Once engulfed in macrophage phagosomes, M. tuberculosis adopts various strategies to take advantage of the host environment for its intracellular survival. Histamine is an organic nitrogen-containing compound that mediates a plethora of cellular processes via different receptors, but the crosstalk mechanism between M. tuberculosis and HRH1 in macrophages is not clear.

Caseous granulomas are pathological hallmarks of tuberculosis (TB), and increasing evidence suggests that TB granuloma composition is highly temporally and spatially heterogenous in both animal models and humans. Traditional pathological techniques are limited in their ability to reveal the heterogeneity present in TB granulomas. Multiplex tissue imaging tools combined with powerful, high resolution spatial analysis have enabled the detection of various cell phenotypes, aiding in the visualization of the granuloma complex and revealing the interactions between immune cells and nonimmune cells. This updated understanding of tuberculous granuloma heterogeneity offers vital insights for researchers aiming to uncover the immunoregulatory mechanisms underlying granuloma formation during TB pathogenesis. More detailed granuloma classification systems will also be of use for precision medicine, and for identifying biological targets for host‐directed therapeutics in TB patients. This article is categorized under: Infectious Diseases > Genetics/Genomics/Epigenetics Infectious Diseases > Biomedical Engineering Infectious Diseases > Molecular and Cellular Physiology

No abstract

Most patients with active pulmonary tuberculosis (TB) are difficult to be differentiated from pneumonia (PN), especially those with acid-fast bacillus smear-negative (AFB -) and interferon-γ release assay-positive (IGRA + ) results. Thus, the aim of the present study was to develop a risk model of low-cost and rapid test for the diagnosis of AFB -IGRA + TB from PN. A total of 41 laboratory variables of 204 AFB -IGRA + TB and 156 PN participants were retrospectively analyzed. Candidate variables were identified by t-statistic test and univariate logistic model. The logistic regression analysis was used to construct the multivariate risk model and nomogram with internal and external validation. A total of 13 statistically differential variables were compared between AFB -IGRA + TB and PN by false discovery rate (FDR) and odds ratio (OR). By integrating five variables, including age, uric acid (UA), albumin (ALB), hemoglobin (Hb) and white blood cell counts (WBC), a multivariate risk model with a concordance index (C-index) of 0.7 (95% CI: 0.61, 0.8) was constructed. The nomogram showed that UA and Hb acted as protective factors with an OR <1, while age, WBC and ALB were risk factors for TB occurrence. Internal and external validation revealed that nomogram prediction was consistent with the actual observations. Collectively, it was revealed that an integration of five biomarkers (age, UA, ALB, Hb and WBC) may be used to quickly predict TB in AFB -IGRA + clinical samples from PN.