Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (774 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The dormant form of tuberculosis where the person shows no obvious symptoms and no sign of the causative agent (Mycobacterium tuberculosis) in the sputum despite being positive for tuberculosis infection skin test.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Latent Tuberculoses; Tuberculoses, Latent; Tuberculosis, Latent; Latent Tuberculosis Infection; Infection, Latent Tuberculosis; Infections, Latent Tuberculosis; Latent Tuberculosis Infections; Tuberculosis Infection, Latent.
Where it fits in the MeSH classification
- Broader topics
- Tuberculosis; Latent Infection
Related concepts
Research landscape
PubMed indexes 4,678 records tagged with the MeSH heading “Latent Tuberculosis” (counts retrieved 2026-10-29). 1,963 of them (42.0%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 2,668 records.
Reviews account for 694 records (14.8%) and meta-analyses for 151. Randomized controlled trials number 83 (1.8%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 72 |
| 2010-2019 | 2,668 |
| 2020 onward | 1,963 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 694 | 14.8% |
| Meta-analyses | 151 | 3.2% |
| Randomized controlled trials | 83 | 1.8% |
Method: PubMed query "Latent Tuberculosis"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in infectious diseases
Infectious disease evidence combines microbiology, epidemiology and clinical trials. Laboratory studies identify pathogens and how they cause harm; surveillance and outbreak investigations describe who is affected and how infection spreads; trials establish whether treatments and vaccines work. Results depend on place and time, because pathogens evolve and resistance patterns and population immunity change. Check when and where the data were collected, how cases were defined and how up to date the evidence is.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB).
- Closing excerpt
- Finally, we discuss the future development directions of ML applied to LTBI diagnosis.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (193 words)
Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 38017571 · DOI · PMC10685516
The interaction of macrophages and CD8 T cells in bronchoalveolar lavage fluid is associated with latent tuberculosis infection [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Mycobacterium tuberculosis (Mtb) infection, including active tuberculosis (TB) and latent Mtb infection (LTBI), leads to diverse outcomes owing to different host immune responses.
- Closing excerpt
- This study improves our understanding of the lung immune landscape during Mtb infection, which may inform future vaccine design for protective immunity.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (178 words)
Mycobacterium tuberculosis (Mtb) infection, including active tuberculosis (TB) and latent Mtb infection (LTBI), leads to diverse outcomes owing to different host immune responses. However, the immune mechanisms that govern the progression from LTBI to TB remain poorly defined in humans. Here, we profiled the lung immune cell populations within the bronchoalveolar lavage fluid (BALF) from patients with LTBI or TB using single-cell RNA sequencing (scRNA-seq). We found that Mtb infection substantially changed the immune cell compartments in the BALF, especially for the three subsets of macrophages, monocyte macrophage (MM)-CCL23, MM-FCN1, and MM-SPP1, which were found to be associated with the disease status of TB infection. Notably, MM-CCL23 cells derived from monocytes after stimulation with Mtb were characterized by high levels of chemokine (CCL23 and CXCL5) production and might serve as a marker for Mtb infection. The MM-CCL23 population mainly recruited CD8-CCR6 T cells through CCL20/CCR6, which was a prominent feature associated with protection immunity in LTBI. This study improves our understanding of the lung immune landscape during Mtb infection, which may inform future vaccine design for protective immunity.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 37470432 · DOI · PMC10399483
Identification of differentially recognized T cell epitopes in the spectrum of tuberculosis infection [3]
Abstract reading summary — source excerpts
- Opening excerpt
- There is still incomplete knowledge of which Mycobacterium tuberculosis (Mtb) antigens can trigger distinct T cell responses at different stages of infection.
- Closing excerpt
- This proteome-wide screen of T cell reactivity identified infection stage-specific epitopes and antigens for potential use in diagnostics and measuring Mtb-specific immune responses.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (155 words)
There is still incomplete knowledge of which Mycobacterium tuberculosis (Mtb) antigens can trigger distinct T cell responses at different stages of infection. Here, a proteome-wide screen of 20,610 Mtb-derived peptides in 21 patients mid-treatment for active tuberculosis (ATB) reveals IFNγ-specific T cell responses against 137 unique epitopes. Of these, 16% are recognized by two or more participants and predominantly derived from cell wall and cell processes antigens. There is differential recognition of antigens, including TB vaccine candidate antigens, between ATB participants and interferon-gamma release assay (IGRA + /-) individuals. We developed an ATB-specific peptide pool (ATB116) consisting of epitopes exclusively recognized by ATB participants. This pool can distinguish patients with pulmonary ATB from IGRA + /- individuals from various geographical locations, with a sensitivity of over 60% and a specificity exceeding 80%. This proteome-wide screen of T cell reactivity identified infection stage-specific epitopes and antigens for potential use in diagnostics and measuring Mtb-specific immune responses.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 38278794 · DOI · PMC10817963
Immunoinformatic Design and Evaluation of a Multi-Epitope mRNA Vaccine RP14914P Targeting Latent Tuberculosis Infection [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Background: Latent tuberculosis infection (LTBI) is the principal reservoir for active tuberculosis, with >85% of cases attributable to reactivation.
- Closing excerpt
- However, these computational predictions require thorough experimental validation to confirm the vaccine's immunogenicity and protective efficacy.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (248 words)
Background: Latent tuberculosis infection (LTBI) is the principal reservoir for active tuberculosis, with >85% of cases attributable to reactivation. Bacillus Calmette-Guérin fails to block this transition, leaving a critical gap in prevention. Methods: An immunoinformatics/reverse-vaccinology pipeline was applied to seven dormancy-related antigens retrieved from Mycobrowser. T-cell epitopes were predicted with NetMHCI/IIpan-4.1 and B-cell epitopes with ABCpred; antigenicity, allergenicity, and toxicity were evaluated with VaxiJen, AllerTOP, and ToxinPred. Secondary/tertiary structures were modeled with PSIPRED and AlphaFold-3; docking to Toll-like receptors (TLR) 2/4 and 100 ns molecular dynamics simulations assessed complex stability. Immune responses were simulated with C-ImmSim, and the mRNA sequence was human-codon-optimized using ExpOptimizer. Results: The resulting construct, RP14914P, encodes 14 cytotoxic T lymphocyte, 9 helper T lymphocyte, and 14 B-cell epitopes within an 866-aa, 90.4 kDa polypeptide. Antigenicity score = 0.7797, immunogenicity score = 8.58629. and no toxicity or allergenicity was predicted. Physicochemical analysis: instability index = 28.65, and solubility = 0.513. Estimated population coverage is 82.35% and 99.67% for Human Leukocyte Antigen (HLA)-I and HLA-II globally. Docking energies: -1477.8 kcal/mol (TLR2) and -1480.1 kcal/mol (TLR4). Molecular dynamics trajectories confirm stable binding. Immune simulation predicts potent activation of Natural Killer cells, macrophages, and dendritic cells, Th1 polarization, high interferon-γ/interleukin-2 secretion, and durable memory. Conclusions: In silico analyses predict that RP14914P exhibits favorable immunogenicity, safety, and broad population coverage, suggesting its potential as a promising mRNA vaccine candidate to prevent LTBI reactivation. However, these computational predictions require thorough experimental validation to confirm the vaccine's immunogenicity and protective efficacy.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41901750 · DOI · PMC13029540
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling [5]
- Research article Latent Mycobacterium tuberculosis infection [6]
- Research article Management of latent Mycobacterium tuberculosis infection: WHO guidelines for low tuberculosis burden countries [7]
- Research article Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection [8]
- Research article The global prevalence of latent tuberculosis: a systematic review and meta-analysis [9]
- Research article The cascade of care in diagnosis and treatment of latent tuberculosis infection: a systematic review and meta-analysis [10]
References
- Li LS, Yang L, Zhuang L, Ye ZY, Zhao WG, Gong WP. From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning. . 2023. PMID 38017571 · DOI · PMC10685516
- Yang Q, Qi F, Ye T, Li J, Xu G, He X, et al.. The interaction of macrophages and CD8 T cells in bronchoalveolar lavage fluid is associated with latent tuberculosis infection. . 2023. PMID 37470432 · DOI · PMC10399483
- Panda S, Morgan J, Cheng C, Saito M, Gilman RH, Ciobanu N, et al.. Identification of differentially recognized T cell epitopes in the spectrum of tuberculosis infection. . 2024. PMID 38278794 · DOI · PMC10817963
- Tian Y, Zhang M, Ali SL, Abduldayeva A, Zhou S, An Y, et al.. Immunoinformatic Design and Evaluation of a Multi-Epitope mRNA Vaccine RP14914P Targeting Latent Tuberculosis Infection. . 2026. PMID 41901750 · DOI · PMC13029540
- Houben RM, Dodd PJ. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. PLoS Med. 2016. PMID 27780211 · DOI · PMC5079585
- Getahun H, Matteelli A, Chaisson RE, Raviglione M. Latent Mycobacterium tuberculosis infection. N Engl J Med. 2015. PMID 26017823 · DOI
- Getahun H, Matteelli A, Abubakar I, Aziz MA, Baddeley A, Barreira D, et al.. Management of latent Mycobacterium tuberculosis infection: WHO guidelines for low tuberculosis burden countries. Eur Respir J. 2015. PMID 26405286 · DOI · PMC4664608
- Drain PK, Bajema KL, Dowdy D, Dheda K, Naidoo K, Schumacher SG, et al.. Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection. Clin Microbiol Rev. 2018. PMID 30021818 · DOI · PMC6148193
- Cohen A, Mathiasen VD, Schön T, Wejse C. The global prevalence of latent tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2019. PMID 31221810 · DOI
- Alsdurf H, Hill PC, Matteelli A, Getahun H, Menzies D. The cascade of care in diagnosis and treatment of latent tuberculosis infection: a systematic review and meta-analysis. Lancet Infect Dis. 2016. PMID 27522233 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.