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Tuberculosis (TB) is caused by Mycobacterium tuberculosis and is one of the primary causes of death worldwide. Rapid and accurate diagnosis of TB is one of the most direct means to reduce the incidence of TB. In this study, urinary proteomic profiling of TB patients and non-TB individual controls (HCs) was performed, and differentially expressed urinary proteins between TB and HCs were compared and exclusively expressed proteins in TB patients were selected to establish a clinically useful disease marker panel. In total, these top 11 targeted proteins with 265 peptides were scheduled for multiple reaction monitoring validation analysis by using urine samples from 52 TB patients and 52 HCs. The result demonstrated that a three-protein combination out of the five-protein panel (namely P22352, Q9P121, P15151, Q13291, and Q8NDA2) exhibited sensitivity rate of 82.7% in the diagnosis of TB. Furthermore, the three-protein combination could differentiate TB from the latent tuberculosis (LTB) effectively, which exhibited specificity rate of 92.3% for the diagnosis of TB from the LTB category. Although more numbers of clinical samples are required for further verification, the results provided preliminary evidence that this "three-protein combination" out of the five-protein panel could probably be a novel TB diagnostic biomarker in clinical application., (© 2021 International Union of Biochemistry and Molecular Biology.)

Background: Complement functions as an important host defense system and complement C5 and C7 have been implicated in immunopathology of tuberculosis. However, little is known about the role of other complement components in tuberculosis., Methods: Complement gene expression in peripheral blood mononuclear cells of tuberculosis patients and controls were determined using whole genome transcriptional microarray assays. The mRNA and protein levels of three C1q components, C1qA, C1qB, and C1qC, were further validated by qRT-PCR and enzyme-linked immunosorbent assay, respectively. The percentages of C1q expression in CD14 positive cells were determined by flow cytometry. Finally, C1qC protein level was quantified in the pleural fluid of tuberculosis and non-tuberculosis pleurisy., Results: C1q expression increases significantly in the peripheral blood of patients with active tuberculosis compared to healthy controls and individuals with latent TB infection. The percentage of C1q-expressing CD14 positive cells is significantly increased in active TB patients. C1q expression in the peripheral blood correlates with sputum smear positivity in tuberculosis patients and is reduced after anti-tuberculosis chemotherapy. Notably, receiver operating characteristic analysis showed that C1qC mRNA levels in peripheral blood efficiently discriminate active from latent tuberculosis infection and healthy controls. Additionally, C1qC protein level in pleural effusion shows improved power in discriminating tuberculosis from non-tuberculosis pleurisy when compared to other inflammatory markers, such as IL-6 and TNF-α., Conclusions: C1q expression correlates with active disease in human tuberculosis. C1q could be a potential diagnostic marker to discriminate active tuberculosis from latent tuberculosis infection as well as tuberculosis pleurisy from non-tuberculosis pleurisy.

Background: Perturbed iron homeostasis is a risk factor for tuberculosis (TB) progression and an indicator of TB treatment failure and mortality. Few studies have evaluated iron homeostasis as a TB diagnostic biomarker.Methods: We recruited participants with TB, latent TB infection (LTBI), cured TB (RxTB), pneumonia (PN) and healthy controls (HCs). We measured serum levels of three iron biomarkers including serum iron, ferritin and transferrin, then established and validated our prediction model.Results: We observed and verified that the three iron biomarker levels correlated with patient status (TB, HC, LTBI, RxTB or PN) and with the degree of lung damage and bacillary load in patients with TB. We then built a TB prediction model, neural network (NNET), incorporating the data of the three iron biomarkers. The model showed good performance for diagnosis of TB, with 83% (95% CI 77 to 87) sensitivity and 86% (95% CI 83 to 89) specificity in the training data set (n=663) and 70% (95% CI 58 to 79) sensitivity and 92% (95% CI 86 to 96) specificity in the test data set (n=220). The area under the curves (AUCs) of the NNET model to discriminate TB from HC, LTBI, RxTB and PN were all >0.83. Independent validation of the NNET model in a separate cohort (n=967) produced an AUC of 0.88 (95% CI 0.85 to 0.91) with 74% (95% CI 71 to 77) sensitivity and 92% (95% CI 87 to 96) specificity.Conclusions: The established NNET TB prediction model discriminated TB from HC, LTBI, RxTB and PN in a large cohort of patients. This diagnostic assay may augment current TB diagnostics. [ABSTRACT FROM AUTHOR]

Background: As a novel biomarker of inflammation, procalcitonin (PCT) has proven useful to guide antibiotic therapy in intensive care unit (ICU). However, there are controversial on mortality. The aim of this study was to evaluate the utility of PCT-guided antibiotic therapy in critically ill adults and determine whether studies are sufficient.Methods: A systematic search in PubMed, Embase and Cochrane was performed. We included only randomized controlled trials which compared the safety and efficacy between PCT-guided or standard antibiotic therapy groups in ICU adults. Trial sequential analysis and GARDE approach were performed.Results: Fifteen studies met our criteria for inclusion finally, with a cumulative number of 5486 ICU patients. There was no difference in 28-day mortality between two compared groups (P = 0.626), but significant decreases were observed in the duration of antibiotic therapy for the first episode of infection (P < 0.001) and length of hospitalization (P = 0.049). No significant deference was found in secondary endpoints except total duration of antibiotic therapy (P < 0.001). TSA revealed that the pooled sample sizes of 28-day mortality and the duration of antibiotic therapy for the first episode of infection exceeded the estimated required information size, but not the length of hospitalization.Conclusions: PCT-guided therapy is a better and safer algorithm to be applied into ICU patients, which appears no effect on 28-day mortality while performing preferable utility in reducing the duration of antibiotic therapy for the first episode of infection. More studies on these endpoints were not recommended. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is a serious infectious disease with high infection and mortality rates. 5%–10% of the latent tuberculosis infections (LTBI) are likely to develop into active TB, and there are currently no clinical biomarkers that can distinguish between LTBI, active TB and other non-tuberculosis populations. Therefore, it is necessary to develop rapid diagnostic methods for active TB and LTBI. In this study, urinary metabolome of 30 active TB samples and the same number of LTBI and non-TB control samples were identified and analyzed by UPLC-Q Exactive MS. In total, 3744 metabolite components were obtained in ESI- mode and 4086 in ESI + mode. Orthogonal partial least square discriminant analysis (OPLS-DA) and hierarchical cluster analysis (HCA) showed that there were significant differences among LTBI, active TB and non-TB. Six differential metabolites were screened in positive and negative mode, 3-hexenoic acid, glutathione (GSH), glycochenodeoxycholate-3-sulfate, N -[4′-hydroxy-(E)-cinnamoyl]- l -aspartic acid, deoxyribose 5-phosphate and histamine. The overlapping pathways differential metabolites involved were mainly related to immune regulation and urea cycle. The results showed that the urine metabolism of TB patients was disordered and many metabolic pathways changed. Multivariate statistical analysis revealed that GSH and histamine were selected as potential molecular markers, with area under curve of receiver operating characteristic curve over 0.75. Among the multiple differential metabolites, GSH and histamine changed to varying degrees in active TB, LTBI and the non-TB control group. The levels of GSH and histamine in 48 urinary samples were measured by ELISA in validation phase, and the result in our study provided the potential for non-invasive biomarkers of TB. [ABSTRACT FROM AUTHOR]