Tuberculosis lymph node granulomas: using transcriptomics to discover immunopathology paradigms and guide host-directed therapy

Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiating between Benign and Malignant Mediastinal and Hilar Lymph Nodes

INTRODUCTION

The aim of the study was to establish an ultrasonographic radiomics machine learning model based on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malignant mediastinal and hilar lymph nodes (LNs).

METHODS

The clinical and ultrasonographic image data of 197 patients were retrospectively analyzed. The radiomics features extracted by EBUS-based radiomics were analyzed by the least absolute shrinkage and selection operator. Then, we used a support vector machine (SVM) algorithm to establish an EBUS-based radiomics model. A total of 205 lesions were randomly divided into training (n = 143) and validation (n = 62) groups. The diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 13 stable radiomics features with non-zero coefficients were selected. The SVM model exhibited promising performance in both groups. In the training group, the SVM model achieved an ROC area under the curve (AUC) of 0.892 (95% CI: 0.885-0.899), with an accuracy of 85.3%, sensitivity of 93.2%, and specificity of 79.8%. In the validation group, the SVM model had an ROC AUC of 0.906 (95% CI: 0.890-0.923), an accuracy of 74.2%, a sensitivity of 70.3%, and a specificity of 74.1%.

CONCLUSION

The EBUS-based radiomics model can be used to differentiate mediastinal and hilar benign and malignant LNs. The SVM model demonstrated excellent potential as a diagnostic tool in clinical practice.

Next-Gen Dual Transcriptomics for Adult Extrapulmonary Tuberculosis Biomarkers and Host-Pathogen Interplay in Human Cells: A Strategic Review

Tuberculosis (TB) is a major public health concern that results in significant morbidity and mortality, particularly in middle- to low-income countries. Extra-pulmonary tuberculosis (EPTB) in adults is a form of TB that affects organs other than the lungs and is challenging to diagnose and treat due to a lack of accurate early diagnostic markers and inadequate knowledge of host immunity. Next-generation sequencing-based approaches have shown potential for identifying diagnostic biomarkers and host immune responses related to EPTB. This strategic review discusses on the significance using primary human cells and cell lines for in vitro transcriptomic studies on common forms of EPTB, such as lymph node TB, brain TB, bone TB, and endometrial TB to derive potential insights. While organoids have shown promise as a model system, primary cell lines still remain a valuable tool for studying host-pathogen interplay due to their conserved immune system, non-iPSC origin, and lack of heterogeneity in cell population. This review outlines a basic workflow for researchers interested in performing transcriptomics studies in EPTB, and also discusses the potential of cell-line based dual RNA-Seq technology for deciphering comprehensive transcriptomic signatures, host-pathogen interplay, and biomarkers from the host and Mycobacterium tuberculosis. Thus, emphasizing the implementation of this technique which can significantly contribute to the global anti-TB effort and advance our understanding of EPTB.

Graphical Abstract