ArticleDiagnostic microbiology and infectious disease2014
A proteomics approach to the identification of plasma biomarkers for latent tuberculosis infection.
Article in Diagnostic microbiology and infectious disease, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Plasma protein signatures altered before and after tuberculosis diagnosis in a population-based cohort.Clinical proteomics · 2026Article
- Advances in the Diagnosis of Latent Tuberculosis Infection.Infection and drug resistance · 2025Review
- Wireless radiation and health: making the case for proteomics research of individual sensitivity.Frontiers in public health · 2024Article
- Protein and Microbial Biomarkers in Sputum Discern Acute and Latent Tuberculosis in Investigation of Pastoral Ethiopian Cohort.Frontiers in cellular and infection microbiology · 2021Article
- Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis.JCI insight · 2020Article
- Identification of Mycobacterium tuberculosis Peptides in Serum Extracellular Vesicles from Persons with Latent Tuberculosis Infection.Journal of clinical microbiology · 2020Article
- Review of the impact of MALDI-TOF MS in public health and hospital hygiene, 2018.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2019Article
- Proteomic profiling for plasma biomarkers of tuberculosis progression.Molecular medicine reports · 2018Article
- Characterization of plasma proteins in children of differentOncotarget · 2017Article
- Identification of Host Proteins Predictive of Early Stage Mycobacterium tuberculosis Infection.EBioMedicine · 2017Article
- Diagnostic 'omics' for active tuberculosis.BMC medicine · 2016Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A proteomic analysis was performed to screen the potential latent tuberculosis infection (LTBI) biomarkers. A training set of spectra was used to generate diagnostic models, and a blind testing set was used to determine the accuracy of the models. Candidate peptides were identified using nano-liquid chromatography-electrospray ionization-tandem mass spectrometry. Based on the training set results, 3 diagnostic models recognized LTBI subjects with good cross-validation accuracy. In the blind testing set, LTBI subjects could be identified with sensitivities and specificities of 85.20% to 88.90% and 85.7% to 100%, respectively. Additionally, 14 potential LTBI biomarkers were identified, and all proteins were identified for the first time through proteomics in the plasma of healthy, latently infected individuals. In all, proteomic pattern analyses can increase the accuracy of LTBI diagnosis, and the data presented here provide novel insights into potential mechanisms involved in LTBI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.