ArticleJournal of inflammation research2024
Development of a Nomogram Based on Transcriptional Signatures, IFN-γ Response and Neutrophils for Diagnosis of Tuberculosis.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- HeptaTB Dx: a diagnostic model leveraging cuproptosis-ferroptosis crosstalk for distinguishing latent from active tuberculosis.Microbiology spectrum · 2026Article
- A Year in Review on Tuberculosis and Non-tuberculous Mycobacteria Disease: A 2025 Update for Clinicians and Scientists.Pathogens & immunity · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Tuberculosis (TB) is a major global health threat and its diagnosis remains challenging. This study aimed to develop a nomogram that incorporated peripheral blood transcriptional signatures and other blood tests for the diagnosis of tuberculosis. Patients and Methods: Patients with TB, patients with other definite pulmonary diseases (OPD), individuals with latent tuberculosis infection (LTBI), and healthy controls (HC) were retrospectively enrolled between May 2017 and April 2018. The results of the interferon-γ release assay (IGRA) and blood counts were obtained from medical records, and the transcripts of 10 genes were detected using reverse transcription polymerase chain reaction (RT-PCR). Variable selection was performed using least absolute shrinkage and selection operator regression (LASSO) and multivariate logistic regression was performed for the optimal prediction model with backward direction. The model was displayed as a nomogram, and its performance was evaluated for discrimination ability, calibration ability, and clinical usefulness. Internal validation of the prediction model was conducted using bootstrap resampling. Results: A total of 185 participants were enrolled, including 84 patients with TB and 101 controls. A prediction nomogram composed of IGRA, percentage of neutrophils, and expression levels of CD64, granzyme A (GZMA), and PR/SET domain 1 (PRDM1) was established. The nomogram demonstrated good discrimination, with an unadjusted area under the curve (AUC) of 0.914 (95% CI: 0.875-0.954) and a bootstrap-corrected AUC of 0.914 (95% CI: 0.874-0.947). With a cutoff value of 0.519, the sensitivity and specificity for discriminating PTB from controls were 0.81 and 0.871, respectively. The nomogram also showed good calibration with the Hosmer-Lemeshow test (P=0.58) and good clinical practicality displayed by the decision curve analysis. Conclusion: A nomogram composed of IGRA, percentage of neutrophils, and expression of CD64, GZMA, and PRDM1 was established. The nomogram demonstrated a sensitivity and specificity of 81% and 87%, respectively, for differentiating TB from controls.
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