Evaluation of a new assay for nontuberculous mycobacteria species identification in diagnostic material and cultures

Smirnova, Tatiana G.; Ustinova, Vera V.; Andreevskaya, Sofya; Larionova, Elena; Ev, Kiseleva; Черноусова, Л. Н.; Varlamov, D. A.; Сочивко, Д. Г.; Ergeshov, Atadzhan

doi:10.1016/j.tube.2021.102124

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…However, Xpert MTB/RIF has a low sensitivity in paucibacillary specimens and the risk of misdiagnosing NTM as Mtb in a high bacterial load setting 16 – 18 . Compared to Xpert MTB/RIF, multiplex PCR sequencing of hsp65 , rpoB , as well as 16S rRNA allows the fast detection of NTM with better specificity and identification of the NTM species in the sample 2 , 16 , 19 . PCR-based tests and mycobacterial culture often utilize sample types such as sputum, bronchial wash, or bronchial lavage.…”

Section: Introductionmentioning

confidence: 99%

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Anh,

Phat,

Thu

et al. 2024

Sci Rep

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Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

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Section: Introductionmentioning

confidence: 99%

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Anh,

Phat,

Thu

et al. 2024

Sci Rep

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“…The NTM refer to mycobacteria species other than the Mycobacterium tuberculosis complex and M. leprae [ 2 ]. More than 200 NTM species were identified and some of which are known as important infectious threats, especially in industrialized countries [ 3 , 4 ]. These bacterial agents exist ubiquitous in the environment and are ubiquitously transmitted by inhalation, ingestion, or direct inoculation in the skin to develop infections [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Nontuberculous Mycobacterial Resistance to Antibiotics and Disinfectants: Challenges Still Ahead

Tarashi

Siadat

Fateh

2022

BioMed Research International

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The mortality incidence from nontuberculous mycobacteria (NTM) infections has been steadily developing globally. These bacterial agents were once thought to be innocent environmental saprophytic that are only dangerous to patients with defective lungs or the immunosuppressed. Nevertheless, the emergence of highly resistant NTM to different antibiotics and disinfectants increased the importance of these agents in the health system. Currently, NTM frequently infect seemingly immunocompetent individuals at rising rates. This is of concern as the resistant NTM are difficult to control and treat. The details behind this NTM development are only beginning to be clarified. The current study will provide an overview of the most important NTM resistance mechanisms to not only antibiotics but also the most commonly used disinfectants. Such evaluations can open new doors to improving control strategies and reducing the risk of NTM infection. Moreover, further studies are crucial to uncover this association.

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Discriminatory power of multiplex PCR for detection of mycobacterial co-infection

Smirnova,

Andreevskaya,

Ustinova

et al. 2024

BRSMU

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The diagnosis of mycobacterial co-infection is one of the pressing public health issues. The study was aimed to determine discriminatory power of multiplex PCR used for species identification when detecting mixed mycobacterial populations. The study involved model samples representing the mixtures of DNA of two mycobacterial species with the ratios of 1 : 1, 1 : 9, 1 : 99, and 1 : 999 and different total DNA concentrations (103 gEq/mL to 106 gEq/mL). The model samples were assessed using the multiplex PCR-based AmpliTube-RV-Differentiation kit (Syntol LLC; Russia). It has been shown that the kit is capable of detecting the mixtures of mycobacterial species with high discriminatory power. The discriminatory power of real-time PCR used for analysis of the mixture of DNA of two mycobacterial species depended on the total DNA content in the sample and varied between 0.1% for high-rate samples (total DNA concentration 106 gEq/mL) and 50% for low-rate samples (total DNA concentration 103 gEq/mL) and corresponded to the amount of DNA of the species in the sample of at least 5 × 102 gEq/mL. When the amount of DNA of each species in the mixture was at least 5 × 102 gEq/mL, the results of PCR test for detection of co-infection did not depend on the mucobacterial species contained in the mixture, which should be taken into account when analyzing PCR results.

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Evaluation of a new assay for nontuberculous mycobacteria species identification in diagnostic material and cultures

Cited by 5 publications

References 33 publications

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Nontuberculous Mycobacterial Resistance to Antibiotics and Disinfectants: Challenges Still Ahead

Discriminatory power of multiplex PCR for detection of mycobacterial co-infection

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