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Review and Updates on the Diagnosis of Tuberculosis
Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570811/ https://www.ncbi.nlm.nih.gov/pubmed/36233689 http://dx.doi.org/10.3390/jcm11195826 |
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author | Huang, Yi Ai, Lin Wang, Xiaochen Sun, Ziyong Wang, Feng |
author_facet | Huang, Yi Ai, Lin Wang, Xiaochen Sun, Ziyong Wang, Feng |
author_sort | Huang, Yi |
collection | PubMed |
description | Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. The aim of this review was to provide an updated overview of current efforts to optimize classical diagnostic methods, as well as new molecular and other methodologies, for accurate diagnosis of patients with Mtb infection. |
format | Online Article Text |
id | pubmed-9570811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95708112022-10-17 Review and Updates on the Diagnosis of Tuberculosis Huang, Yi Ai, Lin Wang, Xiaochen Sun, Ziyong Wang, Feng J Clin Med Review Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. The aim of this review was to provide an updated overview of current efforts to optimize classical diagnostic methods, as well as new molecular and other methodologies, for accurate diagnosis of patients with Mtb infection. MDPI 2022-09-30 /pmc/articles/PMC9570811/ /pubmed/36233689 http://dx.doi.org/10.3390/jcm11195826 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Huang, Yi Ai, Lin Wang, Xiaochen Sun, Ziyong Wang, Feng Review and Updates on the Diagnosis of Tuberculosis |
title | Review and Updates on the Diagnosis of Tuberculosis |
title_full | Review and Updates on the Diagnosis of Tuberculosis |
title_fullStr | Review and Updates on the Diagnosis of Tuberculosis |
title_full_unstemmed | Review and Updates on the Diagnosis of Tuberculosis |
title_short | Review and Updates on the Diagnosis of Tuberculosis |
title_sort | review and updates on the diagnosis of tuberculosis |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570811/ https://www.ncbi.nlm.nih.gov/pubmed/36233689 http://dx.doi.org/10.3390/jcm11195826 |
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