beijing Pediatric Research Institute, Capital Medical University, National Center for Children's Health, Beijing, China.
Wenzhi ShiSchool of Life Sciences, Xiamen Key Laboratory of Multi-Target and Automated Genetic Testing, Xiamen University, Xiamen, China.
Feina LiBeijing 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 Children's Hospital, Beijing Pediatric Research Institute, Capital Medical University, National Center for Children's Health, Beijing, China.
Yajie GuoBeijing 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 Children's Hospital, Beijing Pediatric Research Institute, 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 (Chinese Academy of Preventive Medicine), Beijing, 102206, 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 (Chinese Academy of Preventive Medicine), Beijing, 102206, China. zhaoyl@chinacdc.cn.
Huiwen ZhengBeijing 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 Children's Hospital, Beijing Pediatric Research Institute, Capital Medical University, National Center for Children's Health, Beijing, China. hanhui820@126.com.

Funding

National Key Research and Development Program of China 2022YFC2305204, 2023YFC2307301
6 · The paper itself

Abstract

objectiveTo compare the performance of Sanity 2.0 with that of Xpert MTB/RIF and phenotypic drug susceptibility testing (DST) for the detection of Mycobacterium tuberculosis complex (MTBC) and resistance to rifampicin (RIF) and isoniazid (INH).

methodsAcid-fast staining positive sputum samples, collected from February to July in 2024, were used for solid culture, DST, Xpert MTB/RIF, and Sanity 2.0 assay.

resultsThe limit of detection of the Sanity 2.0 assay was 3 CFU/mL for MTB detection, and 30 CFU/mL for RIF and INH resistance mutations. Compared to Xpert MTB/RIF, complete agreement was observed for the detection of MTB with sensitivity of 99.3% (296/298), as well as for RIF resistance. Using DST as the reference method, Sanity 2.0 exhibited a sensitivity of 96.55% and a specificity of 99.25% for the detection of RIF resistance. For INH resistance, Sanity 2.0 demonstrated a sensitivity of 78.95% and a specificity of 99.22%. Among three cases with discordant rifampicin resistance profiles between Sanity 2.0 and DST, one Sanity 2.0-sensitive/phenotypically-resistant case was confirmed as sensitive by sequencing, and two Sanity 2.0-resistant/phenotypically-sensitive cases carried a mutation in rpoB at codon 531 (TCG→TTG) and 526 (CAC→AGC), respectively. Among two INH-resistant cases by Sanity 2.0 that were sensitive by DST, one isolate carried a mutation in the ahpC promoter region (–15 C > T), and the other had a katG mutation at codon 315 (AGC→ACC). All eight cases that were INH-sensitive by Sanity 2.0 but resistant by DST were confirmed to be sensitive by sequencing.

conclusionSanity 2.0 represents a rapid and automated molecular assay with high sensitivity for MTBC and RIF/INH resistance detection, showing comparable performance to established assays and making it well-suited for decentralized healthcare settings as a complementary diagnostic tool.

Indexed as

Antitubercular AgentsDrug Resistance, Multiple, BacterialIsoniazidMycobacterium tuberculosisRifampinTuberculosis, Multidrug-ResistantBacterial ProteinsCatalaseDNA-Directed RNA PolymerasesDrug Resistance, BacterialHumansMicrobial Sensitivity TestsMutationRapid Diagnostic TestsSensitivity and SpecificitySputumAntitubercular AgentsBacterial ProteinsCatalaseDNA-Directed RNA PolymerasesIsoniazidRifampinrpoB protein, Mycobacterium tuberculosisIsoniazidMolecular diagnosisMycobacterium tuberculosisResistanceRifampin

Identifiers

PMID41790160

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