Diagnostic Value of Nanopore-Based Targeted Sequencing Technology for Subclinical Tuberculosis

Diagnostic Value of Nanopore-Based Targeted Sequencing Technology for Subclinical Tuberculosis | 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 Diagnostic Value of Nanopore-Based Targeted Sequencing Technology for Subclinical Tuberculosis Jianmeng zhu, Su shao, Wenhua zheng, Lili chen, ke zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8876803/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Subclinical Tuberculosis (TB), with low bacterial loads and non-specific symptoms, is difficult to diagnose and promotes transmission. This study aimed to evaluate the diagnostic performance of nanopore-based targeted sequencing (NTS) for subclinical TB in comparison with Mycobacteria Growth Indicator Tube (MGIT) culture and GeneXpert MTB/RIF (Xpert MTB/RIF) assays. Methods This retrospective research included 103 subclinical TB suspects. We tested sputum, bronchoalveolar lavage fluid, or pleural effusion from each patient using NTS, MGIT culture, and Xpert. Diagnostic performance was assessed using a composite standard. Results NTS exhibited higher sensitivity (90.9%) than MGIT culture (40.9%) and Xpert MTB/RIF (50.0%). It had a larger area under the curve (AUC) of 0.923 than Xpert (0.731) and MGIT culture (0.667), as well as an impressive negative predictive value (97.4%) and accuracy (93.2%). Conclusion NTS shows better diagnostic accuracy for subclinical TB than conventional approaches, efficiently identifying cases with low bacterial burdens. It could help find cases of TB earlier, closing the gap in TB control. Subclinical TB Nanopore sequencing Targeted next-generation sequencing Diagnosis accuracy MGIT culture Xpert MTB/RIF Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Subclinical Tuberculosis (TB) denotes a condition where a person carries viable Mycobacterium TB (MTB) without exhibiting any symptoms indicative of TB during screening. However, the disease can be detected through bacteriological tests (such as culture, PCR, and rapid diagnostics) and/or radiographic assessments (like chest X-rays) 1 . It lies between latent infection and clinically active TB. Although there is currently no consensus on the definition of subclinical TB, most national programs define it as “a simple symptom screening of presumed TB patients, regardless of other symptoms, provided they have no cough lasting ≥ 2 weeks.” 2 The global burden of subclinical TB is currently significant. The World Health Organization (WHO) estimates indicate that around 7 million individuals globally experience a subclinical state of TB annually 3 . Recent studies revealed that subclinical TB patients constituted around 50% of all active TB cases worldwide. 2,4 However, the current focus of TB prevention and control efforts remains primarily on symptomatic TB patients, with a significant lack of research on subclinical TB. 5,6 MTB is transmitted through respiratory droplets expelled from the lungs. Coughing is generally considered the primary mode of transmission for TB. 7 Although subclinical TB patients lack obvious clinical symptoms such as persistent coughing, respiratory droplets can still be expelled through activities like singing, speaking, and breathing. Therefore, their contribution to community transmission remains substantial. 8 A recent study on TB transmission found that 40–79% of patients likely transmitted the disease before symptoms appeared. 9 This confirms that while transmission rates during the asymptomatic stage are lower than in the exacerbation phase, they can lead to widespread transmission. 10 However, unlike active TB patients, subclinical TB patients have minimal sputum, usually produce negative bacteriological test findings, and are frequently misdiagnosed due to the absence of TB symptoms. 11 Thus, enhancing diagnostic efficiency for subclinical TB patients is a critical issue. Currently, TB patients lacking pathogenetic evidence and TB-related symptoms often fail to receive early diagnosis. 8,12 Smear microscopy, mycobacterial liquid culture, and the molecular diagnostic technique GeneXpert MTB/RIF (Xpert MTB/RIF) are widely used. However, the sensitivity of smear microscopy is about 30%. 13 Mycobacterial culture is considered the gold standard method, but it is time-consuming, with positive results taking an average of approximately two weeks. 14 This delays treatment and increases the risk of MTB transmission. The Xpert MTB/RIF method is relatively rapid and exhibits acceptable specificity, but its sensitivity remains insufficient to meet clinical needs, primarily because subclinical TB patients typically have scant sputum and low bacterial loads. 15 In cases of negative pathogen detection, clinicians face challenges in diagnosing TB based solely on imaging and immunological test results. Instead of sequencing the genome, nanopore-based targeted sequencing (NTS) analyzes specific DNA or RNA regions and genes related to infection or treatment resistance. 16 Using nanopore sequencing, single-stranded DNA or RNA passes through nanoscale pores, altering ionic currents. These enable NTS to rapidly identify pathogens or genetic markers in clinical samples, including direct detection of MTB without culture 16 . NTS detects low-concentration infections through targeted enrichment, making it ideal for subclinical diseases with low bacterial loads. 17 Its portable design suits resource-constrained environments and requires low sample volumes. 18 However, most NTS studies have focused on symptomatic TB, with few examining low-bacterial-load, asymptomatic subclinical TB. 19–21 This study focuses on the “hidden population” within the TB spectrum: subclinical TB patients. By analyzing and comparing the diagnostic performance of NTS, MGIT culture, and Xpert MTB/RIF technology, we aim to address gaps in TB prevention and control, offering new insights for early screening and intervention. 2 Materials and methods 2.1 Study design This retrospective study was conducted at Chun'an First People's Hospital (a designated TB hospital in Hangzhou City). We reviewed clinical data from patients suspected of MTB infection without typical TB symptoms between March 2024 and December 2025. Inclusion criteria: (1) Patients who underwent acid-fast bacilli smear microscopy, MTB culture, and Xpert MTB/RIF testing; (2) Radiological findings of pulmonary nodules or patchy shadows, or immunological tests (e.g., tuberculin skin test, gamma-interferon release assay, anti-TB antibody detection) suggesting Mycobacterium TB infection. Exclusion criteria: (1) Persistent cough lasting ≥ 2 weeks; (2) Concurrent HIV infection. This study was approved by the Ethics Committee of the Chun'an First People's Hospital. Given the retrospective nature of this study and the anonymity of all data, patient-informed consent was waived. The diagnostic criteria for TB are based on the Health Industry Standard of the People’s Republic of China—Diagnosis for Pulmonary TB (WS 288–2017). The gold standard is a combination of MTB culture and clinical diagnosis, which must meet one or more of the following criteria: 1) Positive mycobacterial culture (species identified as Mycobacterium TB complex) or positive molecular biology test; 2) Pulmonary tissue biopsy demonstrating pathological features consistent with TB; 3) Clinically diagnosed pulmonary TB in culture-negative patients, with reduction or disappearance of pulmonary lesions after 3 months of preventive anti-TB therapy. 2.2 Clinical specimens The specimens analyzed included sputum, bronchoalveolar lavage fluid (BALF), and pleural effusion samples from patients. Sputum specimens included both nocturnal and morning sputum. All sputum specimens were non-putrid, non-dried, and uncontaminated; BALF samples were obtained via bronchoscopy by instilling 50–100 mL of sterile saline into the suspected lesion segment and retrieving 5–20 mL of fluid. Pleural fluid samples were collected under sterile conditions via thoracentesis to aspirate pleural effusion. All sample collection procedures were performed under strict aseptic conditions to prevent contamination from oral, skin, or environmental bacteria. 2.3 MGIT Culture and Species Identification Following NALC-NaOH processing, the specimens were rinsed twice with sterile PBS. Subsequently, 0.5 milliliters of the suspension were inoculated into MGIT culture tubes containing 0.8 milliliters of BACTEC MGIT culture supplement and a mixture of antimicrobial agents. All procedures were conducted within a Class II biosafety cabinet. The MGIT tube was scanned into the BACTEC MGIT instrument and incubated at 37°C. The system automatically recorded fluorescence signals every 60 minutes and reported positive results. When a positive signal was detected, a smear from 0.2 mL of culture was heat-fixed and Ziehl–Neelsen stained to confirm acid-fast bacilli. After confirmation of a positive MGIT culture, the sample was sent to the municipal center for disease control and prevention for mycobacterial identification. The procedure involved mixing 0.4 mL of the positive broth with 4% saline, allowing it to stand for 15 minutes, and adjusting to McFarland 1.0 turbidity. One-hundred-microliter samples were inoculated into three MGIT tubes: a control, one with 500 µg/mL p-nitrobenzoic acid (PNB), and another with 5 µg/mL thiophene-2-carboxylic acid hydrazide (TCH). Tubes were re-incubated and monitored daily for time-to-positivity. Isolates delayed by PNB (> 5 days) were identified as MTB complex; growth within 5 days indicated non-tuberculous mycobacteria (NTM). Within MTBC, growth in TCH within 5 days suggested M. bovis, while no growth indicated MTB. 2.3 Xpert Test Mix the specimen with the sample processing solution at a 1:1–2 ratio. Incubate the mixture at room temperature for 15 minutes. Then, add 2 ml of the mixture to the Xpert MTB/RIF reaction cartridge and place it into the test module for automated testing. Read the MTB test results after 2 hours. 2.4 NTS Samples are aseptically collected to maintain quality, then pre-treated to release nucleic acids and remove impurities. DNA or RNA is extracted using specialized kits, with strict protocols to prevent degradation and contamination, ensuring purity and concentration for further experiments. Design primers or probes specific to target genes or regions, and enrich target nucleic acids using PCR amplification or hybridization capture to reduce interference from non-target sequences. Perform end-repair and A-tailing on enriched nucleic acids, then ligate sequencing adapters to create libraries for nanopore sequencing. Ensure efficient adapter ligation and high library quality for reliable sequencing. Prepare sequencing complexes by mixing the library with nanopore reagents, adjusting library concentration, and adding buffers and enzymes as needed for optimal sequencing. Start sequencing by loading the sequencing complex into the nanopore sequencer flow cell. Molecular motors move nucleic acids through the nanopore, and sensors record ionic current changes to sequence them. Quality checks, filtering, and rectification remove errors and undesired sequences from raw data. Alignment, assembly, and annotation of data using bioinformatics tools reveal sequence information, SNPs, InDels, and other variants. The results are produced by comparing the analyzed data to a reference genome or database. 2.5 Statistical analysis Results were analyzed using SPSS 24 and Origin 2024 software to calculate the sensitivity, specificity, positive predictive value, and negative predictive value for each detection method. Cochran's Q test was used to compare the three testing methods. Paired McNemar's chi-square test was employed to compare the positivity rates between nanopore sequencing technology and either MGIT culture or Xpert methods, with significance indicated as P < 0.05 (*) and high significance as P < 0.01 (**). The Kappa test was used to assess the consistency among the three methods. Origin software was utilized to plot ROC curves and Venn diagrams for comparing the diagnostic performance of the three methods. 3 Results 3.1 Specimen Data As shown in Fig. 1 , initially, we screened a total of 280 patients for NTS. Subsequently, 177 patients were excluded, including 9 who did not complete MGIT culture and GeneXpert testing and 159 who presented with typical TB symptoms (cough with sputum production lasting 2 weeks). Ultimately, 103 suspected subclinical TB patients were enrolled. Each patient provided one sample (sputum, BALF, or pleural fluid), yielding a total of 21 sputum samples, 76 BALF samples, and 6 pleural fluid samples. No patients tested HIV-positive. Table 1 presents the clinical characteristics and demographic information of the enrolled patient samples, and Fig. 2 shows boxplots of clinical data statistics. The average age of 21 TB patients was 62, showing no significant difference compared to the 63-year-old average for non-TB patients. There was also no significant difference in the prevalence rate between males and females. Among laboratory blood test indicators, although the average white blood cell and neutrophil counts in subclinical TB patients were slightly higher than those in non-TB patients, the difference was not statistically significant. Similarly, no significant differences were observed in the average platelet and hemoglobin counts. However, subclinical TB patients exhibited lower lymphocyte levels (Fig. 2 d). This may result from the activation of the immune system following MTB infection, while the pathogen likely suppresses lymphocyte proliferation and activation or induces apoptosis through multiple mechanisms. For example, macrophages may secrete cytokines (such as TNF-α) after phagocytosing MTB, thereby inhibiting T cell proliferation; MTB antigens may also directly induce lymphocyte apoptosis. Table 1 Clinical characteristics of patients enrolled Characteristics PTB (n = 21) Non-PTB (n = 82) P value Age (years) (mean ± SD) Gender 62 ± 12.7 63 ± 11.8 0.69 0.80 Male (n, %) 14 (64%) 58 (72%) Female (n, %) 8(36%) 23(28%) Laboratory examinations Leukocyte ( ∗ 10 9 /L) (Median, IQR) Neutrophil( ∗ 10 9 /L) (Median, IQR) lymphocyte( ∗ 10 9 /L) (Median, IQR) Platelet( ∗ 10 9 /L) (Median, IQR) Hemoglobin(g/L) (Median, IQR) 5.5 (4.6–5.9) 3.6 (2.8-4.0) 1.1 (0.5–1.5) 185 (128–225) 112 (101–125) 5.3 (3.9–6.4) 3.5 (2.4–4.4) 1.4 (1.1–1.6) 183 (143–228) 114 (104–123) 0.86 0.98 0.02 0.89 0.58 3.2 The presence of positive findings for subclinical TB according to the three methods As shown in Fig. 3 , the positive detection cases for NTS, Xpert MTB/RIF testing, and MGIT culture were 25, 14, and 15, respectively. Cochran's Q test revealed significant differences in positive detection rate among the three methods (P = 0.016). Positive samples from NTS produced reads between 34 and 523,333. The average read count is 55,318.16, with a median of 5,208, an interquartile range of 9,615, and a maximum of 523,333. Figure 4 shows the distribution of read counts. Five patients were diagnosed with non-tuberculous mycobacterial (NTM) disease. NTS identified four cases (one patient tested as MTB via NTS), while MGIT culture detected three cases. All NTM patients tested negative by Xpert. Additionally, we compared the three detection methods using a Venn diagram (Fig. 5 ). Figure 5 a showed that NTS and Xpert combined yielded 10 positive results, Xpert and MGIT culture combined yielded 7 positive results, and all three methods together yielded 6 positive results. Figure 5 b showed that the three methods yielded a total of 76 identical results; NTS and Xpert had 84, while NTS and MGIT culture had 83. 3.3 Detection performance of the three methods Based on the final clinical diagnosis, the sensitivities of NTS, MGIT culture, and Xpert testing were 90.9%, 40.9%, and 50.0%, respectively. The specificities were 92.6%, 93.8%, and 96.3%, and the accuracies were 93.2%, 86.4%, and 81.5%, respectively. The Youden indices were calculated as 0.84, 0.35, and 0.46, while the positive predictive values (PPV) were 80.0, 60.0, and 78.6, and the negative predictive values (NPV) were 97.4, 85.2, and 87.6, respectively (see Table 2 ). The Receiver Operating Characteristic (ROC) curves presented in Fig. 6 indicate that the area under the curve (AUC) value for NTS (0.923, 95% CI: 0.854–0.993) is higher than that of the Xpert assay (0.731, 95% CI: 0.630–0.832) and the MGIT culture assay (0.667, 95% CI: 0.553–0.781). Further McNemar chi-square test revealed a significant difference in the positive detection rate between NTS and Xpert (0.01) (Table 3 ) or MGIT cultures (0.03) (Table 4 ). The Kappa values were 0.41 and 0.39, respectively. Table 2 Analysis of diagnostic indicators for NTS, MGIT culture, and Xpert testing. Test Sensitivity (%) Specificity (%) Accuracy (%) Youden’s index PPV (%) NPV (%) AUC Xpert MTB/RIF 50.0 96.3 86.4 0.46 78.6 87.6 73.1 MGIT culture 40.9 93.8 81.5 0.35 60.0 85.2 66.7 NTS 90.9 92.6 93.2 0.84 80.0 97.4 92.3 Note: Sensitivity = [True positives / (True positives + False negatives)]×100%; Specificity = [False negatives / (False positives + True negatives)] × 100%; PPV = [True positives / (True positives + False positives)]×100%; NPV = [False negatives / (False positives + True negatives)]×100%; Youden’s index = Sensitivity + Specificity-1; Accuracy = [(True positives + False negatives)/ Total patients]×100%. Table 3 Comparison of detection performance between NTS and Xpert. Nanopore Xpert MTB/RIF Number χ2 P value kappa value Positive Negative Positive 10 15 25 6.4 0.01 0.41 Negative 4 74 78 Number 14 89 103 Table 4 Comparison of detection performance between NTS and MGIT Culture. Nanopore MGIT culture Number χ2 P value kappa value Positive Negative Positive 10 15 25 5.0 0.03 0.39 Negative 5 73 78 Number 15 88 103 4 Discussion Subclinical TB, a challenging phase between latent infection and active disease, is hard to diagnose due to low bacterial presence and lack of symptoms. This study shows that NTS technology is more effective in diagnosing subclinical TB than traditional methods, with a sensitivity of 90.9% and an AUC of 0.923, outperforming Xpert MTB/RIF (50.0% sensitivity, AUC 0.731) and MGIT culture (40.9% sensitivity, AUC 0.667). The notable performance advantage (P = 0.01 compared to Xpert, P = 0.03 compared to MGIT) is crucial due to the diagnostic difficulties of subclinical TB, where patients often show few or no symptoms and have low bacterial levels. NTS's improved detection fills a vital gap in TB prevention and control, which has typically overlooked this significant "hidden population" that plays a major role in spreading the disease. Our findings support and extend nanopore sequencing investigations for low-bacterial burden TB. For general PTB diagnosis, Yu et al. (2022) showed 94.8% sensitivity using respiratory samples, 22 and Yan et al. (2024) found 83.33% utilizing BALF samples from smear-negative PTB patients. 23 Our 90.9% sensitivity for subclinical TB matches these results and targets populations previously neglected in studies. We also found good specificity (92.6%) and PPV (80.0%), answering concerns about false positives in low-prevalence subclinical screens, a common issue with sensitive molecular tests. Xpert MTB/RIF, a key tool for rapid TB diagnosis, relies on a single-copy gene (rpoB) and has moderate sensitivity, especially in low-bacterial-load cases. 24 NTS trumps it. NTS's targeted enrichment and long-read sequencing, averaging 55,318 bases (Fig. 4 ), allow it to detect MTB at much lower concentrations than conventional methods. Notably, NTS identified 11 additional cases missed by both Xpert and culture (Fig. 5 a), underscoring its ability to reveal hidden infectious reservoirs. The technical benefits of NTS explain its improved subclinical TB diagnosis performance. Multiplex PCR for specific MTB genomic areas enhances signals needed to detect low pathogen levels in samples dominated by host and ambient DNA 17 . The depth of untargeted metagenomic approaches is sometimes insufficient for MTB detection. Long-read nanopore technology can identify MTB-specific sequences from non-tuberculous mycobacteria, simplifying diagnosis. 25 NTS correctly recognized 4 of 5 NTM cases in our investigation, unlike Xpert MTB/RIF, which only detects MTB complexes. Platforms like MinION are portable and have a turnaround in 24–48 hours, making NTS realistic for resource-limited settings and overcoming a significant sequencing diagnostics challenge. 26 The median read count of 5,208 in NTS-positive samples suggests it can quantify bacterial load and disease activity. Our results show that NTS may be an accurate diagnostic technique for subclinical TB, which is crucial for transmission control but underserved by present methods. Including NTS in clinical protocols for asymptomatic TB patients with radiological or immunological evidence could improve detection and curb transmission. NTS can also identify species and test drug resistance, making it appropriate for TB diagnosis and precision treatment in one assay. New studies should test NTS in multiple settings, examine cost-effectiveness for policy guidance, and simplify peripheral lab automated operations. By quantifying bacterial load and disease activity in subclinical TB, this technology provides robust support for the WHO's End TB Strategy. 5 Conclusions This study found that NTS detects low-bacterial-load subclinical TB better than MGIT culture and Xpert MTB/RIF. It effectively diagnoses this "hidden" group, which contributes to community transmission but escapes symptom-based screening. NTS is ideal for high-burden, resource-limited scenarios due to its portability, speed, and cost. To maximize its public health impact, future work should validate its usage in multiple settings, improve cost-effectiveness, and integrate it into routine screening programs. Declarations Declaration of Competing Interest The authors have no competing interests to declare. Ethics approval and consent to participate This study has been approved by the Ethics Committee of Chun'an First People's Hospital and adheres to the Declaration of Helsinki. Given that this is a retrospective study and all data have been anonymised, patient informed consent is waived following approval by the Ethics Committee. Consent for publication Owing to the retrospective nature of this study and the fact that all data were anonymised, patient informed consent was waived following approval by the Ethics Committee. Clinical trial number Not applicable. Note Sensitivity = [True positives / (True positives + False negatives)]×100%; Specificity = [False negatives / (False positives + True negatives)] × 100%; PPV = [True positives / (True positives + False positives)]×100%; NPV = [False negatives / (False positives + True negatives)]×100%; Youden’s index = Sensitivity + Specificity-1; Accuracy = [(True positives + False negatives)/ Total patients]×100%. Funding This research was supported by the Medical and Health Science and Technology Programme of Hangzhou (B20252555, B20262506), Key Projects of Chun'an Medical and Health Science and Technology Programme (2025CAYY006), Medicine and Health Research Foundation of Zhejiang Province (2025KY1246), and Traditional Chinese Medicine Science and Technology Programme Project of Zhejiang Province (2026ZF80). Author Contribution KZ was responsible for the overall planning of the article and conducted the literature search. JZ drafted the core content, provided valuable insights, and assisted in refining the text. SS engaged in in-depth analysis and discussion of the literature, enhancing the comprehensiveness and accuracy of the article. LC performed supplementary literature searches and verification work. Additionally, JZ and LC jointly verified the authenticity of relevant data points within the literature. All authors reviewed and approved the final manuscript. Acknowledgement Thanks to the support and assistance provided by Dian Diagnostics Co., Ltd. in Hangzhou, China. Data availability statement The data are clinical diagnostic reports; they do not require deposition in a public repository. Data will be made available on reasonable request. References Teo AKJ, MacLean EL, Fox GJ. Subclinical tuberculosis: a meta-analysis of prevalence and scoping review of definitions, prevalence and clinical characteristics. Eur Respir Rev Apr. 2024;30(172). 10.1183/16000617.0208-2023 . Frascella B, Richards AS, Sossen B, et al. Subclinical Tuberculosis Disease-A Review and Analysis of Prevalence Surveys to Inform Definitions, Burden, Associations, and Screening Methodology. Clin Infect Dis Aug. 2021;2(3):e830–41. 10.1093/cid/ciaa1402 . Kendall EA, Shrestha S, Dowdy DW. The Epidemiological Importance of Subclinical Tuberculosis. A Critical Reappraisal. Am J Respir Crit Care Med Jan. 2021;15(2):168–74. 10.1164/rccm.202006-2394PP . Odume B, Ogbudebe C, Mukadi Y, et al. The burden of subclinical TB in Nigeria. Public Health Action Dec. 2024;14(4):181–5. 10.5588/pha.24.0038 . Chihota V, Gombe M, Gupta A, et al. Tuberculosis Preventive Treatment in High TB-Burden Settings: A State-of-the-Art Review. Drugs Dec. 2024;28. 10.1007/s40265-024-02131-3 . Goletti D, Matteelli A, Cliff JM, et al. World TB Day 2025 Theme Yes! We Can End TB: Commit, Invest, Deliver can be made a reality through concerted global efforts to advance diagnosis, treatment and research of tuberculosis infection and disease. Int J Infect Dis Mar. 2025;17:107892. 10.1016/j.ijid.2025.107892 . Matteelli A, Lovatti S, Sforza A, Rossi L. Programmatic management of tuberculosis preventive therapy: Past, present, future. Int J Infect Dis May. 2023;130(Suppl 1):S43–6. 10.1016/j.ijid.2023.02.016 . Nguyen HV, Tiemersma E, Nguyen NV, Nguyen HB, Cobelens F. Disease Transmission by Patients With Subclinical Tuberculosis. Clin Infect Dis Jun. 2023;8(11):2000–6. 10.1093/cid/ciad027 . Houben R, Esmail H, Emery JC, et al. Spotting the old foe-revisiting the case definition for TB. Lancet Respir Med. Mar 2019;7(3):199–201. 10.1016/s2213-2600(19)30038-4 . Xu Y, Cancino-Muñoz I, Torres-Puente M, et al. High-resolution mapping of tuberculosis transmission: Whole genome sequencing and phylogenetic modelling of a cohort from Valencia Region, Spain. PLoS Med. Oct 2019;16(10):e1002961. 10.1371/journal.pmed.1002961 . Sarkar M. Incipient and subclinical tuberculosis: a narrative review. Monaldi Arch Chest Dis Jan. 2025;8. 10.4081/monaldi.2025.2982 . Park JH, Choe J, Bae M, et al. Clinical Characteristics and Radiologic Features of Immunocompromised Patients With Pauci-Bacillary Pulmonary Tuberculosis Receiving Delayed Diagnosis and Treatment. Open Forum Infect Dis Feb. 2019;6(2):ofz002. 10.1093/ofid/ofz002 . Asadi L, Croxen M, Heffernan C, et al. How much do smear-negative patients really contribute to tuberculosis transmissions? Re-examining an old question with new tools. EClinicalMedicine Jan. 2022;43:101250. 10.1016/j.eclinm.2021.101250 . Tran BM, Larsson J, Grip A, Karempudi P, Elf J. Phenotypic drug susceptibility testing for Mycobacterium tuberculosis variant bovis BCG in 12 hours. Nat Commun May. 2025;10(1):4366. 10.1038/s41467-025-59736-9 . Otieno JA, Were LM, Lutje V, Scandrett K, Takwoingi Y, Ochodo EA. Impact of rapid nucleic acid amplification tests for tuberculosis on patient outcomes. Cochrane Database Syst Rev Dec. 2025;18(12):Cd016194. 10.1002/14651858.Cd016194 . Cabibbe AM, Moghaddasi K, Batignani V, Morgan GSK, Di Marco F, Cirillo DM. Nanopore-based targeted sequencing test for direct tuberculosis identification, genotyping, and detection of drug resistance mutations: a side-by-side comparison of targeted next-generation sequencing technologies. J Clin Microbiol Oct. 2024;16(10):e0081524. 10.1128/jcm.00815-24 . Yu S, Liu N, Xie Z, et al. Nanopore sequencing for precise detection of Mycobacterium tuberculosis and drug resistance: a retrospective multicenter study in China. J Clin Microbiol Apr. 2025;9(4):e0181324. 10.1128/jcm.01813-24 . Gómez-González PJ, Campino S, Phelan JE, Clark TG. Portable sequencing of Mycobacterium tuberculosis for clinical and epidemiological applications. Brief Bioinform Sep. 2022;20(5). 10.1093/bib/bbac256 . Chen J, Fan Q, Gong S, et al. Diagnosis of Drug-Resistant Tuberculosis: Rapid Evaluation of Drug Susceptibility with Nanopore Targeted Sequencing. Clin Chem Aug. 2025;1(8):908–19. 10.1093/clinchem/hvaf061 . Nilgiriwala K, Rabodoarivelo MS, Hall MB, et al. Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination, and Drug Susceptibility Prediction. J Clin Microbiol Mar. 2023;23(3):e0157822. 10.1128/jcm.01578-22 . Schwab TC, Joseph L, Moono A, et al. Field evaluation of nanopore targeted next-generation sequencing to predict drug-resistant tuberculosis from native sputum in South Africa and Zambia. J Clin Microbiol Mar. 2025;12(3):e0139024. 10.1128/jcm.01390-24 . Yu G, Shen Y, Zhong F, et al. Diagnostic accuracy of nanopore sequencing using respiratory specimens in the diagnosis of pulmonary tuberculosis. Int J Infect Dis Sep. 2022;122:237–43. 10.1016/j.ijid.2022.06.001 . Yan X, Yang G, Wang Y, et al. Nanopore sequencing for smear-negative pulmonary tuberculosis-a multicentre prospective study in China. Ann Clin Microbiol Antimicrob Jun. 2024;14(1):51. 10.1186/s12941-024-00714-2 . Horne DJ, Zifodya JS, Shapiro AE, et al. Xpert MTB/RIF Ultra assay for pulmonary tuberculosis and rifampicin resistance in adults and adolescents. Cochrane Database Syst Rev Jul. 2025;29(7):Cd009593. 10.1002/14651858.CD009593.pub6 . Fu S, Zhang Y, Wang R, et al. A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens. J Environ Manage Nov. 2023;1:345:118737. 10.1016/j.jenvman.2023.118737 . Zhang T, Li H, Jiang M, et al. Nanopore sequencing: flourishing in its teenage years. J Genet Genomics Dec. 2024;51(12):1361–74. 10.1016/j.jgg.2024.09.007 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8876803","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600174756,"identity":"b4d7377f-c6c0-4d15-8aa2-3c9a47c6106a","order_by":0,"name":"Jianmeng zhu","email":"","orcid":"","institution":"Chun’an Branch of Zhejiang Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianmeng","middleName":"","lastName":"zhu","suffix":""},{"id":600174759,"identity":"22e77f5f-cc1a-47d3-8f2e-f793aa6ae556","order_by":1,"name":"Su shao","email":"","orcid":"","institution":"Chun’an Branch of Zhejiang Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"shao","suffix":""},{"id":600174761,"identity":"575787e1-cf95-4081-a491-7cf0535ef042","order_by":2,"name":"Wenhua zheng","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Chun'an","correspondingAuthor":false,"prefix":"","firstName":"Wenhua","middleName":"","lastName":"zheng","suffix":""},{"id":600174762,"identity":"e1ed9839-dad5-4825-96e8-b460878e96f0","order_by":3,"name":"Lili chen","email":"","orcid":"","institution":"Chun’an Branch of Zhejiang Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"chen","suffix":""},{"id":600174764,"identity":"60f58d90-737b-4a91-a241-3615558fff0e","order_by":4,"name":"ke zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACCR4Gxgcf/0jIsbG3HyBaC7PhzAYLYz6eMwlEa2ET5m2oSJwn4WBAnBbJnuPPGHh3SKS3STAkMPyo2EZYizRvQ9oDyTMSuW3SjQcYe87cJqxFjp/huIEBG1CLzIEEZsY2orQwtkkksEmks0kkGBCnRZq3mU3iIFgXsVoke44xGzackTBsAwbyQaL8InEm/eHjPxV18vLt7Qcf/KggQgsKOECi+lEwCkbBKBgFuAAAksE41DSZJLYAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College","correspondingAuthor":true,"prefix":"","firstName":"ke","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2026-02-14 04:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8876803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8876803/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104171856,"identity":"7196da66-ef43-44bf-ab15-c4f21d8b07b0","added_by":"auto","created_at":"2026-03-08 14:55:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2343644,"visible":true,"origin":"","legend":"\u003cp\u003eScreening flow chart of enrolled patients.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8876803/v1/0ae84faa358bc56b0033b710.png"},{"id":104171854,"identity":"e1b6cad2-4bdb-4a81-85dd-fbec61131d52","added_by":"auto","created_at":"2026-03-08 14:55:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1342955,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical boxplots of age distribution and clinical hematology laboratory data for enrolled patients. (a) Age data; (b) Leukocyte counts; (c) Neutrophil counts; (d) Lymphocyte counts; (e) Platelet counts; and (f) Haemoglobin counts for TB and non-TB patients.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8876803/v1/ad1ad6ea35f4f74e6a206223.png"},{"id":104171855,"identity":"f2e94f48-31dd-41d7-ba49-3dae60bab9d2","added_by":"auto","created_at":"2026-03-08 14:55:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":594962,"visible":true,"origin":"","legend":"\u003cp\u003ePositive cases detected by NTS, Xpert MTB/RIF, and MGIT culture.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8876803/v1/3a9e8f9981dcbadebc01c5b4.png"},{"id":104403630,"identity":"4603e9b7-eda3-40f2-961c-a836029c8997","added_by":"auto","created_at":"2026-03-11 12:18:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":466967,"visible":true,"origin":"","legend":"\u003cp\u003eSequence numbers of MTB-positive results obtained through NTS.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8876803/v1/37c8c2099c0a6cd532efeef9.png"},{"id":104171859,"identity":"0d0deb47-8592-47f9-b3ce-b66b2dd36288","added_by":"auto","created_at":"2026-03-08 14:55:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":840476,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing the results detected by NTS, Xpert MTB/RIF, and MGIT culture. (a) Venn diagram of positive results. (b) Venn diagram of overall results.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8876803/v1/9269bc35911dd7a02f774756.png"},{"id":104171857,"identity":"f0a72705-0348-4287-bb25-bed68e33c19c","added_by":"auto","created_at":"2026-03-08 14:55:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1414030,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of NTS, MGIT culture, and Xpert MTB/RIF.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8876803/v1/3883a918c324dcdc35639b56.png"},{"id":104408751,"identity":"1579c9cc-2a8f-4838-bba3-0e5d419e38e3","added_by":"auto","created_at":"2026-03-11 12:43:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7697583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876803/v1/e31cf359-1c2c-4130-9714-c3f3dbb7d929.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Value of Nanopore-Based Targeted Sequencing Technology for Subclinical Tuberculosis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSubclinical Tuberculosis (TB) denotes a condition where a person carries viable Mycobacterium TB (MTB) without exhibiting any symptoms indicative of TB during screening. However, the disease can be detected through bacteriological tests (such as culture, PCR, and rapid diagnostics) and/or radiographic assessments (like chest X-rays) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It lies between latent infection and clinically active TB. Although there is currently no consensus on the definition of subclinical TB, most national programs define it as \u0026ldquo;a simple symptom screening of presumed TB patients, regardless of other symptoms, provided they have no cough lasting\u0026thinsp;\u0026ge;\u0026thinsp;2 weeks.\u0026rdquo; \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe global burden of subclinical TB is currently significant. The World Health Organization (WHO) estimates indicate that around 7\u0026nbsp;million individuals globally experience a subclinical state of TB annually\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Recent studies revealed that subclinical TB patients constituted around 50% of all active TB cases worldwide. \u003csup\u003e2,4\u003c/sup\u003e However, the current focus of TB prevention and control efforts remains primarily on symptomatic TB patients, with a significant lack of research on subclinical TB. \u003csup\u003e5,6\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMTB is transmitted through respiratory droplets expelled from the lungs. Coughing is generally considered the primary mode of transmission for TB. \u003csup\u003e7\u003c/sup\u003e Although subclinical TB patients lack obvious clinical symptoms such as persistent coughing, respiratory droplets can still be expelled through activities like singing, speaking, and breathing. Therefore, their contribution to community transmission remains substantial. \u003csup\u003e8\u003c/sup\u003e A recent study on TB transmission found that 40\u0026ndash;79% of patients likely transmitted the disease before symptoms appeared. \u003csup\u003e9\u003c/sup\u003e This confirms that while transmission rates during the asymptomatic stage are lower than in the exacerbation phase, they can lead to widespread transmission. \u003csup\u003e10\u003c/sup\u003e However, unlike active TB patients, subclinical TB patients have minimal sputum, usually produce negative bacteriological test findings, and are frequently misdiagnosed due to the absence of TB symptoms. \u003csup\u003e11\u003c/sup\u003e Thus, enhancing diagnostic efficiency for subclinical TB patients is a critical issue.\u003c/p\u003e \u003cp\u003eCurrently, TB patients lacking pathogenetic evidence and TB-related symptoms often fail to receive early diagnosis. \u003csup\u003e8,12\u003c/sup\u003e Smear microscopy, mycobacterial liquid culture, and the molecular diagnostic technique GeneXpert MTB/RIF (Xpert MTB/RIF) are widely used. However, the sensitivity of smear microscopy is about 30%.\u003csup\u003e13\u003c/sup\u003e Mycobacterial culture is considered the gold standard method, but it is time-consuming, with positive results taking an average of approximately two weeks. \u003csup\u003e14\u003c/sup\u003e This delays treatment and increases the risk of MTB transmission. The Xpert MTB/RIF method is relatively rapid and exhibits acceptable specificity, but its sensitivity remains insufficient to meet clinical needs, primarily because subclinical TB patients typically have scant sputum and low bacterial loads. \u003csup\u003e15\u003c/sup\u003e In cases of negative pathogen detection, clinicians face challenges in diagnosing TB based solely on imaging and immunological test results.\u003c/p\u003e \u003cp\u003eInstead of sequencing the genome, nanopore-based targeted sequencing (NTS) analyzes specific DNA or RNA regions and genes related to infection or treatment resistance. \u003csup\u003e16\u003c/sup\u003e Using nanopore sequencing, single-stranded DNA or RNA passes through nanoscale pores, altering ionic currents. These enable NTS to rapidly identify pathogens or genetic markers in clinical samples, including direct detection of MTB without culture\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. NTS detects low-concentration infections through targeted enrichment, making it ideal for subclinical diseases with low bacterial loads. \u003csup\u003e17\u003c/sup\u003e Its portable design suits resource-constrained environments and requires low sample volumes. \u003csup\u003e18\u003c/sup\u003e However, most NTS studies have focused on symptomatic TB, with few examining low-bacterial-load, asymptomatic subclinical TB. \u003csup\u003e19\u0026ndash;21\u003c/sup\u003e This study focuses on the \u0026ldquo;hidden population\u0026rdquo; within the TB spectrum: subclinical TB patients. By analyzing and comparing the diagnostic performance of NTS, MGIT culture, and Xpert MTB/RIF technology, we aim to address gaps in TB prevention and control, offering new insights for early screening and intervention.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis retrospective study was conducted at Chun'an First People's Hospital (a designated TB hospital in Hangzhou City). We reviewed clinical data from patients suspected of MTB infection without typical TB symptoms between March 2024 and December 2025. Inclusion criteria: (1) Patients who underwent acid-fast bacilli smear microscopy, MTB culture, and Xpert MTB/RIF testing; (2) Radiological findings of pulmonary nodules or patchy shadows, or immunological tests (e.g., tuberculin skin test, gamma-interferon release assay, anti-TB antibody detection) suggesting Mycobacterium TB infection. Exclusion criteria: (1) Persistent cough lasting\u0026thinsp;\u0026ge;\u0026thinsp;2 weeks; (2) Concurrent HIV infection. This study was approved by the Ethics Committee of the Chun'an First People's Hospital. Given the retrospective nature of this study and the anonymity of all data, patient-informed consent was waived.\u003c/p\u003e \u003cp\u003eThe diagnostic criteria for TB are based on the Health Industry Standard of the People\u0026rsquo;s Republic of China\u0026mdash;Diagnosis for Pulmonary TB (WS 288\u0026ndash;2017). The gold standard is a combination of MTB culture and clinical diagnosis, which must meet one or more of the following criteria: 1) Positive mycobacterial culture (species identified as Mycobacterium TB complex) or positive molecular biology test; 2) Pulmonary tissue biopsy demonstrating pathological features consistent with TB; 3) Clinically diagnosed pulmonary TB in culture-negative patients, with reduction or disappearance of pulmonary lesions after 3 months of preventive anti-TB therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical specimens\u003c/h2\u003e \u003cp\u003eThe specimens analyzed included sputum, bronchoalveolar lavage fluid (BALF), and pleural effusion samples from patients. Sputum specimens included both nocturnal and morning sputum. All sputum specimens were non-putrid, non-dried, and uncontaminated; BALF samples were obtained via bronchoscopy by instilling 50\u0026ndash;100 mL of sterile saline into the suspected lesion segment and retrieving 5\u0026ndash;20 mL of fluid. Pleural fluid samples were collected under sterile conditions via thoracentesis to aspirate pleural effusion. All sample collection procedures were performed under strict aseptic conditions to prevent contamination from oral, skin, or environmental bacteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MGIT Culture and Species Identification\u003c/h2\u003e \u003cp\u003eFollowing NALC-NaOH processing, the specimens were rinsed twice with sterile PBS. Subsequently, 0.5 milliliters of the suspension were inoculated into MGIT culture tubes containing 0.8 milliliters of BACTEC MGIT culture supplement and a mixture of antimicrobial agents. All procedures were conducted within a Class II biosafety cabinet. The MGIT tube was scanned into the BACTEC MGIT instrument and incubated at 37\u0026deg;C. The system automatically recorded fluorescence signals every 60 minutes and reported positive results. When a positive signal was detected, a smear from 0.2 mL of culture was heat-fixed and Ziehl\u0026ndash;Neelsen stained to confirm acid-fast bacilli. After confirmation of a positive MGIT culture, the sample was sent to the municipal center for disease control and prevention for mycobacterial identification. The procedure involved mixing 0.4 mL of the positive broth with 4% saline, allowing it to stand for 15 minutes, and adjusting to McFarland 1.0 turbidity. One-hundred-microliter samples were inoculated into three MGIT tubes: a control, one with 500 \u0026micro;g/mL p-nitrobenzoic acid (PNB), and another with 5 \u0026micro;g/mL thiophene-2-carboxylic acid hydrazide (TCH). Tubes were re-incubated and monitored daily for time-to-positivity. Isolates delayed by PNB (\u0026gt;\u0026thinsp;5 days) were identified as MTB complex; growth within 5 days indicated non-tuberculous mycobacteria (NTM). Within MTBC, growth in TCH within 5 days suggested M. bovis, while no growth indicated MTB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Xpert Test\u003c/h2\u003e \u003cp\u003eMix the specimen with the sample processing solution at a 1:1\u0026ndash;2 ratio. Incubate the mixture at room temperature for 15 minutes. Then, add 2 ml of the mixture to the Xpert MTB/RIF reaction cartridge and place it into the test module for automated testing. Read the MTB test results after 2 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 NTS\u003c/h2\u003e \u003cp\u003eSamples are aseptically collected to maintain quality, then pre-treated to release nucleic acids and remove impurities. DNA or RNA is extracted using specialized kits, with strict protocols to prevent degradation and contamination, ensuring purity and concentration for further experiments. Design primers or probes specific to target genes or regions, and enrich target nucleic acids using PCR amplification or hybridization capture to reduce interference from non-target sequences. Perform end-repair and A-tailing on enriched nucleic acids, then ligate sequencing adapters to create libraries for nanopore sequencing. Ensure efficient adapter ligation and high library quality for reliable sequencing. Prepare sequencing complexes by mixing the library with nanopore reagents, adjusting library concentration, and adding buffers and enzymes as needed for optimal sequencing.\u003c/p\u003e \u003cp\u003eStart sequencing by loading the sequencing complex into the nanopore sequencer flow cell. Molecular motors move nucleic acids through the nanopore, and sensors record ionic current changes to sequence them. Quality checks, filtering, and rectification remove errors and undesired sequences from raw data. Alignment, assembly, and annotation of data using bioinformatics tools reveal sequence information, SNPs, InDels, and other variants. The results are produced by comparing the analyzed data to a reference genome or database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eResults were analyzed using SPSS 24 and Origin 2024 software to calculate the sensitivity, specificity, positive predictive value, and negative predictive value for each detection method. Cochran's Q test was used to compare the three testing methods. Paired McNemar's chi-square test was employed to compare the positivity rates between nanopore sequencing technology and either MGIT culture or Xpert methods, with significance indicated as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*) and high significance as P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (**). The Kappa test was used to assess the consistency among the three methods. Origin software was utilized to plot ROC curves and Venn diagrams for comparing the diagnostic performance of the three methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1 Specimen Data\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, initially, we screened a total of 280 patients for NTS. Subsequently, 177 patients were excluded, including 9 who did not complete MGIT culture and GeneXpert testing and 159 who presented with typical TB symptoms (cough with sputum production lasting 2 weeks). Ultimately, 103 suspected subclinical TB patients were enrolled. Each patient provided one sample (sputum, BALF, or pleural fluid), yielding a total of 21 sputum samples, 76 BALF samples, and 6 pleural fluid samples. No patients tested HIV-positive. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the clinical characteristics and demographic information of the enrolled patient samples, and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows boxplots of clinical data statistics. The average age of 21 TB patients was 62, showing no significant difference compared to the 63-year-old average for non-TB patients. There was also no significant difference in the prevalence rate between males and females. Among laboratory blood test indicators, although the average white blood cell and neutrophil counts in subclinical TB patients were slightly higher than those in non-TB patients, the difference was not statistically significant. Similarly, no significant differences were observed in the average platelet and hemoglobin counts. However, subclinical TB patients exhibited lower lymphocyte levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This may result from the activation of the immune system following MTB infection, while the pathogen likely suppresses lymphocyte proliferation and activation or induces apoptosis through multiple mechanisms. For example, macrophages may secrete cytokines (such as TNF-α) after phagocytosing MTB, thereby inhibiting T cell proliferation; MTB antigens may also directly induce lymphocyte apoptosis.\u003c/p\u003e \u003cp\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\u003eClinical characteristics of patients enrolled\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTB (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eNon-PTB (n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP 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) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003cp\u003e0.80\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\u003e14 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e58 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003e8(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e23(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory examinations\u003c/p\u003e \u003cp\u003eLeukocyte (\u003csup\u003e\u0026lowast;\u003c/sup\u003e10\u003csup\u003e9\u003c/sup\u003e/L) (Median, IQR)\u003c/p\u003e \u003cp\u003eNeutrophil(\u003csup\u003e\u0026lowast;\u003c/sup\u003e10\u003csup\u003e9\u003c/sup\u003e/L) (Median, IQR)\u003c/p\u003e \u003cp\u003elymphocyte(\u003csup\u003e\u0026lowast;\u003c/sup\u003e10\u003csup\u003e9\u003c/sup\u003e/L) (Median, IQR)\u003c/p\u003e \u003cp\u003ePlatelet(\u003csup\u003e\u0026lowast;\u003c/sup\u003e10\u003csup\u003e9\u003c/sup\u003e/L) (Median, IQR)\u003c/p\u003e \u003cp\u003eHemoglobin(g/L) (Median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5.5 (4.6\u0026ndash;5.9)\u003c/p\u003e \u003cp\u003e3.6 (2.8-4.0)\u003c/p\u003e \u003cp\u003e1.1 (0.5\u0026ndash;1.5)\u003c/p\u003e \u003cp\u003e185 (128\u0026ndash;225)\u003c/p\u003e \u003cp\u003e112 (101\u0026ndash;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3 (3.9\u0026ndash;6.4)\u003c/p\u003e \u003cp\u003e3.5 (2.4\u0026ndash;4.4)\u003c/p\u003e \u003cp\u003e1.4 (1.1\u0026ndash;1.6)\u003c/p\u003e \u003cp\u003e183 (143\u0026ndash;228)\u003c/p\u003e \u003cp\u003e114 (104\u0026ndash;123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e0.89\u003c/p\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 The presence of positive findings for subclinical TB according to the three methods\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the positive detection cases for NTS, Xpert MTB/RIF testing, and MGIT culture were 25, 14, and 15, respectively. Cochran's Q test revealed significant differences in positive detection rate among the three methods (P\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePositive samples from NTS produced reads between 34 and 523,333. The average read count is 55,318.16, with a median of 5,208, an interquartile range of 9,615, and a maximum of 523,333. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the distribution of read counts. Five patients were diagnosed with non-tuberculous mycobacterial (NTM) disease. NTS identified four cases (one patient tested as MTB via NTS), while MGIT culture detected three cases. All NTM patients tested negative by Xpert. Additionally, we compared the three detection methods using a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea showed that NTS and Xpert combined yielded 10 positive results, Xpert and MGIT culture combined yielded 7 positive results, and all three methods together yielded 6 positive results. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb showed that the three methods yielded a total of 76 identical results; NTS and Xpert had 84, while NTS and MGIT culture had 83.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Detection performance of the three methods\u003c/h2\u003e \u003cp\u003eBased on the final clinical diagnosis, the sensitivities of NTS, MGIT culture, and Xpert testing were 90.9%, 40.9%, and 50.0%, respectively. The specificities were 92.6%, 93.8%, and 96.3%, and the accuracies were 93.2%, 86.4%, and 81.5%, respectively. The Youden indices were calculated as 0.84, 0.35, and 0.46, while the positive predictive values (PPV) were 80.0, 60.0, and 78.6, and the negative predictive values (NPV) were 97.4, 85.2, and 87.6, respectively (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Receiver Operating Characteristic (ROC) curves presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicate that the area under the curve (AUC) value for NTS (0.923, 95% CI: 0.854\u0026ndash;0.993) is higher than that of the Xpert assay (0.731, 95% CI: 0.630\u0026ndash;0.832) and the MGIT culture assay (0.667, 95% CI: 0.553\u0026ndash;0.781). Further McNemar chi-square test revealed a significant difference in the positive detection rate between NTS and Xpert (0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) or MGIT cultures (0.03) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Kappa values were 0.41 and 0.39, respectively.\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\u003eAnalysis of diagnostic indicators for NTS, MGIT culture, and Xpert testing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXpert MTB/RIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMGIT culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Sensitivity = [True positives / (True positives\u0026thinsp;+\u0026thinsp;False negatives)]\u0026times;100%; Specificity = [False negatives / (False positives\u0026thinsp;+\u0026thinsp;True negatives)] \u0026times; 100%; PPV = [True positives / (True positives\u0026thinsp;+\u0026thinsp;False positives)]\u0026times;100%; NPV = [False negatives / (False positives\u0026thinsp;+\u0026thinsp;True negatives)]\u0026times;100%; Youden\u0026rsquo;s index\u0026thinsp;=\u0026thinsp;Sensitivity\u0026thinsp;+\u0026thinsp;Specificity-1; Accuracy = [(True positives\u0026thinsp;+\u0026thinsp;False negatives)/ Total patients]\u0026times;100%.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\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 detection performance between NTS and Xpert.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNanopore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eXpert MTB/RIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ekappa value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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 detection performance between NTS and MGIT Culture.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNanopore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMGIT culture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ekappa value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eSubclinical TB, a challenging phase between latent infection and active disease, is hard to diagnose due to low bacterial presence and lack of symptoms. This study shows that NTS technology is more effective in diagnosing subclinical TB than traditional methods, with a sensitivity of 90.9% and an AUC of 0.923, outperforming Xpert MTB/RIF (50.0% sensitivity, AUC 0.731) and MGIT culture (40.9% sensitivity, AUC 0.667). The notable performance advantage (P\u0026thinsp;=\u0026thinsp;0.01 compared to Xpert, P\u0026thinsp;=\u0026thinsp;0.03 compared to MGIT) is crucial due to the diagnostic difficulties of subclinical TB, where patients often show few or no symptoms and have low bacterial levels. NTS's improved detection fills a vital gap in TB prevention and control, which has typically overlooked this significant \"hidden population\" that plays a major role in spreading the disease.\u003c/p\u003e \u003cp\u003eOur findings support and extend nanopore sequencing investigations for low-bacterial burden TB. For general PTB diagnosis, Yu et al. (2022) showed 94.8% sensitivity using respiratory samples, \u003csup\u003e22\u003c/sup\u003e and Yan et al. (2024) found 83.33% utilizing BALF samples from smear-negative PTB patients. \u003csup\u003e23\u003c/sup\u003e Our 90.9% sensitivity for subclinical TB matches these results and targets populations previously neglected in studies. We also found good specificity (92.6%) and PPV (80.0%), answering concerns about false positives in low-prevalence subclinical screens, a common issue with sensitive molecular tests. Xpert MTB/RIF, a key tool for rapid TB diagnosis, relies on a single-copy gene (rpoB) and has moderate sensitivity, especially in low-bacterial-load cases. \u003csup\u003e24\u003c/sup\u003e NTS trumps it. NTS's targeted enrichment and long-read sequencing, averaging 55,318 bases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), allow it to detect MTB at much lower concentrations than conventional methods. Notably, NTS identified 11 additional cases missed by both Xpert and culture (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), underscoring its ability to reveal hidden infectious reservoirs.\u003c/p\u003e \u003cp\u003eThe technical benefits of NTS explain its improved subclinical TB diagnosis performance. Multiplex PCR for specific MTB genomic areas enhances signals needed to detect low pathogen levels in samples dominated by host and ambient DNA\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The depth of untargeted metagenomic approaches is sometimes insufficient for MTB detection. Long-read nanopore technology can identify MTB-specific sequences from non-tuberculous mycobacteria, simplifying diagnosis. \u003csup\u003e25\u003c/sup\u003e NTS correctly recognized 4 of 5 NTM cases in our investigation, unlike Xpert MTB/RIF, which only detects MTB complexes. Platforms like MinION are portable and have a turnaround in 24\u0026ndash;48 hours, making NTS realistic for resource-limited settings and overcoming a significant sequencing diagnostics challenge. \u003csup\u003e26\u003c/sup\u003e The median read count of 5,208 in NTS-positive samples suggests it can quantify bacterial load and disease activity.\u003c/p\u003e \u003cp\u003eOur results show that NTS may be an accurate diagnostic technique for subclinical TB, which is crucial for transmission control but underserved by present methods. Including NTS in clinical protocols for asymptomatic TB patients with radiological or immunological evidence could improve detection and curb transmission. NTS can also identify species and test drug resistance, making it appropriate for TB diagnosis and precision treatment in one assay. New studies should test NTS in multiple settings, examine cost-effectiveness for policy guidance, and simplify peripheral lab automated operations. By quantifying bacterial load and disease activity in subclinical TB, this technology provides robust support for the WHO's End TB Strategy.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study found that NTS detects low-bacterial-load subclinical TB better than MGIT culture and Xpert MTB/RIF. It effectively diagnoses this \"hidden\" group, which contributes to community transmission but escapes symptom-based screening. NTS is ideal for high-burden, resource-limited scenarios due to its portability, speed, and cost. To maximize its public health impact, future work should validate its usage in multiple settings, improve cost-effectiveness, and integrate it into routine screening programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study has been approved by the Ethics Committee of Chun'an First People's Hospital and adheres to the Declaration of Helsinki. Given that this is a retrospective study and all data have been anonymised, patient informed consent is waived following approval by the Ethics Committee.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e Owing to the retrospective nature of this study and the fact that all data were anonymised, patient informed consent was waived following approval by the Ethics Committee.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eNote\u003c/h2\u003e \u003cp\u003eSensitivity = [True positives / (True positives\u0026thinsp;+\u0026thinsp;False negatives)]\u0026times;100%; Specificity = [False negatives / (False positives\u0026thinsp;+\u0026thinsp;True negatives)] \u0026times; 100%; PPV = [True positives / (True positives\u0026thinsp;+\u0026thinsp;False positives)]\u0026times;100%; NPV = [False negatives / (False positives\u0026thinsp;+\u0026thinsp;True negatives)]\u0026times;100%; Youden\u0026rsquo;s index\u0026thinsp;=\u0026thinsp;Sensitivity\u0026thinsp;+\u0026thinsp;Specificity-1; Accuracy = [(True positives\u0026thinsp;+\u0026thinsp;False negatives)/ Total patients]\u0026times;100%.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by the Medical and Health Science and Technology Programme of Hangzhou (B20252555, B20262506), Key Projects of Chun'an Medical and Health Science and Technology Programme (2025CAYY006), Medicine and Health Research Foundation of Zhejiang Province (2025KY1246), and Traditional Chinese Medicine Science and Technology Programme Project of Zhejiang Province (2026ZF80).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKZ was responsible for the overall planning of the article and conducted the literature search. JZ drafted the core content, provided valuable insights, and assisted in refining the text. SS engaged in in-depth analysis and discussion of the literature, enhancing the comprehensiveness and accuracy of the article. LC performed supplementary literature searches and verification work. Additionally, JZ and LC jointly verified the authenticity of relevant data points within the literature. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThanks to the support and assistance provided by Dian Diagnostics Co., Ltd. in Hangzhou, China.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe data are clinical diagnostic reports; they do not require deposition in a public repository. Data will be made available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTeo AKJ, MacLean EL, Fox GJ. Subclinical tuberculosis: a meta-analysis of prevalence and scoping review of definitions, prevalence and clinical characteristics. Eur Respir Rev Apr. 2024;30(172). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/16000617.0208-2023\u003c/span\u003e\u003cspan address=\"10.1183/16000617.0208-2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrascella B, Richards AS, Sossen B, et al. Subclinical Tuberculosis Disease-A Review and Analysis of Prevalence Surveys to Inform Definitions, Burden, Associations, and Screening Methodology. Clin Infect Dis Aug. 2021;2(3):e830\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cid/ciaa1402\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciaa1402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKendall EA, Shrestha S, Dowdy DW. The Epidemiological Importance of Subclinical Tuberculosis. A Critical Reappraisal. Am J Respir Crit Care Med Jan. 2021;15(2):168\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.202006-2394PP\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202006-2394PP\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdume B, Ogbudebe C, Mukadi Y, et al. The burden of subclinical TB in Nigeria. Public Health Action Dec. 2024;14(4):181\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5588/pha.24.0038\u003c/span\u003e\u003cspan address=\"10.5588/pha.24.0038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChihota V, Gombe M, Gupta A, et al. Tuberculosis Preventive Treatment in High TB-Burden Settings: A State-of-the-Art Review. Drugs Dec. 2024;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40265-024-02131-3\u003c/span\u003e\u003cspan address=\"10.1007/s40265-024-02131-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoletti D, Matteelli A, Cliff JM, et al. World TB Day 2025 Theme Yes! We Can End TB: Commit, Invest, Deliver can be made a reality through concerted global efforts to advance diagnosis, treatment and research of tuberculosis infection and disease. Int J Infect Dis Mar. 2025;17:107892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijid.2025.107892\u003c/span\u003e\u003cspan address=\"10.1016/j.ijid.2025.107892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatteelli A, Lovatti S, Sforza A, Rossi L. Programmatic management of tuberculosis preventive therapy: Past, present, future. Int J Infect Dis May. 2023;130(Suppl 1):S43\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijid.2023.02.016\u003c/span\u003e\u003cspan address=\"10.1016/j.ijid.2023.02.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen HV, Tiemersma E, Nguyen NV, Nguyen HB, Cobelens F. Disease Transmission by Patients With Subclinical Tuberculosis. Clin Infect Dis Jun. 2023;8(11):2000\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cid/ciad027\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciad027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouben R, Esmail H, Emery JC, et al. Spotting the old foe-revisiting the case definition for TB. Lancet Respir Med. Mar 2019;7(3):199\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2213-2600(19)30038-4\u003c/span\u003e\u003cspan address=\"10.1016/s2213-2600(19)30038-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Cancino-Mu\u0026ntilde;oz I, Torres-Puente M, et al. High-resolution mapping of tuberculosis transmission: Whole genome sequencing and phylogenetic modelling of a cohort from Valencia Region, Spain. PLoS Med. Oct 2019;16(10):e1002961. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pmed.1002961\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1002961\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar M. Incipient and subclinical tuberculosis: a narrative review. Monaldi Arch Chest Dis Jan. 2025;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4081/monaldi.2025.2982\u003c/span\u003e\u003cspan address=\"10.4081/monaldi.2025.2982\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JH, Choe J, Bae M, et al. Clinical Characteristics and Radiologic Features of Immunocompromised Patients With Pauci-Bacillary Pulmonary Tuberculosis Receiving Delayed Diagnosis and Treatment. Open Forum Infect Dis Feb. 2019;6(2):ofz002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ofid/ofz002\u003c/span\u003e\u003cspan address=\"10.1093/ofid/ofz002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsadi L, Croxen M, Heffernan C, et al. How much do smear-negative patients really contribute to tuberculosis transmissions? Re-examining an old question with new tools. EClinicalMedicine Jan. 2022;43:101250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eclinm.2021.101250\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2021.101250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran BM, Larsson J, Grip A, Karempudi P, Elf J. Phenotypic drug susceptibility testing for Mycobacterium tuberculosis variant bovis BCG in 12 hours. Nat Commun May. 2025;10(1):4366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-025-59736-9\u003c/span\u003e\u003cspan address=\"10.1038/s41467-025-59736-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtieno JA, Were LM, Lutje V, Scandrett K, Takwoingi Y, Ochodo EA. Impact of rapid nucleic acid amplification tests for tuberculosis on patient outcomes. Cochrane Database Syst Rev Dec. 2025;18(12):Cd016194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.Cd016194\u003c/span\u003e\u003cspan address=\"10.1002/14651858.Cd016194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabibbe AM, Moghaddasi K, Batignani V, Morgan GSK, Di Marco F, Cirillo DM. Nanopore-based targeted sequencing test for direct tuberculosis identification, genotyping, and detection of drug resistance mutations: a side-by-side comparison of targeted next-generation sequencing technologies. J Clin Microbiol Oct. 2024;16(10):e0081524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/jcm.00815-24\u003c/span\u003e\u003cspan address=\"10.1128/jcm.00815-24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu S, Liu N, Xie Z, et al. Nanopore sequencing for precise detection of Mycobacterium tuberculosis and drug resistance: a retrospective multicenter study in China. J Clin Microbiol Apr. 2025;9(4):e0181324. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/jcm.01813-24\u003c/span\u003e\u003cspan address=\"10.1128/jcm.01813-24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Gonz\u0026aacute;lez PJ, Campino S, Phelan JE, Clark TG. Portable sequencing of Mycobacterium tuberculosis for clinical and epidemiological applications. Brief Bioinform Sep. 2022;20(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bib/bbac256\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbac256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Fan Q, Gong S, et al. Diagnosis of Drug-Resistant Tuberculosis: Rapid Evaluation of Drug Susceptibility with Nanopore Targeted Sequencing. Clin Chem Aug. 2025;1(8):908\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/clinchem/hvaf061\u003c/span\u003e\u003cspan address=\"10.1093/clinchem/hvaf061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilgiriwala K, Rabodoarivelo MS, Hall MB, et al. Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination, and Drug Susceptibility Prediction. J Clin Microbiol Mar. 2023;23(3):e0157822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/jcm.01578-22\u003c/span\u003e\u003cspan address=\"10.1128/jcm.01578-22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwab TC, Joseph L, Moono A, et al. Field evaluation of nanopore targeted next-generation sequencing to predict drug-resistant tuberculosis from native sputum in South Africa and Zambia. J Clin Microbiol Mar. 2025;12(3):e0139024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/jcm.01390-24\u003c/span\u003e\u003cspan address=\"10.1128/jcm.01390-24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Shen Y, Zhong F, et al. Diagnostic accuracy of nanopore sequencing using respiratory specimens in the diagnosis of pulmonary tuberculosis. Int J Infect Dis Sep. 2022;122:237\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijid.2022.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ijid.2022.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan X, Yang G, Wang Y, et al. Nanopore sequencing for smear-negative pulmonary tuberculosis-a multicentre prospective study in China. Ann Clin Microbiol Antimicrob Jun. 2024;14(1):51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12941-024-00714-2\u003c/span\u003e\u003cspan address=\"10.1186/s12941-024-00714-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorne DJ, Zifodya JS, Shapiro AE, et al. Xpert MTB/RIF Ultra assay for pulmonary tuberculosis and rifampicin resistance in adults and adolescents. Cochrane Database Syst Rev Jul. 2025;29(7):Cd009593. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.CD009593.pub6\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD009593.pub6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu S, Zhang Y, Wang R, et al. A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens. J Environ Manage Nov. 2023;1:345:118737. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jenvman.2023.118737\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2023.118737\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang T, Li H, Jiang M, et al. Nanopore sequencing: flourishing in its teenage years. J Genet Genomics Dec. 2024;51(12):1361\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jgg.2024.09.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jgg.2024.09.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Subclinical TB, Nanopore sequencing, Targeted next-generation sequencing, Diagnosis accuracy, MGIT culture, Xpert MTB/RIF","lastPublishedDoi":"10.21203/rs.3.rs-8876803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8876803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSubclinical Tuberculosis (TB), with low bacterial loads and non-specific symptoms, is difficult to diagnose and promotes transmission. This study aimed to evaluate the diagnostic performance of nanopore-based targeted sequencing (NTS) for subclinical TB in comparison with Mycobacteria Growth Indicator Tube (MGIT) culture and GeneXpert MTB/RIF (Xpert MTB/RIF) assays.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective research included 103 subclinical TB suspects. We tested sputum, bronchoalveolar lavage fluid, or pleural effusion from each patient using NTS, MGIT culture, and Xpert. Diagnostic performance was assessed using a composite standard.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNTS exhibited higher sensitivity (90.9%) than MGIT culture (40.9%) and Xpert MTB/RIF (50.0%). It had a larger area under the curve (AUC) of 0.923 than Xpert (0.731) and MGIT culture (0.667), as well as an impressive negative predictive value (97.4%) and accuracy (93.2%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNTS shows better diagnostic accuracy for subclinical TB than conventional approaches, efficiently identifying cases with low bacterial burdens. It could help find cases of TB earlier, closing the gap in TB control.\u003c/p\u003e","manuscriptTitle":"Diagnostic Value of Nanopore-Based Targeted Sequencing Technology for Subclinical Tuberculosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:55:37","doi":"10.21203/rs.3.rs-8876803/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T07:17:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T19:23:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T17:23:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248864316625144252518952130772033443238","date":"2026-04-28T07:40:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290856872481754611151410662221535407372","date":"2026-04-26T01:57:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T05:58:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72749730303970439080015407331587062221","date":"2026-03-02T09:49:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T17:07:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-26T17:03:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T11:04:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T08:26:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-02-25T08:21:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f23a1e3-e1ae-4d69-887f-d7cdbd5f14b7","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T07:17:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T19:23:43+00:00","index":85,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T17:23:48+00:00","index":84,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T07:25:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:55:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8876803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8876803","identity":"rs-8876803","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}