Combining Interferon-γ Release Assays and Metagenomic Next-generation Sequencing for Diagnosis of Pulmonary Tuberculosis: A Retrospective Study

Combining Interferon-γ Release Assays and Metagenomic Next-generation Sequencing for Diagnosis of Pulmonary Tuberculosis: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Combining Interferon-γ Release Assays and Metagenomic Next-generation Sequencing for Diagnosis of Pulmonary Tuberculosis: A Retrospective Study Yanyan Liu, Miaohong Fang, Chenxi Yuan, Yi Yang, Liang Yu, Yasheng Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4629309/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted 4 You are reading this latest preprint version Abstract Background Rapid diagnosis of pulmonary tuberculosis (PTB) is urgently needed. We aimed to improve diagnosis rates by combining tuberculosis-interferon (IFN)-γ release assays (TB-IGRA) with metagenomic next-generation sequencing (mNGS) for PTB diagnosis. Methods A retrospective study of 29 PTB and 32 non-TB patients from our hospital was conducted between October 2022 and June 2023. Samples were processed for TB-IGRA and mNGS tests according to the manufacturer’s protocol. Results The levels of IFN-γ release in PTB patients were significantly higher than those -in non-TB patients (604.15 ± 112.18 pg/mL, and 1.04 ± 0.38 pg/mL, respectively; p < 0.0001). Regarding presenting symptoms or signs, cough and thoracalgia were less common in PTB patients than in non-TB patients ( p = 0.001 and p = 0.024, respectively). Total protein and albumin levels in the sera of PTB patients were significantly elevated compared to non-TB patients ( p = 0.039 and p = 0.004, respectively). The area under the ROC curve (AUC) for TB-IGRA in PTB diagnosis was 0.939. With an optimal IFN-γ cut-off value of 14.3 pg/mL( Youden’s index 0.831) sensitivity was 86.2% and specificity was 96.9%. ROC curve analysis for mNGS and TB-IGRA combined with mNGS showed AUCs of 0.879 and 1, respectively. Conclusions TB-IGRA combined with mNGS is an effective method for diagnosing tuberculosis, and can be used in the clinical diagnosis of PTB. TB-IGRA tuberculosis mNGS IFN-γ sputum smears Figures Figure 1 Figure 2 Background Tuberculosis (TB) has been recognized as a global public health emergency for the past two decades and remains the leading cause of death among adults worldwide due to an infectious disease [ 1 ]. According to the Global Tuberculosis Report 2020, more than 10 million new TB and 1.4 million deaths have been reported to the World Health Organization (WHO) according to the Global Tuberculosis Report 2020 [ 2 ]. The target of the end TB strategy is a 17% annual decline from 2025 to 2035, but the global incidence of TB is currently declining at an average rate of approximately 2% per year [ 3 ]. TB, is caused by Mycobacterium tuberculosis , an airborne infectious disease, with pulmonary tuberculosis (PTB) accounting for approximately 80% of all cases. Bacteriological examination methods, such as smear microscopy, are the gold standards for TB diagnosis. However, the low smear positivity rate and long incubation period pose challenges for clinical diagnosis [ 4 ]. The diagnosis of PTB is delayed, particularly in smear-negative cases. The morbidity and spread of TB can be reduced by early detection and accurate diagnosis, which are key elements of TBcontrol [ 5 ]. The gold standard for TB diagnosis involves detecting Mycobacterium tuberculosis in sputum or tissue samples, using methods such as sputum smears, bacterial culture, and PCR amplification [ 6 ]. Despite wide spread use in China, the TB antibody (TB-Ab) test generally achieves a, its diagnostic rate of < 70%. In 2010, the WHO recommended the Xpert MTB/RIF assay to test for tuberculosis testing; however, this method has limited sensitivity for detecting PTB, false positivity, and restricted utility [ 7 ]. Therefore, a rapid and sensitive technology is needed to improve positivity rate. A is used in the tuberculosis-interferon (IFN)-γ release assays (TB-IGRA) use a special antigen to stimulate an immune response, detected by enzyme-linked immunosorbent assay (ELISA). TB IGRA demonstrates high specificity and sensitivity making it a promising diagnostic tool for TB [ 8 , 9 ]. The assay detect inflammatory cytokine of IFN-γ released by T cells in TB-IGRAs, which is an ex vivo blood test for the T cell immune response. It is stimulated by antigens specific to M. tuberculosis , including culture filtrate antigen (CFP-10) and early secretory antigenic target (ESAT-6) [ 10 , 11 ]. However, the immune function and inflammatory status can influence TB-IGRA detection results [ 12 ], potentially causing TB-IGRA to miss detection in immunocompromised patients with TB-IGRA test. Metagenomic next-generation sequencing (mNGS) offers broad unbiased coverage and can unpredictably identify[ 13 ]. Bacteria, fungi, and viruses simultaneously in a single sample [ 14 , 15 ]. mNGS plays an important role in clinical diagnosis and rational drug use [ 16 ]. making it widely used in clinical practice. In addition, the absence of culture and pathogen detection results within 24–48 hours is an advantage of mNGS [ 17 ]. M. tuberculosis and other potential pathogens can be detected using the mNGS thus enhancing the diagnostic accuracy for mixed infections [ 18 ].The mNGS test is widely used for etiological detection in clinical practice [ 19 – 22 ]. However, M. tuberculosis ’s intracellular bacterium and thick capsule can lead to false-negative results in the pathogen detection by the mNGS test [ 23 ]. According to the WHO’s report on TB, no single test is 100% accurate. .Therefore, combining TB-IGRA and mNGS as a supplementary diagnostic method could potentially enhance tuberculosis diagnosis by leveraging the strengths of both tests. This combined approach could be particularly beneficial in complex cases, such as patients with negative or indeterminate TB IGRA results, or when rapid diagnosis is critical to initiate treatment and preventing TB spread. While TB IGRA and mNGS may incur additional costs, the long-term benefits of accurate diagnosis, appropriate treatment, and reduced transmission may outweigh the initial expenses. Moreover, the costs of misdiagnosis or delayed treatment may be significantly higher in some settings. In this study, we investigated the sensitivity and specificity of PTB detection using TB-IGRA, mNGS, and clinical laboratory indices. This study aimed to develop improved strategies for PTB diagnosis by combining multiple parameters. Materials and Methods Patients and Sample Collection This study was a retrospective cohort study. We reviewed patients with pulmonary infections from the First Affiliated Hospital of Anhui Medical University between October 2022 and June 2023 using a hospital electronic medical record system (Fig. 1 ). All enrolled patients were classified as having PTB or non-TB according to the Clinical Diagnosis Standards for TB in China (WS 288–2017). The Chinese diagnostic criteria for TB, outlined in the "WS288-2017" standard, provide a comprehensive approach to diagnosing pulmonary tuberculosis: (1) Suspected Cases: Defined by specific criteria including epidemiological history combined with clinical symptoms or imaging findings; (2) Clinical Diagnosis Cases: Identified by imaging findings along with clinical symptoms, tuberculin skin test results, or auxiliary examination results; (3) Confirmed Cases: Diagnosed by positive sputum smear microscopy, culture, or pathological diagnosis of tuberculosis lesions. The inclusion criteria were as follows: (1) PTB: Patients with pulmonary infection positive for TB-IGRA and/or mNGS and positive mycobacterial tuberculosis cultures. (2) Non-TB: Patients with pulmonary infection negative for TB-IGRA and mNGS of TB, with no bacteriological (sputum smear microscopy, mycobacterial sputum culture, or nucleic acid amplification assays) or radiological evidence of PTB, and no history of TB. Patients with the following characteristics were excluded from this study: (1) patients aged less than 18 years, (2) those who received anti-TB treatment for > 2 weeks before admission to our hospital, and (3) those with incomplete information. Information on age, gender, smoking history, fever, cough, thoracalgia, wheezing, white blood cells (WBCs), C-reactive protein (CRP), neutrophils, procalcitonin (PCT), lymphocytes, erythrocyte sedimentation rate (ESR), D-dimer, total protein, albumin, interferon-γ (IFN-γ), smear-negative PTB and Chest CT scans were obtained from medical records. This study was approved by the Ethics Committee of the First Affiliated Hospital of the Anhui Medical University (PJ2023-13-35). The Ethics Committee agreed to a waiver of informed consent because this was a retrospective study. TB-IGRA test All subjects were tested using TB-IGRA according to the manufacturer’s instructions (Wantai Biology Ltd., Beijing, China). Briefly, were injected 1mL heparinized whole blood from each patient was injected into three special culture tubes (T/P/N) for TB-IGRA: T (test tube) coated with a recombinant fusion protein of CFP-10 and ESAT-6 ( M. tuberculosis -specific antigens), P (positive control tube) containing phytohemagglutinin (PHA), and N (negative control tube). All the tubes were then incubated for 22 ± 2 h at 37°C, then centrifuged at 3000 × g for 15 min, and the serum was collected. First, 20 µL of the serum was diluted, and 50 µL of the sample plasma was added to the sample wells, with calibration solution added to standard wells. The wells were mixed and incubated for 60 min at 37°C. Subsequently, the was added about 50 µL of enzyme-labeled antibody was added to both sample and standard wells, mixed, and incubated for 60 min at 37°C. After washing the wells five times, 50 µL of chromogenic solutions A and B were added and incubated for 15 min at 37°C. Finally, the absorbance was measured at 450 nm using a microplate reader. Standard curves were prepared for each experiment. The ELISA immunosorbent assay was used to measure IFN-γ levels. Positive samples were identified according to the criteria listed in Table 1 . Table 1 TB-IGRA detection criterion N P-N T-N Results ≤ 400 Any value ≥ 14 and ≥ N/4 Positive ≥ 20 < 14 Negative ≥ 14 but ≥ N/4 < 20 < 14 Uncertain ≥ 14 but ≥ N/4 ≥ 400 Any value Any value Uncertain Notes: TB-IGRA, interferon-γ release assays; N (Unit: pg/ml), negative control tube content value; P (Unit: pg/ml), positive control tube content value; T (Unit: pg/ml), testing tube content value; P-N, difference of content value between positive control tube and negative control tube; T-N, difference of content value between testing tube and negative control tube. Metagenomic next-generation sequencing and analysis DNA samples were extracted and purified using a QIAamp DNA Micro Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s recommendations. The samples were transcribed using the QIAamp Viral RNA Mini Kit (QIAGEN, Hilden, Germany) and the SMART MMLV Reverse Transcriptase kit (Takara Biotechnology Co. Ltd., Dalian, China). The concentration and quality of the extracts was determined using Qubit 4.0 (Thermo Fisher Scientific, MA, United States). DNA and RNA libraries were constructed using the QIAseq Ultralow Input Library Kit (QIAGEN, Hilden, Germany) and TruePrep DNA Library Prep Kit (Vazyme, Jiangsu, China), respectively. Sequencing of the libraries was performed on the NextSeq 550 platform (Illumina, San Diego, CA, USA) was used to sequence libraries. High-quality sequencing data were generated by removing low-quality, low-complexity, and short reads (< 35 base pairs). Bowtie2 was used to obtain clean reads by mapping human reads to the human reference genome (hg38). Then, Burrows-Wheeler Aligner software was used to align the clean sequences to the microbial pan-genome database. Meanwhile the same procedure and bioinformatics analysis were used for mNGS of the negative and positive controls. The number of specific reads and reads per million (RPM) were calculated. For detected bacteria and fungi, an mNGS result was defined as positive when the genome coverage of detected sequences belonging to this microorganism ranked top 10 of the same kind of microbes or when RPM sample /RPM NTC >10 if RPM NTC ≠0, and the microorganism was not detected in the negative control (NTC). For viruses, mNGS was considereda positive result when at least one specific read was mapped to a species or when RPM sample /RPM NTC was > 5 if RPM NTC ≠0 and the virus was not detected in the NTC. Statistical analyses All statistical analyses were performed using SPSS (version 17.0; SPSS Inc., Chicago, IL, USA). Data are presented as mean ± standard error of the mean (SEMs). Differences in PCT, age, WBCs, lymphocytes, CRP, neutrophils, ESR, D-dimer, total protein, albumin, and IFN-γ levels was assessed by the Mann-Whitney U test. The chi-square (χ 2 ) test was used to assess the differences in smoking history, gender proportion, fever, cough, thoracalgia, wheezing, smear-negative PTB, and chest CT findings. Receiver operating characteristic (ROC) curves were used to plot sensitivity versus 1-specificity (evaluated at several different diagnostic thresholds of IFN-γ concentrations). Youden’s index was used to determine the optimal cutoff threshold. Statistical significance was set at p < 0.05. Results General Characteristics of the Enrolled Cohort Between October 2022 and June 2023, 89 patients with suspected pulmonary infections at the First Affiliated Hospital of Anhui Medical University were enrolled in this study. All enrolled patients underwent analysis using TB-IGRA and mNGS test. A total of 28 patients were excluded due to the loss of key clinical data (n = 20), lack of raw sequence data (n = 7), and duplication (n = 1). Eventually, 61 eligible patients were enrolled, comprising 29 patients with PTB and 32 patients without PTB ( Fig. 1 ) . Comparison of clinical parameters in PTB and non-TB patients The mean age of the 29 PTB patients was 50.0 ± 3.23 years, and the mean age of the 32 non-TB patients was 58.1 ± 3.16 years. Regarding presenting symptoms or signs, cough and thoracalgia were significantly less common in PTB patients than in non-TB patients ( p = 0.001 and p = 0.024, respectively; Table 2 ). The levels of total protein and albumin in the sera of PTB patients were significantly elevated compared to those in non-TB patients ( p = 0.039 and p = 0.004, respectively). However, CRP levels in PTB patients were lower than those in non-TB patients ( p = 0.024; Table 2 ). No significant differences in the other parameters were identified between the PTB and non-TB groups (Table 2 ). The specific bacteria responsible for the non-TB group are listed in Supplementary Table 1 . Table 2 Demographic and clinical characteristics of PTB and non-TB Patients. Clinical parameters PTB (n = 29) non-TB (n = 32) p value* Age (years) § 50.0 ± 3.23 58.1 ± 3.16 0.078 Gender 0.099 Male (n, %) 12 (41.4%) 20 (62.5%) Female (n, %) 17 (58.6%) 12 (37.5%) Smoke history (n, %) 2 (6.9%) 4 (12.5%) 0.458 Presenting symptoms or signs Fever (n, %) 14 (48.3%) 20 (62.5%) 0.264 Cough (n, %) 9 (31.0%) 23 (71.9%) 0.001 Thoracalgia (n, %) 8 (27.6%) 18 (56.3%) 0.024 Wheezing (n, %) 5 (17.2%) 11 (34.4%) 0.129 Laboratory parameters WBCs (*10 9 /L) § 7.24 ± 0.61 7.48 ± 0.64 0.977 Neutrophils (*10 9 /L) § 5.58 ± 0.58 5.60 ± 0.67 0.707 Lymphocyte (*10 9 /L) § 1.03 ± 0.08 1.37 ± 0.19 0.180 PCT (ng/L) § 0.09 ± 0.02 1.01 ± 0.51 0.374 CRP (mg/L) § 31.29 ± 7.57 65.58 ± 14.95 0.024 ESR (mm/h) § 41.34 ± 7.59 31.63 ± 4.54 0.649 D-dimer(µg/L) § 1.63 ± 0.41 3.11 ± 1.04 0.484 Total protein (g/L) § 64.99 ± 1.53 60.23 ± 1.32 0.039 Albumin (g/L) § 37.25 ± 0.95 32.83 ± 1.00 0.004 Smear-negative PTB 20 (69.0%) 32 (100%) 0.0001 Chest CT scan 0.608 Bilateral 22 (75.9%) 26 (81.2%) Unilateral 7 (24.1%) 6 (18.8%) Notes: PTB, pulmonary tuberculosis; non-TB, patients with pulmonary infection negative for tuberculosis; WBCs, white blood cell; PCT, procalcitonin; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; CT, Computed Tomography. §Data are shown as means ± SEMs. *From Chi-square (χ 2 ) test and Mann-Whitney U test. Statistical significance was set at p < 0.05. Comparison of IFN-γ release levels in PTB and non-TB patients The levels of IFN-γ release in PTB and non-TB patients were 604.15 ± 112.18 pg/mL, and 1.04 ± 0.38 pg/mL, respectively. PTB patients exhibited significantly higher IFN-γ release than non-TB patients ( p < 0.0001; Table 3 ). Table 3 Comparison of IFN-γ levels in PTB and non-TB patients. Variables PTB (n = 29) non-TB (n = 32) p value* IFN-γ (pg/mL) § 604.15 ± 112.18 1.04 ± 0.38 < 0.0001 Notes: PTB, pulmonary tuberculosis; non-TB, patients with pulmonary infection negative for tuberculosis; IFN-γ, interferon-γ; n, number. §Data are shown as means ± SEMs. *Comparison of IFN-γ level between PTB and non-TB patients by Mann-Whitney U test. Statistical significance was set at p < 0.05. Comparison of IFN-γ release levels in PTB patients with positive and negative sputum smears The levels of IFN-γ release in PTB patients with positive and negative sputum smears were 1243.35 ± 412.44 pg/mL, and 425.56 ± 106.70 pg/mL, respectively. PTB patients with positive sputum smears had significantly higher IFN-γ release than those with negative sputum smears ( p = 0.023, Table 4 ). Table 4 Comparison of IFN-γ release levels in PTB patients with positive and negative sputum smears Variables PTB with positive sputum smears (n = 9) PTB with negative sputum smears (n = 20) p value* IFN-γ (pg/mL) § 1243.35 ± 412.44 425.56 ± 106.70 0.023 Notes: PTB, pulmonary tuberculosis; IFN-γ, interferon-γ; n, number. §Data are shown as means ± SEMs. *Comparison of IFN-γ level between PTB patients with positive and negative sputum smears by Mann-Whitney U test. Statistical significance was set at p < 0.05. ROC, receiver operating characteristics; TB-IGRA, tuberculosis-interferon-γ release assays; mNGS, metagenomic next-generation sequencing; PTB, pulmonary tuberculosis. ROC curve analysis of the sensitivity and specificity of TB-IGRA, mNGS, and TB-IGRA combined with mNGS ROC curve analysis for IFN-γ showed that the area under the ROC curve (AUC) for PTB diagnosis was 0.939 (95%CI: 0.876-1), with a sensitivity of 86.2% and specificity of 96.9% when the recommended cut-off value of 14.3 pg/mL (Youden’s index 0.831) was applied. When a cutoff value of 32.12 pg/mL was used, the sensitivity was 82.8% and the specificity was 100%, although Youden’s index slightly decreased to 0.828. ROC curve analysis for mNGS showed that the AUC for PTB diagnosis was 0.879 (95%CI: 0.782–0.977). ROC curve analysis for TB-IGRA combined with mNGS showed that the AUC for PTB diagnosis was 1. ( Fig. 2 ) Discussion The diagnosis of TB depends on the observation of clinical symptoms, smears and cultures of clinical samples, and radiographic examination [ 4 ]. However, the symptoms of TB have become increasingly insidious and the signs more atypical. Clinical diagnosis remains a major challenge due to the limitations of existing diagnostic methods and the high negative rate of cultures and smears. TB-IGRA is widely used for the diagnosing PTB. Compared to the Xpert MTB/RIF assay, TB-IGRA is highly specific for M. tuberculosis infection, reducing the risk of false positives that can occur with the GeneXpert assay because of non-tuberculous mycobacteria (NTM) or Bacillus Calmette-Guérin (BCG) vaccination, which is also particularly useful for diagnosing latent TB infection [ 7 ]. However, the body’s inflammatory status can influence TB IGRA results. mNGS is becoming a promising option for detecting M. tuberculosis , results within a short timeframe, similar to the GeneXpert assay, but with the added benefit of pathogen identification from a single test, allowing the detection of unexpected or novel pathogens [ 13 ]. This could also improve our understanding of tuberculosis transmission. However, M. tuberculosis migh not be detected by mNGS in some patients due to its thick capsule [ 24 ]. In this study, the TB-IGRA was combined with mNGS to determine whether this combination could reduce the rate of misdiagnoses and missed diagnoses. We evaluated the clinical characteristics and demographics of 29 patients with PTB and 32 patients without TB. We found that the levels of total albumin, protein, and IFN-γ in the sera of PTB patients were significantly higher compared with those in non-TB patients. Our results are consistent with previous studies [ 25 – 27 ], indicating that IFN-γ release could be influence by the immune function of the body. We also found that the incidence of cough and thoracalgia was significantly lower in PTB patients than in non-TB patients, indicating that pulmonary symptoms were relatively mild in these patients. It is worth noting that TB patients experienced fewer coughs, and thoracalgia might have led to delayed diagnosis because the onset of TB might have gone unnoticed. Therefore, early diagnosis is crucial for the effective treatment and prevention of disease progression. The levels of total protein and albumin in the sera of PTB patients were significantly higher than those in non-TB patients ( p < 0.05 ). This might be due to the small sample size; however, it did not affect the overall analysis results as the inclusion criteria for PTB were positive for TB-IGRA and/or mNGS, with positive mycobacterial tuberculosis cultures, while non-TB was defined as a pulmonary infection caused by pathogens other than tuberculosis. There were no significant differences in age, sex, WBCs count, lymphocyte count, PCT level, neutrophil count, or chest CT findings between PTB and non-TB patients. Additionally, IFN-γ release in PTB patients with positive sputum smears were higher compared to PTB patients with negative smears. However, Ji et al. found there was no difference in the IFN-γ levels of smear-negative and smear-positive PTB patients (89.6% vs. 90.8%, p > 0.05) [ 28 ]. The different results might be due to the different sources of specimens and number of cases. Our results showed that the AUC of TB-IGRA, mNGS, and TB-IGRA combined with mNGS for the diagnosis was 0.939, 0.879, and 1, respectively. This indicates that the sensitivity of TB-IGRA combined with mNGS was significantly higher than that of TB-IGRA and mNGS alone ( p < 0.05). Clinical studies have shown that T lymphocyte reduction can lead to a lower positivity rate of TB-IGRA in immunocompromised patients [ 29 , 30 ]. However, Our study did not address this issue. Therefore, additional data are required for further analysis. We found that the sensitivity was 86.2% and the specificity was 96.9%, when the optimal cut-off value for IFN-γ (14.3 pg/mL, Youden’s index 0.831) was applied. This suggests that the cut-off value for TB-IGRA is consistent with the diagnostic criteria, aligning with of previous studies [ 25 , 28 ], indicating that TB-IGRA is a reliable diagnostic tool for PTB infection. Additionally, mNGS has been gradually applied to the diagnosis of TB infection due to its rapid and highly sensitive characteristics [ 31 ]. However, the specificity of mNGS is not high compared to the final clinical diagnosis [ 32 ]. The principle of TB-IGRA is that IFN-γ is secreted by T lymphocytes in PTB patients under specific antigen stimulation, making its sensitivity higher than that of mNGS. The main advantage of mNGS is its ability to eliminate interference from non-TB patients. Our results revealed that the combination of TB-IGRA and mNGS was superior to mNGS alone for PTB diagnosis. Therefore, it is necessary to perform a TB-IGRA combined with mNGS for PTBinfections. These data may be helpful in evaluating and diagnosing PTB in elderly and immunocompromised patients in future studies, as well as in the patients at stages of infection that are currently difficult to diagnose. We hope that the combination of TB-IGRA and mNGS can be implemented in clinical settings. A limitation of our study was the small sample size owing to the design of this study, which obtained examination results from medical records. Meanwhile, TB-IGRA and mNGS examinations were not routinely performed at the First Affiliated Hospital of Anhui Medical University. This resulted in a substantial exclusion of subjects. Further prospective studies with larger sample sizes are required to evaluate the combination of TB-IGRA and mNGS for rapid diagnosis of TB. Conclusion TB-IGRA combined with mNGS is an effective method for detecting PTB infection and holds significance in the clinical diagnosis of PTB. Therefore, the clinical application of this combination should be advocated and promoted. Abbreviations PTB - Pulmonary Tuberculosis TB-IGRA - Tuberculosis-Interferon Gamma Release Assays mNGS - Metagenomic Next-Generation Sequencing IFN-γ - Interferon-gamma WHO - World Health Organization PCR - Polymerase Chain Reaction ELISA - Enzyme-Linked Immunosorbent Assay CFP-10 - Culture Filtrate Protein 10 ESAT-6 - Early Secretory Antigenic Target 6 NTM - Non-Tuberculous Mycobacteria BCG - Bacillus Calmette-Guérin CRP - C-Reactive Protein WBC - White Blood Cell PCT - Procalcitonin ESR - Erythrocyte Sedimentation Rate CT - Computed Tomography ROC - Receiver Operating Characteristic AUC - Area Under the Curve NGS - Next-Generation Sequencing NTC - Negative Control RPM - Reads Per Million Declarations Acknowledgements The authors highly acknowledge the contribution of all the associated personnel who contributed to the completion of this study. Authors ’ contributions Conception and design: Yanyan Liu, Miaohong Fang, Chenxi Yuan. Provision of study materials or patients: Yi Yang, Liang Yu. Collection and assembly of data: Chenxi Yuan, Yi Yang. Data analysis and interpretation: Yasheng Li, Lifen Hu. Manuscript writing: Yanyan Liu, Miaohong Fang, Chenxi Yuan. Final approval of manuscript: Yanyan Liu and Jiabin Li. All authors read and approved the final manuscript. Funding This study was supported by the Natural Science Foundation in Anhui Province (No. 2208085MH264) , the Project Supported by Anhui Medical University (2021xkj138), Anhui Province clinical medical research transformation special project (202304295107020032, 202304295107020043), Anhui university scientific research project (2023AH010083), and China Primary Health Care Foundation (No. MTP2022A015) Data availability The datasets used and/or analyzed during the current study are available at National Genomics Data Center (NGDC) (https://ngdc.cncb.ac.cn/), reference number PRJCA020154. Ethics a pproval and consent to participate The study was approved by the ethical research committee of the First Affiliated Hospital of Anhui Medical University (Approval No. PJ-2023-13-35) and was conducted in accordance with the Declaration of Helsinki. As the study was based on a retrospective review of anonymous medical records and did not involve patient interaction, informed consents of the patients were waived by the ethical research committee of the First Affiliated Hospital of Anhui Medical University. Consent for Publication Not applicable. Competing interests All authors have no conflicting interests in this work. References Furin J, Cox H, Pai M.Tuberculosis. Lancet . 2019;393(10181):1642-1656. doi: 10.1016/S0140-6736(19)30308-3. Chakaya J, Khan M, Ntoumi F, et al. Global Tuberculosis Report 2020 - Reflections on the Global TB burden, treatment and prevention efforts. Int J Infect Dis . 2021;113 Suppl 1(Suppl 1):S7-S12. doi: 10.1016/j.ijid.2021.02.107. Chaw L, Chien LC, Wong J, et al. Global trends and gaps in research related to latent tuberculosis infection. BMC Public Health . 2020;20(1):352. doi: 10.1186/s12889-020-8419-0. Park MY, Kim YJ, Hwang SH, et al. Evaluation of an immunochromatographic assay kit for rapid identification of Mycobacterium tuberculosis complex in clinical isolates. J Clin Microbiol . 2009;47(2):481-484. doi: 10.1128/JCM.01253-08. Kontsevaya I, Cabibbe AM, Cirillo DM, et al. Update on the diagnosis of tuberculosis. Clin Microbiol Infect . 2023;S1198-743X(23):340-343. doi: 10.1016/j.cmi.2023.07.014. Alonzi T, Repele F, Goletti D. Research tests for the diagnosis of tuberculosis infection. Expert Rev Mol Diagn . 2023;23(9):783-795. doi: 10.1080/14737159.2023.2240230. Li K, Hu Q, Liu J, Liu S, He Y. Effects of sputum bacillary load and age on GeneXpert and traditional methods in pulmonary tuberculosis: a 4-year retrospective comparative study. BMC Infect Dis, 2023;23(1):831. doi: 10.1186/s12879-023-08832-6. Dai Y, Feng Y, Xu R, et al. Evaluation of interferon-gamma release assays for the diagnosis of tuberculosis: an updated meta-analysis. Eur J Clin Microbiol Infect Dis . 2012;31(11):3127-37. doi: 10.1007/s10096-012-1674-y. Slater M, Tran MC, Platt L, et al. In vitro immunomodulation for enhancing T cell-based diagnosis of Mycobacterium tuberculosis infection. Diagn Microbiol Infect Dis . 2015;83(1):41-5. doi: 10.1016/j.diagmicrobio.2015.05.007. Pai M, Behr M. Latent Mycobacterium tuberculosis infection and interferongamma release assays. Microbiol Spectr. 2016;4(5). doi:10.1128/ microbiolspec.TBTB2-0023-2016.2. Higuchi K, Kawabe Y, Mitarai S, et al. Comparison of performance in two diagnostic methods for tuberculosis infection. Med Microbiol Immunol. 2009 Feb;198(1):33-7. doi: 10.1007/s00430-008-0102-5. Ai L, Feng P, Chen D, et al. Clinical value of interferon-γ release assay in the diagnosis of active tuberculosis. Exp Ther Med . 2019;18(2):1253-1257. doi: 10.3892/etm.2019.7696. Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet . 2019 ;20(6):341-355. doi: 10.1038/s41576-019-0113-7. Chen H, Liang Y, Wang R, et al. Metagenomic next-generation sequencing for the diagnosis of Pneumocystis jirovecii Pneumonia in critically pediatric patients. Ann Clin Microbiol Antimicrob . 2023;22(1):6. doi: 10.1186/s12941-023-00555-5. Diao Z, Han D, Zhang R, Li J. Metagenomics next-generation sequencing tests take the stage in the diagnosis of lower respiratory tract infections. J Adv Res . 2021;38:201-212. doi: 10.1016/j.jare.2021.09.012. Xing XW, Yu SF, Zhang JT, et al. Metagenomic Next-Generation Sequencing of Cerebrospinal Fluid for the Diagnosis of Cerebral Aspergillosis. Front Microbiol . 2021;12:787863. doi: 10.3389/fmicb.2021.787863. Miao Q, Ma Y, Wang Q, et al. Microbiological Diagnostic Performance of Metagenomic Next-generation Sequencing When Applied to Clinical Practice. Clin Infect Dis . 2018;67(suppl_2):S231-S240. doi: 10.1093/cid/ciy693. Li Y, Bian W, Wu S, et al. Metagenomic next-generation sequencing for Mycobacterium tuberculosis complex detection: a meta-analysis. Front Public Health . 2023;11:1224993. doi: 10.3389/fpubh.2023.1224993. Zhou X, Wu H, Ruan Q, et al. Clinical Evaluation of Diagnosis Efficacy of Active Mycobacterium tuberculosis Complex Infection via Metagenomic Next-Generation Sequencing of Direct Clinical Samples. Front Cell Infect Microbiol . 2019;9:351. doi: 10.3389/fcimb.2019.00351. Zhu N, Zhou D, Li S. Diagnostic Accuracy of Metagenomic Next-Generation Sequencing in Sputum-Scarce or Smear-Negative Cases with Suspected Pulmonary Tuberculosis. Biomed Res Int . 2021;2021:9970817. doi: 10.1155/2021/9970817. Shi CL, Han P, Tang PJ, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. J Infect . 2020;81(4):567-574. doi: 10.1016/j.jinf.2020.08.004. Sun W, Lu Z, Yan L. Clinical efficacy of metagenomic next-generation sequencing for rapid detection of Mycobacterium tuberculosis in smear-negative extrapulmonary specimens in a high tuberculosis burden area. Int J Infect Diseases . 2021;103:91-96. doi: 10.1016/j.ijid.2020.11.165. Jin Y, Hu S, Feng J, Ni J. Clinical value of metagenomic next-generation sequencing using spinal tissue in the rapid diagnosis of spinal tuberculosis. Infect Drug Resist. 2023;16:3305-3313. doi: 10.2147/idr.S410914. Satta G, Lipman M, Smith GP, et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential? Clin Microbiol Infect . 2018;24(6):604-609. doi: 10.1016/j.cmi.2017.10.030. Zhang B, Xiao L, Qiu Q, et al. Association between IL-18, IFN-γ and TB susceptibility: a systematic review and meta-analysis. Ann Palliat Med . 2021;10(10):10878-10886. doi: 10.21037/apm-21-2582. Tan Y, Tan Y, Li J, et al. Combined IFN-γ and IL-2 release assay for detect active pulmonary tuberculosis: a prospective multicentre diagnostic study in China. J Transl Med . 2021;19(1):289. doi: 10.1186/s12967-021-02970-8. Goletti D, Delogu G, Matteelli A, Migliori GB. The role of IGRA in the diagnosis of tuberculosis infection, differentiating from active tuberculosis, and decision making for initiating treatment or preventive therapy of tuberculosis infection. Int J Infect Dis . 2022;124 Suppl 1:S12-S19. doi: 10.1016/j.ijid.2022.02.047. Ji L, Lou YL, Wu ZX, et al. Usefulness of interferon-γ release assay for the diagnosis of sputum smear-negative pulmonary and extra-pulmonary TB in Zhejiang Province, China. Infect Dis Poverty . 2017;6(1):121. doi: 10.1186/s40249-017-0331-1. Lv D, Liu Y, Guo F, et al. Combining interferon-γ release assays with lymphocyte enumeration for diagnosis of Mycobacterium tuberculosis infection. J Int Med Res . 2020;48(6):300060520925660. doi: 10.1177/0300060520925660. Boyd AE, Ashcroft A, Lipman M, Bothamley GH. Limited added value of T-SPOT.TB blood test in diagnosing active TB: a prospective bayesian analysis. J Infect . 2011;62(6):456-61. doi: 10.1016/j.jinf.2011.04.003. Li Y, Bian W, Wu S, et al. Metagenomic next-generation sequencing for Mycobacterium tuberculosis complex detection: a meta-analysis. Front Public Health . 2023;11:1224993. doi: 10.3389/fpubh.2023.1224993. Shi CL, Han P, Tang PJ, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. J Infect . 2020;81(4):567-574. doi: 10.1016/j.jinf.2020.08.004. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2024 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 27 Jun, 2024 Editor assigned by journal 26 Jun, 2024 Submission checks completed at journal 26 Jun, 2024 First submitted to journal 24 Jun, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4629309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319756870,"identity":"6628568c-4507-43d4-b563-c0a0ed312381","order_by":0,"name":"Yanyan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACPmYYS/7x8Q8fGA6A2RL4tLDBtTCkpTHOIEoLgpljxsxDlBZ2HjOJnzsOy5szHDB7bFNzJ9rgAPPB2zwMdnm4HcZjJtl75rDhzsaGdOOcY89yNxxgS7bmYUguxqdFgrftMOOGwwwHpHPYDgO18JhJA12Y2IDPlr9th+03HGNskLb4B9LC/42gFmmgLYkbzjCzSTO2gW1hI6CFrdhati09ecMNNmbD3r7DuTMPsxlbzjFIxqmFn//wxptv26xtN9zg//jgx7fDuX3Hmx/eeFNhh1MLELAAY6EZiQ+OXAPc6kFKPjAw1OFVMQpGwSgYBSMcAADUQ1fwiCdRvgAAAABJRU5ErkJggg==","orcid":"","institution":"the First Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Liu","suffix":""},{"id":319756873,"identity":"70809704-4755-4221-af55-9a864cd0b1ec","order_by":1,"name":"Miaohong Fang","email":"","orcid":"","institution":"the First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Miaohong","middleName":"","lastName":"Fang","suffix":""},{"id":319756877,"identity":"1de71810-9155-4653-b297-d561457ffb62","order_by":2,"name":"Chenxi Yuan","email":"","orcid":"","institution":"the First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenxi","middleName":"","lastName":"Yuan","suffix":""},{"id":319756878,"identity":"47f10c04-7b09-4f6c-869f-472dff668db3","order_by":3,"name":"Yi Yang","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Yang","suffix":""},{"id":319756879,"identity":"dbfdd33b-0704-440e-ac2a-152b95f4a9c6","order_by":4,"name":"Liang Yu","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Yu","suffix":""},{"id":319756880,"identity":"83de9453-f641-49e7-83d3-4a9c9a3147c3","order_by":5,"name":"Yasheng Li","email":"","orcid":"","institution":"Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yasheng","middleName":"","lastName":"Li","suffix":""},{"id":319756881,"identity":"2cb93ecd-cdf4-4db3-8782-85562facde0f","order_by":6,"name":"Lifen Hu","email":"","orcid":"","institution":"the First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lifen","middleName":"","lastName":"Hu","suffix":""},{"id":319756882,"identity":"f6e0b350-de59-45bb-8d6f-2f6fc706be82","order_by":7,"name":"Jiabin Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiabin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-24 10:06:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4629309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4629309/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-024-10206-5","type":"published","date":"2024-11-18T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60708926,"identity":"6e42ddba-500d-4445-b682-5b249c20bbd6","added_by":"auto","created_at":"2024-07-19 19:51:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":969391,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of cases inclusion and exclusion.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4629309/v1/9c3934d365289589e4310a55.jpg"},{"id":60710037,"identity":"6fc37ff2-8f7e-4940-8689-abf1638b205d","added_by":"auto","created_at":"2024-07-19 19:59:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48098,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of TB-IGRA, mNGS and TB-IGRA combined with mNGS for PTB diagnosis\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristics; TB-IGRA, tuberculosis-interferon-γ release assays; mNGS, metagenomic next-generation sequencing; PTB, pulmonary tuberculosis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4629309/v1/ea93b866b44f0212e389e86a.png"},{"id":69835208,"identity":"30c0022b-56db-4268-aeaf-2909e1dec2cf","added_by":"auto","created_at":"2024-11-25 16:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1704036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4629309/v1/2968948c-033b-476f-becf-63a0aca766e7.pdf"},{"id":60708928,"identity":"0705610a-676c-4be9-ab3d-b1d4e010240a","added_by":"auto","created_at":"2024-07-19 19:51:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17554,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4629309/v1/d5791e613ca79648ed357bc1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining Interferon-γ Release Assays and Metagenomic Next-generation Sequencing for Diagnosis of Pulmonary Tuberculosis: A Retrospective Study","fulltext":[{"header":"Background","content":"\u003cp\u003eTuberculosis (TB) has been recognized as a global public health emergency for the past two decades and remains the leading cause of death among adults worldwide due to an infectious disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the Global Tuberculosis Report 2020, more than 10\u0026nbsp;million new TB and 1.4\u0026nbsp;million deaths have been reported to the World Health Organization (WHO) according to the Global Tuberculosis Report 2020 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The target of the end TB strategy is a 17% annual decline from 2025 to 2035, but the global incidence of TB is currently declining at an average rate of approximately 2% per year [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. TB, is caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, an airborne infectious disease, with pulmonary tuberculosis (PTB) accounting for approximately 80% of all cases. Bacteriological examination methods, such as smear microscopy, are the gold standards for TB diagnosis. However, the low smear positivity rate and long incubation period pose challenges for clinical diagnosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The diagnosis of PTB is delayed, particularly in smear-negative cases.\u003c/p\u003e \u003cp\u003eThe morbidity and spread of TB can be reduced by early detection and accurate diagnosis, which are key elements of TBcontrol [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The gold standard for TB diagnosis involves detecting Mycobacterium tuberculosis in sputum or tissue samples, using methods such as sputum smears, bacterial culture, and PCR amplification [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite wide spread use in China, the TB antibody (TB-Ab) test generally achieves a, its diagnostic rate of \u0026lt;\u0026thinsp;70%. In 2010, the WHO recommended the Xpert MTB/RIF assay to test for tuberculosis testing; however, this method has limited sensitivity for detecting PTB, false positivity, and restricted utility [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, a rapid and sensitive technology is needed to improve positivity rate. A is used in the tuberculosis-interferon (IFN)-γ release assays (TB-IGRA) use a special antigen to stimulate an immune response, detected by enzyme-linked immunosorbent assay (ELISA). TB IGRA demonstrates high specificity and sensitivity making it a promising diagnostic tool for TB [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The assay detect inflammatory cytokine of IFN-γ released by T cells in TB-IGRAs, which is an ex vivo blood test for the T cell immune response. It is stimulated by antigens specific to \u003cem\u003eM. tuberculosis\u003c/em\u003e, including culture filtrate antigen (CFP-10) and early secretory antigenic target (ESAT-6) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the immune function and inflammatory status can influence TB-IGRA detection results [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], potentially causing TB-IGRA to miss detection in immunocompromised patients with TB-IGRA test.\u003c/p\u003e \u003cp\u003eMetagenomic next-generation sequencing (mNGS) offers broad unbiased coverage and can unpredictably identify[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Bacteria, fungi, and viruses simultaneously in a single sample [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. mNGS plays an important role in clinical diagnosis and rational drug use [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. making it widely used in clinical practice. In addition, the absence of culture and pathogen detection results within 24\u0026ndash;48 hours is an advantage of mNGS [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. \u003cem\u003eM. tuberculosis\u003c/em\u003e and other potential pathogens can be detected using the mNGS thus enhancing the diagnostic accuracy for mixed infections [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].The mNGS test is widely used for etiological detection in clinical practice [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, \u003cem\u003eM. tuberculosis\u003c/em\u003e\u0026rsquo;s intracellular bacterium and thick capsule can lead to false-negative results in the pathogen detection by the mNGS test [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. According to the WHO\u0026rsquo;s report on TB, no single test is 100% accurate. .Therefore, combining TB-IGRA and mNGS as a supplementary diagnostic method could potentially enhance tuberculosis diagnosis by leveraging the strengths of both tests. This combined approach could be particularly beneficial in complex cases, such as patients with negative or indeterminate TB IGRA results, or when rapid diagnosis is critical to initiate treatment and preventing TB spread. While TB IGRA and mNGS may incur additional costs, the long-term benefits of accurate diagnosis, appropriate treatment, and reduced transmission may outweigh the initial expenses. Moreover, the costs of misdiagnosis or delayed treatment may be significantly higher in some settings.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the sensitivity and specificity of PTB detection using TB-IGRA, mNGS, and clinical laboratory indices. This study aimed to develop improved strategies for PTB diagnosis by combining multiple parameters.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Sample Collection\u003c/h2\u003e \u003cp\u003eThis study was a retrospective cohort study. We reviewed patients with pulmonary infections from the First Affiliated Hospital of Anhui Medical University between October 2022 and June 2023 using a hospital electronic medical record system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All enrolled patients were classified as having PTB or non-TB according to the Clinical Diagnosis Standards for TB in China (WS 288\u0026ndash;2017). The Chinese diagnostic criteria for TB, outlined in the \"WS288-2017\" standard, provide a comprehensive approach to diagnosing pulmonary tuberculosis: (1) Suspected Cases: Defined by specific criteria including epidemiological history combined with clinical symptoms or imaging findings; (2) Clinical Diagnosis Cases: Identified by imaging findings along with clinical symptoms, tuberculin skin test results, or auxiliary examination results; (3) Confirmed Cases: Diagnosed by positive sputum smear microscopy, culture, or pathological diagnosis of tuberculosis lesions. The inclusion criteria were as follows: (1) PTB: Patients with pulmonary infection positive for TB-IGRA and/or mNGS and positive mycobacterial tuberculosis cultures. (2) Non-TB: Patients with pulmonary infection negative for TB-IGRA and mNGS of TB, with no bacteriological (sputum smear microscopy, mycobacterial sputum culture, or nucleic acid amplification assays) or radiological evidence of PTB, and no history of TB. Patients with the following characteristics were excluded from this study: (1) patients aged less than 18 years, (2) those who received anti-TB treatment for \u0026gt;\u0026thinsp;2 weeks before admission to our hospital, and (3) those with incomplete information. Information on age, gender, smoking history, fever, cough, thoracalgia, wheezing, white blood cells (WBCs), C-reactive protein (CRP), neutrophils, procalcitonin (PCT), lymphocytes, erythrocyte sedimentation rate (ESR), D-dimer, total protein, albumin, interferon-γ (IFN-γ), smear-negative PTB and Chest CT scans were obtained from medical records. This study was approved by the Ethics Committee of the First Affiliated Hospital of the Anhui Medical University (PJ2023-13-35). The Ethics Committee agreed to a waiver of informed consent because this was a retrospective study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTB-IGRA test\u003c/h2\u003e \u003cp\u003eAll subjects were tested using TB-IGRA according to the manufacturer\u0026rsquo;s instructions (Wantai Biology Ltd., Beijing, China). Briefly, were injected 1mL heparinized whole blood from each patient was injected into three special culture tubes (T/P/N) for TB-IGRA: T (test tube) coated with a recombinant fusion protein of CFP-10 and ESAT-6 (\u003cem\u003eM. tuberculosis\u003c/em\u003e-specific antigens), P (positive control tube) containing phytohemagglutinin (PHA), and N (negative control tube). All the tubes were then incubated for 22\u0026thinsp;\u0026plusmn;\u0026thinsp;2 h at 37\u0026deg;C, then centrifuged at 3000 \u0026times; g for 15 min, and the serum was collected. First, 20 \u0026micro;L of the serum was diluted, and 50 \u0026micro;L of the sample plasma was added to the sample wells, with calibration solution added to standard wells. The wells were mixed and incubated for 60 min at 37\u0026deg;C. Subsequently, the was added about 50 \u0026micro;L of enzyme-labeled antibody was added to both sample and standard wells, mixed, and incubated for 60 min at 37\u0026deg;C. After washing the wells five times, 50 \u0026micro;L of chromogenic solutions A and B were added and incubated for 15 min at 37\u0026deg;C. Finally, the absorbance was measured at 450 nm using a microplate reader. Standard curves were prepared for each experiment. The ELISA immunosorbent assay was used to measure IFN-γ levels. Positive samples were identified according to the criteria listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTB-IGRA detection criterion\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14 and \u0026ge;\u0026thinsp;N/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14 but \u0026ge;\u0026thinsp;N/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14 but \u0026ge;\u0026thinsp;N/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAny value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: TB-IGRA, interferon-γ release assays; N (Unit: pg/ml), negative control tube content value; P (Unit: pg/ml), positive control tube content value; T (Unit: pg/ml), testing tube content value; P-N, difference of content value between positive control tube and negative control tube; T-N, difference of content value between testing tube and negative control tube.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic next-generation sequencing and analysis\u003c/h2\u003e \u003cp\u003eDNA samples were extracted and purified using a QIAamp DNA Micro Kit (QIAGEN, Hilden, Germany) according to the manufacturer\u0026rsquo;s recommendations. The samples were transcribed using the QIAamp Viral RNA Mini Kit (QIAGEN, Hilden, Germany) and the SMART MMLV Reverse Transcriptase kit (Takara Biotechnology Co. Ltd., Dalian, China). The concentration and quality of the extracts was determined using Qubit 4.0 (Thermo Fisher Scientific, MA, United States). DNA and RNA libraries were constructed using the QIAseq Ultralow Input Library Kit (QIAGEN, Hilden, Germany) and TruePrep DNA Library Prep Kit (Vazyme, Jiangsu, China), respectively. Sequencing of the libraries was performed on the NextSeq 550 platform (Illumina, San Diego, CA, USA) was used to sequence libraries.\u003c/p\u003e \u003cp\u003eHigh-quality sequencing data were generated by removing low-quality, low-complexity, and short reads (\u0026lt;\u0026thinsp;35 base pairs). Bowtie2 was used to obtain clean reads by mapping human reads to the human reference genome (hg38). Then, Burrows-Wheeler Aligner software was used to align the clean sequences to the microbial pan-genome database. Meanwhile the same procedure and bioinformatics analysis were used for mNGS of the negative and positive controls. The number of specific reads and reads per million (RPM) were calculated. For detected bacteria and fungi, an mNGS result was defined as positive when the genome coverage of detected sequences belonging to this microorganism ranked top 10 of the same kind of microbes or when RPM\u003csub\u003esample\u003c/sub\u003e/RPM\u003csub\u003eNTC\u003c/sub\u003e\u0026gt;10 if RPM\u003csub\u003eNTC\u003c/sub\u003e\u0026ne;0, and the microorganism was not detected in the negative control (NTC). For viruses, mNGS was considereda positive result when at least one specific read was mapped to a species or when RPM\u003csub\u003esample\u003c/sub\u003e/RPM\u003csub\u003eNTC\u003c/sub\u003e was \u0026gt;\u0026thinsp;5 if RPM\u003csub\u003eNTC\u003c/sub\u003e\u0026ne;0 and the virus was not detected in the NTC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS (version 17.0; SPSS Inc., Chicago, IL, USA). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEMs). Differences in PCT, age, WBCs, lymphocytes, CRP, neutrophils, ESR, D-dimer, total protein, albumin, and IFN-γ levels was assessed by the Mann-Whitney U test. The chi-square (χ\u003csup\u003e2\u003c/sup\u003e) test was used to assess the differences in smoking history, gender proportion, fever, cough, thoracalgia, wheezing, smear-negative PTB, and chest CT findings. Receiver operating characteristic (ROC) curves were used to plot sensitivity versus 1-specificity (evaluated at several different diagnostic thresholds of IFN-γ concentrations). Youden\u0026rsquo;s index was used to determine the optimal cutoff threshold. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Characteristics of the Enrolled Cohort\u003c/h2\u003e \u003cp\u003eBetween October 2022 and June 2023, 89 patients with suspected pulmonary infections at the First Affiliated Hospital of Anhui Medical University were enrolled in this study. All enrolled patients underwent analysis using TB-IGRA and mNGS test. A total of 28 patients were excluded due to the loss of key clinical data (n\u0026thinsp;=\u0026thinsp;20), lack of raw sequence data (n\u0026thinsp;=\u0026thinsp;7), and duplication (n\u0026thinsp;=\u0026thinsp;1). Eventually, 61 eligible patients were enrolled, comprising 29 patients with PTB and 32 patients without PTB \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparison of clinical parameters in PTB and non-TB patients\u003c/h2\u003e \u003cp\u003eThe mean age of the 29 PTB patients was 50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23 years, and the mean age of the 32 non-TB patients was 58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16 years. Regarding presenting symptoms or signs, cough and thoracalgia were significantly less common in PTB patients than in non-TB patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The levels of total protein and albumin in the sera of PTB patients were significantly elevated compared to those in non-TB patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, respectively). However, CRP levels in PTB patients were lower than those in non-TB patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No significant differences in the other parameters were identified between the PTB and non-TB groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The specific bacteria responsible for the non-TB group are listed in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of PTB and non-TB Patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTB (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-TB (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke history (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresenting symptoms or signs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (71.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoracalgia (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheezing (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBCs (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (*10\u003csup\u003e9\u003c/sup\u003e/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT (ng/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.58\u0026thinsp;\u0026plusmn;\u0026thinsp;14.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer(\u0026micro;g/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (g/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmear-negative PTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (69.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest CT scan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: PTB, pulmonary tuberculosis; non-TB, patients with pulmonary infection negative for tuberculosis; WBCs, white blood cell; PCT, procalcitonin; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; CT, Computed Tomography.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026sect;Data are shown as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEMs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*From Chi-square (χ\u003csup\u003e2\u003c/sup\u003e) test and Mann-Whitney U test. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eComparison of IFN-γ release levels in PTB and non-TB patients\u003c/h2\u003e \u003cp\u003eThe levels of IFN-γ release in PTB and non-TB patients were 604.15\u0026thinsp;\u0026plusmn;\u0026thinsp;112.18 pg/mL, and 1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 pg/mL, respectively. PTB patients exhibited significantly higher IFN-γ release than non-TB patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of IFN-γ levels in PTB and non-TB patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTB (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-TB (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFN-γ (pg/mL)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e604.15\u0026thinsp;\u0026plusmn;\u0026thinsp;112.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: PTB, pulmonary tuberculosis; non-TB, patients with pulmonary infection negative for tuberculosis; IFN-γ, interferon-γ; n, number.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026sect;Data are shown as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEMs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Comparison of IFN-γ level between PTB and non-TB patients by Mann-Whitney U test. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of IFN-γ release levels in PTB patients with positive and negative\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003esputum smears\u003c/h2\u003e \u003cp\u003eThe levels of IFN-γ release in PTB patients with positive and negative sputum smears were 1243.35\u0026thinsp;\u0026plusmn;\u0026thinsp;412.44 pg/mL, and 425.56\u0026thinsp;\u0026plusmn;\u0026thinsp;106.70 pg/mL, respectively. PTB patients with positive sputum smears had significantly higher IFN-γ release than those with negative sputum smears (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of IFN-γ release levels in PTB patients with positive and negative sputum smears\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTB with positive\u003c/p\u003e \u003cp\u003e sputum smears (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePTB with negative \u003c/p\u003e \u003cp\u003esputum smears (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFN-γ (pg/mL)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1243.35\u0026thinsp;\u0026plusmn;\u0026thinsp;412.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425.56\u0026thinsp;\u0026plusmn;\u0026thinsp;106.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: PTB, pulmonary tuberculosis; IFN-γ, interferon-γ; n, number.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026sect;Data are shown as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEMs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Comparison of IFN-γ level between PTB patients with positive and negative sputum smears by Mann-Whitney U test. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eROC, receiver operating characteristics; TB-IGRA, tuberculosis-interferon-γ release assays; mNGS, metagenomic next-generation sequencing; PTB, pulmonary tuberculosis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eROC curve analysis of the sensitivity and specificity of TB-IGRA, mNGS, and TB-IGRA combined with mNGS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis for IFN-γ showed that the area under the ROC curve (AUC) for PTB diagnosis was 0.939 (95%CI: 0.876-1), with a sensitivity of 86.2% and specificity of 96.9% when the recommended cut-off value of 14.3 pg/mL (Youden\u0026rsquo;s index 0.831) was applied. When a cutoff value of 32.12 pg/mL was used, the sensitivity was 82.8% and the specificity was 100%, although Youden\u0026rsquo;s index slightly decreased to 0.828. ROC curve analysis for mNGS showed that the AUC for PTB diagnosis was 0.879 (95%CI: 0.782\u0026ndash;0.977). ROC curve analysis for TB-IGRA combined with mNGS showed that the AUC for PTB diagnosis was 1. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe diagnosis of TB depends on the observation of clinical symptoms, smears and cultures of clinical samples, and radiographic examination [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the symptoms of TB have become increasingly insidious and the signs more atypical. Clinical diagnosis remains a major challenge due to the limitations of existing diagnostic methods and the high negative rate of cultures and smears. TB-IGRA is widely used for the diagnosing PTB. Compared to the Xpert MTB/RIF assay, TB-IGRA is highly specific for \u003cem\u003eM. tuberculosis\u003c/em\u003e infection, reducing the risk of false positives that can occur with the GeneXpert assay because of non-tuberculous mycobacteria (NTM) or Bacillus Calmette-Gu\u0026eacute;rin (BCG) vaccination, which is also particularly useful for diagnosing latent TB infection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the body\u0026rsquo;s inflammatory status can influence TB IGRA results. mNGS is becoming a promising option for detecting \u003cem\u003eM. tuberculosis\u003c/em\u003e, results within a short timeframe, similar to the GeneXpert assay, but with the added benefit of pathogen identification from a single test, allowing the detection of unexpected or novel pathogens [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This could also improve our understanding of tuberculosis transmission. However, \u003cem\u003eM. tuberculosis\u003c/em\u003e migh not be detected by mNGS in some patients due to its thick capsule [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, the TB-IGRA was combined with mNGS to determine whether this combination could reduce the rate of misdiagnoses and missed diagnoses.\u003c/p\u003e \u003cp\u003eWe evaluated the clinical characteristics and demographics of 29 patients with PTB and 32 patients without TB. We found that the levels of total albumin, protein, and IFN-γ in the sera of PTB patients were significantly higher compared with those in non-TB patients. Our results are consistent with previous studies [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], indicating that IFN-γ release could be influence by the immune function of the body. We also found that the incidence of cough and thoracalgia was significantly lower in PTB patients than in non-TB patients, indicating that pulmonary symptoms were relatively mild in these patients. It is worth noting that TB patients experienced fewer coughs, and thoracalgia might have led to delayed diagnosis because the onset of TB might have gone unnoticed. Therefore, early diagnosis is crucial for the effective treatment and prevention of disease progression. The levels of total protein and albumin in the sera of PTB patients were significantly higher than those in non-TB patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ). This might be due to the small sample size; however, it did not affect the overall analysis results as the inclusion criteria for PTB were positive for TB-IGRA and/or mNGS, with positive mycobacterial tuberculosis cultures, while non-TB was defined as a pulmonary infection caused by pathogens other than tuberculosis. There were no significant differences in age, sex, WBCs count, lymphocyte count, PCT level, neutrophil count, or chest CT findings between PTB and non-TB patients. Additionally, IFN-γ release in PTB patients with positive sputum smears were higher compared to PTB patients with negative smears. However, Ji et al. found there was no difference in the IFN-γ levels of smear-negative and smear-positive PTB patients (89.6% vs. 90.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The different results might be due to the different sources of specimens and number of cases.\u003c/p\u003e \u003cp\u003eOur results showed that the AUC of TB-IGRA, mNGS, and TB-IGRA combined with mNGS for the diagnosis was 0.939, 0.879, and 1, respectively. This indicates that the sensitivity of TB-IGRA combined with mNGS was significantly higher than that of TB-IGRA and mNGS alone (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Clinical studies have shown that T lymphocyte reduction can lead to a lower positivity rate of TB-IGRA in immunocompromised patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, Our study did not address this issue. Therefore, additional data are required for further analysis. We found that the sensitivity was 86.2% and the specificity was 96.9%, when the optimal cut-off value for IFN-γ (14.3 pg/mL, Youden\u0026rsquo;s index 0.831) was applied. This suggests that the cut-off value for TB-IGRA is consistent with the diagnostic criteria, aligning with of previous studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], indicating that TB-IGRA is a reliable diagnostic tool for PTB infection. Additionally, mNGS has been gradually applied to the diagnosis of TB infection due to its rapid and highly sensitive characteristics [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the specificity of mNGS is not high compared to the final clinical diagnosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The principle of TB-IGRA is that IFN-γ is secreted by T lymphocytes in PTB patients under specific antigen stimulation, making its sensitivity higher than that of mNGS. The main advantage of mNGS is its ability to eliminate interference from non-TB patients. Our results revealed that the combination of TB-IGRA and mNGS was superior to mNGS alone for PTB diagnosis. Therefore, it is necessary to perform a TB-IGRA combined with mNGS for PTBinfections. These data may be helpful in evaluating and diagnosing PTB in elderly and immunocompromised patients in future studies, as well as in the patients at stages of infection that are currently difficult to diagnose. We hope that the combination of TB-IGRA and mNGS can be implemented in clinical settings.\u003c/p\u003e \u003cp\u003e A limitation of our study was the small sample size owing to the design of this study, which obtained examination results from medical records. Meanwhile, TB-IGRA and mNGS examinations were not routinely performed at the First Affiliated Hospital of Anhui Medical University. This resulted in a substantial exclusion of subjects. Further prospective studies with larger sample sizes are required to evaluate the combination of TB-IGRA and mNGS for rapid diagnosis of TB.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTB-IGRA combined with mNGS is an effective method for detecting PTB infection and holds significance in the clinical diagnosis of PTB. Therefore, the clinical application of this combination should be advocated and promoted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePTB - Pulmonary Tuberculosis\u003c/p\u003e\n\u003cp\u003eTB-IGRA - Tuberculosis-Interferon Gamma Release Assays\u003c/p\u003e\n\u003cp\u003emNGS - Metagenomic Next-Generation Sequencing\u003c/p\u003e\n\u003cp\u003eIFN-\u0026gamma; - Interferon-gamma\u003c/p\u003e\n\u003cp\u003eWHO - World Health Organization\u003c/p\u003e\n\u003cp\u003ePCR - Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003eELISA - Enzyme-Linked Immunosorbent Assay\u003c/p\u003e\n\u003cp\u003eCFP-10 - Culture Filtrate Protein 10\u003c/p\u003e\n\u003cp\u003eESAT-6 - Early Secretory Antigenic Target 6\u003c/p\u003e\n\u003cp\u003eNTM - Non-Tuberculous Mycobacteria\u003c/p\u003e\n\u003cp\u003eBCG - Bacillus Calmette-Gu\u0026eacute;rin\u003c/p\u003e\n\u003cp\u003eCRP - C-Reactive Protein\u003c/p\u003e\n\u003cp\u003eWBC - White Blood Cell\u003c/p\u003e\n\u003cp\u003ePCT - Procalcitonin\u003c/p\u003e\n\u003cp\u003eESR - Erythrocyte Sedimentation Rate\u003c/p\u003e\n\u003cp\u003eCT - Computed Tomography\u003c/p\u003e\n\u003cp\u003eROC - Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eAUC - Area Under the Curve\u003c/p\u003e\n\u003cp\u003eNGS - Next-Generation Sequencing\u003c/p\u003e\n\u003cp\u003eNTC - Negative Control\u003c/p\u003e\n\u003cp\u003eRPM - Reads Per Million\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors highly acknowledge the contribution of all the associated personnel who contributed to the completion of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design:\u0026nbsp;Yanyan Liu,\u0026nbsp;Miaohong Fang,\u0026nbsp;Chenxi Yuan.\u0026nbsp;Provision of study materials or patients:\u0026nbsp;Yi Yang,\u0026nbsp;Liang Yu. Collection and assembly of data:\u0026nbsp;Chenxi Yuan,\u0026nbsp;Yi Yang. Data analysis and interpretation:\u0026nbsp;Yasheng Li,\u0026nbsp;Lifen Hu. Manuscript writing:\u0026nbsp;Yanyan Liu,\u0026nbsp;Miaohong Fang,\u0026nbsp;Chenxi Yuan.\u0026nbsp;Final approval of manuscript:\u0026nbsp;Yanyan Liu\u0026nbsp;and\u0026nbsp;Jiabin Li. All authors read and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation in Anhui Province (No. 2208085MH264) , the Project Supported by Anhui Medical University (2021xkj138), Anhui Province clinical medical research transformation special project (202304295107020032, 202304295107020043), Anhui university scientific research project (2023AH010083), and China Primary Health Care Foundation (No. MTP2022A015)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available at National Genomics Data Center (NGDC) (https://ngdc.cncb.ac.cn/), reference number PRJCA020154.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003epproval\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethical research committee of the First Affiliated Hospital of Anhui Medical University (Approval No. PJ-2023-13-35) and was conducted in accordance with the Declaration of Helsinki. As the study was based on a retrospective review of anonymous medical records and did not involve patient interaction, informed consents of the patients were waived by the ethical research committee of the First Affiliated Hospital of Anhui Medical University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicting interests in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFurin J, Cox H, Pai M.Tuberculosis.\u003cem\u003e\u0026nbsp;Lancet\u003c/em\u003e. 2019;393(10181):1642-1656. doi: 10.1016/S0140-6736(19)30308-3.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChakaya J, Khan M, Ntoumi F, et al. Global Tuberculosis Report 2020 - Reflections on the Global TB burden, treatment and prevention efforts. \u003cem\u003eInt J Infect Dis\u003c/em\u003e. 2021;113 Suppl 1(Suppl 1):S7-S12. doi: 10.1016/j.ijid.2021.02.107.\u003c/li\u003e\n \u003cli\u003eChaw L, Chien LC, Wong J, et al. Global trends and gaps in research related to latent tuberculosis infection. \u003cem\u003eBMC Public Health\u003c/em\u003e. 2020;20(1):352. doi: 10.1186/s12889-020-8419-0.\u003c/li\u003e\n \u003cli\u003ePark MY, Kim YJ, Hwang SH, et al. Evaluation of an immunochromatographic assay kit for rapid identification of Mycobacterium tuberculosis complex in clinical isolates. \u003cem\u003eJ Clin Microbiol\u003c/em\u003e. 2009;47(2):481-484. doi: 10.1128/JCM.01253-08.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKontsevaya I, Cabibbe AM, Cirillo DM, et al. Update on the diagnosis of tuberculosis. \u003cem\u003eClin Microbiol Infect\u003c/em\u003e. 2023;S1198-743X(23):340-343. doi: 10.1016/j.cmi.2023.07.014.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAlonzi T, Repele F, Goletti D. Research tests for the diagnosis of tuberculosis infection. \u003cem\u003eExpert Rev Mol Diagn\u003c/em\u003e. 2023;23(9):783-795. doi: 10.1080/14737159.2023.2240230.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi K, Hu Q, Liu J, Liu S, He Y.\u0026nbsp;Effects of sputum bacillary load and age on GeneXpert and traditional methods in pulmonary tuberculosis: a 4-year retrospective comparative study.\u0026nbsp;BMC Infect Dis,\u0026nbsp;2023;23(1):831. doi: 10.1186/s12879-023-08832-6.\u003c/li\u003e\n \u003cli\u003eDai Y, Feng Y, Xu R, et al. Evaluation of interferon-gamma release assays for the diagnosis of tuberculosis: an updated meta-analysis. \u003cem\u003eEur J Clin Microbiol Infect Dis\u003c/em\u003e. 2012;31(11):3127-37. doi: 10.1007/s10096-012-1674-y.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSlater M, Tran MC, Platt L, et al. In vitro immunomodulation for enhancing T cell-based diagnosis of Mycobacterium tuberculosis infection. \u003cem\u003eDiagn Microbiol Infect Dis\u003c/em\u003e. 2015;83(1):41-5. doi: 10.1016/j.diagmicrobio.2015.05.007.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePai M, Behr M. Latent Mycobacterium tuberculosis infection and interferongamma release assays. Microbiol Spectr. 2016;4(5). doi:10.1128/ microbiolspec.TBTB2-0023-2016.2.\u003c/li\u003e\n \u003cli\u003eHiguchi K, Kawabe Y, Mitarai S, et al. Comparison of performance in two diagnostic methods for tuberculosis infection. \u003cem\u003eMed Microbiol Immunol.\u003c/em\u003e 2009 Feb;198(1):33-7. doi: 10.1007/s00430-008-0102-5.\u003c/li\u003e\n \u003cli\u003eAi L, Feng P, Chen D, et al. Clinical value of interferon-\u0026gamma; release assay in the diagnosis of active tuberculosis. \u003cem\u003eExp Ther Med\u003c/em\u003e. 2019;18(2):1253-1257. doi: 10.3892/etm.2019.7696.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChiu CY, Miller SA. Clinical metagenomics. \u003cem\u003eNat Rev Genet\u003c/em\u003e. 2019 ;20(6):341-355. doi: 10.1038/s41576-019-0113-7.\u003c/li\u003e\n \u003cli\u003eChen H, Liang Y, Wang R, et al. Metagenomic next-generation sequencing for the diagnosis of Pneumocystis jirovecii Pneumonia in critically pediatric patients. \u003cem\u003eAnn Clin Microbiol Antimicrob\u003c/em\u003e. 2023;22(1):6. doi: 10.1186/s12941-023-00555-5.\u003c/li\u003e\n \u003cli\u003eDiao Z, Han D, Zhang R, Li J. Metagenomics next-generation sequencing tests take the stage in the diagnosis of lower respiratory tract infections. \u003cem\u003eJ Adv Res\u003c/em\u003e. 2021;38:201-212. doi: 10.1016/j.jare.2021.09.012.\u003c/li\u003e\n \u003cli\u003eXing XW, Yu SF, Zhang JT, et al. Metagenomic Next-Generation Sequencing of Cerebrospinal Fluid for the Diagnosis of Cerebral Aspergillosis. \u003cem\u003eFront Microbiol\u003c/em\u003e. 2021;12:787863. doi: 10.3389/fmicb.2021.787863.\u003c/li\u003e\n \u003cli\u003eMiao Q, Ma Y, Wang Q, et al. Microbiological Diagnostic Performance of Metagenomic Next-generation Sequencing When Applied to Clinical Practice. \u003cem\u003eClin Infect Dis\u003c/em\u003e. 2018;67(suppl_2):S231-S240. doi: 10.1093/cid/ciy693.\u003c/li\u003e\n \u003cli\u003eLi Y, Bian W, Wu S, et al. Metagenomic next-generation sequencing for Mycobacterium tuberculosis complex detection: a meta-analysis. \u003cem\u003eFront Public Health\u003c/em\u003e. 2023;11:1224993. doi: 10.3389/fpubh.2023.1224993.\u003c/li\u003e\n \u003cli\u003eZhou X, Wu H, Ruan Q, et al. Clinical Evaluation of Diagnosis Efficacy of Active Mycobacterium tuberculosis Complex Infection via Metagenomic Next-Generation Sequencing of Direct Clinical Samples. \u003cem\u003eFront Cell Infect Microbiol\u003c/em\u003e. 2019;9:351. doi: 10.3389/fcimb.2019.00351.\u003c/li\u003e\n \u003cli\u003eZhu N, Zhou D, Li S. Diagnostic Accuracy of Metagenomic Next-Generation Sequencing in Sputum-Scarce or Smear-Negative Cases with Suspected Pulmonary Tuberculosis. \u003cem\u003eBiomed Res Int\u003c/em\u003e. 2021;2021:9970817. doi: 10.1155/2021/9970817.\u003c/li\u003e\n \u003cli\u003eShi CL, Han P, Tang PJ, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. \u003cem\u003eJ Infect\u003c/em\u003e. 2020;81(4):567-574. doi: 10.1016/j.jinf.2020.08.004.\u003c/li\u003e\n \u003cli\u003eSun W, Lu Z, Yan L. Clinical efficacy of metagenomic next-generation sequencing for rapid detection of Mycobacterium tuberculosis in smear-negative extrapulmonary specimens in a high tuberculosis burden area. \u003cem\u003eInt J Infect Diseases\u003c/em\u003e. 2021;103:91-96. doi: 10.1016/j.ijid.2020.11.165.\u003c/li\u003e\n \u003cli\u003eJin Y, Hu S, Feng J, Ni J. Clinical value of metagenomic next-generation sequencing using spinal tissue in the rapid diagnosis of spinal tuberculosis. Infect Drug Resist. 2023;16:3305-3313. doi: 10.2147/idr.S410914.\u003c/li\u003e\n \u003cli\u003eSatta G, Lipman M, Smith GP, et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential? \u003cem\u003eClin Microbiol Infect\u003c/em\u003e. 2018;24(6):604-609. doi: 10.1016/j.cmi.2017.10.030.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang B, Xiao L, Qiu Q, et al. Association between IL-18, IFN-\u0026gamma; and TB susceptibility: a systematic review and meta-analysis. \u003cem\u003eAnn Palliat Med\u003c/em\u003e. 2021;10(10):10878-10886. doi: 10.21037/apm-21-2582.\u003c/li\u003e\n \u003cli\u003eTan Y, Tan Y, Li J, et al. Combined IFN-\u0026gamma; and IL-2 release assay for detect active pulmonary tuberculosis: a prospective multicentre diagnostic study in China. \u003cem\u003eJ Transl Med\u003c/em\u003e. 2021;19(1):289. doi: 10.1186/s12967-021-02970-8.\u003c/li\u003e\n \u003cli\u003eGoletti D, Delogu G, Matteelli A, Migliori GB. The role of IGRA in the diagnosis of tuberculosis infection, differentiating from active tuberculosis, and decision making for initiating treatment or preventive therapy of tuberculosis infection. \u003cem\u003eInt J Infect Dis\u003c/em\u003e. 2022;124 Suppl 1:S12-S19. doi: 10.1016/j.ijid.2022.02.047.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJi L, Lou YL, Wu ZX, et al. Usefulness of interferon-\u0026gamma; release assay for the diagnosis of sputum smear-negative pulmonary and extra-pulmonary TB in Zhejiang Province, China. \u003cem\u003eInfect Dis Poverty\u003c/em\u003e. 2017;6(1):121. doi: 10.1186/s40249-017-0331-1.\u003c/li\u003e\n \u003cli\u003eLv D, Liu Y, Guo F, et al. Combining interferon-\u0026gamma; release assays with lymphocyte enumeration for diagnosis of Mycobacterium tuberculosis infection. \u003cem\u003eJ Int Med Res\u003c/em\u003e. 2020;48(6):300060520925660. doi: 10.1177/0300060520925660.\u003c/li\u003e\n \u003cli\u003eBoyd AE, Ashcroft A, Lipman M, Bothamley GH. Limited added value of T-SPOT.TB blood test in diagnosing active TB: a prospective bayesian analysis. \u003cem\u003eJ Infect\u003c/em\u003e. 2011;62(6):456-61. doi: 10.1016/j.jinf.2011.04.003.\u003c/li\u003e\n \u003cli\u003eLi Y, Bian W, Wu S, et al. Metagenomic next-generation sequencing for Mycobacterium tuberculosis complex detection: a meta-analysis. \u003cem\u003eFront Public Health\u003c/em\u003e. 2023;11:1224993. doi: 10.3389/fpubh.2023.1224993.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShi CL, Han P, Tang PJ, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. \u003cem\u003eJ Infect\u003c/em\u003e. 2020;81(4):567-574. doi: 10.1016/j.jinf.2020.08.004.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"TB-IGRA, tuberculosis, mNGS, IFN-γ, sputum smears","lastPublishedDoi":"10.21203/rs.3.rs-4629309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4629309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRapid diagnosis of pulmonary tuberculosis (PTB) is urgently needed. We aimed to improve diagnosis rates by combining tuberculosis-interferon (IFN)-γ release assays (TB-IGRA) with metagenomic next-generation sequencing (mNGS) for PTB diagnosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e \u003cb\u003eA\u003c/b\u003e retrospective study of 29 PTB and 32 non-TB patients from our hospital was conducted between October 2022 and June 2023. Samples were processed for TB-IGRA and mNGS tests according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe levels of IFN-γ release in PTB patients were significantly higher than those -in non-TB patients (604.15\u0026thinsp;\u0026plusmn;\u0026thinsp;112.18 pg/mL, and 1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 pg/mL, respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Regarding presenting symptoms or signs, cough and thoracalgia were less common in PTB patients than in non-TB patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024, respectively). Total protein and albumin levels in the sera of PTB patients were significantly elevated compared to non-TB patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, respectively). The area under the ROC curve (AUC) for TB-IGRA in PTB diagnosis was 0.939. With an optimal IFN-γ cut-off value of 14.3 pg/mL( Youden\u0026rsquo;s index 0.831) sensitivity was 86.2% and specificity was 96.9%. ROC curve analysis for mNGS and TB-IGRA combined with mNGS showed AUCs of 0.879 and 1, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTB-IGRA combined with mNGS is an effective method for diagnosing tuberculosis, and can be used in the clinical diagnosis of PTB.\u003c/p\u003e","manuscriptTitle":"Combining Interferon-γ Release Assays and Metagenomic Next-generation Sequencing for Diagnosis of Pulmonary Tuberculosis: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 19:51:51","doi":"10.21203/rs.3.rs-4629309/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-27T11:36:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-27T01:01:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-27T01:00:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-06-24T10:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18cd8edf-d592-4f18-8d04-4f7c27fded5f","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:07:29+00:00","versionOfRecord":{"articleIdentity":"rs-4629309","link":"https://doi.org/10.1186/s12879-024-10206-5","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2024-11-18 15:57:52","publishedOnDateReadable":"November 18th, 2024"},"versionCreatedAt":"2024-07-19 19:51:51","video":"","vorDoi":"10.1186/s12879-024-10206-5","vorDoiUrl":"https://doi.org/10.1186/s12879-024-10206-5","workflowStages":[]},"version":"v1","identity":"rs-4629309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4629309","identity":"rs-4629309","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}