Evaluation of Xpert MTB Host Response assay for the diagnosis of patients with Active tuberculosis in China

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The host peripheral blood 3-gene (GBP5, DUSP3 and KLF2) was found and verified to have high diagnostic value for active tuberculosis (ATB)(2, 3). The clinical diagnostic value of the new 3-genes ( GBP5, DUSP3 and TBP ) modified by Cepheid company has not been evaluated Methods: We used a retrospective cohort study of 297 clinical ATB patients, 103 patients with other pulmonary diseases (OPD), and 79 healthy subjects are used as healthy controls (HC).The receiver operating characteristic curve ( ROC curve ) was used to analyze the value of TB score in the diagnosis of ATB. Results: The AUC of TB score between ATB group and HC group was 0.879 and OPD group, respectively. The treatment duration and bacterial burden of ATB will affect the diagnostic efficacy of TB score. When only ATB patients within 3 days were included, the AUC was 0.895 and 0.715 and 0.715 for ATB and AUC was 0.952 and 0.778, respectively. Positive patients within 3 days were included, the TB score AUC was 0.936 and 0.788 for ATB from HC and OPD. Conclusion : 3-gene TB score test can be used as a rapid blood screening test for clinical ATB patients, and its own bacterial load is an important factor affecting its detection. In addition, with increasing treatment duration in ATB patients, TB scores have increased, with some potential to monitor treatment response. Xpert MTB Host Response assay Active tuberculosis Diagnose Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Tuberculosis is a global disease caused by Mycobacterium tuberculosis (MTB) infection, which is mainly respiratory tract transmitted and seriously harmful to human health(4). The early and accurate diagnosis of the disease is the key to controlling the tuberculosis epidemic(5). According to the latest WHO 2023 Global Tuberculosis Report, there were about 10.6 million new patients worldwide in 2022, and about 3.1 million of them were not diagnosed or registered(6)。 At present, the laboratory diagnosis of TB mainly relies on smear, culture and molecular biology techniques to test the etiology of sputum or invasive sampling samples, but sputum testing is difficult to apply in children, critically ill patients and patients without sputum(7). γ -Interferon release test (interferon gamma release assays, IGRAs) assists the diagnosis of TB by detecting the release of specific IFN- γ in blood samples, but some non-tuberculous mycobacterial infections may cause false positive IGRA results, so it is still necessary to find new detection targets to make up for the lack of clinical diagnosis and treatment(8)。 As early as 2014, WHO made the development of sputum-based test reagents a priority for TB diagnosis(1). Previous studies have shown that host transcription markers in whole blood can be used for the diagnosis of TB(9-11). A multi-center global cohort study found that TB scores, composed of GBP5, DUSP3 and KLF2 genes, had a high diagnostic efficacy in distinguishing ATB from healthy people, latent tuberculosis infection (LTBI) and other lung diseases(2). Subsequently, several cohort studies have validated the better diagnostic performance of TB score in ATB patients(3, 12-16). At present, Cepheid（Sunnyvale, CA, USA) has further optimized the principle and detection method of the three genes, replacing the original KLF2 gene with the housekeeping gene TBP, and the improved three genes have not yet been verified in clinical patients. This paper analyzed the expression of GBP5, DUSP3 and TBP and the diagnostic value of TB score in whole blood of clinical ATB patients. Subject and Methods 1.1 Subject selection This study used a retrospective cohort study of patients recruited from the Pulmonary and Respiratory Departments of the Infectious Diseases Hospital affiliated to Soochow University from May 22 to August 29,2023, with inclusion criteria of age older than 14 years. Finally, 297 ATB patients were enrolled, and the diagnosis met the Chinese diagnostic criteria for tuberculosis, WS288-2017, 103 patients with other pulmonary diseases (OPD), including pneumonia, lung cancer, Nontuberculous Mycobacteria, etc. In addition, 79 healthy controls (HC) with normal imaging examination and negative IGRA test were included. Subject clinical data and concurrent laboratory test results were collected for later analysis. This study was approved by the Ethics Committee of the Affiliated Infectious Diseases Hospital of Soochow University. 1.2 Reagents and Methods 1.2.1 instruments and reagents Bactec MGIT 960 Instrument (Becton Dickinson, Cockeysville, MD, USA); MBP 64 Antigen Test kit (Capilia, Hangzhou, China); The Xpert MTB/RIF kit (Cepheid, Sunnyvale, USA); GeneXpert Instrument (Cepheid, Sunnyvale, USA); The Xpert-MTB-HR kit (Cepheid, Sunnyvale, USA); The QuantiFERON-TB Gold (QFT) test kit (QIAGEN, Germany) 1.2.2. Experimental methods MTB etiology test: Sputum or bronchoalveolar lavage fluid samples（BALF） were collected for bacterial smear, tuberculosis culture and molecular biological detection. Bacterial smears were examined by acid-fast staining. Tuberculosis culture was cultured using Bactec MGIT 960 instrument, and culture positive strains were confirmed using MBP 64 antigen detection kit, and 42 days without positive was judged negative. Molecular biology testing was performed using the Xpert MTB / RIF kit, detected on a GeneXpert instrument. Three gene tests: Peripheral blood of the subjects was collected and anti-coagulated with EDKA-2K and mixed upside down.Within 3 hours, 50ul blood sample was added to the Xpert-MTB-HR kit and tested on the GeneXpert platform. The samples that were not detected in time need to be stored at 4 °, and the detection should be completed within 8 hours.TB score was calculated from the cycle threshold (threshold cycle, CT) = ([CT (GBP 5) + CT (DUSP 3)] / 2) -CT (TBP). IGRA test: operate according to the instructions of the QFT test kit. 1.3 Statistical analysis SPSS Statistics 26 software, Graph Pad Prism 9 software and R ( version 4.3.3 ) were used for data analysis and graphics drawing. Median mean, median, range, inter-quartile range, variance and standard deviation of TB scores were calculated for different populations. The area under the ROC curve（AUC）was used to evaluate diagnostic efficacy, the optimal diagnostic threshold was calculated with the Youden index, and the AUC of different ROC curves was compared by the DeLong’s test. Furthermore, pairwise comparisons of mean TB scores between different groups were performed using the Tukey's multiple comparison test. Grade correlation was analyzed by Spearson correlation, with P <0.05 indicating statistical significance. Results 2.1 Basic information of the subjects This study included 479 participants, including 297 ATB patients and 182 controls, and the controls included 103 OPD patients and 79 healthy individuals, and the basic information is shown in Table 1. There was no statistical difference in gender and age distribution between the ATB and OPD groups (p> 0.05). 14.8% ATB and 7.8% OPD had a previous history of TB (p> 0.05). At least one TB-related symptom was reported in 78.1% of ATB patients, compared with 64.1% of OPD patients (p =0.04). A younger age and higher proportion of women in the HC group compared with ATB and OPD (p <0.05). Of 297 ATB patients, sputum smear positive was 34.0%, culture positive 44.5% (118 / 256) and Xpert MTB / RIF positive 54.1% (120 / 222). ATB patients were divided into 6 groups, with 40% within 3 days of medication. Table 1: Clinical data of the three groups of study subjects ATB patients n（%） OPD n（%） Healthy controls n（%） Total 297 103 79 Age <65y 173(58.2) 47(45.6) 76(96.2) ≥65y 124(41.8) 56(54.4) 3(3.8) Sex Female 98 (33.0) 45(43.7) 59(74.7) Male 199(67.0) 58(56.3) 20(25.3) *Any TB symptoms No 65(21.9) 37(35.9) NA Yes 232(78.1) 65(64.1) NA History of TB 44(14.8) 8(7.8) NA Drug-resistant TB 43(14.5) NA NA Smear positive 101(34.0) 6/41(6.83) NA Culture positive（TB） 118/256(44.5) 0/39(0) NA GeneXpert MTB/RIF Positive or weak positive 120/222(54.1) 0/37(0) NA Negative 119/244(45.9) 37/37(100) NA Anti-TB treatment time 6m 22(7.4) NA NA 2.2 Distribution of genes and TB scores in the three population groups We compared the expression of genes GBP5 and DUSP3 and TB scores in the three populations. In ATB patients, the Ct value of GBP5 was significantly lower than in the other two groups (p <0.0001), although the Ct value in the OPD group was also significantly lower than HC group (p <0.05), the difference was relatively small (Figure 1A). The Ct value of the DUSP3 gene was significantly lower in the ATB group than in the HC group (p <0.001), but not significant from the OPD group (Figure 1B). The TB score values varied significantly between the three groups (p <0.0001), and the overall TB score was significantly lower in the ATB group compared to the other two groups (Figure 1C). 2.3 Diagnostic performance of the TB score for the ATB patients After analysis of the diagnostic performance of TB score, the AUC of TB score was 0.879 (95% CI, 0.844 ~ 0.914), and the optimum sensitivity was 76.09% and 93.27% specific by the Youden index (Figure 2A). When the specificity was fixed at 70%, the sensitivity of the TB score was 96.2%, satisfying the minimum TPP criteria for the triage test. In contrast, the TB score had a lower AUC for identifying ATB and OPD: 0.689 (95% CI, 0.628 to 0.750) (Fig. 2A). Next, we analyzed whether age, sex had an effect on the diagnostic performance of TB score in R language. A new model was included in the ROC model of TB score identifying ATB from OPD, and Delong’s tested no statistical difference in AUC between the two ROC models (p> 0.05) (Figure 2B). With the treatment of ATB patients, the results of pathogenic examination turned negative. We compared the positive detection rate of TB culture, Xpert MTB / RIF test and TB score for the enrolled ATB population. According to the results, the detection rate of ATB by TB score was much higher than culture and Xpert MTB / RIF in both the type of sample of sputum and BALF obtained from invasive sampling (Table 2). When TB score and TB culture were combined detection, the diagnostic efficacy was not significantly improved (p>0.05) (Figure 2C), and when TB score and Xpert MTB / RIF were combined diagnosed, the diagnostic efficacy was significantly improved compared with TB score alone (p <0.00001) (Figure 2D). Table 2 : Comparison of TB score, tuberculosis culture and Xpert MTB / RIF detection in the detection rate of ATB patients. Sample type Sputum sample BALF Detect method Xpert MTB / RIF MTB culture And Xpert MTB / RIF MTB culture Positive number 69 63 51 51 Negative number 59 100 42 42 Total 128 163 93 93 ATB, relevance ratio 0.5391 0.3865 0.5484 0.5484 The detection rate of ATB by TB score 0.7969 0.7546 0.7849 0.7634 2.3 Effect of treatment duration of ATB patients on TB score To assess the effect of treatment duration on the detection of TB scores, we divided ATB patients into six subgroups according to the duration of anti-TB drug use and observed the distribution of TB scores in the different subgroups. The results showed that after the start of treatment, the mean TB score of TB patients increased significantly, and decreased to the initial level 2 months later. With the further increase of treatment duration, the mean TB score increased from -1.591 to -0.972, gradually approached the cutoff value and tended to stabilize (Figure 3A). To reduce the interference of treatment factors, we separately analyzed ATB patients within 3 days and found that TB scores within 2 months of medication was higher than that of patients within 3 days of medication, but the difference was not statistically significant (p> 0.05). However, when administered, older than 2 months, the patients' TB scores changed significantly (p <0.01) (Figure 3B). Next, we did the ROC curve to further analyze whether the difference in treatment duration will affect the detection of ATB patients by TB score.The results showed that the AUC of TB score was 0.895 (95%CI: 0.857~0.934) in distinguishing ATB patients and healthy people within 3 days of medication, and the optimal sensitivity was 79.68% and the specificity was 92.41%. When OPD patients were used as a negative control, the AUC of ATB patients within 3 days of medication was 0.715 (95%CI: 0.642 ~0.770), and the optimal sensitivity was 76.47% and the specificity was 61.17%. For ATB patients within 2 months of medication, the AUC distinguishing TB score from OPD patients was 0.720 (95%CI: 0.651 to 0.790), similar to ATB patients within 3 days (Figure 3C). 2.3 Correlation between bacterial load and TB score We collected concurrent laboratory findings from ATB patients and performed descriptive analysis of TB scores for smear microscopy, Xpert MTB / RIF testing, TB culture, and IGRA test results as shown in Table 3. Patients with smear positive ATB had lower TB scores (mean: -1.92) than those with smear negative (mean: -1.16). Similarly, ATB patients with high Xpert MTB / RIF semi-quantitative results ( mean : -2.07 ) were significantly lower than those with very low semi-quantitative results ( mean : -1.35 ).The TB scoreof ATB patients with positive tuberculosis culture ( mean : -1.83 ) was lower than that of ATB patients with negative culture ( mean : -1.18 ).In addition, the TB score of ATB patients who tested negative for IGRA (mean: -1.82) was lower than the TB score tested positive for IGRA (mean: -1.47). To further explore the relationship between MTB bacterial burden and TB score, we divided ATB patients with positive for Xpert MTB / RIF test into four groups: high, moderate, low and very low load based on the semi-quantitative results of bacterial burden. Spearman correlation analysis showed that the bacterial load was significantly negatively correlated with TB score ( p< 0.01 ). In the group comparison, ATB patients with high / medium load had significantly lower TB scores than those with low load (p <0.01) and those with very low load (p 0.05). Similarly, we found that only 5 out of the 18 enrolled clinically diagnosed cases were identified by the TB score (Figure 4A). Next, we separately analyzed the performance of the TB score in patients with bacteria-positive ATB (Culture or Xpert MTB / RIF test positive). ROC results showed that the AUC of TB score distinguishing patients with positive ATB and healthy persons was 0.952 (95%CI: 0.651 ~ 0.790), optimal sensitivity was 90.54% and specificity was 93.67%; AUC was 0.778 (95%CI: 0.716-0.839), and optimal sensitivity was 89.86% and specificity was 61.17% (Figure 4B). Considering the influence of treatment duration and bacterial volume load on TB score simultaneously, we analyzed bacteria-positive ATB patients within 3 days of medication. The ROC curve results showed that the AUC was 0.936 (95%CI: 0.890 ~ 0.981) and 0.788 (95%CI: 0.717~ 0.859), respectively, with the optimal sensitivity of 84.38% and 81.25%, and specificity of 97.47% and 70.87%, respectively (Figure 4C). When considering the effect of treatment duration, bacteria-negative ATB patients（Culture and Xpert MTB / RIF test were negative）within 3 days were analyzed, the AUC of TB score was 0.808 (95%CI: 0.715-0.901), with an optimal sensitivity of 70.00% and a specificity of 86.08%. However, the TB score that the AUC between bacteria-negative patients and OPD patients was only 0.595 (95%CI: 0.492 and 0.699) (Figure 4D). Table3：Comparison of 3-gene TB score in participants with ATB against smear, Xpert MTB/RIF, culture time to positivity and IGRA results. TB score N Minimum Maximum Mean Medium IQR Varience Standard deviation Smear Positive 101 -5.10 0.15 -1.92 -1.85 -2.65, -1.85 1.01 1.00 Negative 182 -3.80 0.55 -1.16 -1.02 -1.67, -0.43 0.85 0.92 MTB/RIF Negative 102 -3.14 0.35 -1.25 -1.06 -1.92, -0.57 0.86 0.93 Very low 29 -3.70 0.06 -1.35 -1.31 -1.71, -0.81 0.65 0.80 Low 45 -3.65 -0.05 -1.56 -1.34 -2.14, -0.89 0.78 0.88 Medium 45 -4.23 -0.44 -2.20 -2.25 -2.91, -1.58 0.84 0.91 High 11 -4.35 0.15 -2.07 -2.05 -3.13, -0.95 1.82 1.35 Culture Positive 114 -5.10 0.15 -1.83 -1.68 -2.60，-1.08 1.03 1.01 Negative 142 -3.70 0.35 -1.18 -0.99 -1.71，-0.52 0.83 0.91 IGRA Positive 210 -4.23 0.35 -1.47 -1.33 -2.19,-1.33 0.91 0.95 Negative 27 -5.10 0.00 -1.82 -1.78 -2.65,-0.81 1.64 1.28 Discussion TB remains a major public health problem for human health, and good diagnostic tools are essential for early TB detection and prevention of its continued transmission(6). Currently, several studies have shown that the transcript levels of the three genes GBP5, DUSP3, and KLF2 in the host blood can be used for the diagnosis of ATB patients(2). However, the diagnostic efficacy of the modified three gene GBP5, DUSP3 and TBP in clinical ATB patients has not been analyzed yet verified. In this retrospective cohort study, 297 patients with clinically diagnosed ATB and 103 patients with other lung diseases were included, and 79 healthy subjects were recruited. By detecting the expression levels of three genes GBP5, DUSP3 and TBP in peripheral blood of all subjects and calculating the TB score, the diagnostic performance of the improved three genes in clinical ATB patients was verified. The results show that the TB score is highly accurate in distinguishing healthy people from ATB patients, meeting the minimum performance requirements of WHO for TB triage reagents(17), But had lower performance in distinguishing between OPD and ATB patients. For the reason, we first excluded the influence of age and sex on the detection performance of TB score. Later, we found that TB score was closely related to the patient's own bacterial load, and in the positive patients, the detection sensitivity of TB score was as high as 89.86%. Furthermore, we found that TB score correlated with treatment duration in ATB patients. In the first 2 months of taking anti-TB drugs, the TB score gradually approached the cutoff value and stabilized with the duration of medication. When considering the influence of drug duration and bacterial load, the AUC of TB score was 0.788, the optimal detection sensitivity was 81.25%, and the specificity was 70.87%, which was slightly higher than the results of previous studies in the clinical cohort researched (18)。 This study included three different populations, assessing the possibility of TB score as a TB triage reagent in two different scenarios. First, the screening of the general population, the WHO recommends that systematic TB screening in the general population is required in areas where the estimated TB prevalence is 0.5% or higher, but an ideal screening tool is still lacking(19). Our study showed that the TB score can accurately distinguish between ATB patients and healthy people, with a sensitivity of 96.2% when the minimum specificity was 70%. Because of its simplicity, speed and non-sputum specimens, TB score detection has broad application prospects in the TB screening of ordinary people. It is worth noting that the sample of healthy people in our study was obtained from the hospital physical examination center, which can not represent the real situation of TB screening in high-prevalence areas, and should be further evaluated. Second, we evaluated the ability of the TB score to distinguish between clinical OPD and ATB. As a country with high burden of TB, the incidence of TB in China is lower than the global average, but the proportion of patients in the early stage of the disease is high, therefore, appropriate tools for clinical TB detection are urgently needed to achieve early detection and early diagnosis of patients(20, 21). Our results indicate that the diagnostic performance of TB score in distinguishing early ATB patients within 2 months of medication is similar to that within 3 days, but the diagnostic performance is not high, which is different from previous studies(3, 12, 15), Among them, the difference in the bacterial load and the disease spectrum of ATB patients may be important factors affecting the diagnostic performance of TB score. Previous studies have shown that MTB load in patient sputum is correlated with systemic inflammation levels(22). Patients with high bacterial load may have a stronger immune response, while previous studies reported that the gene GBP5 is involved in promoting the formation of the AIM2 and NLRP3 inflammasome(23, 24), DUSP3 is a signaling regulator of both JNK and ERK, and both are involved in the inflammatory response(25). Therefore, patients with high bacterial load may have lower TB scores and be more likely to be detected, which is consistent with the results of the bacterial load analyzed above.In the case of the same medication time, we analyzed the efficacy of TB score in distinguishing bacteria-positive ATB patients from bacteria-negative patients within three days of medication. The results showed that the efficacy of distinguishing bacteria-negative patients was much lower than that of bacteria-positive patients. Clinically diagnosed cases in ATB usually refer to patients who have no laboratory bacterial detection results, but clinical manifestations and medical history suggest that there may be MTB infection(26). We found that the detection rate of TB score for these patients was very low (5/18). These results suggest that the bacterial load is closely related to the detection efficiency of TB score. Previous studies also showed that the bacterial volume load can have an impact on the detection efficacy of TB score. In a multi-center study, 71% of TB patients had a moderate or high bacterial load, with a TB score specificity of 86% and a sensitivity of 90%(3). However, in a study from Brazil, only 31% of TB patients had a moderate or high bacterial load, with a TB score specificity of 53% and a sensitivity of 90%(27). In this study, when we only included bacteria-positive patients within 3 days of medication for analysis, the performance of TB score was improved significantly, with sensitivity of 81.25% and specificity of 70.87%. In addition, the samples we included came from the national tertiary tuberculosis hospitals, and the patients are more complicated and critical, which may also bias the research results, and more people need to be included for multi-center research and evaluation in the future. In this retrospective study, we also included ATB patients with different treatment days to analyze the change in TB score with the length of treatment. The sustained increase in TB score during the first 2 months of treatment may indicate effective therapy for early clearance of bacteria. With increasing treatment length, TB scores tending to cutoff values may indicate cure in patients. This is consistent with the results of previous studies(28), However, to determine whether the TB score can be used as an early and accurate indicator of the treatment success of ATB patients, follow-up testing of individual patients and large-scale and comprehensive studies are still needed. In conclusion, we have for the first time investigated the efficacy of the TB score consisting of GBP5, DUSBP3 and TBP in whole blood through a retrospective cohort study. We found that TB score has high accuracy in distinguishing healthy people and ATB patients, broad application in large-scale TB screening, but low efficacy in distinguishing OPD patients from ATB patients, and would be affected by patient treatment duration and bacterial volume load. Meanwhile, the TB score also shows the potential to monitor the treatment response of ATB patients, which still needs to be further evaluated in large-scale multi-center studies in the future. Declarations Funding This work was funded by the Provincial Department of Science and Technology of the Jiangsu province , grant number BE2023718, the Science and Technology Plan of Suzhou, China, grant number SLT2021012. Ethics approval and consent to participate The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance the tenets of the Helsinki Declaration, and has been approved by the Ethics Committee of the Fifth People’s Hospital of Suzhou. Patients have signed informed consent. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Estimating the value of active case finding for tuberculosis in South Africa, China, and India. BMC Medicine. 2014;12:216. doi: 10.1186/s12916-014-0216-0. PubMed PMID: 25358459; PubMed Central PMCID: PMCQ1. Chen J-O, Qiu Y-B, Rueda ZV, Hou J-L, Lu K-Y, Chen L-P, et al. Role of community-based active case finding in screening tuberculosis in Yunnan province of China. Infectious Diseases of Poverty. 2019;8(1):92. doi: 10.1186/s40249-019-0602-0. PubMed PMID: 31661031; PubMed Central PMCID: PMCQ1. Mesquita EDD, Gil-Santana L, Ramalho D, Tonomura E, Silva EC, Oliveira MM, et al. Associations between systemic inflammation, mycobacterial loads in sputum and radiological improvement after treatment initiation in pulmonary TB patients from Brazil: a prospective cohort study. BMC Infectious Diseases. 2016;16:368. doi: 10.1186/s12879-016-1736-3. PubMed PMID: 27494953; PubMed Central PMCID: PMCQ3. Meunier E, Wallet P, Dreier RF, Costanzo S, Anton L, Rühl S, et al. Guanylate-binding proteins promote activation of the AIM2 inflammasome during infection with Francisella novicida. Nature Immunology. 2015;16(5):476-84. doi: 10.1038/ni.3119. PubMed PMID: 25774716; PubMed Central PMCID: PMCQ1. Shenoy AR, Wellington DA, Kumar P, Kassa H, Booth CJ, Cresswell P, et al. GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals. Science (New York, NY). 2012;336(6080):481-5. doi: 10.1126/science.1217141. PubMed PMID: 22461501; PubMed Central PMCID: PMCQ1. Alonso A, Saxena M, Williams S, Mustelin T. Inhibitory role for dual specificity phosphatase VHR in T cell antigen receptor and CD28-induced Erk and Jnk activation. The Journal of Biological Chemistry. 2001;276(7):4766-71. PubMed PMID: 11085983; PubMed Central PMCID: PMCQ2. WHO Guidelines Approved by the Guidelines Review Committee. WHO consolidated guidelines on tuberculosis: Module 3: diagnosis – rapid diagnostics for tuberculosis detection. Geneva: World Health Organization © World Health Organization 2021.; 2021. Moreira FMF, Verma R, Pereira Dos Santos PC, Leite A, da Silva Santos A, de Araujo RCP, et al. Blood-based host biomarker diagnostics in active case finding for pulmonary tuberculosis: A diagnostic case-control study. EClinicalMedicine. 2021;33:100776. doi: 10.1016/j.eclinm.2021.100776. PubMed PMID: 33842866; PubMed Central PMCID: PMCQ1. Zimmer AJ, Schumacher SG, Södersten E, Mantsoki A, Wyss R, Persing DH, et al. A novel blood-based assay for treatment monitoring of tuberculosis. BMC Research Notes. 2021;14(1):247. doi: 10.1186/s13104-021-05663-z. PubMed PMID: 34193258. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4591433","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321141173,"identity":"4dcc5f17-2870-4752-ba76-f15a80a3ab1e","order_by":0,"name":"Miaomiao Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACef7mAwYfeGqYQQzitBjOOJZQOEPmGDuIQaQ1B3IMPvPYMPODGMTpYGw4YLiZJ4dNmrHhzMcbbxjs5HQbCGhhZ25INpxzRsaYnbl3s+UchmRjswOEbTlm8LaHLZmx4ew2aR6GA4nbCGkBqmn/wfuPub7hQM4zYrUkMxjy8DAzA73PRpwWYNgCMc8xZiDD2HKOARF+kefv/wCLyoc33lTYyRHUggIkeIiMGmQtpOoYBaNgFIyCEQEAIAlE1tHbzugAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Miaomiao","middleName":"","lastName":"Zhao","suffix":""},{"id":321141174,"identity":"f18f9ea6-7cf1-48f1-8707-f99fd8e4d3ec","order_by":1,"name":"Ping Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Xu","suffix":""},{"id":321141175,"identity":"e65c32c5-da91-4f1e-b94d-af24602bb650","order_by":2,"name":"Lulu Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-06-17 03:01:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4591433/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4591433/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60443240,"identity":"738f8d97-b356-4e56-a70f-b19656137972","added_by":"auto","created_at":"2024-07-16 20:20:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71229,"visible":true,"origin":"","legend":"\u003cp\u003eXpert-MTB-HR test results for ATB, OPD and HC groups.（a) GBP5, (b) DUSP3 (c) TB score ,*, P \u0026lt;0.05; * *, P \u0026lt;0.01; * * *, P \u0026lt;0.001; * * * *, P \u0026lt;0.0001; ns,\u003c/p\u003e\n\u003cp\u003eno significance.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4591433/v1/3b973565651fe3702cd4eb0a.png"},{"id":60443243,"identity":"ef442ef0-200a-4cd0-8dfc-1d71ea874a6d","added_by":"auto","created_at":"2024-07-16 20:20:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103717,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagnostic efficacy of TB score alone or in combination with other tests for ATB patients.( a ) TB score alone diagnosis ( red line with HC group as negative control, blue line with OPD group as negative control ) ( b ) TB score combined with gender and age ( red line TB score alone analysis, blue line TB score combined with gender and age ) ( c ) TB score combined with tuberculosis culture ( red line TB score alone analysis, blue line TB score combined with culture ) ( d ) TB score combined with Xpert MTB / RIF detection ( red line TB score alone analysis, blue line TB score combined with Xpert MTB / RIF)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4591433/v1/a6cd04408f92f2fd28366776.png"},{"id":60443242,"identity":"f7dc35c1-ad8d-4f70-a3c1-e469f0eff21b","added_by":"auto","created_at":"2024-07-16 20:20:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62856,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of treatment duration on TB score in ATB patients ( a ) Distribution of TB scores in ATB patients with different medication intervals.( b ) Comparison of TB scores in ATB patients with different medication intervals ( c ) ROC analysis of the diagnostic efficacy of TB scores in ATB patients with different medication intervals ( red line to distinguish ATB patients and HC group within 3 days of medication, blue line to distinguish ATB patients and OPD group within 3 days of medication, green line to distinguish ATB patients and OPD group within 2 months of medication ).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4591433/v1/d38446a04a47bd4e1cf523b9.png"},{"id":60443244,"identity":"8e5e9da5-c64c-4a7c-a91f-9c0e7d4a4db4","added_by":"auto","created_at":"2024-07-16 20:20:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79403,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of bacterial load on TB score ( a ) ROC curve analysis of the detection efficiency of TB score for bacterial positive ATB patients ( red line with HC group as negative control, blue line with OPD group as negative control ) ( b ) ROC curve analysis of the diagnostic efficiency of TB score for bacterial positive ATB patients within 3 days of medication ( red line with HC group as negative control, blue line with OPD group as negative control ) ( c ) ROC curve analysis of the diagnostic efficiency of TB score for bacterial negative ATB patients within 3 days of medication ( red line with HC group as negative control, blue line with OPD group as control ) ( d ) distribution of TB scores in ATB patients with different bacterial load.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4591433/v1/253aec8369df8b96b5bba509.png"},{"id":66911834,"identity":"26624765-c1a0-4269-95c3-6cc2b966bce7","added_by":"auto","created_at":"2024-10-17 23:01:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":796511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4591433/v1/c9025bc1-7f9a-4d4d-8071-2ecf9646edcd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Xpert MTB Host Response assay for the diagnosis of patients with Active tuberculosis in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis is a global disease caused by Mycobacterium tuberculosis (MTB) infection, which is mainly respiratory tract transmitted and seriously harmful to human health(4). The early and accurate diagnosis of the disease is the key to controlling the tuberculosis epidemic(5). According to the latest WHO 2023 Global Tuberculosis Report, there were about 10.6 million new patients worldwide in 2022, and about 3.1 million of them were not diagnosed or registered(6)。\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;At present, the laboratory diagnosis of TB mainly relies on smear, culture and molecular biology techniques to test the etiology of sputum or invasive sampling samples, but sputum testing is difficult to apply in children, critically ill patients and patients without sputum(7). γ -Interferon release test (interferon gamma release assays, IGRAs) assists the diagnosis of TB by detecting the release of specific IFN- γ in blood samples, but some non-tuberculous mycobacterial infections may cause false positive IGRA results, so it is still necessary to find new detection targets to make up for the lack of clinical diagnosis and treatment(8)。\u003c/p\u003e\n\u003cp\u003eAs early as 2014, WHO made the development of sputum-based test reagents a priority for TB diagnosis(1). Previous studies have shown that host transcription markers in whole blood can be used for the diagnosis of TB(9-11). A multi-center global cohort study found that TB scores, composed of GBP5, DUSP3 and KLF2 genes, had a high diagnostic efficacy in distinguishing ATB from healthy people, latent tuberculosis infection (LTBI) and other lung diseases(2). Subsequently, several cohort studies have validated the better diagnostic performance of TB score in ATB patients(3, 12-16). At present, Cepheid（Sunnyvale, CA, USA) has further optimized the principle and detection method of the three genes, replacing the original KLF2 gene with the housekeeping gene TBP, and the improved three genes have not yet been verified in clinical patients. This paper analyzed the expression of GBP5, DUSP3 and TBP and the diagnostic value of TB score in whole blood of clinical ATB patients.\u003c/p\u003e"},{"header":"Subject and Methods","content":"\u003ch2\u003e1.1 Subject selection\u003c/h2\u003e\n\u003cp\u003eThis study used a retrospective cohort study of patients recruited from the Pulmonary and Respiratory Departments of the Infectious Diseases Hospital affiliated to Soochow University from May 22 to August 29,2023, with inclusion criteria of age older than 14 years. Finally, 297 ATB patients were enrolled, and the diagnosis met the Chinese diagnostic criteria for tuberculosis, WS288-2017, 103 patients with other pulmonary diseases (OPD), including pneumonia, lung cancer, Nontuberculous Mycobacteria, etc. In addition, 79 healthy controls (HC) with normal imaging examination and negative IGRA test were included. Subject clinical data and concurrent laboratory test results were collected for later analysis. This study was approved by the Ethics Committee of the Affiliated Infectious Diseases Hospital of Soochow University.\u003c/p\u003e\n\u003ch2\u003e1.2 Reagents and Methods\u003c/h2\u003e\n\u003ch3\u003e1.2.1 instruments and reagents\u003c/h3\u003e\n\u003cp\u003eBactec MGIT 960 Instrument (Becton Dickinson, Cockeysville, MD, USA); MBP 64 Antigen Test kit (Capilia, Hangzhou, China); The Xpert MTB/RIF kit (Cepheid, Sunnyvale, USA); GeneXpert Instrument (Cepheid, Sunnyvale, USA); The Xpert-MTB-HR kit (Cepheid, Sunnyvale, USA); The QuantiFERON-TB Gold (QFT) test kit (QIAGEN, Germany)\u003c/p\u003e\n\u003ch3\u003e1.2.2. Experimental methods\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp;MTB etiology test: Sputum or bronchoalveolar lavage fluid samples（BALF） were collected for bacterial smear, tuberculosis culture and molecular biological detection. Bacterial smears were examined by acid-fast staining. Tuberculosis culture was cultured using Bactec MGIT 960 instrument, and culture positive strains were confirmed using MBP 64 antigen detection kit, and 42 days without positive was judged negative. Molecular biology testing was performed using the Xpert MTB / RIF kit, detected on a GeneXpert instrument.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Three gene tests: Peripheral blood of the subjects was collected and anti-coagulated with EDKA-2K and mixed upside down.Within 3 hours, 50ul blood sample was added to the Xpert-MTB-HR kit and tested on the GeneXpert platform. The samples that were not detected in time need to be stored at 4 °, and the detection should be completed within 8 hours.TB score was calculated from the cycle threshold (threshold cycle, CT) = ([CT (GBP 5) + CT (DUSP 3)] / 2) -CT (TBP).\u003c/p\u003e\n\u003cp\u003eIGRA test: operate according to the instructions of the QFT test kit.\u003c/p\u003e\n\u003ch2\u003e1.3 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eSPSS Statistics 26 software, Graph Pad Prism 9 software and R ( version 4.3.3 ) were used for data analysis and graphics drawing. Median mean, median, range, inter-quartile range, variance and standard deviation of TB scores were calculated for different populations. The area under the ROC curve（AUC）was used to evaluate diagnostic efficacy, the optimal diagnostic threshold was calculated with the Youden index, and the AUC of different ROC curves was compared by the DeLong’s test. Furthermore, pairwise comparisons of mean TB scores between different groups were performed using the Tukey's multiple comparison test. Grade correlation was analyzed by Spearson correlation, with P \u0026lt;0.05 indicating statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e2.1 Basic information of the subjects\u003c/h2\u003e\n\u003cp\u003eThis study included 479 participants, including 297 ATB patients and 182 controls, and the controls included 103 OPD patients and 79 healthy individuals, and the basic information is shown in Table 1. There was no statistical difference in gender and age distribution between the ATB and OPD groups (p\u0026gt; 0.05). 14.8% ATB and 7.8% OPD had a previous history of TB (p\u0026gt; 0.05). At least one TB-related symptom was reported in 78.1% of ATB patients, compared with 64.1% of OPD patients (p =0.04). A younger age and higher proportion of women in the HC group compared with ATB and OPD (p \u0026lt;0.05). Of 297 ATB patients, sputum smear positive was 34.0%, culture positive 44.5% (118 / 256) and Xpert MTB / RIF positive 54.1% (120 / 222). ATB patients were divided into 6 groups, with 40% within 3 days of medication.\u003c/p\u003e\n\u003cp\u003eTable 1: \u0026nbsp;Clinical data of the three groups of study subjects\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003eATB\u0026nbsp;patients\u003c/p\u003e\n \u003cp\u003en（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eOPD\u0026nbsp;n（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eHealthy\u0026nbsp;controls\u0026nbsp;n（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e\u0026lt;65y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e173(58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e47(45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e76(96.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e\u0026ge;65y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e124(41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e56(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e3(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e98 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e45(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e59(74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e199(67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e58(56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e20(25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e*Any\u0026nbsp;TB\u0026nbsp;symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e65(21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e37(35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e232(78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e65(64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eHistory\u0026nbsp;of\u0026nbsp;TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e44(14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e8(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eDrug-resistant\u0026nbsp;TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e43(14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eSmear\u0026nbsp;positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e101(34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e6/41(6.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eCulture \u0026nbsp;positive（TB）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e118/256(44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e0/39(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eGeneXpert\u0026nbsp;MTB/RIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003ePositive\u0026nbsp;or\u0026nbsp;weak\u0026nbsp;positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e120/222(54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e0/37(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e119/244(45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e37/37(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003eAnti-TB\u0026nbsp;treatment\u0026nbsp;time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e\u0026lt;3d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e119(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e3d-2w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e68(22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e2w-2m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e46(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e2m\u0026mdash;4m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e25(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e4m\u0026mdash;6m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e17(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.53833049403748%\"\u003e\n \u003cp\u003e\u0026gt;6m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.33901192504259%\"\u003e\n \u003cp\u003e22(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.63543441226576%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e2.2 Distribution of genes and TB scores in the three population groups\u003c/h2\u003e\n\u003cp\u003eWe compared the expression of genes GBP5 and DUSP3 and TB scores in the three populations. In ATB patients, the Ct value of GBP5 was significantly lower than in the other two groups (p \u0026lt;0.0001), although the Ct value in the OPD group was also significantly lower than HC group (p \u0026lt;0.05), the difference was relatively small (Figure 1A). The Ct value of the DUSP3 gene was significantly lower in the ATB group than in the HC group (p \u0026lt;0.001), but not significant from the OPD group (Figure 1B). The TB score values varied significantly between the three groups (p \u0026lt;0.0001), and the overall TB score was significantly lower in the ATB group compared to the other two groups (Figure 1C).\u003c/p\u003e\n\u003ch2\u003e2.3 Diagnostic performance of the TB score for the ATB patients\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;After analysis of the diagnostic performance of TB score, the AUC of TB score was 0.879 (95% CI, 0.844 ~ 0.914), and the optimum sensitivity was 76.09% and 93.27% specific by the Youden index (Figure 2A). When the specificity was fixed at 70%, the sensitivity of the TB score was 96.2%, satisfying the minimum TPP criteria for the triage test. In contrast, the TB score had a lower AUC for identifying ATB and OPD: 0.689 (95% CI, 0.628 to 0.750) (Fig. 2A).\u003c/p\u003e\n\u003cp\u003eNext, we analyzed whether age, sex had an effect on the diagnostic performance of TB score in R language. A new model was included in the ROC model of TB score identifying ATB from OPD, and Delong\u0026rsquo;s tested no statistical difference in AUC between the two ROC models (p\u0026gt; 0.05) (Figure 2B). With the treatment of ATB patients, the results of pathogenic examination turned negative. We compared the positive detection rate of TB culture, Xpert MTB / RIF test and TB score for the enrolled ATB population. According to the results, the detection rate of ATB by TB score was much higher than culture and Xpert MTB / RIF in both the type of sample of sputum and BALF obtained from invasive sampling (Table 2). When TB score and TB culture were combined detection, the diagnostic efficacy was not significantly improved (p\u0026gt;0.05) (Figure 2C), and when TB score and Xpert MTB / RIF were combined diagnosed, the diagnostic efficacy was significantly improved compared with TB score alone (p \u0026lt;0.00001) (Figure 2D).\u003c/p\u003e\n\u003cp\u003eTable 2 : Comparison of TB score, tuberculosis culture and Xpert MTB / RIF detection in the detection rate of ATB patients.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"670\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.47761194029851%\"\u003e\n \u003cp\u003eSample type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.3134328358209%\" colspan=\"2\"\u003e\n \u003cp\u003eSputum sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.208955223880594%\" colspan=\"2\"\u003e\n \u003cp\u003eBALF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.51420029895366%\"\u003e\n \u003cp\u003eDetect method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.720478325859492%\"\u003e\n \u003cp\u003e\u0026nbsp;Xpert MTB / RIF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.499252615844544%\"\u003e\n \u003cp\u003eMTB culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.411061285500747%\"\u003e\n \u003cp\u003eAnd Xpert MTB / RIF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.855007473841555%\"\u003e\n \u003cp\u003eMTB culture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.51420029895366%\"\u003e\n \u003cp\u003ePositive number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.720478325859492%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.499252615844544%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.411061285500747%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.855007473841555%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.51420029895366%\"\u003e\n \u003cp\u003eNegative number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.720478325859492%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.499252615844544%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.411061285500747%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.855007473841555%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.51420029895366%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.720478325859492%\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.499252615844544%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.411061285500747%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.855007473841555%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.51420029895366%\"\u003e\n \u003cp\u003eATB, relevance ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.720478325859492%\"\u003e\n \u003cp\u003e0.5391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.499252615844544%\"\u003e\n \u003cp\u003e0.3865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.411061285500747%\"\u003e\n \u003cp\u003e0.5484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.855007473841555%\"\u003e\n \u003cp\u003e0.5484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.51420029895366%\"\u003e\n \u003cp\u003eThe detection rate of ATB by TB score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.720478325859492%\"\u003e\n \u003cp\u003e0.7969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.499252615844544%\"\u003e\n \u003cp\u003e0.7546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.411061285500747%\"\u003e\n \u003cp\u003e0.7849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.855007473841555%\"\u003e\n \u003cp\u003e0.7634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e2.3 Effect of treatment duration of ATB patients on TB score\u003c/h2\u003e\n\u003cp\u003eTo assess the effect of treatment duration on the detection of TB scores, we divided ATB patients into six subgroups according to the duration of anti-TB drug use and observed the distribution of TB scores in the different subgroups. The results showed that after the start of treatment, the mean TB score of TB patients increased significantly, and decreased to the initial level 2 months later. With the further increase of treatment duration, the mean TB score increased from -1.591 to -0.972, gradually approached the cutoff value and tended to stabilize (Figure 3A). To reduce the interference of treatment factors, we separately analyzed ATB patients within 3 days and found that TB scores within 2 months of medication was higher than that of patients within 3 days of medication, but the difference was not statistically significant (p\u0026gt; 0.05). However, when administered, older than 2 months, the patients\u0026apos; TB scores changed significantly (p \u0026lt;0.01) (Figure 3B). Next, we did the ROC curve to further analyze whether the difference in treatment duration will affect the detection of ATB patients by TB score.The results showed that the AUC of TB score was 0.895 (95%CI: 0.857~0.934) in distinguishing ATB patients and healthy people within 3 days of medication, and the optimal sensitivity was 79.68% and the specificity was 92.41%. When OPD patients were used as a negative control, the AUC of ATB patients within 3 days of medication was 0.715 (95%CI: 0.642 ~0.770), and the optimal sensitivity was 76.47% and the specificity was 61.17%. For ATB patients within 2 months of medication, the AUC distinguishing TB score from OPD patients was 0.720 (95%CI: 0.651 to 0.790), similar to ATB patients within 3 days (Figure 3C).\u003c/p\u003e\n\u003ch2\u003e2.3 Correlation between bacterial load and TB score\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;We collected concurrent laboratory findings from ATB patients and performed descriptive analysis of TB scores for smear microscopy, Xpert MTB / RIF testing, TB culture, and IGRA test results as shown in Table 3. Patients with smear positive ATB had lower TB scores (mean: -1.92) than those with smear negative (mean: -1.16). Similarly, ATB patients with high Xpert MTB / RIF semi-quantitative results ( mean : -2.07 ) were significantly lower than those with very low semi-quantitative results ( mean : -1.35 ).The TB scoreof ATB patients with positive tuberculosis culture \u0026nbsp;( mean : -1.83 ) was lower than that of ATB patients with negative culture ( mean : -1.18 ).In addition, the TB score of ATB patients who tested negative for IGRA (mean: -1.82) was lower than the TB score tested positive for IGRA (mean: -1.47).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To further explore the relationship between MTB bacterial burden and TB score, we divided ATB patients with positive for Xpert MTB / RIF test into four groups: high, moderate, low and very low load based on the semi-quantitative results of bacterial burden. Spearman correlation analysis showed that the bacterial load was significantly negatively correlated with TB score ( p\u0026lt; 0.01 ). In the group comparison, ATB patients with high / medium load had significantly lower TB scores than those with low load (p \u0026lt;0.01) and those with very low load (p\u0026lt;0.001). However, although the TB score was lower in patients with low load than those with very low load, the difference was not statistically significant (p\u0026gt; 0.05). Similarly, we found that only 5 out of the 18 enrolled clinically diagnosed cases were identified by the TB score (Figure 4A). Next, we separately analyzed the performance of the TB score in patients with bacteria-positive ATB (Culture or Xpert MTB / RIF test positive). ROC results showed that the AUC of TB score distinguishing patients with positive ATB and healthy persons was 0.952 (95%CI: 0.651 ~ 0.790), optimal sensitivity was 90.54% and specificity was 93.67%; AUC was 0.778 (95%CI: 0.716-0.839), and optimal sensitivity was 89.86% and specificity was 61.17% (Figure 4B).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Considering the influence of treatment duration and bacterial volume load on TB score simultaneously, we analyzed bacteria-positive ATB patients within 3 days of medication. The ROC curve results showed that the AUC was 0.936 (95%CI: 0.890 ~ 0.981) and 0.788 (95%CI: 0.717~ 0.859), respectively, with the optimal sensitivity of 84.38% and 81.25%, and specificity of 97.47% and 70.87%, respectively (Figure 4C). When considering the effect of treatment duration, bacteria-negative ATB patients（Culture and Xpert MTB / RIF test were negative）within 3 days were analyzed, the AUC of TB score was 0.808 (95%CI: 0.715-0.901), with an optimal sensitivity of 70.00% and a specificity of 86.08%. However, the TB score that the AUC between bacteria-negative patients and OPD patients was only 0.595 (95%CI: 0.492 and 0.699) (Figure 4D).\u003c/p\u003e\n\u003cp\u003eTable3：Comparison of 3-gene TB score in participants with ATB against smear, Xpert MTB/RIF, culture time to positivity and IGRA results.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"763\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eTB\u0026nbsp;score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003eVarience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003eStandard\u0026nbsp;deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003eSmear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-2.65,\u0026nbsp;-1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-1.67,\u0026nbsp;-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003eMTB/RIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-1.92,\u0026nbsp;-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eVery\u0026nbsp;low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-1.71,\u0026nbsp;-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-2.14,\u0026nbsp;-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-2.91,\u0026nbsp;-1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-3.13, \u0026nbsp;-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003eCulture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-2.60，-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-1.71，-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003eIGRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-2.19,-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.317585301837271%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.10498687664042%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.136482939632545%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58005249343832%\"\u003e\n \u003cp\u003e-5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.286089238845145%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.398950131233596%\"\u003e\n \u003cp\u003e-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.698162729658792%\"\u003e\n \u003cp\u003e-2.65,-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eTB remains a major public health problem for human health, and good diagnostic tools are essential for early TB detection and prevention of its continued transmission(6). Currently, several studies have shown that the transcript levels of the three genes GBP5, DUSP3, and KLF2 in the host blood can be used for the diagnosis of ATB patients(2). However, the diagnostic efficacy of the modified three gene GBP5, DUSP3 and TBP in clinical ATB patients has not been analyzed yet verified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this retrospective cohort study, 297 patients with clinically diagnosed ATB and 103 patients with other lung diseases were included, and 79 healthy subjects were recruited. By detecting the expression levels of three genes GBP5, DUSP3 and TBP in peripheral blood of all subjects and calculating the TB score, the diagnostic performance of the improved three genes in clinical ATB patients was verified.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The results show that the TB score is highly accurate in distinguishing healthy people from ATB patients, meeting the minimum performance requirements of WHO for TB triage reagents(17), But had lower performance in distinguishing between OPD and ATB patients. For the reason, we first excluded the influence of age and sex on the detection performance of TB score. Later, we found that TB score was closely related to the patient\u0026apos;s own bacterial load, and in the positive patients, the detection sensitivity of TB score was as high as 89.86%. Furthermore, we found that TB score correlated with treatment duration in ATB patients. In the first 2 months of taking anti-TB drugs, the TB score gradually approached the cutoff value and stabilized with the duration of medication. When considering the influence of drug duration and bacterial load, the AUC of TB score was 0.788, the optimal detection sensitivity was 81.25%, and the specificity was 70.87%, which was slightly higher than the results of previous studies in the clinical cohort researched\u0026nbsp;(18)。\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study included three different populations, assessing the possibility of TB score as a TB triage reagent in two different scenarios. First, the screening of the general population, the WHO recommends that systematic TB screening in the general population is required in areas where the estimated TB prevalence is 0.5% or higher, but an ideal screening tool is still lacking(19). Our study showed that the TB score can accurately distinguish between ATB patients and healthy people, with a sensitivity of 96.2% when the minimum specificity was 70%. Because of its simplicity, speed and non-sputum specimens, TB score detection has broad application prospects in the TB screening of ordinary people. It is worth noting that the sample of healthy people in our study was obtained from the hospital physical examination center, which can not represent the real situation of TB screening in high-prevalence areas, and should be further evaluated.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Second, we evaluated the ability of the TB score to distinguish between clinical OPD and ATB. As a country with high burden of TB, the incidence of TB in China is lower than the global average, but the proportion of patients in the early stage of the disease is high, therefore, appropriate tools for clinical TB detection are urgently needed to achieve early detection and early diagnosis of patients(20, 21). Our results indicate that the diagnostic performance of TB score in distinguishing early ATB patients within 2 months of medication is similar to that within 3 days, but the diagnostic performance is not high, which is different from previous studies(3, 12, 15), Among them, the difference in the bacterial load and the disease spectrum of ATB patients may be important factors affecting the diagnostic performance of TB score.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Previous studies have shown that MTB load in patient sputum is correlated with systemic inflammation levels(22). Patients with high bacterial load may have a stronger immune response, while previous studies reported that the gene GBP5 is involved in promoting the formation of the AIM2 and NLRP3 inflammasome(23, 24), DUSP3 is a signaling regulator of both JNK and ERK, and both are involved in the inflammatory response(25). Therefore, patients with high bacterial load may have lower TB scores and be more likely to be detected, which is consistent with the results of the bacterial load analyzed above.In the case of the same medication time, we analyzed the efficacy of TB score in distinguishing bacteria-positive ATB patients from bacteria-negative patients within three days of medication. The results showed that the efficacy of distinguishing bacteria-negative patients was much lower than that of bacteria-positive patients. Clinically diagnosed cases in ATB usually refer to patients who have no laboratory bacterial detection results, but clinical manifestations and medical history suggest that there may be MTB infection(26). We found that the detection rate of TB score for these patients was very low (5/18). These results suggest that the bacterial load is closely related to the detection efficiency of TB score.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Previous studies also showed that the bacterial volume load can have an impact on the detection efficacy of TB score. In a multi-center study, 71% of TB patients had a moderate or high bacterial load, with a TB score specificity of 86% and a sensitivity of 90%(3). However, in a study from Brazil, only 31% of TB patients had a moderate or high bacterial load, with a TB score specificity of 53% and a sensitivity of 90%(27). In this study, when we only included bacteria-positive patients within 3 days of medication for analysis, the performance of TB score was improved significantly, with sensitivity of 81.25% and specificity of 70.87%. In addition, the samples we included came from the national tertiary tuberculosis hospitals, and the patients are more complicated and critical, which may also bias the research results, and more people need to be included for multi-center research and evaluation in the future.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In this retrospective study, we also included ATB patients with different treatment days to analyze the change in TB score with the length of treatment. The sustained increase in TB score during the first 2 months of treatment may indicate effective therapy for early clearance of bacteria. With increasing treatment length, TB scores tending to cutoff values may indicate cure in patients. This is consistent with the results of previous studies(28), However, to determine whether the TB score can be used as an early and accurate indicator of the treatment success of ATB patients, follow-up testing of individual patients and large-scale and comprehensive studies are still needed.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we have for the first time investigated the efficacy of the TB score consisting of GBP5, DUSBP3 and TBP in whole blood through a retrospective cohort study. We found that TB score has high accuracy in distinguishing healthy people and ATB patients, broad application in large-scale TB screening, but low efficacy in distinguishing OPD patients from ATB patients, and would be affected by patient treatment duration and bacterial volume load. Meanwhile, the TB score also shows the potential to monitor the treatment response of ATB patients, which still needs to be further evaluated in large-scale multi-center studies in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Provincial Department of Science and Technology of the Jiangsu province , grant number BE2023718, the Science and Technology Plan of Suzhou, China, grant number SLT2021012.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance the tenets of the Helsinki Declaration, and has been approved by the Ethics Committee of the Fifth People’s Hospital of Suzhou. Patients have signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMiaomiao Zhao: Writing-review\u0026amp;editing, Writing-original draft,Investigation, Data curation. Ping Xu: Project administration, Supervision, Funding acquisition, Conceptualization. Lulu Xu: Resources.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health O. High priority target product profiles for new tuberculosis diagnostics: report of a consensus meeting, 28-29 April 2014, Geneva, Switzerland. Geneva: World Health Organization, 2014 2014. Report No.: Contract No.: WHO/HTM/TB/2014.18.\u003c/li\u003e\n \u003cli\u003eSweeney TE, Braviak L, Tato CM, Khatri P. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. The Lancet Respiratory Medicine. 2016;4(3):213-24. doi: 10.1016/S2213-2600(16)00048-5. PubMed PMID: 26907218; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eSutherland JS, van der Spuy G, Gindeh A, Thuong NTT, Namuganga A, Owolabi O, et al. Diagnostic Accuracy of the Cepheid 3-gene Host Response Fingerstick Blood Test in a Prospective, Multi-site Study: Interim Results. Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America. 2022;74(12):2136-41. doi: 10.1093/cid/ciab839. PubMed PMID: 34550342; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eFurin J, Cox H, Pai M. Tuberculosis. Lancet (London, England). 2019;393(10181):1642-56. doi: 10.1016/S0140-6736(19)30308-3. PubMed PMID: 30904262; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eRangaka MX, Cavalcante SC, Marais BJ, Thim S, Martinson NA, Swaminathan S, et al. Controlling the seedbeds of tuberculosis: diagnosis and treatment of tuberculosis infection. Lancet (London, England). 2015;386(10010):2344-53. doi: 10.1016/S0140-6736(15)00323-2. PubMed PMID: 26515679; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eWorld Health O. Global tuberculosis report 2023. Geneva: World Health Organization; 2023 2023.\u003c/li\u003e\n \u003cli\u003eSingh P, Saket VK, Kachhi R. Diagnosis of TB: From conventional to modern molecular protocols. Frontiers In Bioscience (Elite Edition). 2019;11(1):38-60. PubMed PMID: 30468637.\u003c/li\u003e\n \u003cli\u003eWorld Health O. Practical manual on tuberculosis laboratory strengthening. 2022 update ed. Geneva: World Health Organization; 2022 2022.\u003c/li\u003e\n \u003cli\u003eMutavhatsindi H, van der Spuy GD, Malherbe ST, Sutherland JS, Geluk A, Mayanja-Kizza H, et al. Validation and Optimization of Host Immunological Bio-Signatures for a Point-of-Care Test for TB Disease. Frontiers In Immunology. 2021;12:607827. doi: 10.3389/fimmu.2021.607827. PubMed PMID: 33717089; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eLeong S, Zhao Y, Joseph NM, Hochberg NS, Sarkar S, Pleskunas J, et al. Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from south India. Tuberculosis (Edinburgh, Scotland). 2018;109:41-51. doi: 10.1016/j.tube.2018.01.002. PubMed PMID: 29559120; PubMed Central PMCID: PMCQ3.\u003c/li\u003e\n \u003cli\u003eMulenga H, Zauchenberger C-Z, Bunyasi EW, Mbandi SK, Mendelsohn SC, Kagina B, et al. Performance of diagnostic and predictive host blood transcriptomic signatures for Tuberculosis disease: A systematic review and meta-analysis. PloS One. 2020;15(8):e0237574. doi: 10.1371/journal.pone.0237574. PubMed PMID: 32822359; PubMed Central PMCID: PMCQ2.\u003c/li\u003e\n \u003cli\u003eS\u0026ouml;dersten E, Ongarello S, Mantsoki A, Wyss R, Persing DH, Banderby S, et al. Diagnostic Accuracy Study of a Novel Blood-Based Assay for Identification of Tuberculosis in People Living with HIV. Journal of Clinical Microbiology. 2021;59(3). doi: 10.1128/JCM.01643-20. PubMed PMID: 33298607; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eTurner CT, Gupta RK, Tsaliki E, Roe JK, Mondal P, Nyawo GR, et al. Blood transcriptional biomarkers for active pulmonary tuberculosis in a high-burden setting: a prospective, observational, diagnostic accuracy study. The Lancet Respiratory Medicine. 2020;8(4):407-19. doi: 10.1016/S2213-2600(19)30469-2. PubMed PMID: 32178775; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eHoang LT, Jain P, Pillay TD, Tolosa-Wright M, Niazi U, Takwoingi Y, et al. Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. The Lancet Infectious Diseases. 2021;21(3):366-75. doi: 10.1016/S1473-3099(20)30928-2. PubMed PMID: 33508221; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eWarsinske HC, Rao AM, Moreira FMF, Santos PCP, Liu AB, Scott M, et al. Assessment of Validity of a Blood-Based 3-Gene Signature Score for Progression and Diagnosis of Tuberculosis, Disease Severity, and Treatment Response. JAMA Network Open. 2018;1(6):e183779. doi: 10.1001/jamanetworkopen.2018.3779. PubMed PMID: 30646264; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eAhmad R, Xie L, Pyle M, Suarez MF, Broger T, Steinberg D, et al. A rapid triage test for active pulmonary tuberculosis in adult patients with persistent cough. Science Translational Medicine. 2019;11(515). doi: 10.1126/scitranslmed.aaw8287. PubMed PMID: 31645455; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eDenkinger CM, Schumacher SG, Gilpin C, Korobitsyn A, Wells WA, Pai M, et al. Guidance for the Evaluation of Tuberculosis Diagnostics That Meet the World Health Organization (WHO) Target Product Profiles: An Introduction to WHO Process and Study Design Principles. The Journal of Infectious Diseases. 2019;220(220 Suppl 3):S91-S8. doi: 10.1093/infdis/jiz097. PubMed PMID: 31593596; PubMed Central PMCID: PMCQ1.\u003c/li\u003e\n \u003cli\u003eWu X, Tan G, Ma J, Yang J, Guo Y, Lu H, et al. 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PubMed PMID: 34193258.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Xpert MTB Host Response assay, Active tuberculosis, Diagnose","lastPublishedDoi":"10.21203/rs.3.rs-4591433/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4591433/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: The World Health Organization regards the development of rapid non-sputum diagnostic reagents as a high priority for TB diagnosis(1). The host peripheral blood 3-gene (GBP5, DUSP3 and KLF2) was found and verified to have high diagnostic value for active tuberculosis (ATB)(2, 3). The clinical diagnostic value of the new 3-genes ( GBP5, DUSP3 and TBP ) modified by Cepheid company has not been evaluated\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe used a retrospective cohort study of 297 clinical ATB patients, 103 patients with other pulmonary diseases (OPD), and 79 healthy subjects are used as healthy controls (HC).The receiver operating characteristic curve ( ROC curve ) was used to analyze the value of TB score in the diagnosis of ATB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The AUC of TB score between ATB group and HC group was 0.879 and OPD group, respectively. The treatment duration and bacterial burden of ATB will affect the diagnostic efficacy of TB score. When only ATB patients within 3 days were included, the AUC was 0.895 and 0.715 and 0.715 for ATB and AUC was 0.952 and 0.778, respectively. Positive patients within 3 days were included, the TB score AUC was 0.936 and 0.788 for ATB from HC and OPD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: 3-gene TB score test can be used as a rapid blood screening test for clinical ATB patients, and its own bacterial load is an important factor affecting its detection. In addition, with increasing treatment duration in ATB patients, TB scores have increased, with some potential to monitor treatment response.\u003c/p\u003e","manuscriptTitle":"Evaluation of Xpert MTB Host Response assay for the diagnosis of patients with Active tuberculosis in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 20:20:34","doi":"10.21203/rs.3.rs-4591433/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1afbb071-b388-455d-9764-3e85a72d6aec","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-17T22:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 20:20:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4591433","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4591433","identity":"rs-4591433","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}