Evaluation of a lateral flow assay-based IFN-γ release assay as a point-of-care test for the diagnosis of latent tuberculosis infection

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

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.

Evaluation of ichroma™ COVID-19 interferon gamma release assay for detection of vaccine-induced immunity in healthcare workers

OBJECTIVES

We compared the performance of a new interferon gamma release assay (IGRA) format assay, the ichroma™ COVID-19 IGRA (IGRA-SARS), with that of the widely used QuantiFERON SARS-CoV-2 ELISA kit (QFN-SARS) in vaccinated healthcare workers (HCWs). Additionally, we analyzed the long-term changes in IGRA results after the final vaccine dose.

METHODS

A total of 383 specimens from 281 HCWs were enrolled in this study, and the results of SARS-IGRA and QFN-SARS assays were compared. In addition, we performed the receive operator curve analysis to estimate the optimal cut-off value for IGRA-SARS.

RESULTS

For all specimens, IGRA-SARS and QFN-SARS showed 75.7% and 64.2% of the positive results, respectively. The absolute agreement between IGRA-SARS and QFN-SARS was 80.0%, and the Fleiss' κ value was 0.525, indicating moderate agreement. ROC curve analysis of the IGRA-SARS results showed a cut-off value of >0.254 IU/mL, which was consistent with the manufacturer's specifications. The positive rates of both IGRA assays decreased significantly after a postvaccination period of 6 months.

CONCLUSIONS

IGRA-SARS showed acceptable performance in the detection of vaccine-induced immunity against COVID-19; however, harmonization of IGRA assays has not yet been achieved. Additionally, the significant decline of positive rates of IGRA after the last vaccination would support the necessity of booster vaccination after a postvaccination period of 6 months.