High Sensitivity of Targeted Next-Generation Sequencing on Patients with Suspected Mycobacteria Infectious Diseases: A Prospective Matched Cohort Study

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Methods We consecutively enrolled patients with suspected Mycobacterium infectious diseases, collected samples (respiratory tract sample and tissue biopsy), and tested them using M-tNGS and GeneXpert MTB RIF (Xpert) assays. The results of these tests were compared with the clinical diagnosis and Mycobacterium culture results. Results Overall,244 patients were enrolled, from whom 206 respiratory samples and 38 tissue samples were obtained. The sensitivity of M-tNGS was superior to that of Xpert in both respiratory and biopsy samples (92.2% vs. 43.6% and 90.0% vs. 46.0%, respectively). Conversely, M-tNGS was less specific than Xpert in respiratory and biopsy samples (79.7% vs. 100% and 87.5% vs. 100% , respectively). Meanwhile, M-tNGS more frequently detected drug resistance and nontubercular mycobacteria (NTM), with sensitivity of 70.91% and 13.11%, respectively. Based on comparison with a composite reference standard, M-tNGS was more accurate than the Xpert assay and Mycobacteriumtuberculosis(MTB) culture, with areas under the curve of 0.86, 0.72, and 0.6, respectively. Conclusion M-tNGS is suitable for the early screening of Mycobacterium infectious diseases. Notably, M-tNGS can provide more information on drug resistance and Mycobacterium species identification, facilitating accurate treatment. Mycobacterium tuberculosis nontuberculous mycobacteria Mycobacterium-targeted next-generation sequencing Xpert MTB/RIF sensitivity Figures Figure 1 Figure 2 Background Tuberculosis (TB)—which is caused by Mycobacterium tuberculosis (MTB)—is recognized as an infectious disease worldwide, accounting for approximately 1.6 million deaths per year [ 1 ]. The use of a rapid and accurate tool for the early diagnosis of MTB is crucial for its control and elimination [ 1 ]. Currently, acid-fast staining smear microscopy (AFB) remains a major rapid detection tool for MTB. However, this tool cannot distinguish MTB from other acid-fast bacteria and has relatively low sensitivity [ 2 ]. Meanwhile, Mycobacterium culture in a solid or liquid medium is still considered the gold standard for Mycobacterium tuberculosis detection; however, it takes at least 2–4 weeks [ 3 ]. Some nucleic acid amplification assays, such as the GeneXpert MTB/RIF assay (Xpert), have been recommended by the World Health Organization for the initial diagnosis of suspected tuberculosis [ 4 ]. The Xpert assay has been reported to have good sensitivity and a short turnover time. However, its diagnostic sensitivity is suboptimal and occasionally low in different TB populations, such as those with low bacilli loads, or after antibiotic exposure [ 5 ]. Moreover, this method cannot be used for the diagnosis of other types of mycobacterial diseases [ 6 ] and is limited to the detection of rifampicin resistance. Mycobacterium -targeted next-generation sequencing (M-tNGS) is a novel pathogenic microbial detection method based on a combination of ultra-multiple polymerase chain reaction (PCR) and next-generation high-throughput sequencing technology. Specific primers are designed for multiple targets and gene loci of mycobacteria, such as 16SrRNA, hsp65, ropB, and sod gene intervals, which can theoretically improve the clinical detection rate of Mycobacterium pathogens and identification of Mycobacterium species and resistance gene types. To date, the relevant literature has demonstrated that M-tNGS effectively detects drug resistance of Mycobacterium tuberculosis [ 7 , 8 ]; however, there have been limited reports on the clinical diagnostic efficacy of M-tNGS. Thus, this study aimed to explore the clinical diagnostic efficacy of M-tNGS in patients with suspected Mycobacterium infection and compare its advantages and disadvantages with those of the Xpert assay. Methods Study design and participants We consecutively enrolled individuals with suspected MTB in the Nanjing Second Hospital—a provincial tertiary care hospital for infectious diseases. Inclusion criteria for these individuals were as follows: patients with suspected MTB or other mycobacterial infections, those who volunteered to undergo M-tNGS, and those who submitted samples for culture identification testing and Xpert assay at the same time. Exclusion criteria were as follows: patients with incomplete clinical or laboratory data for major data analyses, those who provided insufficient samples for tests, and those who withdrew their informed consent. The final diagnosis of pulmonary and extrapulmonary tuberculosis was obtained 3–6 months after follow-up; this diagnosis was consistent with the WHO’s revised definitions and reporting framework for tuberculosis [ 9 ]. NTM diseases were diagnosed by experienced clinicians who followed the practice consensus guidelines [ 10 ]. This study was approved by the Ethics Committee of Nanjing Second Hospital (SN: 2019-LS-ky004). Written informed consent was obtained from all study participants. Mycobacterium-tNGS test procedures Nucleic acid extraction: The original sample was pretreated (through processes such as sputum liquefaction), and the deoxyribonucleic acid (DNA) was released by bead milling, enzymatic hydrolysis, chemical reagent cracking, and other steps. Further, nucleic acid purification was performed to remove reagents, ions, enzymes, and other impurities via column purification with sterile nucleic acid-free water elution. Multiplex PCR: The specific target of mycobacteria was continuously replicated under the guidance of multiple primers and DNA polymerase. Purification of the first round of PCR products: The enzyme, primer, and other impurities in the sample were removed by magnetic bead purification, and the amplicon fragment was eluted with sterile nucleic acid-free water. Library amplification and enrichment: The amplicon was extended under the guidance of an index primer and DNA polymerase to obtain the sequencing library. Purification of the second round of PCR products: The enzyme, primer, and other impurities in the sample were removed by magnetic bead purification, and the fragments with significantly large or small amplicon fragments (i.e., nonspecific amplified fragments) were removed. Further, the sequencing library was eluted with sterile nucleic acid-free water. Library mixing: According to the requirements of M-tNGS sequencing data, the library was diluted to a certain molar concentration using Illumina sequencing. Sequencing data analysis: The data were compared with those of the tNGS target library to identify mycobacteria and specific genes using the Bseq sequencing platform (model: Illumina MiniSeq, sequencing strategy: SE 75, and sequencing throughput: 25M). The Xpert MTB/RIF Notably, Xpert assays were performed as per the manufacturer’s instructions. Briefly, a sample reagent was added to the initially collected sample at a dilution ratio of 2:1, and a 2.0-mL aliquot of the resulting mixture was added to the Xpert cartridge. Further, the samples were analyzed using standard four-module GeneXpert instruments with automated readouts for tuberculosis detection. After the reaction, the results were directly evaluated. Mycobacterium culture and species identification The Mycobacterium growth indicator tube(MGIT) 960 media comprised 4 mL of Middlebrook 7H9 broth with an oxygen-sensitive fluorescent sensor embedded in silicon at the bottom of the tube that emits fluorescence under ultraviolet light when oxygen is depleted, indicating mycobacterial growth. Uninoculated tubes were used as negative controls, whereas tubes inoculated with H37RV were used as positive controls. Further, the MGIT-positive cultures were subjected to the paranitro benzoic acid test to differentiate them from nontubercular mycobacteria (NTM). PCR-reverse dot blot hybridization for Mycobacterium species identification, Mycobacterium species identification kit for gene detection, PCR amplification instrument, and molecular hybridization instrument were provided by the company. Overall, 22 specific oligonucleotide probes were designed according to the 16S rRNA sequence and hybridized with biotin-labeled PCR amplification products. Further, we determined whether the probes were hybridized with the DNA fragment based on whether the specific position of the membrane strip was colored, and 22 common pathogenic mycobacteria were clinically identified. According to the instructions provided in the kit, sample collection, sample treatment, nucleic acid extraction, reagent configuration, PCR amplification, analyses, and result interpretation were conducted. Sample size consideration and statistical analysis Based on our preliminary study, we assumed a sensitivity of 85% for M-tNGS and 65% for the Xpert assay and found that a population size of 140 was sufficient to determine the difference between the two assays based on a two-sided test with α = 0.05 and power = 80%(PASS 11.0). Statistical analyses were performed using SPSS 25.0 (IBM, Armonk, NY, USA) and Graphpad Prism 8. Continuous variables were expressed as mean ± standard deviations. Furthermore, 2 × 2 contingency tables were derived to determine true positives, true negatives, false negatives, false positives, sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). All statistical values are reported as absolute values with 95% confidence intervals (95% CIs).The paired McNemar chi-square test was used to compare the diagnostic accuracy of M-tNGS with that of the culture identification test or Xpert assay. A two-sided P-value of < 0.05 was considered statistically significant. The consistency between the diagnosis using three diagnostic methods and the final clinical diagnosis was evaluated using kappa values and 95% CIs. Results Clinical Characteristics of the Participants Overall, 261 patients with suspected MTB were screened between March 1, 2022 and June 30, 2022. Of them, 17 were excluded from the study owing to incomplete clinical data or indeterminate diagnosis. Finally, the remaining 244 participants were enrolled in the study, with an equal number of samples obtained for M-tNGS and Xpert assay, including 206 (84.4%) respiratory tract samples and 38 (15.6%) biopsy samples (i.e., 2 pulmonary and 26 extrapulmonary tissue samples) (Fig. 1). The median age of our study participants was 44.0 years. Overall, 129 of 244 (53%) participants were females: 188 were diagnosed with MTB, 29 were diagnosed with NTM-PD, and 27 were not diagnosed with MTB (Table 1 ). Table 1 Clinical characteristics of enrolled patients with suspected Mycobacterium infectious diseases. Pulmonary tuberculosis (N = 166) Extrapulmonary Tuberculosis (N = 22) NTM-PD (N = 29) Not tuberculosis (N = 27) All (N = 244) Gender(n, %) Female 79(48) 18(82) 20(69) 11(41) 129(53) Age (years) 0 to 25 36(22) 6(27) 3(10) 2(7) 47(19) 26 to 45 59(36) 8(36) 3(10) 10(37) 80(33) 46 to 65 54(33) 5(23) 16(55) 11(41) 86(35) 66 and above 17(10) 3(14) 7()24 4(15) 31(13) Xpert (n, %) Positive 70(42) 8(36) 0(0) 0(0) 79(32) Negative 93(56) 9(41) 28(97) 26(96) 155(64) NA 3(2) 5(23) 1(3) 1(4) 10(4) Culture Identification test(n, %) Positive 43(26) 0 22(76) 0(0) 65(27) Negative 115(69) 0 7()(24) 19(70) 141(58) NA 8(5) 22(1) 0(0) 8(30) 38(16) TSOPT(n, %) Positive 99(6) 10(45) 11(38) 12(44) 132(54) Negative 26(16) 2(9) 12(41) 10(37) 50(20) NA 41(25) 10(45) 6(21) 5(19) 62(25) M-tNGS (n, %) Positive 154(93) 19(86) 26(90) 3(11) 202(83) Negative 12(7) 3(14) 3(10) 24(89) 42(17) NOTE:NA,not applicable,TSPOT,T-SPOT.TB Diagnostic performance of M-tNGS for suspected MTB Regarding the detection of pulmonary tuberculosis, the sensitivity of M-tNGS was found to be higher than that of the Xpert assay and MTB culture (92.21% vs. 43.59% vs. 26.28%, respectively; P < 0.05) based on comparison with a composite reference standard. The specificity of M-tNGS was lower than that of the Xpert assay and MTB culture (79.17%, 100%, and 100%, respectively; P < 0.05). The area under the curve (AUC) for M-tNGS (0.86, 95% CI: 0.76–0.95) was greater than that for other detection tests, with AUCs of 0.72 (95% CI: 0.65–0.79) and 0.6 (95% CI: 0.54–0.73) for the Xpert assay and culture identification test, respectively.The PPV and NPV were 96.6% and 61.29% for M-tNGS and 100% and 34.33% for the Xpert assay, respectively. The clinical consistency between M-tNGS and Xpert assay was 90.45% vs. 56.44% (P < 0.001) (Table 2 ).Drug sensitivity test results are presented in the Additional file 1: Supplementary Table. Table 2 Diagnostic performance of M-tNGS, Xpertand Culture Identification testin PTB among 206 respiratory samples. detection method Sensitivity (%, 95%CI) Specificity (%, 95%CI) PPV (%, 95%CI) NPV (%, 95%CI) AUC (%, 95%CI) Clinical concordance rate (%, 95%CI) M-tNGS 92.21(86.49–95.73) 79.17(57.3-92.07) 96.6(91.83–98.74) 61.29(42.29–77.58) 0.86(0.76–0.95) 90.45(84.91–94.17) Xpert 43.59(35.75–51.75) * 100(90.4–100)* 100(93.34–100) 34.33(26.49–43.09) 0.72(0.65–0.79) 56.44(49.30-63.33)** Culture identification test 26.28(19.72–34.04) * 100(82.83–100)* 100(89.33–100) 17.27(11.59–24.81) 0.6(0.54–0.73) 36.11(29.19–43.63)** NOTE: PPV: positive predictive values; NPV: Negative predictive value; AUC: Areas under the curve; CI: Confidence interval. The paired McNemar chi-square test was used to compare the diagnostic accuracy of M-tNGS with that of Xpert assay or the culture identification test. *: p < 0.05.**༚p < 0.001. Table 3 Diagnostic Performance of M-tNGS, Culture Identification test in NTM pulmonary diseases(NTM-PD). Detection method Sensitivity (%, 95%CI) Specificity (%, 95%CI) PPV (%, 95%CI) NPV (%, 95%CI) AUC (%, 95%CI) Clinical concordance rate (%, 95%CI) M-tNGS 91.3(70.49–98.48)* 82.86(65.71–92.83) 77.78(57.27–90.63) 93.55(77.16–98.88) 0.87(0.77–0.97) 86.21(74.027–93.44) Culture identification test 75(54.78–88.57)* 99.26(95.33–99.96) 95.45(75.11–99.76) 95.04(89.66–97.81) 0.87(0.77–0.97) 95.09(90.22–97.70) NOTE: PPV: positive predictive values; NPV: Negative predictive value; AUC: Areas under the curve; CI: Confidence interval. The paired McNemar chi-square test was used to compare the diagnostic sensitivity of M-tNGS with culture identification test. *: p < 0.05. Diagnostic performance of M-tNGS in different types of samples Notably, M-tNGS has similar sensitivity and specificity in respiratory and biopsy samples; however, the causes of false positivity in the two samples were different. Regarding respiratory samples, M-tNGS was more sensitive (92.2% vs. 43.6%, P < 0.05) but less specific (79.7% vs. 100%, P < 0.05) than the Xpert assay. Regarding biopsy samples, the sensitivity and specificity of M-tNGS were 90.0% and 87.5%, respectively (Fig. 2). Positive yield of M-tNGS for detection of NTM pulmonary diseases(NTM-PD) In this study, M-tNGS identified 27 NTM strains. Of these, 77.78% (21/27) were confirmed as NTM-PD. Based on comparison with a composite reference standard, the sensitivity of M-tNGS in detecting NTM was higher than that of the culture identification test (91.3% vs. 75%, P < 0.05; Table 2 ). Strain identification are presented in the Additional file 1: Supplementary Table. Discussion Rapid nucleic acid testing for Mycobacterium tuberculosis offers the advantages of a short turnaround time, low biosafety requirements, and ability to provide information regarding drug susceptibility. The World Health Organization recommends the use of this test for the initial diagnosis of tuberculosis [11]. However, existing nucleic acid testing technologies cannot achieve optimal diagnostic sensitivity. In a systematic review of studies on respiratory secretions, as a supplementary test following % sensitivity and negative smear microscopy, the Xpert assay had 67% sensitivity and 99% specificity [ 12 ]. The sensitivity of the Xpert assay in formaldehyde-fixed paraffin-embedded extrapulmonary tissues was 53.2%, which was substantially lower than that in fresh tissue samples [ 13 , 14 ].In the present study, we evaluated a novel nucleic acid assay for Mycobacterium tuberculosis that utilizes a multiplex PCR design to improve assay sensitivity. The results revealed that 89.27% (183/205) of the cases had negative results on smear microscopy of respiratory secretions. It is encouraging that in this situation, M-tNGS still exhibits a high detection rate, sensitivity, and clinical consistency for Mycobacterium tuberculosis . Moreover, we grouped fresh tissues and formalin-fixed paraffin-embedded tissues in all biopsy samples, and the results revealed no statistically significant difference in the sensitivity of M-tNGS (P > 0.05). This finding expands the application scope of M-tNGS in clinical samples, especially for tissue samples that are difficult to obtain repeatedly, and increases the detection rate of pathogens. Hence, M-tNGS is a promising alternative tool for the diagnosis of infectious diseases caused by mycobacteria. Notably, the high sensitivity of M-tNGS comes at the expense of reduced specificity. This is also true for other ultrasensitive tools, such as the Xpert Ultra assay, which uses additional detection primers and lowers the detection threshold. The Xpert Ultra assay has been reported to have high sensitivity; however, in patients with a history of tuberculosis, it may yield false-positive results owing to the presence of mycobacterial DNA or inactive intact mycobacteria [ 15 ]. We reviewed all false-positive cases, among which six had positive MTB (five were ultimately diagnosed with NTM-PD and one was diagnosed with organizing pneumonia). Whether these conditions were induced by tuberculosis infection is still being discussed. Among six patients with positive NTM, four were diagnosed with pulmonary tuberculosis and two were excluded from the diagnosis. A phenomenon that cannot be ignored is the similarity between MTB and NTM strains, which often results in false-positive results. This may be mainly attributed to contaminating DNA, incorrect selection of gene PCR primers, and PCR competition [ 16 ]. The contaminating DNA is derived from the environment (laboratory and sample collection space) and/or reagents/consumables used during sample processing. In addition, multiple hypervariable regions of each gene exhibit different degrees of sequence diversity, varying from genus to genus, resulting in false positivity. Differences in the PCR amplification efficiency of multi-gene targets within a polymicrobial clinical sample may provide biased (or even false) outcomes. In this study, a multiple-PCR technique was introduced for M-tNGS testing, which led to increased sensitivity but decreased specificity, likely because of the involvement of multiple gene targets in the technique. For respiratory samples, M-tNGS is more suitable as a diagnostic tool to rule out TB because of its high sensitivity and relatively low specificity, especially in tertiary healthcare services where patients may have undergone pretesting and experienced antibiotic exposure. Nonetheless, the specificity of M-tNGS for biopsy samples is significantly higher than that for respiratory samples (87.5% vs. 79.17%). Notably, the current detection rate of cerebrospinal fluid pathogens is not promising, and the sensitivity of the Xpert assay is only 81% [ 17 ]. In one cerebrospinal fluid sample of this study, the Xpert assay failed to detect Mycobacterium tuberculosis ; however, M-tNGS yielded a positive result, which was consistent with the culture results, indicating that for biopsy samples, M-tNGS is a promising and valuable novel diagnostic tool. In the diagnosis of nontuberculous mycobacterial lung disease, the culture identification test has a long culture cycle. Meanwhile, PCR-reverse dot blot hybridization strain identification technology has poor sensitivity and can only identify 22 common pathogenic mycobacteria in clinical practice; however, the identification species are limited. Meanwhile, M-tNGS enables the direct classification of 60 NTM strains within 24 hours, directly providing drug resistance guidance and greatly improving the diagnostic efficiency. In this study, the NTM detection rate, diagnostic sensitivity, clinical consistency rate, and specificity of M-tNGS were comparable to those of the culture identification test, and the 27 detected cases were directly typed. The types of strains detected in this study were consistent with those reported in previous studies [19]. The detection rate of the culture identification test was 10.68% (22/206). Only three cases each of Mycobacterium intracellulare and Mycobacterium abscessus infection were detected, and we could not identify the specific strains in 16 cases, which may delay early diagnosis and treatment. It is suggested that M-tNGS offers obvious advantages over the culture identification test in the clinical diagnosis of NTM-PD. The agreement between M-tNGS and phenotypic drug susceptibility testing (pDST) was almost perfect for RIF, INH, EMB, LFX, AMK, and SM in drug resistance testing for isolated strains of Mycobacterium tuberculosis [ 7 , 8 ]. We used pDST as the gold standard to analyze the performance of M-tNGS in detecting drug resistance to the abovementioned drugs, and the results indicated that the overall sensitivity, specificity, and consistency rate of M-tNGS were 45.45% (95% CI: 25.07–67.32), 1.32% (95% CI: 87.09–94.30), and 87.80% (95% CI: 83.31–91.24), respectively. The results may have been affected by sample quality and background bacterial interference. However, the sample size for detecting drug resistance is insufficient to validate the value of M-tNGS in clinical practice. However, this study has some limitations. First, although this study demonstrated the excellent performance of M-tNGS in the diagnosis of mycobacterial diseases, the sample size used to validate the value of M-tNGS for drug resistance was relatively insufficient. Second, considering the lack of experience among clinicians regarding the application of this technology and its relatively high cost, patients with negative Xpert assay results or samples who cannot undergo Xpert assay are currently recommended to undergo further testing. This may indicate a selection bias, which also explains why the detection rate and sensitivity of the Xpert assay and tuberculosis culture for Mycobacterium tuberculosis in this study were lower than those in other studies. Conclusions As a targeted high-throughput sequencing technology, Mycobacterium -targeted next-generation sequencing can considerably improve the early etiological detection rate of patients with suspected Mycobacterium infection diseases, enable specific strain typing in the early stage, and provide a reference for drug resistance, which acts as a basis for formulating accurate anti-tuberculosis programs. Abbreviations MTB Mycobacterium tuberculosis M-tNGS Mycobacterium-targeted Next-Generation Sequencing Xpert GeneXpert MTB RIF NTM Non-tubercular mycobacteria DNA Deoxyribonucleic acid AFB Acid-fast staining smear microscopy PCR Polymerase chain reaction AUC Areas under the curve CI Confidence interval NPV Negative pdictive values PPV Positive pdictive values MGITMycobacterium growth indicator tube NTM-PDNTM pulmonary diseases pDSTPhenotypic drug susceptibility testing Declarations • Ethics approval and consent to participate This study was approved by the Ethics Committee of Nanjing Second Hospital (SN: 2019-LS-ky004). Written informed consent was obtained from all of the participants. • Consent for publication All authors have read and approved the manuscript. • Availability of data and materials Data relating to this study are contained and presented in this document. Other materials are available from the corresponding authors on reasonable request. • Competing interests The authors declare that they have no competing interests. • Funding This work was supported by the Innovation center for infectious disease of Jiangsu Province (CXZX202232) ,Natural Science Foundation of Jiangsu Province (BK20221722),and “333 talent project”of Jiangsu province. • Authors’ contributions TXH wrote the draft. YC conducted statistical analysis. YYR collected clinical data. CMH and XHL conceived the study and revised the manuscript. The author(s) read and approved the final manuscript. References World Health Organization. Licence: CC BY-NC-SA 3.0 IGO; Geneva: 2021. Global tuberculosis report. Geneva: World Health Organization; 2021. Ryu YJ, Koh WJ, Daley CL. Diagnosis and Treatment of Nontuberculous Mycobacterial Lung Disease: Clinicians' Perspectives. Tuberc Respir Dis (Seoul). 2016;79:74–84. Acharya B, Acharya A, Gautam S, Ghimire SP, Mishra G, Parajuli N, et al. 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Diagnostic accuracy of Xpert MTB/RIF fortuberculous meningitis: systematic review and meta-analysis. Trop Med Int Health. 2021;26:122–32. Hu C, Huang L, Cai M, Wang W, Shi X, Chen W. Characterization of non-tuberculous mycobacterial pulmonary disease in Nanjing district of China. BMCInfect Dis. 2019;19:764. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4128802","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282925465,"identity":"5971e91c-9934-448d-808b-a13b1f1a2adc","order_by":0,"name":"Tian-Xing Hang","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tian-Xing","middleName":"","lastName":"Hang","suffix":""},{"id":282925466,"identity":"2cd01f27-6757-4110-aae0-33c8673e56d5","order_by":1,"name":"Yu Chen","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Chen","suffix":""},{"id":282925467,"identity":"e2f88d7f-9a36-4940-baf2-8ec7b553c41a","order_by":2,"name":"Yun-Yao Ren","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yun-Yao","middleName":"","lastName":"Ren","suffix":""},{"id":282925468,"identity":"9b2445cc-6400-4e51-a2a0-4cda72e46e9c","order_by":3,"name":"Xu-Hui Liu","email":"","orcid":"","institution":"The Third People's Hospital of Shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Xu-Hui","middleName":"","lastName":"Liu","suffix":""},{"id":282925469,"identity":"54fdf382-930b-4a23-af74-465dddb3e78e","order_by":4,"name":"Chun-Mei Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFCCAwwMCQY1cmAGEDA2EKXlQ8UxY1K0AFXNOMOcCFNJWIs54xkzad42tvTtjGcMP91gsJHdcID52QN8WiwbwFpkcnc2nDGWzmFIM95wgM3cAJ8WgwMQW3I3ABnMOQyHEzcc4GGTIEILc7oBRMt/4rRIAr2fANVygBgtx4otgIFsuAHIkM4xSDaeeZjNDL+WG4c33gBGpTyI8Tmnwk6273jzM7xaGCROmEAUSBwAmQDEzHjVAwF/++MPEEYDIaWjYBSMglEwUgEA1BFRggoJV6cAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Chun-Mei","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-03-19 09:00:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4128802/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4128802/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53582495,"identity":"03656b38-2440-4e73-80fe-3cda28ee52b4","added_by":"auto","created_at":"2024-03-27 17:41:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":507712,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4128802/v1/772b81f6541c6dab0e3c6999.jpeg"},{"id":53582496,"identity":"ccd48789-4ebe-472c-8176-5af4f8c1e748","added_by":"auto","created_at":"2024-03-27 17:41:06","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":738028,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4128802/v1/2e11da254c2c823bcf1169dd.jpeg"},{"id":53615094,"identity":"e38dab4b-ed65-4ac4-92e7-d57a7d8479b1","added_by":"auto","created_at":"2024-03-28 06:30:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":580244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4128802/v1/25fb3b6b-cec1-457e-860d-dcda171559ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High Sensitivity of Targeted Next-Generation Sequencing on Patients with Suspected Mycobacteria Infectious Diseases: A Prospective Matched Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eTuberculosis (TB)\u0026mdash;which is caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB)\u0026mdash;is recognized as an infectious disease worldwide, accounting for approximately 1.6\u0026nbsp;million deaths per year [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The use of a rapid and accurate tool for the early diagnosis of MTB is crucial for its control and elimination [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, acid-fast staining smear microscopy (AFB) remains a major rapid detection tool for MTB. However, this tool cannot distinguish MTB from other acid-fast bacteria and has relatively low sensitivity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Meanwhile, \u003cem\u003eMycobacterium\u003c/em\u003e culture in a solid or liquid medium is still considered the gold standard for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e detection; however, it takes at least 2\u0026ndash;4 weeks [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Some nucleic acid amplification assays, such as the GeneXpert MTB/RIF assay (Xpert), have been recommended by the World Health Organization for the initial diagnosis of suspected tuberculosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Xpert assay has been reported to have good sensitivity and a short turnover time. However, its diagnostic sensitivity is suboptimal and occasionally low in different TB populations, such as those with low bacilli loads, or after antibiotic exposure [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, this method cannot be used for the diagnosis of other types of mycobacterial diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and is limited to the detection of rifampicin resistance.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMycobacterium\u003c/em\u003e-targeted next-generation sequencing (M-tNGS) is a novel pathogenic microbial detection method based on a combination of ultra-multiple polymerase chain reaction (PCR) and next-generation high-throughput sequencing technology. Specific primers are designed for multiple targets and gene loci of mycobacteria, such as 16SrRNA, hsp65, ropB, and sod gene intervals, which can theoretically improve the clinical detection rate of \u003cem\u003eMycobacterium\u003c/em\u003e pathogens and identification of \u003cem\u003eMycobacterium\u003c/em\u003e species and resistance gene types. To date, the relevant literature has demonstrated that M-tNGS effectively detects drug resistance of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; however, there have been limited reports on the clinical diagnostic efficacy of M-tNGS. Thus, this study aimed to explore the clinical diagnostic efficacy of M-tNGS in patients with suspected \u003cem\u003eMycobacterium\u003c/em\u003e infection and compare its advantages and disadvantages with those of the Xpert assay.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe consecutively enrolled individuals with suspected MTB in the Nanjing Second Hospital\u0026mdash;a provincial tertiary care hospital for infectious diseases. Inclusion criteria for these individuals were as follows: patients with suspected MTB or other mycobacterial infections, those who volunteered to undergo M-tNGS, and those who submitted samples for culture identification testing and Xpert assay at the same time. Exclusion criteria were as follows: patients with incomplete clinical or laboratory data for major data analyses, those who provided insufficient samples for tests, and those who withdrew their informed consent. The final diagnosis of pulmonary and extrapulmonary tuberculosis was obtained 3\u0026ndash;6 months after follow-up; this diagnosis was consistent with the WHO\u0026rsquo;s revised definitions and reporting framework for tuberculosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. NTM diseases were diagnosed by experienced clinicians who followed the practice consensus guidelines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This study was approved by the Ethics Committee of Nanjing Second Hospital (SN: 2019-LS-ky004). Written informed consent was obtained from all study participants.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMycobacterium-tNGS test procedures\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNucleic acid extraction: The original sample was pretreated (through processes such as sputum liquefaction), and the deoxyribonucleic acid (DNA) was released by bead milling, enzymatic hydrolysis, chemical reagent cracking, and other steps. Further, nucleic acid purification was performed to remove reagents, ions, enzymes, and other impurities via column purification with sterile nucleic acid-free water elution.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMultiplex PCR: The specific target of mycobacteria was continuously replicated under the guidance of multiple primers and DNA polymerase.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePurification of the first round of PCR products: The enzyme, primer, and other impurities in the sample were removed by magnetic bead purification, and the amplicon fragment was eluted with sterile nucleic acid-free water.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLibrary amplification and enrichment: The amplicon was extended under the guidance of an index primer and DNA polymerase to obtain the sequencing library.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePurification of the second round of PCR products: The enzyme, primer, and other impurities in the sample were removed by magnetic bead purification, and the fragments with significantly large or small amplicon fragments (i.e., nonspecific amplified fragments) were removed. Further, the sequencing library was eluted with sterile nucleic acid-free water.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLibrary mixing: According to the requirements of M-tNGS sequencing data, the library was diluted to a certain molar concentration using Illumina sequencing.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSequencing data analysis: The data were compared with those of the tNGS target library to identify mycobacteria and specific genes using the Bseq sequencing platform (model: Illumina MiniSeq, sequencing strategy: SE 75, and sequencing throughput: 25M).\u003cem\u003eThe\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eXpert MTB/RIF\u003c/h2\u003e \u003cp\u003eNotably, Xpert assays were performed as per the manufacturer\u0026rsquo;s instructions. Briefly, a sample reagent was added to the initially collected sample at a dilution ratio of 2:1, and a 2.0-mL aliquot of the resulting mixture was added to the Xpert cartridge. Further, the samples were analyzed using standard four-module GeneXpert instruments with automated readouts for tuberculosis detection. After the reaction, the results were directly evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMycobacterium culture and species identification\u003c/h2\u003e \u003cp\u003eThe Mycobacterium growth indicator tube(MGIT) 960 media comprised 4 mL of Middlebrook 7H9 broth with an oxygen-sensitive fluorescent sensor embedded in silicon at the bottom of the tube that emits fluorescence under ultraviolet light when oxygen is depleted, indicating mycobacterial growth. Uninoculated tubes were used as negative controls, whereas tubes inoculated with H37RV were used as positive controls. Further, the MGIT-positive cultures were subjected to the paranitro benzoic acid test to differentiate them from nontubercular mycobacteria (NTM).\u003c/p\u003e \u003cp\u003ePCR-reverse dot blot hybridization for \u003cem\u003eMycobacterium\u003c/em\u003e species identification, \u003cem\u003eMycobacterium\u003c/em\u003e species identification kit for gene detection, PCR amplification instrument, and molecular hybridization instrument were provided by the company. Overall, 22 specific oligonucleotide probes were designed according to the 16S rRNA sequence and hybridized with biotin-labeled PCR amplification products. Further, we determined whether the probes were hybridized with the DNA fragment based on whether the specific position of the membrane strip was colored, and 22 common pathogenic mycobacteria were clinically identified. According to the instructions provided in the kit, sample collection, sample treatment, nucleic acid extraction, reagent configuration, PCR amplification, analyses, and result interpretation were conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size consideration and statistical analysis\u003c/h2\u003e \u003cp\u003eBased on our preliminary study, we assumed a sensitivity of 85% for M-tNGS and 65% for the Xpert assay and found that a population size of 140 was sufficient to determine the difference between the two assays based on a two-sided test with α\u0026thinsp;=\u0026thinsp;0.05 and power\u0026thinsp;=\u0026thinsp;80%(PASS 11.0). Statistical analyses were performed using SPSS 25.0 (IBM, Armonk, NY, USA) and Graphpad Prism 8. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. Furthermore, 2 \u0026times; 2 contingency tables were derived to determine true positives, true negatives, false negatives, false positives, sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). All statistical values are reported as absolute values with 95% confidence intervals (95% CIs).The paired McNemar chi-square test was used to compare the diagnostic accuracy of M-tNGS with that of the culture identification test or Xpert assay. A two-sided P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. The consistency between the diagnosis using three diagnostic methods and the final clinical diagnosis was evaluated using kappa values and 95% CIs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics of the Participants\u003c/h2\u003e \u003cp\u003eOverall, 261 patients with suspected MTB were screened between March 1, 2022 and June 30, 2022. Of them, 17 were excluded from the study owing to incomplete clinical data or indeterminate diagnosis. Finally, the remaining 244 participants were enrolled in the study, with an equal number of samples obtained for M-tNGS and Xpert assay, including 206 (84.4%) respiratory tract samples and 38 (15.6%) biopsy samples (i.e., 2 pulmonary and 26 extrapulmonary tissue samples) (Fig.\u0026nbsp;1). The median age of our study participants was 44.0 years. Overall, 129 of 244 (53%) participants were females: 188 were diagnosed with MTB, 29 were diagnosed with NTM-PD, and 27 were not diagnosed with MTB (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of enrolled patients with suspected Mycobacterium infectious diseases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulmonary\u003c/p\u003e \u003cp\u003etuberculosis\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;166)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtrapulmonary\u003c/p\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNTM-PD\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot tuberculosis\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;244)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eGender(n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79(48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129(53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 to 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47(19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26 to 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80(33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46 to 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86(35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7()24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31(13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eXpert (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79(32)\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\u003e93(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155(64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCulture Identification test(n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65(27)\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\u003e115(69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7()(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141(58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38(16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTSOPT(n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132(54)\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\u003e26(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50(20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62(25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eM-tNGS (n, %)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154(93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202(83)\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\u003e12(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42(17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNOTE:NA,not applicable,TSPOT,T-SPOT.TB\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic performance of M-tNGS for suspected MTB\u003c/h2\u003e \u003cp\u003eRegarding the detection of pulmonary tuberculosis, the sensitivity of M-tNGS was found to be higher than that of the Xpert assay and MTB culture (92.21% vs. 43.59% vs. 26.28%, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) based on comparison with a composite reference standard. The specificity of M-tNGS was lower than that of the Xpert assay and MTB culture (79.17%, 100%, and 100%, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The area under the curve (AUC) for M-tNGS (0.86, 95% CI: 0.76\u0026ndash;0.95) was greater than that for other detection tests, with AUCs of 0.72 (95% CI: 0.65\u0026ndash;0.79) and 0.6 (95% CI: 0.54\u0026ndash;0.73) for the Xpert assay and culture identification test, respectively.The PPV and NPV were 96.6% and 61.29% for M-tNGS and 100% and 34.33% for the Xpert assay, respectively. The clinical consistency between M-tNGS and Xpert assay was 90.45% vs. 56.44% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).Drug sensitivity test results are presented in the Additional file 1: Supplementary Table.\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\u003eDiagnostic performance of M-tNGS, Xpertand Culture Identification testin PTB among 206 respiratory samples.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003edetection method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003cp\u003econcordance rate\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-tNGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.21(86.49\u0026ndash;95.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.17(57.3-92.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.6(91.83\u0026ndash;98.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.29(42.29\u0026ndash;77.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86(0.76\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.45(84.91\u0026ndash;94.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXpert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.59(35.75\u0026ndash;51.75)\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(90.4\u0026ndash;100)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100(93.34\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.33(26.49\u0026ndash;43.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72(0.65\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.44(49.30-63.33)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulture\u003c/p\u003e \u003cp\u003eidentification\u003c/p\u003e \u003cp\u003etest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.28(19.72\u0026ndash;34.04)\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(82.83\u0026ndash;100)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100(89.33\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.27(11.59\u0026ndash;24.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6(0.54\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.11(29.19\u0026ndash;43.63)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNOTE: PPV: positive predictive values; NPV: Negative predictive value; AUC: Areas under the curve; CI: Confidence interval. The paired McNemar chi-square test was used to compare the diagnostic accuracy of M-tNGS with that of Xpert assay or the culture identification test. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.**༚p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eDiagnostic Performance of M-tNGS, Culture Identification test in NTM pulmonary diseases(NTM-PD).\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetection\u003c/p\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003cp\u003econcordance rate\u003c/p\u003e \u003cp\u003e(%, 95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-tNGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.3(70.49\u0026ndash;98.48)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.86(65.71\u0026ndash;92.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.78(57.27\u0026ndash;90.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.55(77.16\u0026ndash;98.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87(0.77\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86.21(74.027\u0026ndash;93.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulture\u003c/p\u003e \u003cp\u003eidentification\u003c/p\u003e \u003cp\u003etest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75(54.78\u0026ndash;88.57)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.26(95.33\u0026ndash;99.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.45(75.11\u0026ndash;99.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.04(89.66\u0026ndash;97.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87(0.77\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95.09(90.22\u0026ndash;97.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNOTE: PPV: positive predictive values; NPV: Negative predictive value; AUC: Areas under the curve; CI: Confidence interval. The paired McNemar chi-square test was used to compare the diagnostic sensitivity of M-tNGS with culture identification test. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic performance of M-tNGS in different types of samples\u003c/h2\u003e \u003cp\u003eNotably, M-tNGS has similar sensitivity and specificity in respiratory and biopsy samples; however, the causes of false positivity in the two samples were different. Regarding respiratory samples, M-tNGS was more sensitive (92.2% vs. 43.6%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but less specific (79.7% vs. 100%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than the Xpert assay. Regarding biopsy samples, the sensitivity and specificity of M-tNGS were 90.0% and 87.5%, respectively (Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePositive yield of M-tNGS for detection of NTM pulmonary diseases(NTM-PD)\u003c/h2\u003e \u003cp\u003eIn this study, M-tNGS identified 27 NTM strains. Of these, 77.78% (21/27) were confirmed as NTM-PD. Based on comparison with a composite reference standard, the sensitivity of M-tNGS in detecting NTM was higher than that of the culture identification test (91.3% vs. 75%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Strain identification are presented in the Additional file 1: Supplementary Table.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRapid nucleic acid testing for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e offers the advantages of a short turnaround time, low biosafety requirements, and ability to provide information regarding drug susceptibility. The World Health Organization recommends the use of this test for the initial diagnosis of tuberculosis [11]. However, existing nucleic acid testing technologies cannot achieve optimal diagnostic sensitivity. In a systematic review of studies on respiratory secretions, as a supplementary test following % sensitivity and negative smear microscopy, the Xpert assay had 67% sensitivity and 99% specificity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The sensitivity of the Xpert assay in formaldehyde-fixed paraffin-embedded extrapulmonary tissues was 53.2%, which was substantially lower than that in fresh tissue samples [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e].In the present study, we evaluated a novel nucleic acid assay for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e that utilizes a multiplex PCR design to improve assay sensitivity. The results revealed that 89.27% (183/205) of the cases had negative results on smear microscopy of respiratory secretions. It is encouraging that in this situation, M-tNGS still exhibits a high detection rate, sensitivity, and clinical consistency for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e. Moreover, we grouped fresh tissues and formalin-fixed paraffin-embedded tissues in all biopsy samples, and the results revealed no statistically significant difference in the sensitivity of M-tNGS (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This finding expands the application scope of M-tNGS in clinical samples, especially for tissue samples that are difficult to obtain repeatedly, and increases the detection rate of pathogens. Hence, M-tNGS is a promising alternative tool for the diagnosis of infectious diseases caused by mycobacteria.\u003c/p\u003e \u003cp\u003eNotably, the high sensitivity of M-tNGS comes at the expense of reduced specificity. This is also true for other ultrasensitive tools, such as the Xpert Ultra assay, which uses additional detection primers and lowers the detection threshold. The Xpert Ultra assay has been reported to have high sensitivity; however, in patients with a history of tuberculosis, it may yield false-positive results owing to the presence of mycobacterial DNA or inactive intact mycobacteria [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We reviewed all false-positive cases, among which six had positive MTB (five were ultimately diagnosed with NTM-PD and one was diagnosed with organizing pneumonia). Whether these conditions were induced by tuberculosis infection is still being discussed. Among six patients with positive NTM, four were diagnosed with pulmonary tuberculosis and two were excluded from the diagnosis. A phenomenon that cannot be ignored is the similarity between MTB and NTM strains, which often results in false-positive results. This may be mainly attributed to contaminating DNA, incorrect selection of gene PCR primers, and PCR competition [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The contaminating DNA is derived from the environment (laboratory and sample collection space) and/or reagents/consumables used during sample processing. In addition, multiple hypervariable regions of each gene exhibit different degrees of sequence diversity, varying from genus to genus, resulting in false positivity. Differences in the PCR amplification efficiency of multi-gene targets within a polymicrobial clinical sample may provide biased (or even false) outcomes. In this study, a multiple-PCR technique was introduced for M-tNGS testing, which led to increased sensitivity but decreased specificity, likely because of the involvement of multiple gene targets in the technique.\u003c/p\u003e \u003cp\u003eFor respiratory samples, M-tNGS is more suitable as a diagnostic tool to rule out TB because of its high sensitivity and relatively low specificity, especially in tertiary healthcare services where patients may have undergone pretesting and experienced antibiotic exposure. Nonetheless, the specificity of M-tNGS for biopsy samples is significantly higher than that for respiratory samples (87.5% vs. 79.17%). Notably, the current detection rate of cerebrospinal fluid pathogens is not promising, and the sensitivity of the Xpert assay is only 81% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In one cerebrospinal fluid sample of this study, the Xpert assay failed to detect \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e; however, M-tNGS yielded a positive result, which was consistent with the culture results, indicating that for biopsy samples, M-tNGS is a promising and valuable novel diagnostic tool.\u003c/p\u003e \u003cp\u003eIn the diagnosis of nontuberculous mycobacterial lung disease, the culture identification test has a long culture cycle. Meanwhile, PCR-reverse dot blot hybridization strain identification technology has poor sensitivity and can only identify 22 common pathogenic mycobacteria in clinical practice; however, the identification species are limited. Meanwhile, M-tNGS enables the direct classification of 60 NTM strains within 24 hours, directly providing drug resistance guidance and greatly improving the diagnostic efficiency. In this study, the NTM detection rate, diagnostic sensitivity, clinical consistency rate, and specificity of M-tNGS were comparable to those of the culture identification test, and the 27 detected cases were directly typed. The types of strains detected in this study were consistent with those reported in previous studies [19]. The detection rate of the culture identification test was 10.68% (22/206). Only three cases each of \u003cem\u003eMycobacterium intracellulare\u003c/em\u003e and \u003cem\u003eMycobacterium abscessus\u003c/em\u003e infection were detected, and we could not identify the specific strains in 16 cases, which may delay early diagnosis and treatment. It is suggested that M-tNGS offers obvious advantages over the culture identification test in the clinical diagnosis of NTM-PD.\u003c/p\u003e \u003cp\u003eThe agreement between M-tNGS and phenotypic drug susceptibility testing (pDST) was almost perfect for RIF, INH, EMB, LFX, AMK, and SM in drug resistance testing for isolated strains of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We used pDST as the gold standard to analyze the performance of M-tNGS in detecting drug resistance to the abovementioned drugs, and the results indicated that the overall sensitivity, specificity, and consistency rate of M-tNGS were 45.45% (95% CI: 25.07\u0026ndash;67.32), 1.32% (95% CI: 87.09\u0026ndash;94.30), and 87.80% (95% CI: 83.31\u0026ndash;91.24), respectively. The results may have been affected by sample quality and background bacterial interference. However, the sample size for detecting drug resistance is insufficient to validate the value of M-tNGS in clinical practice.\u003c/p\u003e \u003cp\u003eHowever, this study has some limitations. First, although this study demonstrated the excellent performance of M-tNGS in the diagnosis of mycobacterial diseases, the sample size used to validate the value of M-tNGS for drug resistance was relatively insufficient. Second, considering the lack of experience among clinicians regarding the application of this technology and its relatively high cost, patients with negative Xpert assay results or samples who cannot undergo Xpert assay are currently recommended to undergo further testing. This may indicate a selection bias, which also explains why the detection rate and sensitivity of the Xpert assay and tuberculosis culture for \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e in this study were lower than those in other studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAs a targeted high-throughput sequencing technology, \u003cem\u003eMycobacterium\u003c/em\u003e-targeted next-generation sequencing can considerably improve the early etiological detection rate of patients with suspected \u003cem\u003eMycobacterium\u003c/em\u003e infection diseases, enable specific strain typing in the early stage, and provide a reference for drug resistance, which acts as a basis for formulating accurate anti-tuberculosis programs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMTB\u003cem\u003eMycobacterium\u003c/em\u003etuberculosis \u003c/p\u003e\n\u003cp\u003eM-tNGS Mycobacterium-targeted Next-Generation Sequencing\u003c/p\u003e\n\u003cp\u003eXpert GeneXpert MTB RIF \u003c/p\u003e\n\u003cp\u003eNTM Non-tubercular mycobacteria\u003c/p\u003e\n\u003cp\u003eDNA Deoxyribonucleic acid\u003c/p\u003e\n\u003cp\u003eAFB Acid-fast staining smear microscopy\u003c/p\u003e\n\u003cp\u003ePCR Polymerase chain reaction \u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp;\u0026nbsp; Areas under the curve\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Confidence interval\u003c/p\u003e\n\u003cp\u003eNPV\u0026nbsp; \u0026nbsp;\u0026nbsp; Negative pdictive values\u003c/p\u003e\n\u003cp\u003ePPV\u0026nbsp; \u0026nbsp; \u0026nbsp; Positive pdictive values\u003c/p\u003e\n\u003cp\u003eMGITMycobacterium growth indicator tube\u003c/p\u003e\n\u003cp\u003eNTM-PDNTM pulmonary diseases\u003c/p\u003e\n\u003cp\u003epDSTPhenotypic drug susceptibility testing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Nanjing Second Hospital (SN: 2019-LS-ky004). Written informed consent was obtained from all of the participants.\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData relating to this study are contained and presented in this document. Other materials are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;the\u0026nbsp;Innovation center for infectious disease of Jiangsu Province (CXZX202232) ,Natural Science Foundation of Jiangsu Province (BK20221722),and\u0026nbsp;\u0026ldquo;333 talent project\u0026rdquo;of Jiangsu province.\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTXH wrote the draft. YC conducted statistical analysis. YYR collected clinical data. CMH and XHL conceived the study and revised the manuscript. The author(s) read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Licence: CC BY-NC-SA 3.0 IGO; Geneva: 2021. Global tuberculosis report. Geneva: World Health Organization; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyu YJ, Koh WJ, Daley CL. Diagnosis and Treatment of Nontuberculous Mycobacterial Lung Disease: Clinicians' Perspectives. Tuberc Respir Dis (Seoul). 2016;79:74\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcharya B, Acharya A, Gautam S, Ghimire SP, Mishra G, Parajuli N, et al. Advances in diagnosis of Tuberculosis: an update into molecular diagnosis of Mycobacterium tuberculosis. Mol Biol Rep. 2020;47:4065\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO Endorses New Rapid Tuberculosis Test. London: World Health Organization. Geneva; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey S, Congdon J, McInnes B, Pop A, Coulter C. Evaluation of the GeneXpert MTB/RIF assay on extrapulmonary and respiratory samples other than sputum: a low burden country experience. Pathology. 2017;49:70\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong L, Xie B, Liu Q, Hua L, Bhusal A, Bao C, et al. Application of acid-fast staining combined with GeneXpert MTB/RIF in the diagnosis of non-tuberculous mycobacteria pulmonary disease. Int J Infect Dis. 2021;104:711\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSibandze DB, Kay A, Dreyer V, Sikhondze W, Dlamini Q, DiNardo A, et al. Rapid molecular diagnostics of tuberculosis resistance by targeted stool sequencing. Genome Med. 2022;14(1):52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu SH, Xiao YX, Hsiao HC, Jou R. Development and Assessment of a Novel Whole-Gene-Based Targeted Next-Generation Sequencing Assay for Detecting the Susceptibility of Mycobacterium tuberculosis to 14 Drugs. Microbiol Spectr. 2022;10(6):e0260522.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEurosurveillance editorial team. WHO revised definitions and reporting framework for tuberculosis. Euro Surveill. 2013;18:20455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaley CL, Iaccarino JM, Lange C, Cambau E, Wallace RJ Jr, Andrejak C et al. Treatment of nontuberculous mycobacterial pulmonary disease: an official ATS/ERS/ESCMID/IDSA clinical practice guideline. Eur Respir J. 2020;56:2000535. 11.World Health Organization. WHO Standard: Universal Access to Rapid Tuberculosis Diagnostics. 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BMCInfect Dis. 2019;19:764.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mycobacterium tuberculosis, nontuberculous mycobacteria, Mycobacterium-targeted next-generation sequencing, Xpert MTB/RIF, sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-4128802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4128802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the diagnostic accuracy of \u003cem\u003eMycobacterium\u003c/em\u003e-targeted next-generation sequencing (M-tNGS)technique forpatients with suspected \u003cem\u003eMycobacterium\u003c/em\u003e infectious diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe consecutively enrolled patients with suspected \u003cem\u003eMycobacterium\u003c/em\u003e infectious diseases, collected samples (respiratory tract sample and tissue biopsy), and tested them using M-tNGS and GeneXpert MTB RIF (Xpert) assays. The results of these tests were compared with the clinical diagnosis and \u003cem\u003eMycobacterium\u003c/em\u003e culture results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall,244 patients were enrolled, from whom 206 respiratory samples and 38 tissue samples were obtained. The sensitivity of M-tNGS was superior to that of Xpert in both respiratory and biopsy samples (92.2% vs. 43.6% and 90.0% vs. 46.0%, respectively). Conversely, M-tNGS was less specific than Xpert in respiratory and biopsy samples (79.7% vs. 100% and 87.5% vs. 100% , respectively). Meanwhile, M-tNGS more frequently detected drug resistance and nontubercular mycobacteria (NTM), with sensitivity of 70.91% and 13.11%, respectively. Based on comparison with a composite reference standard, M-tNGS was more accurate than the Xpert assay and Mycobacteriumtuberculosis(MTB) culture, with areas under the curve of 0.86, 0.72, and 0.6, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM-tNGS is suitable for the early screening of \u003cem\u003eMycobacterium\u003c/em\u003e infectious diseases. Notably, M-tNGS can provide more information on drug resistance and \u003cem\u003eMycobacterium\u003c/em\u003e species identification, facilitating accurate treatment.\u003c/p\u003e","manuscriptTitle":"High Sensitivity of Targeted Next-Generation Sequencing on Patients with Suspected Mycobacteria Infectious Diseases: A Prospective Matched Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-27 17:41:01","doi":"10.21203/rs.3.rs-4128802/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8961f55f-1422-4e29-8d5d-585ca3baa3a2","owner":[],"postedDate":"March 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-28T06:30:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-27 17:41:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4128802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4128802","identity":"rs-4128802","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}