Performance of microbiological tests for tuberculosis diagnostic according to the type of respiratory specimen: A 10-year retrospective study

Unmasking Pott Disease: A Diagnostic Challenge Mimicking Metastatic Lung Cancer - A Case Report

BACKGROUND Tuberculosis spondylitis, also known as Pott disease, is a rare form of the ancient infectious disease tuberculosis. It bears a complex clinical and radiological profile, often necessitating an extensive differential diagnostic approach for accurate identification. The disease was named in honor of the first diagnosed patient, highlighting its historical significance. CASE REPORT We report a case involving a 69-year-old male initially admitted to the Pulmonology Department under the suspicion of a left lung tumor, as indicated by a chest X-ray. A subsequent CT scan revealed a tumor-hilar mass, enlarged subcarineal lymph nodes, and a pathological mass at the C6/C7 vertebral level. Despite negative tuberculosis tests, the patient was misdiagnosed with disseminated lung cancer with spinal metastases. Following radiotherapy targeting the cervical and thoracic spine, the definitive diagnosis of spinal tuberculosis was confirmed via histopathological examination from an open biopsy of the C6 and C7 vertebrae. CONCLUSIONS Tuberculosis can present with an insidious and misleading clinical picture, often mimicking other diseases such as cancer. Early and accurate diagnostic processes are crucial for effective treatment. This case underscores the importance of considering tuberculosis in the differential diagnosis, especially when clinical presentations are ambiguous.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification. We identified 41 DMs and 39 pathways that changed during intensive phase completion. Notably, levels of certain amino acids including histidine, bile acids, and metabolites of purine metabolism were dramatically increased. The altered pathways included those involved in the metabolism of amino acids, glycerophospholipids, and purine. At the end of treatment, 44 DMs were discovered. The levels of glutamine, bile acids, and lysophosphatidylinositol significantly increased compared to baseline; the levels of carboxylates and hypotaurine declined. In addition, 37 pathways principally associated with the metabolism of amino acids, carbohydrates, and glycan altered at treatment completion. The potential of each DM for diagnosing TB was examined using a cohort consisting of TB patients, those with latent infections, and controls. Logistic regression revealed four biomarkers (taurine, methionine, glutamine, and acetyl-carnitine) that exhibited excellent performance in differential diagnosis. In conclusion, we identified metabolites that could serve as useful metabolic signatures for TB management and elucidated underlying biological processes affected by the crosstalk between host and TB pathogen during treatment.

Diagnostic value of the dual-modal imaging radiomics model for subpleural pulmonary lesions

PURPOSE

To investigate the clinical value of the radiomics model of grayscale ultrasound (GUS) and contrast-enhanced ultrasound (CEUS) to diagnosis subpleural pulmonary tuberculosis and nonpulmonary tuberculosis based on GUS and CEUS images.

METHODS

This study included 221 patients with 228 lesions diagnosed using the composite reference standard. The patients were randomly divided into training (n = 183) and test (n = 45) cohorts in an 8:2 ratio. The regions of interest of the GUS and CEUS images were manually segmented to extract the radiomic features. The GUS, CEUS and GUS+CEUS radiomics models were constructed via the multistep selection of highly correlated features. Receiver operating characteristic curves of the different models were plotted, and the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value (NPV) of the different models were compared.

RESULTS

Following Least Absolute Shrinkage and Selection Operator dimension reduction we selected 4, 9, and 11 features to construct the GUS, CEUS, and GUS+CEUS radiomics models, respectively. The AUC values of the three groups in the test cohort were 0.689, 0.748 and 0.779, respectively, and they did not differ significantly. In the test cohort, the GUS+CEUS radiomics model exhibited the highest AUC (0.779), accuracy (75.56%), and NPV (68.7%) of the three models.

CONCLUSIONS

The GUS+CEUS radiomics model possesses good clinical value in diagnosing pulmonary tuberculosis.