ArticleFrontiers in cellular and infection microbiology2017
Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.
Article in Frontiers in cellular and infection microbiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Diagnostic performance of biomarkers for differentiating active tuberculosis from latent tuberculosis: a systematic review and Bayesian network meta-analysis.Frontiers in microbiology · 2024Pooled it
- Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods.Diseases (Basel, Switzerland) · 2026Review
- HeptaTB Dx: a diagnostic model leveraging cuproptosis-ferroptosis crosstalk for distinguishing latent from active tuberculosis.Microbiology spectrum · 2026Article
- Immunological testing and machine learning in detecting latent tuberculosis among high-risk groups (nature review).Frontiers in medicine · 2025Review
- Review and Updates on the Diagnosis of Tuberculosis.Journal of clinical medicine · 2022Review
- Use of DosR and Rpf antigens from Mycobacterium tuberculosis to screen for latent and relapse tuberculosis infection in a tuberculosis endemic community of Huainan City.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2022Article
- Using TBAg/PHA Ratio for Monitoring TB Treatment: A Prospective Multicenter Study.Journal of clinical medicine · 2022Article
- A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis.Frontiers in cellular and infection microbiology · 2022Article
- Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy.Frontiers in microbiology · 2021Review
- Lymphocyte Non-Specific Function Detection Facilitating the Stratification ofFrontiers in immunology · 2021Article
- Predicting Grating Orientations With Cross-Frequency Coupling and Least Absolute Shrinkage and Selection Operator in V1 and V4 of Rhesus Monkeys.Frontiers in computational neuroscience · 2020Article
- Combination of Xpert MTB/RIF and TBAg/PHA Ratio for Prompt Diagnosis of Active Tuberculosis: A Two-Center Prospective Cohort Study.Frontiers in medicine · 2020Article
- A Model Based on the Combination of IFN-γ, IP-10, Ferritin and 25-Hydroxyvitamin D for Discriminating Latent From Active Tuberculosis in Children.Frontiers in microbiology · 2019Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) is a leading global public health problem. To achieve the end TB strategy, non-invasive markers for diagnosis and treatment monitoring of TB disease are urgently needed, especially in high-endemic countries such as China. Interferon-gamma release assays (IGRAs) and tuberculin skin test (TST), frequently used immunological methods for TB detection, are intrinsically unable to discriminate active tuberculosis (ATB) from latent tuberculosis infection (LTBI). Thus, the specificity of these methods in the diagnosis of ATB is dependent upon the local prevalence of LTBI. The pathogen-detecting methods such as acid-fast staining and culture, all have limitations in clinical application. ImmunoScore (IS) is a new promising prognostic tool which was commonly used in tumor. However, the importance of host immunity has also been demonstrated in TB pathogenesis, which implies the possibility of using IS model for ATB diagnosis and therapy monitoring. In the present study, we focused on the performance of IS model in the differentiation between ATB and LTBI and in treatment monitoring of TB disease. We have totally screened five immunological markers (four non-specific markers and one TB-specific marker) and successfully established IS model by using Lasso logistic regression analysis. As expected, the IS model can effectively distinguish ATB from LTBI (with a sensitivity of 95.7% and a specificity of 92.1%) and also has potential value in the treatment monitoring of TB disease.
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