Autophagy-related molecular clusters identified as indicators for distinguishing active and latent TB infection in pediatric patients.

BACKGROUND: Autophagy is crucial for controlling the manifestation of tuberculosis. This study intends to discover autophagy-related molecular clusters as biomarkers for discriminating between latent tuberculosis (LTBI) and active tuberculosis (ATB) in children through gene expression profile analysis. METHODS: The expression of autophagy modulators was examined in pediatric patients with LTBI and ATB utilizing public datasets from the Gene Expression Omnibus (GEO) collection (GSE39939 and GSE39940). RESULTS: In a training dataset (GSE39939), patients with LTBI and ATB exhibited the expression of autophagy-related genes connected with their active immune responses. Two molecular clusters associated with autophagy were identified. Compared to Cluster 1, Cluster 2 was distinguished through decreased adaptive cellular immune response and enhanced inflammatory activation, according to single-sample gene set enrichment analysis (ssGSEA). Per the study of gene set variation, Cluster 2's differentially expressed genes (DEGs) played a role in synthesizing transfer RNA, DNA repair and recombination, and primary immunodeficiency. The peak variation efficiency, root mean square error, and area under the curve (AUC) (AUC = 0.950) were all lowered in random forest models. Finally, a seven-gene-dependent random forest profile was created utilizing the CD247, MAN1C1, FAM84B, HSZFP36, SLC16A10, DTX3, and SIRT4 genes, which performed well against the validation dataset GSE139940 (AUC = 0.888). The nomogram calibration and decision curves performed well in identifying ATB from LTBI. CONCLUSIONS: In summary, according to the present investigation, autophagy and the immunopathology of TB might be correlated. Furthermore, this investigation established a compelling prediction expression profile for measuring autophagy subtype development risks, which might be employed as possible biomarkers in children to differentiate ATB from LTBI.

Characterizing the Spectrum of Latent Mycobacterium tuberculosis in the Cynomolgus Macaque Model: Clinical, Immunologic, and Imaging Features of Evolution.

Mycobacterium tuberculosis infection outcomes have been described as active tuberculosis or latent infection but a spectrum of outcomes is now recognized. We used a nonhuman primate model, which recapitulates human infection, to characterize the clinical, microbiologic, and radiographic patterns associated with developing latent M. tuberculosis infection. Four patterns were identified. "Controllers" had normal erythrocyte sedimentation rate (ESR) without M. tuberculosis growth in bronchoalveolar lavage or gastric aspirate (BAL/GA). "Early subclinicals" showed transient ESR elevation and/or M. tuberculosis growth on BAL/GA for 60 days postinfection, "mid subclinicals" were positive for 90 days, and "late subclinicals" were positive intermittently, despite the absence of clinical disease. Variability was noted regarding granuloma formation, lung/lymph node metabolic activity, lung/lymph node bacterial burden, gross pathology, and extrapulmonary disease. Like human M. tuberculosis infection, this highlights the heterogeneity associated with the establishment of latent infection, underscoring the need to understand the clinical spectrum and risk factors associated with severe disease.

Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children.

BACKGROUND: Cell death plays a crucial role in the progression of active tuberculosis (ATB) from latent infection (LTBI). Cuproptosis, a novel programmed cell death, has been reported to be associated with the pathology of various diseases. We aimed to identify cuproptosis-related molecular subtypes as biomarkers for distinguishing ATB from LTBI in pediatric patients. METHOD: The expression profiles of cuproptosis regulators and immune characteristics in pediatric patients with ATB and LTBI were analyzed based on GSE39939 downloaded from the Gene Expression Omnibus. From the 52 ATB samples, we investigated the molecular subtypes based on differentially expressed cuproptosis-related genes (DE-CRGs) via consensus clustering and related immune cell infiltration. Subtype-specific differentially expressed genes (DEGs) were found using the weighted gene co-expression network analysis. The optimum machine model was then determined by comparing the performance of the eXtreme Gradient Boost (XGB), the random forest model (RF), the general linear model (GLM), and the support vector machine model (SVM). Nomogram and test datasets (GSE39940) were used to verify the prediction accuracy. RESULTS: Nine DE-CRGs (NFE2L2, NLRP3, FDX1, LIPT1, PDHB, MTF1, GLS, DBT, and DLST) associated with active immune responses were ascertained between ATB and LTBI patients. Two cuproptosis-related molecular subtypes were defined in ATB pediatrics. Single sample gene set enrichment analysis suggested that compared with Subtype 2, Subtype 1 was characterized by decreased lymphocytes and increased inflammatory activation. Gene set variation analysis showed that cluster-specific DEGs in Subtype 1 were closely associated with immune and inflammation responses and energy and amino acids metabolism. The SVM model exhibited the best discriminative performance with a higher area under the curve (AUC = 0.983) and relatively lower root mean square and residual error. A final 5-gene-based (MAN1C1, DKFZP434N035, SIRT4, BPGM, and APBA2) SVM model was created, demonstrating satisfactory performance in the test datasets (AUC = 0.905). The decision curve analysis and nomogram calibration curve also revealed the accuracy of differentiating ATB from LTBI in children. CONCLUSION: Our study suggested that cuproptosis might be associated with the immunopathology of Mycobacterium tuberculosis infection in children. Additionally, we built a satisfactory prediction model to assess the cuproptosis subtype risk in ATB, which can be used as a reliable biomarker for the distinguishment between pediatric ATB and LTBI.

Serum biomarkers in patients with unilateral or bilateral active pulmonary tuberculosis: Immunological networks and promising diagnostic applications.

The present observational study was designed to characterize the integrative profile of serum soluble mediators to describe the immunological networks associated with clinical findings and identify putative biomarkers for diagnosis and prognosis of active tuberculosis. The study population comprises 163 volunteers, including 84 patients with active pulmonary tuberculosis/(TB), and 79 controls/(C). Soluble mediators were measured by multiplexed assay. Data analysis demonstrated that the levels of CCL3, CCL5, CXCL10, IL-1ß, IL-6, IFN-γ, IL-1Ra, IL-4, IL-10, PDGF, VEGF, G-CSF, IL-7 were increased in TB as compared to C. Patients with bilateral pulmonary involvement/(TB-BI) exhibited higher levels of CXCL8, IL-6 and TNF with distinct biomarker signatures (CCL11, CCL2, TNF and IL-10) as compared to patients with unilateral infiltrates/(TB-UNI). Analysis of biomarker networks based in correlation power graph demonstrated small number of strong connections in TB and TB-BI. The search for biomarkers with relevant implications to understand the pathogenetic mechanisms and useful as complementary diagnosis tool of active TB pointed out the excellent performance of single analysis of IL-6 or CXCL10 and the stepwise combination of IL-6 â†’ CXCL10 (Accuracy = 84 %; 80 % and 88 %, respectively). Together, our finding demonstrated that immunological networks of serum soluble biomarkers in TB patients differ according to the unilateral or bilateral pulmonary involvement and may have relevant implications to understand the pathogenetic mechanisms involved in the clinical outcome of Mtb infection.

The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm.

BACKGROUND: Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis infection. METHODS: A total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study .46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model's predictive performance for dentifying active and latent tuberculosis infection. RESULTS: After verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing set. CONCLUSIONS: The machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection.

Tuberculosis screening practices and outcomes in an australian dialysis unit.

BACKGROUND: The World Health Organisation (WHO) recommends all dialysis patients undertake routine screening for latent tuberculosis infection (LTBI) in high income countries such as Australia. However, we employ a targeted screening approach in our Australian dialysis unit in line with local and some international guidelines. We analysed our practices to assess the validity of our approach. METHODS: A retrospective review of new dialysis patients during the period 2012-2018 was undertaken. Patient records were reviewed for basic demographic data, comorbidities, LTBI screening using Quantiferon Gold (QFG), and outcomes, including episodes of active TB, to June 2020. RESULTS: 472 patients were included. WHO high risk country of origin patients accounted for 22% (n = 103). 229 patients (48.5%) were screened using QFG. The single main indication for screening was transplantation waitlisting. 34 patients had a positive QFG result. Active tuberculosis developed in two patients during the observation period. Both occurred in the screened cohort, the cases having previously tested negative via QFG at 11 and 16 months, prior to the development of active tuberculosis. No patients in the unscreened cohort developed active tuberculosis during the observation period. WHO high risk country of origin was associated with positive QFG status, odds ratio 10.4 (95% CI 3.3-31.2). CONCLUSION: The data failed to show a benefit from widening of the screening program within our dialysis unit. However, a much larger sample size will be required to confidently assess the impact of the current approach on patient outcomes. Analysis of current screening practices and outcomes across all Australian dialysis services is warranted to assess the risks and benefits of widening the screening practices to include all dialysis patients as recommended by the WHO.

Excluding Participants With Mycobacteria Infections From Clinical Trials: A Critical Consideration in Evaluating the Efficacy of BCG Against COVID-19.

In the context of the coronavirus disease 2019 (COVID-19) pandemic, Bacillus Calmette-Guérin (BCG), a tuberculosis (TB) vaccine, has been investigated for its potential to prevent COVID-19 with conflicting outcomes. Currently, over 50 clinical trials have been conducted to assess the effectiveness of BCG in preventing COVID-19, but the results have shown considerable variations. After scrutinizing the data, it was discovered that some trials had enrolled individuals with active TB, latent TB infection, or a history of TB. This finding raises concerns about the reliability and validity of the trial outcomes. In this study, we explore the potential consequences of including these participants in clinical trials, including impaired host immunity, immune exhaustion, and the potential masking of the BCG vaccine's protective efficacy against COVID-19 by persistent mycobacterial infections. We also put forth several suggestions for future clinical trials. Our study underscores the criticality of excluding individuals with active or latent TB from clinical trials evaluating the efficacy of BCG in preventing COVID-19.

Decoding the similarities and specific differences between latent and active tuberculosis infections based on consistently differential expression networks.

Although intensive efforts have been devoted to investigating latent tuberculosis (LTB) and active tuberculosis (PTB) infections, the similarities and differences in the host responses to these two closely associated stages remain elusive, probably due to the difficulty in identifying informative genes related to LTB using traditional methods. Herein, we developed a framework known as the consistently differential expression network to identify tuberculosis (TB)-related gene pairs by combining microarray profiles and protein-protein interactions. We thus obtained 774 and 693 pairs corresponding to the PTB and LTB stages, respectively. The PTB-specific genes showed higher expression values and fold-changes than the LTB-specific genes. Furthermore, the PTB-related pairs generally had higher expression correlations and would be more activated compared to their LTB-related counterparts. The module analysis implied that the detected gene pairs tended to cluster in the topological and functional modules. Functional analysis indicated that the LTB- and PTB-specific genes were enriched in different pathways and had remarkably different locations in the NF-κB signaling pathway. Finally, we showed that the identified genes and gene pairs had the potential to distinguish TB patients in different disease stages and could be considered as drug targets for the specific treatment of patients with LTB or PTB.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.

BACKGROUND: The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. METHODS: T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. RESULTS: A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively. CONCLUSIONS: Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

Whole blood GBP5 protein levels in patients with and without active tuberculosis.

BACKGROUND: The host blood transcriptional levels of several genes, such as guanylate binding protein 5 (GBP5), have been reported as potential biomarkers for active tuberculosis (aTB) diagnosis. The aim of this study was to investigate whole blood GBP5 protein levels in aTB and non-tuberculosis patients. METHODS: An in-house immunoassay for testing GBP5 protein levels in whole blood was developed, and suspected aTB patients were recruited. Whole blood samples were collected and tested at enrolment using interferon-gamma release assay (IGRA) and the GBP5 assay. RESULTS: A total of 470 participants were enrolled, and 232 and 238 patients were finally diagnosed with aTB and non-TB, respectively. The GBP5 protein levels of aTB patients were significantly higher than those of non-tuberculosis patients (p < 0.001), and the area under the ROC curve of the GBP5 assay for aTB diagnosis was 0.76. The reactivity of the GBP5 assay between pulmonary and extrapulmonary tuberculosis patients was comparable (p = 0.661). With the optimal cut-off value, the sensitivity and specificity of the GBP5 assay for diagnosing aTB were 78.02 and 66.81%, respectively, while those of IGRA were 77.59 and 76.47%. The combination of the GBP5 assay and IGRA results in 88.52% accuracy for diagnosing aTB in 63.83% of suspected patients with a positive predictive value of 89.57% and a negative predictive value of 87.59%. CONCLUSIONS: Whole blood GBP5 protein is a valuable biomarker for diagnosing of aTB. This study provides an important idea for realizing the clinical application of whole blood transcriptomics findings by immunological methods.

The Impact of Borderline Quantiferon-TB Gold Plus Results for Latent Tuberculosis Screening under Routine Conditions in a Low-Endemicity Setting.

Quantiferon-TB Gold Plus (QFT-Plus) is an interferon gamma release assay used to diagnose latent tuberculosis (LTB). A borderline range (0.20 to 0.99 IU/ml) around the cutoff (0.35 IU/ml) has been suggested for the earlier QFT version. Our aims were to evaluate the borderline range for QFT-Plus and the contribution of the new TB2 antigen tube. QFT-Plus results were collected from clinical laboratories in Sweden and linked to incident active TB within 3 to 24 months using the national TB registry. Among QFT-Plus results from 58,539 patients, 83% were negative (<0.20 IU/ml), 2.4% were borderline negative (0.20 to 0.34 IU/ml), 3.4% were borderline positive (0.35 to 0.99 IU/ml), 9.6% were positive (≥1.0 IU/ml), and 1.6% were indeterminate. Follow-up tests after initial borderline results were negative (<0.20 IU/ml) in 38.3%, without any cases of incident active TB within 2 years. Applying the 0.35-IU/ml cutoff, 1.5% of TB1 and TB2 results were discrepant, of which 52% were within the borderline range. A TB2 result of ≥0.35 IU/ml with a TB1 result of <0.20 IU/ml was found in 0.4% (231/58,539) of all included baseline QFT-Plus test results, including 1.8% (1/55) of incident TB cases. A borderline range for QFT-Plus is clinically useful as more than one-third of those with borderline results are convincingly negative upon retesting, without developing incident active TB. The TB2 tube contribution to LTB diagnosis appears limited.

The TBAg/PHA ratio in T-SPOT.TB assay has high prospective value in the diagnosis of active tuberculosis: a multicenter study in China.

BACKGROUND: The positive rate of pathogenic examination about tuberculosis is low. It is still difficult to achieve early diagnosis for some TB patients. The value of Interferon-gamma release assays (IGRA) in the diagnosis of active tuberculosis remains controversial. The purpose of this multicenter prospective study was to verify and validate the role of TBAg/PHA ratio (TB-specific antigen to phytohaemagglutinin) of T-SPOT.TB assay in diagnosing ATB. METHODS: We prospectively enrolled 2390 suspected pulmonary tuberculosis patients with positive T-SPOT assay results from three tertiary hospitals. RESULTS: A total of 1549 ATB (active tuberculosis) patients (including 1091 confirmed and 458 probable ATB) and 724 non-tuberculosis (non-TB) patients with positive T-SPOT results were included. The results of this study showed that ESAT-6 and CFP-10 in the T-SPOT.TB assay were significantly higher in the ATB group compared with the non-TB group, while PHA was lower in the ATB group. Results of ESAT-6, CFP-10 and PHA show a certain diagnostic performance, but moderate sensitivity and specificity. The TBAg/PHA ratio, a further calculation of ESAT-6, CFP-10 and PHA in T-SPOT.TB assay showed improved performance in the diagnosis of active Tuberculosis. If using the threshold value of 0.2004, the specificity and sensitivity of TBAg/PHA ratio in distinguishing ATB from non-TB were 92.3% and 74.4%, PPV was 95.4, PLR was 9.6. CONCLUSION: By recalculating the results of T-SPOT.TB Assay, the TBAg/PHA ratio shows high prospect value in the diagnosis of active tuberculosis in high prediction areas.

Gene expression pattern analysis using dual-color RT-MLPA and integrative genome-wide association studies of eQTL for tuberculosis suscepitibility.

BACKGROUND: When infected with Mycobacterium tuberculosis, only a small proportion of the population will develop active TB, and the role of host genetic factors in different TB infection status was not fully understood. METHODS: Forty-three patients with active tuberculosis and 49 with latent tuberculosis were enrolled in the prospective cohort. Expressing levels of 27 candidate mRNAs, which were previously demonstrated to differentially expressed in latent and active TB, were measured by dual color reverse transcription multiplex ligation dependent probe amplification assay (dcRT-MLPA). Using expression levels of these mRNAs as quantitative traits, associations between expression abundance and genome-wild single nucleotide polymorphisms (SNPs) were calculated. Finally, identified candidate SNPs were further assessed for their associations with TB infection status in a validation cohort with 313 Chinese Han cases. RESULTS: We identified 9 differentially expressed mRNAs including il7r, il4, il8, tnfrsf1b, pgm5, ccl19, il2ra, marco and fpr1 in the prospective cohort. Through expression quantitative trait loci mapping, we screened out 8 SNPs associated with these mRNAs. Then, CG genotype of the SNP rs62292160 was finally verified to be significantly associated with higher transcription levels of IL4 in LTBI than in TB patients. CONCLUSION: We reported that the SNP rs62292160 in Chinese Han population may link to higher expression of il4 in latent tuberculosis. Our findings provided a new genetic variation locus for further exploration of the mechanisms of TB and a possible target for TB genetic susceptibility studies, which might aid the clinical decision to precision treatment of TB.

Heme oxygenase-1 and neopterin plasma/serum levels and their role in diagnosing active and latent TB among HIV/TB co-infected patients: a cross sectional study.

BACKGROUND: Tuberculosis (TB) diagnosis in the context of HIV co-infection remains challenging. Heme oxygenase 1 (HO-1) and neopterin have been validated as potential biomarkers for TB diagnosis. Latent TB infection (LTBI) is diagnosed using tuberculin skin test (TST) and interferon gamma release assays (T-Spot and QuantiFERON TB gold tests, respectively). However, these tests have shown challenges and yet diagnosing LTBI is important for the overall control of TB. This study was conducted to determine the levels of H0-1 and neopterin, and their role in the diagnosis of TB among individuals enrolled in the Community Health and Social Network of Tuberculosis (COHSONET) study and the Kampala TB Drug Resistance Survey (KDRS). METHODS: This was a nested cross-sectional study. Plasma and serum samples collected from 140 patients at Mulago National Referral Hospital, Kampala Uganda were used. M.tb culture was performed on sputum to confirm active TB(ATB) and QuantiFERON TB gold test to confirm latent TB infection (LTBI). ELISAs were performed to determine the levels of HO-1 and neopterin. Data analysis was done using t-test and Receiver Operating Characteristic curves to determine the diagnostic accuracy. RESULTS: HO-1 levels among active tuberculosis (ATB)/HIV-infected patients and LTBI/HIV-infected patients were 10.7 ng/ml (IQR: 7.3-12.7 ng/ml) and 7.5 ng/ml (IQR: 5.4-14.1 ng/ml) respectively. Neopterin levels among ATB/HIV-positive patients and LTBI/HIV-positive patients were 11.7 ng/ml (IQR: 5.2.4 ng/ml) and 8.8 ng/ml (IQR: 2.4-19.8 ng/ml), respectively. HO-1 showed a sensitivity of 58.57% and a specificity of 67.14% with area under the curve (AUC) of 0.57 when used to discriminate between ATB and LTB. Neopterin showed an AUC of 0.62 with a sensitivity of 57.14% and a specificity of 60.0% when used to distinguish ATB from LTB. CONCLUSION: There was no in significant difference in HO-1 concentration levels of ATB individuals compared to LTB individuals. There was a significant difference in neopterin concentrations levels of ATB individuals compared to latently infected individuals. Findings from this study, show that HO-1 and neopterin have poor ability to distinguish between ATB and LTB.

Interferon-gamma release assay levels and risk of progression to active tuberculosis: a systematic review and dose-response meta-regression analysis.

BACKGROUND: Identifying and treating individuals with high risk of progression from latent tuberculosis infection to active tuberculosis (TB) disease is critical for eliminating the disease. We aimed to conduct a systematic review and meta-regression analysis to quantify the dose-response relationship between interferon-gamma release assay (IGRA) levels and the risk of progression to active TB. METHODS: We searched PubMed and Embase from 1 January 2001 to 10 May 2020 for longitudinal studies that reported the risk of progression from latent to active TB as a function of baseline IGRA values. We used a novel Bayesian meta-regression method to pool effect sizes from included studies and generate a continuous dose-response risk curve. Our modeling framework enabled us to incorporate random effects across studies, and include data with different IGRA ranges across studies. The quality of included studies were assessed using the Newcastle-Ottawa scale (NOS). RESULTS: We included 34 studies representing 581,956 person-years of follow-up with a total of 788 incident cases of TB in the meta-regression analysis. Higher levels of interferon-gamma were associated with increased risk of progression to active tuberculosis. In the dose-response curve, the risk increased sharply between interferon-gamma levels 0 and 5 IU/ml, after which the risk continued to increase moderately but at a slower pace until reaching about 15 IU/ml where the risk levels off. Compared to 0 IU/ml, the relative risk of progression to active TB among those with interferon-gamma levels of 0.35, 1, 5, 10, 15, and 20 IU/ml were: 1.64 (1.28-2.08), 2.90 (2.02-3.88), 11.38 (6.64-16.38), 19.00 (13.08-26.90), 21.82 (14.65-32.57), and 22.31 (15.43-33.00), respectively. The dose-response relationship remains consistent when limiting the analysis to studies that scored highest in the NOS. CONCLUSION: The current practice of dichotomizing IGRA test results simplifies the TB infection disease continuum. Evaluating IGRA test results over a continuous scale could enable the identification of individuals at greatest risk of progression to active TB.

Metformin and Rifampicin combination augments active to latent tuberculosis conversion: A computational study.

Tuberculosis, a global threat, is a highly infectious disease intensified by the emergence of drug-resistant strains. In tuberculosis disease spectrum, a typical situation is a dormant or latent phase where a person exposed to Mycobacterium tuberculosis has the reservoir of the disease that may or may not result in an active state. Existence of the dormant state is retarding the eradication of tuberculosis. Transcription of several genes helps M. tuberculosis to survive in nonreplicative mode. DosR transcription factor is the hallmark for this genesis. Diabetes mellitus is a predisposition factor leading to the development of tuberculosis and latent tuberculosis. High plasma insulin concentrations in the prediabetic state can increase the tuberculosis bacterium. On the other hand, antidiabetic drug metformin is known to reduce active tuberculosis disease when provided in combination with antitubercular therapy. However, the effect of the same on latent tuberculosis is still unknown. In the present work using tools of computational biology, we have tried to find the consequence of adding metformin in combination with rifampicin, a well-known antitubercular drug, on molecular mechanisms of latent tuberculosis. We have investigated whether metformin and rifampicin interact with DosR machinery or not. Our results indicate that if metformin-bound DosR-DNA complex binds with rifampicin, it will result in the conversion of active tuberculosis to latent tuberculosis.

Pediatric Tuberculosis Disease during Years of High Refugee Arrivals: A 6-Year National Prospective Surveillance Study.

BACKGROUND: In Europe, surveillance and monitoring of pediatric tuberculosis (TB) remains important, particularly in the light of migration in recent years. The aim of the study was to evaluate incidence rates of childhood TB and detailed diagnostic pathways and treatment. METHODS: Data were collected through the Swiss Pediatric Surveillance Unit (SPSU) from December 2013 to November 2019. Monthly -notifications are obtained from the 33 pediatric hospitals in the SPSU, and a detailed questionnaire was sent out upon notification. Inclusion criteria were children and adolescents aged up to 15 years with culture- or molecular-confirmed TB disease or for whom a treatment with ≥3 antimycobacterial drugs had been initiated. Data were compared with age-matched notification data from the Swiss Federal Office of Public Health (FOPH). RESULTS: Of the 172 cases notified to SPSU, a detailed questionnaire was returned for 161 (93%) children, of which 139 met the inclusion criteria. Reasons for exclusion were age >15 years, double reporting, and not fulfilling the criteria for TB disease. During the same time period, 172 pediatric TB cases were reported to the FOPH, resulting in an incidence of 2.1 per 100,000, ranging from 1.4 to 2.8 per year, without a clear trend over time. In the 64 (46.0%) foreign-born children, incidence rates were higher and peaked in 2016, with 13.7 per 100,000 (p = 0.018). The median interval between arrival in Switzerland and TB diagnosis was 5 (IQR 1-21) months, and 80% were diagnosed within 24 months of arrival. In 58% of the cases, TB disease was confirmed by culture or molecular assays. Age >10 years, presence of fever, or weight loss were independent factors associated with confirmed TB. CONCLUSION: The annual pediatric TB incidence rate only varied among foreign-born children and was highest in 2016 when refugee influx peaked in Europe. Importantly, most foreign-born children with TB were diagnosed within 2 years after arrival in Switzerland. Thus, the early period after arrival in Switzerland is associated with a higher risk of TB disease in children, and this should be considered for screening guidance in refugees.

Clinical evaluation of QuantiFERON®-TB Gold Plus directly compared with QuantiFERON®-TB Gold In-Tube and T-Spot®.TB for active pulmonary tuberculosis in the elderly.

BACKGROUND: Reduced sensitivity of tuberculosis (TB) interferon-γ release assays (IGRAs) among the elderly has been reported, which is presumably due to diminished immune function. We evaluated the clinical performance of QuantiFERON®-TB Gold plus (QFT-Plus) compared with QuantiFERON®-TB Gold In-Tube (QFT-GIT) and T-Spot®.TB (T-SPOT) in the elderly. METHODS: Blood samples for all three IGRAs were drawn at the same time from all the participants. Both CD4 and CD8 T-cell counts in patients' peripheral blood were also measured. RESULTS: A total of 142 active pulmonary TB patients (median age: 84, interquartile range; 76-89 years) were recruited. The sensitivities of the tested IGRAs (excluding invalid/indeterminate cases) were as follows: QFT-Plus, 93.6%; QFT-GIT, 91.4%; and T-SPOT 68.1%. QFT-Plus displayed significantly higher sensitivity than T-SPOT (p < 0.00001). All three IGRAs exhibited the same specificity (100%), as assessed using blood samples from healthy, low TB-risk individuals (n = 118; median age: 39, IQR; 32-47 years). Positivity in 43 active TB patients with CD4 T-cell counts <200/µL, 39 of whom were ≥80 years of age, was as follows: QFT-Plus, 83.7%; QFT-GIT, 74.4%; and T-SPOT, 58.1%. The difference between TB2-TB1 of the QFT-Plus assay was statistically correlated with CD8 but not CD4 T-cell counts in blood (r = 0.193, p = 0.0298). CONCLUSIONS: QFT-Plus showed high performance in the detection of TB infection in patients irrespective of their advanced age (≥80 years) or lower CD4 counts. QFT-Plus can be useful for the diagnosis of TB infection in all patients, including those who are elderly and/or immunocompromised.

Next-Generation Digital Biomarkers for Tuberculosis and Antibiotic Stewardship: Perspective on Novel Molecular Digital Biomarkers in Sweat, Saliva, and Exhaled Breath.

The internet of health care things enables a remote connection between health care professionals and patients wearing smart biosensors. Wearable smart devices are potentially affordable, sensitive, specific, user-friendly, rapid, robust, lab-independent, and deliverable to the end user for point-of-care testing. The datasets derived from these devices are known as digital biomarkers. They represent a novel patient-centered approach to collecting longitudinal, context-derived health insights. Adding automated, analytical smartphone applications will enable their use in high-, middle-, and low-income countries. So far, digital biomarkers have been focused primarily on accelerometer data and heart rate due to well-established sensors originating from the consumer market. Novel emerging smart biosensors will detect biomarkers (or compounds) independent of a lab and noninvasively in sweat, saliva, and exhaled breath. These molecular digital biomarkers are a promising novel approach to reduce the burden from 2 major infectious diseases with urgent unmet needs: tuberculosis and infections with multidrug resistant pathogens. Active tuberculosis (aTbc) is one of the deadliest diseases from an infectious agent. However, a simple and reliable test for its detection is still missing. Furthermore, inappropriate antimicrobial use leads to the development of antimicrobial resistance, which is associated with high mortality and health care costs. From this perspective, we discuss the innovative approach of a noninvasive and lab-independent collection of novel biomarkers to detect aTbc, which at the same time may additionally serve as a scalable therapeutic drug monitoring approach for antibiotics. These molecular digital biomarkers are next-generation digital biomarkers and have the potential to shape the future of infectious diseases.

Depression and recovery of IL-17A secretion in mitogen responses in patients with active tuberculosis-a prospective observational study.

BACKGROUND/PURPOSE: T-helper cell 17 (Th17) is a distinct subset of CD4+ T lymphocytes that is important in the pathogenesis of Mycobacterium tuberculosis infection. This study aims to investigate the characteristics of interleukin (IL)-17A and Th17-related cytokines after stimulation with phytohemagglutinin in patients with active tuberculosis (TB). METHODS: This prospective cohort study enrolled patients with culture-confirmed active TB. QuantiFERON-TB Gold In-Tube (QFT-GIT) assay was performed upon TB diagnosis and at 2 months after TB treatment. Their non-TB-specific secretion of IL-17A and Th17-related cytokines were measured in supernatants of mitogen tubes in QFT-GIT and compared to those of active TB contacts with or without latent TB infection. We analyzed the association between IL-17A secretions and TB presentation and treatment outcomes. RESULTS: A total of 108 patients with TB and 64 non-TB cases were enrolled. The secretion of IL-17A, IL-21, IL-23, and IL-6 were lower in active TB patients upon TB diagnosis. In active TB patients, lower IL-17A secretions were associated with higher grades of sputum smear. In the multivariate analysis, lower IL-17A secretions served as an independent factor associated with 2-month culture non-conversion (odds ratio 23.04, 95% confidence interval [CI] 1.69-84.78) and on-treatment mortality (hazard ratio 28.54, 95% CI 1.30-99.25). The levels of IL-23, and IL-6 significantly increased after 2 months of anti-TB treatment. CONCLUSION: The non-TB-specific IL-17A secretions were lower in active TB patients upon TB diagnosis and associated with higher disease severity and worse treatment outcomes. Trend of recovery of the depressed Th17-related cytokines was noted after effective anti-TB treatment.