<i>Mycobacterium tuberculosis</i>antigen-specific T-cell responses in smear-negative pulmonary tuberculosis patients

Esmael, Ahmed; Abebe, Tamrat; Mihret, Adane; Mussa, Daniel; Neway, Sebsib; Ernst, Joel D.; Rengarajan, Jyothi; Wassie, Liya; Howe, Rawleigh

doi:10.1093/cei/uxac049

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Besides, it has been observed that the expression of Ki-67 would decrease following treatment [18,19]. The heightened Ki-67 expression in CD4 + and CD8 + T cell subpopulations following in vitro restimulation with MTB antigen in PTM patients suggested a greater burden of mycobacterium antigen in slow responders, hinting at the possibility of PTM being a chronic infectious disease involving both innate and acquired immunity in response to TB antigen [20]. The disparity in CD4 + and CD8 + T cell subset activation implied that immune dysregulation might be a characteristic of PTM.…”

Section: Discussionmentioning

confidence: 99%

Clinical predictive model for identifying high-risk factors in Pleurisy Tuberculoma patients

Gao,

Yang,

Wang

et al. 2024

Preprint

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Introduction : Pleurisy tuberculoma(PTM) manifests as a tumor-like lesion in either the parietal or visceral pleura. The majority of PTM cases arise during the management of tuberculous pleural effusion, underscoring a strong association between the two conditions, despite the precise pathogenesis remaining elusive. To explore the high-risk factors for the development of PTM, we constructed a clinical predictive model aimed at providing more powerful clues for PTM treatment. Methods A retrospective study was conducted on patients with PTM or TPE who were treated at Nanjing Chest Hospital and Nanjing Second Hospital between March 2013 and April 2024. The demographic information and clinical characteristics were sorted out, and the model was constructed by logistic regression, lasso regression, and best subset selection. ROC curves, calibration curves, DCA curves, and clinical impact curves were analyzed for all constructed models to determine the final clinical prediction model. In the internal validation, the performance of the selected model was further evaluated. Results The final predictive model incorporated two risk factors associated with PTM development: Ki-67+CD4+T and Ki-67+CD8+T. Despite its limited calibration, the model has a strong discriminating ability (c-statistic = 0.987; 95% confidence interval: 0.969-1.000) and holds significant clinical value (as indicated by DCA analysis, maintaining a high net benefit across a wide threshold range). During internal validation, the model exhibited excellent predictive performance (AUC: 0.98; Accuracy: 0.96; Kappa: 0.93; Sensitivity: 0.95; Specificity: 0.96). Conclusion A predictive model based on two indicators may help clinicians make early predictions of PTM based on laboratory data. The results of this study may motivate healthcare providers to further investigate the immune role of T lymphocytes in PTM individuals, enhancing comprehension of the development of PTM.

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Section: Discussionmentioning

confidence: 99%

Clinical predictive model for identifying high-risk factors in Pleurisy Tuberculoma patients

Gao,

Yang,

Wang

et al. 2024

Preprint

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“…In our study, the proportion of sputum smear-negative TB in these patients was the lowest. TB patients with smear-negative were found to have higher levels of immune cells and IFN- γ [ 41 , 42 ]. As mentioned above, both glycemic control and statin use can improve immune functions.…”

Section: Discussionmentioning

confidence: 99%

Interaction of Glycemic Control and Statin Use on Diabetes‐Tuberculosis Treatment Outcome: A Nested Case‐Control Study

Meng,

Zheng,

et al. 2024

Canadian Journal of Infectious Diseases and Medical Microbiolog

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This study aims to explore the interaction of glycemic control and statin use on the treatment outcomes of pulmonary tuberculosis‐diabetes comorbidity (PTB‐DM) patients. A nested case‐control study was conducted in a tuberculosis patients’ cohort. We defined cases as patients who experienced unfavorable outcomes. Glycemic control was estimated at the baseline. Statin use was obtained from medical records. The multivariate logistic regression models were developed, and the interaction table invented by Andersson was adopted to analyze the interaction of glycemic control and statin use on treatment outcomes. A total of 2,047 patients were included in this study. There was a significant interaction between glycemic control and statin use on the treatment outcomes. Patients with good glycemic control and no statin use (OR = 0.464, 95% CI: 0.360–0.623) had a lower risk of unfavorable outcomes than those with poor glycemic control and statin use (OR = 0.604, 95% CI: 0.401–0.734). Patients with good glycemic control and statin use had the lowest risk of unfavorable outcomes (OR = 0.394, 95% CI: 0.264–0.521). Glycemic control in diabetes‐tuberculosis treatment should be paid considerable attention. Patients can benefit from statin use even if they have poor glycemic control. Patients with good glycemic control and statin use can have the best outcomes.

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“…T uberculosis (TB) continues to pose a considerable global health burden, and smear-negative pulmonary tuberculosis (SNPT) represents a significant proportion of TB cases. 1 , 2 Smear-negative pulmonary tuberculosis is defined by the absence of detectable bacteria in sputum smears, despite clinical and radiological indications of pulmonary TB. 3 The limited bacterial presence in SNPT complicates diagnosis using conventional techniques, resulting in delays in treatment initiation and heightened risk of disease transmission.…”

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confidence: 99%

Retrospective analysis of the clinical utility of multi-cytokine profiles in smear-negative pulmonary tuberculosis

Li,

Zhao,

Han

et al. 2024

SMJ

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Objectives: To evaluate cytokine profiles and interferon-gamma release assay (IGRA) for their diagnostic capabilities in the differentiation of tuberculosis (TB) from non-TB conditions, as well as smear-negative pulmonary tuberculosis (SNPT) from smear-positive pulmonary tuberculosis (SPPT). Methods: A total of 125 participants were included, 77 of whom had TB and 48 who didn’t, and demographic, clinical, and laboratory data were collected, including cytokine levels and IGRA results. The TB patients were further divided into 2 subgroups: SNPT (n=42) and SPPT (n=35). Results: Compared to non-TB, the TB group had lower BMI, higher WBC, neutrophils, monocytes, ESR and CRP ( p <0.05). TB patients showed higher IL-2, IL-6, IFN-γ, IL-8 ( p <0.001) and higher IGRA positivity (88.3% versus [vs.] 29.2%, p <0.001). Between SNPT and SPPT, moderate effect sizes were observed for IFN-α, IL-2, IL-10, IL-8 (Cohen’s d 0.59-0.76), with lower IGRA positivity in SNPT (81.0% vs. 97.1%, p =0.015). ROC analysis indicated IFN-α, IL-2, IL-10, IL-8 had moderate accuracy for SNPT diagnosis (AUCs 0.668-0.734), and combining these improved accuracy (AUC 0.759, 80% sensitivity, 64.2% specificity). Conclusion: A multi-biomarker approach combining these cytokines demonstrates enhanced diagnostic accuracy for tuberculosis.

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Mycobacterium tuberculosisantigen-specific T-cell responses in smear-negative pulmonary tuberculosis patients

Cited by 4 publications

References 26 publications

Clinical predictive model for identifying high-risk factors in Pleurisy Tuberculoma patients

Clinical predictive model for identifying high-risk factors in Pleurisy Tuberculoma patients

Interaction of Glycemic Control and Statin Use on Diabetes‐Tuberculosis Treatment Outcome: A Nested Case‐Control Study

Retrospective analysis of the clinical utility of multi-cytokine profiles in smear-negative pulmonary tuberculosis

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