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Arbués MD, Rossetti MLR. Evaluation of the GeneXpert MTB/RIF to diagnose tuberculosis in a public health laboratory. Rev Saude Publica 2024; 58:03. [PMID: 38381893 PMCID: PMC10878686 DOI: 10.11606/s1518-8787.2024058005306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/31/2023] [Indexed: 02/23/2024] Open
Abstract
OBJECTIVES To evaluate the performance of geneXpert MTB/Rif versus conventional methods (bacilloscopy and culture) in the diagnosis of tuberculosis in a Central Public Health Laboratory (LACEN, Tocantins), Northern Brazil. METHODS Retrospective study, with information from 1,973 suspected cases of tuberculosis from patients treated from January 2015 to December 2020. RESULTS From the culture (reference standard), the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the geneXpert MTB/Rif were 100%, 97%, 74%, 100%, and 97%, respectively, against 85%, 98%, 80%, 98%, and 97% of bacilloscopy. CONCLUSIONS The geneXpert MTB/Rif performed similarly to culture and better than bacilloscopy. Although positive cases with negative culture should be evaluated with caution, its routine use is important for the early detection of tuberculosis.
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Affiliation(s)
- Mohanna Damasceno Arbués
- Universidade Luterana do BrasilPrograma de Doutorado em Biologia Molecular e Celular Aplicada à SaúdeCanoasRSBrasilUniversidade Luterana do Brasil. Programa de Doutorado em Biologia Molecular e Celular Aplicada à Saúde. Canoas, RS, Brasil
| | - Maria Lúcia Rosa Rossetti
- Universidade Luterana do BrasilPrograma de Doutorado em Biologia Molecular e Celular Aplicada à SaúdeCanoasRSBrasilUniversidade Luterana do Brasil. Programa de Doutorado em Biologia Molecular e Celular Aplicada à Saúde. Canoas, RS, Brasil
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Patnaik N, Dey RJ. Label-Free Citrate-Stabilized Silver Nanoparticles-Based, Highly Sensitive, Cost-Effective, and Rapid Visual Method for the Differential Detection of Mycobacterium tuberculosis and Mycobacterium bovis. ACS Infect Dis 2024; 10:426-435. [PMID: 38112513 DOI: 10.1021/acsinfecdis.3c00390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Tuberculosis poses a global health challenge, and it demands improved diagnostics and therapies. Distinguishing between Mycobacterium tuberculosis (M. tb) and Mycobacterium bovis (M. bovis) infections holds critical "One Health" significance due to the zoonotic nature of these infections and inherent resistance of M. bovis to pyrazinamide, a key part of the directly observed treatment, short-course (DOTS) regimen. Furthermore, most of the currently used molecular detection methods fail to distinguish between the two species. To address this, our study presents an innovative molecular-biosensing strategy. We developed a label-free citrate-stabilized silver nanoparticle aggregation assay that offers sensitive, cost-effective, and swift detection. For molecular detection, genomic markers unique to M. tb and M. bovis were targeted using species-specific primers. In addition to amplifying species-specific regions, these primers also aid the detection of characteristic deletions in each of the mycobacterial species. Post polymerase chain reaction (PCR), we compared two highly sensitive visual detection methods with respect to the traditional agarose gel electrophoresis. The paramagnetic bead-based bridging flocculation assay successfully discriminates M. tb from M. bovis with a sensitivity of ∼40 bacilli. The second strategy exploits citrate-stabilized silver nanoparticles, which aggregate in the absence of amplified dsDNA on the addition of sodium chloride (NaCl). This technique enables the precise, sensitive, and differential detection of as few as ∼4 bacilli. Our study hence advances tuberculosis detection, overcoming the challenges of M. tb and M. bovis differentiation and offering a quicker alternative to time-consuming methods.
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Affiliation(s)
- Naresh Patnaik
- Department of Biological Sciences, BITS Pilani Hyderabad Campus, Hyderabad, Telangana State 500078, India
| | - Ruchi Jain Dey
- Department of Biological Sciences, BITS Pilani Hyderabad Campus, Hyderabad, Telangana State 500078, India
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Heupink TH, Verboven L, Warren RM, Van Rie A. Comprehensive and accurate genetic variant identification from contaminated and low-coverage Mycobacterium tuberculosis whole genome sequencing data. Microb Genom 2021; 7:000689. [PMID: 34793294 PMCID: PMC8743552 DOI: 10.1099/mgen.0.000689] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/09/2021] [Indexed: 12/30/2022] Open
Abstract
Improved understanding of the genomic variants that allow Mycobacterium tuberculosis (Mtb ) to acquire drug resistance, or tolerance, and increase its virulence are important factors in controlling the current tuberculosis epidemic. Current approaches to Mtb sequencing, however, cannot reveal Mtb ’s full genomic diversity due to the strict requirements of low contamination levels, high Mtb sequence coverage and elimination of complex regions. We have developed the XBS (compleX Bacterial Samples) bioinformatics pipeline, which implements joint calling and machine-learning-based variant filtering tools to specifically improve variant detection in the important Mtb samples that do not meet these criteria, such as those from unbiased sputum samples. Using novel simulated datasets, which permit exact accuracy verification, XBS was compared to the UVP and MTBseq pipelines. Accuracy statistics showed that all three pipelines performed equally well for sequence data that resemble those obtained from culture isolates of high depth of coverage and low-level contamination. In the complex genomic regions, however, XBS accurately identified 9.0 % more SNPs and 8.1 % more single nucleotide insertions and deletions than the WHO-endorsed unified analysis variant pipeline. XBS also had superior accuracy for sequence data that resemble those obtained directly from sputum samples, where depth of coverage is typically very low and contamination levels are high. XBS was the only pipeline not affected by low depth of coverage (5–10×), type of contamination and excessive contamination levels (>50 %). Simulation results were confirmed using whole genome sequencing (WGS) data from clinical samples, confirming the superior performance of XBS with a higher sensitivity (98.8%) when analysing culture isolates and identification of 13.9 % more variable sites in WGS data from sputum samples as compared to MTBseq, without evidence for false positive variants when rRNA regions were excluded. The XBS pipeline facilitates sequencing of less-than-perfect Mtb samples. These advances will benefit future clinical applications of Mtb sequencing, especially WGS directly from clinical specimens, thereby avoiding in vitro biases and making many more samples available for drug resistance and other genomic analyses. The additional genetic resolution and increased sample success rate will improve genome-wide association studies and sequence-based transmission studies.
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Affiliation(s)
- Tim H. Heupink
- Family Medicine and Population Health (FAMPOP), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Lennert Verboven
- Family Medicine and Population Health (FAMPOP), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Robin M. Warren
- South African Medical Research Council Centre for Tuberculosis Research and DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Stellenbosch University, Stellenbosch, South Africa
| | - Annelies Van Rie
- Family Medicine and Population Health (FAMPOP), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
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Liu Z, Tong Y, Wu J, Du L, Wei C, Cui W, Cao Y, Chen M, Cai Z, Chen W, Ding H, Guan M, Guo W, Gao C, Hao X, Hu C, Huang S, Jiang Y, Li J, Li P, Li Z, Ming L, Pan S, Shen Z, Su J, Sun Z, Wang H, Wang J, Xu B, Yu N, Zheng L, Zhang Y, Zhang X, Zhang Y, Duan Y, Wang C. Chinese Expert Consensus on the Nucleic Acid Detection of SARS-CoV-2. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1631. [PMID: 33490143 PMCID: PMC7812184 DOI: 10.21037/atm-20-4060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The coronavirus disease 2019 (COVID-19) has already become a pandemic wherein the infection's timely diagnosis has proven beneficial to patient treatment and disease control. Nucleic acid detection has been the primary laboratory diagnostic method for the detection of SARS-CoV-2. To ensure laboratory staff safety and quality nucleic acid testing, the Chinese Society of Laboratory Medicine formulated this consensus, based on the Chinese National Recommendations and previous literature for nucleic acid detection. A working group comprises 34 hospital professionals experience with real-time polymerase chain reactions (PCR) testing for SARS-CoV-2 drafted guidance statements during online discussions. A modified Delphi methodology was used in forming a consensus among a wider group of hospital professionals with SARS-CoV-2 detection experience. Guidance statements were developed for four categories: (I) specimen type, priority, collecting, transportation and receiving; (II) nucleic acid isolation and amplification; (III) quality control; (IV) biosafety management and decontamination. The modified Delphi voting process included a total of 29 guidance statements and final agreement. Consensus was reached after two rounds of voting. Recommendations were established for the detection of SARS-CoV-2 using real time PCR testing based on evidence and group consensus. The manuscript was evaluated against The Appraisal of Guidelines for Research & Evaluation Instrument (AGREE II) and was developed to aid medical laboratory staff in the detection of the ribonucleic acid (RNA) of SARS-CoV-2.
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Affiliation(s)
- Zijie Liu
- Yunnan Key Laboratory of Laboratory Medicine, Kunming, China.,Department of Laboratory Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yongqing Tong
- Laboratory Medicine Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Wu
- Department of Clinical Laboratory, Shanghai General Hospital Jiading Branch, Shanghai, China
| | - Lutao Du
- Laboratory Medicine Center of The Second Hospital of Shandong University, Jinan, China
| | - Chaojun Wei
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China
| | - Wei Cui
- Department of Laboratory Medicine, Cancer Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Yongtong Cao
- Laboratory Department of China-Japan Friendship Hospital, Beijing, China
| | - Ming Chen
- Laboratory Department of Southwest Hospital, Chongqing, China
| | - Zhen Cai
- Department of Laboratory Medicine, Nanfang Hospital of Southern Medical University, Guangzhou, China
| | - Wei Chen
- Department of Laboratory Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Haitao Ding
- Department of Laboratory Medicine, People's Hospital of Inner Mongolia Autonomous Region, Hohhot, China
| | - Ming Guan
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Guo
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chunfang Gao
- Department of Laboratory Medicine, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Xiaoke Hao
- Department of Laboratory Medicine, Xijing Hospital, Air Force Military Medical University, Xi'an, China
| | - Chengjin Hu
- Department of Laboratory Diagnosis, 960th Hospital of Chinese PLA, Jinan, China
| | - Shan Huang
- Guizhou Province Center for Clinical Laboratory, Guiyang, China
| | - Yanfang Jiang
- Gene Diagnostic Center of the First Hospital of Jilin University, Changchun, China
| | - Jinming Li
- National Center for Clinical Laboratories, Beijing, China
| | - Ping Li
- Medical Laboratory and Pathology Center, the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Changsha, China
| | - Zhuo Li
- Department of Laboratory Medicine, the First Affiliated Hospital of Xi'an Medical College, Xian, China
| | - Liang Ming
- Department of Laboratory Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shiyang Pan
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zuojun Shen
- Scientific Research Department of the First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Jianrong Su
- Department of Laboratory Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Wang
- Department of Laboratory Medicine, Peking University People's Hospital, Beijing, China
| | - Junjun Wang
- Department of Laboratory Medicine, Eastern Theater General Hospital; Nanjing, China
| | - Bin Xu
- Provincial Clinical Inspection Center of Jiangsu Cancer Hospital, Nanjing, China
| | - Nong Yu
- Laboratory of Suzhou Branch of Xinhua Hospital Affiliated to Shanghai Jiaotong University, Suzhou, China
| | - Lei Zheng
- Department of Laboratory Medicine, Nanfang Hospital of Southern Medical University, Guangzhou, China
| | - Yi Zhang
- Department of Laboratory Medicine, Qilu Hospital of Shandong University, Jinan, China
| | - Xin Zhang
- Department of Laboratory Medicine of Xinjiang Production and Construction Corps Hospital, Urumqi, China
| | - Ying Zhang
- Department of Clinical Laboratory Medicine, the First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yong Duan
- Yunnan Key Laboratory of Laboratory Medicine, Kunming, China.,Department of Laboratory Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chengbin Wang
- Department of Clinical Laboratory Medicine, the First Medical Center, Chinese PLA General Hospital, Beijing, China
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