RT-PCR-based diagnosis of infectious diseases with special reference to non- tuberculous mycobacteria in a corporate hospital in Bangladesh

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Abu Sayem, Md. Akram Hossain, Md. Abdus Salam, Ayesha Ahmed Khan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9251620/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Timely detection, prevention, and response to infectious diseases are essential for enhancing quality of life, societal well-being, and reducing healthcare costs. Advanced diagnostics like reverse transcription polymerase chain reaction (RT-PCR) improve accuracy, mitigate suffering, limit transmission, and decrease mortality. This study evaluated utility of RT-PCR for detecting infectious agents, including Nontuberculous mycobacteria (NTM), to guide clinical decision-making. Methods This retrospective study analyzed infectious disease data from Apollo Imperial Hospitals Ltd., Chattogram, Bangladesh from June 2019 to July 2024. Of 810 anonymized records extracted from the molecular biology laboratory, 786 samples with complete diagnostic information undergoing RT-PCR testing were included after excluding incomplete entries. As a hospital-based analysis, no eligible cases were overlooked beyond incomplete records. Results Among 786 patients (mean age 38.8 ± 19.3 years), 53.7% were female, and 87.1% were adults or elderly. Samples comprised blood (31.6%), tissue (27.2%), body fluids (19.3%), swabs (8.1%), sputum (6.1%), pus (4.8%), and urine (2.8%). Mycobacteria were detected in 6.4% of samples, including Mycobacterium tuberculosis (MTB; 2.9%) and nontuberculous mycobacteria (NTM; 3.5%). Overall RT-PCR positivity was 12.7%. Associations between positivity and socio-demographic/clinical factors (e.g., gender, sample type, pathogen) were analyzed, revealing higher NTM than MTB rates, a concern for policymakers, in particular National Tuberculosis Program (NTP). Conclusions Rapid identification and treatment of infectious diseases are essential. NTM warrants particular attention due to symptom overlapping with tuberculosis. The NTP should prioritize accurate diagnostics and management to avert misdiagnosis and inappropriate therapy. Annual NTP and WHO reports should highlight NTM prevalence and challenges to inform policy. RT-PCR Infectious disease diagnostics Clinical samples Nontuberculous mycobacteria Mycobacterium tuberculosis Bangladesh Figures Figure 1 Figure 2 Introduction Globally, infectious diseases remain a leading cause of morbidity and mortality, responsible for approximately one-quarter of annual deaths. Recent years have seen resurgences of respiratory infections (e.g., pneumonia), the COVID-19 pandemic, and outbreaks of vector-borne diseases like dengue [ 1 – 4 ]. Enteric infections causing diarrheal diseases are major contributors to childhood mortality, with rotavirus, Shigella , and Campylobacter ranking among the leading pathogens. In 2019, rotavirus accounted for 19% of global diarrheal deaths in children, followed by shigellosis and campylobacteriosis at 12% each [ 5 ]. Bloodstream infections caused nearly 3 million deaths globally, with ~ 26% attributable to gram-negative bacteria [ 6 ]. Infectious diseases are more prevalent in low- and middle-income countries (LMICs) due to prevailing conducive factors [ 7 ]. Bangladesh as one of the LMICs is facing a complex, evolving infectious disease burden, dominated by recurrent dengue outbreaks, acute respiratory infections, and diarrheal diseases [ 4 , 8 ]. In 2025, Bangladesh reported over 102,000 dengue cases and 412 deaths, marking a severe outbreak that extended beyond urban areas and overwhelmed healthcare resources [ 9 ]. Bangladesh rank 7th among 30 high tuberculosis burden countries globally [ 10 ]. Nontuberculous mycobacteria (NTM) pose an additional critical challenge worldwide, including in Bangladesh [ 11 ]. Tropical climate of Bangladesh also favors the persistence of malaria, lymphatic filariasis, helminthiases, viral hepatitis, diarrheal diseases, and cholera [ 12 – 14 ]. Accurate and timely identification of infections is crucial for reducing morbidity, costs, enabling prompt treatment, and informing public health responses. Molecular diagnostics have transformed infectious disease detection worldwide, with real-time reverse transcription polymerase chain reaction (RT-PCR) established as the gold standard for pathogen identification in clinical settings [ 2 ]. In Bangladesh, both public and private hospitals expanded molecular laboratory capacities during large-scale outbreaks, notably the COVID-19 pandemic. RT-PCR offers superior sensitivity, specificity, and precision, making it indispensable for confirming infections across diverse pathogens and informing clinical decision-making and patient management [ 15 , 16 ]. Moreover, RT-PCR yields consistent, reliable results, enabling more accurate, efficient, and cost-effective detection and management of infectious diseases [ 16 ]. Despite advances in molecular diagnostics, most Bangladeshi healthcare facilities continue to rely on conventional microbiological and serological methods, which suffer from low sensitivity, prolonged turnaround times, and limited ability to detect emerging pathogens [ 14 ]. RT-PCR adoption has markedly enhanced diagnostic precision by detecting specific genetic sequences at low concentrations, proving invaluable for early or atypical infections. It identifies diverse agents including viruses, fungi, Mycoplasma , Chlamydia , atypical bacteria, and enteric pathogens such as Salmonella typhi and Salmonella paratyphi A [ 17 , 18 ]. RT-PCR plays a crucial role in detecting diverse infectious diseases, including tuberculosis, HIV/AIDS, and pathogens responsible for sporadic, endemic, epidemic, and pandemic outbreaks. Apollo Imperial Hospitals Ltd., a leading private healthcare facility in Chattogram, Bangladesh, has been utilizing RT-PCR since its inception in 2022. The hospital maintains comprehensive records of patient socio-demographic, clinical, and laboratory data to monitor treatment outcomes and laboratory performance. This study analyzed these records to correlate patient characteristics with RT-PCR-confirmed infectious diseases, identify key challenges, and provide policy recommendations for optimization. Materials and methods Study settings This retrospective study utilized secondary data from Apollo Imperial Hospitals Ltd., a 120-bed facility in Chattogram, Bangladesh, equipped with inpatient (IPD), outpatient (OPD), and emergency departments. The molecular biology laboratory, a key diagnostic unit, provided RT-PCR data for infectious diseases. Duty physicians referred IPD and OPD patients for laboratory workups, including RT-PCR when indicated. Test results were delivered to patients and routinely documented in the hospital's medical records system. Data collection was approved by the appropriate authorities. Notably, diarrheal pathogen panels, encephalitis/meningitis panels, genitourinary panels, dengue, malaria, and other common pathogens were not assessed by RT-PCR due to low demand and availability of alternatives such as culture or rapid diagnostic tests (RDTs). Samples Data on molecular diagnostics for infectious diseases from June 2019 to July 2024 were retrospectively collected from the hospital's molecular biology laboratory. A total of 810 anonymized patient records were extracted; after excluding of incomplete entries, 786 samples data were used for analysis. Samples were categorized as follows: sputum, pus, urine; the cervical swab, wound, nasal, nasopharyngeal, and penile swabs were grouped as "swab" (n = 64); plasma, serum, and whole blood as "blood" (n = 248); pericardial, peritoneal, synovial, cerebrospinal, pleural, eye, gastric aspirate, mouth discharge, tracheal, bronchial, and other fluids as "body fluid" (n = 152); and granuloma, colon, breast, terminal ileum, excised block, cervical lymph node, skin scraping, neck tissue, fallopian tube, endotracheal, alveolar, endometrial tissues, fine-needle aspirate cytology (FNAC) material, bone marrow, and other tissues as "tissue" (n = 214). Inclusion and exclusion criteria All RT-PCR-requested samples by duty physicians from inpatient (IPD) or outpatient (OPD) departments, regardless of patient age or gender, were included. Samples requested for conventional tests were excluded. The patients from emergency department usually being transferred to IPD immediately after managing critical condition. Hence, there was no sample from emergency department. Data analysis Data were stored in Microsoft Excel and imported into SPSS (version 25.0) and Stata (version 14.0) for analysis. Entries were verified, cleaned, coded, and analyzed. Independent variables included age group, gender, patient source (IPD or OPD), sample type, and targeted infectious agent. The primary outcome was RT-PCR positivity. Associations were assessed using chi-square tests, and multivariable binary logistic regression model evaluated the independent factors influencing the outcome. Statistical significance was set at P < 0.05. Results Of the 786 patients, 53.7% were female with adults (18–59.99 years) comprising the majority (58.7%), followed by elderly (≥ 60 years; 28.4%) and newborns to adolescents (13.0%) (Table 1 ). Most samples originated from outpatient department (OPD) (85.8%). Blood (31.6%) and tissue (27.2%) were the most common sample types, followed by body fluids (19.3%), swabs (8.1%), sputum (6.1%), pus (4.8%), and urine (2.8%). Mycobacterial testing predominated (66.0%), followed by HBV (19.2%), with fewer tests for other pathogens: tissue (HCV 3.2%), HPV (4.6%), HSV (2.4%), CMV (2.9%), and RPP (1.7%) (Table 1 ). Table 1 Socio-demographic characteristics of patients and details of clinical samples tested by RT-PCR (n = 786) Characteristics Frequency % Age 0-17.99 years (Newborn to adolescents group) 102 13.0 18-59.99 years (Adult age group) 461 58.7 ≥ 60 years (Old age group) 223 28.4 Gender Male 364 46.3 Female 422 53.7 Source of patient Inpatient department (IPD) 112 14.2 Outpatient department (OPD) 674 85.8 Clinical samples Sputum 48 06.1 Pus 38 04.8 Urine 22 02.8 Swab 64 08.1 Body fluid 152 19.3 Blood 248 31.6 Tissue 214 27.2 Samples tested for Cytomegalovirus (CMV) 23 02.9 Hepatitis B virus (HBV) 151 19.2 Hepatitis C virus (HCV) 25 03.2 Human Papillomavirus (HPV) 36 04.6 Herpes Simplex Virus (HSV) 19 02.4 Mycobacterium 519 66.0 Respiratory Pathogen Panel (RPP) 13 01.7 The overall cumulative pathogen detection rate by RT-PCR was 12.7% across 786 clinical samples (Fig. 1 ). This low positivity likely reflects interlinked factors, including sample quality, physician-assessed clinical signs and symptoms, personnel training, and other factors. Mycobacteria dominated across most sample types, comprising 100% of sputum (n = 48), pus (n = 38), and tissue (n = 214) detections. Mycobacteria was tested among 87.5% of body fluids (n = 133), 36.4% of urine (n = 08), 25.4% of blood (n = 63), and 23.4% of swabs (n = 15) (Table 2 ). HBV was prominent in blood (60.9%), while swabs showed diverse detections: HPV (56.3%), RPP (20.3%), and mycobacteria (23.4%). CMV and HCV were detected primarily in urine, body fluid and blood respectively; HSV mainly in body fluids. Overall, mycobacteria accounted for 66.0% of all detections (519/786), followed by HBV (19.2%) (Table 2 ). Table 2 Frequency distribution of pathogens detected by RT-PCR across clinical sample types (n = 786); values represent counts (row percentages) Sample group Number (%) CMV HBV HCV HPV HSV Mycobact. RPP Sputum 00 (00.0) 00 (00.0) 00 (00.0) 00 (00.0) 00 (00.0) 48 (100.0) 00 (00.0) Pus 00 (00.0) 00 (00.0) 00 (00.0) 00 (00.0) 00 (00.0) 38 (100.0) 00 (00.0) Urine 14 (63.6) 00 (00.0) 00 (00.0) 00 (00.0) 00 (00.0) 08 (36.4) 00 (00.0) Swab 00 (00.0) 00 (00.0) 00 (00.0) 36 (56.3) 00 (00.0) 15 (23.4) 13 (20.3) Body fluid 02 (01.3) 00 (00.0) 00 (00.0) 00 (00.0) 17 (11.2) 133 (87.5) 00 (00.0) Blood 07 (02.8) 151 (60.9) 25 (10.1) 00 (00.0) 02 (00.8) 63 (25.4) 00 (00.0) Tissue 00 (00.0) 00 (00.0) 00 (00.0) 00 (00.0) 00 (00.0) 214 (100.0) 00 (00.0) Total 23 (02.9) 151 (19.2) 25 (03.2) 36 (04.6) 19 (02.4) 519 (66.0) 13 (01.7) N.B. CMV: Cytomegalovirus, HBV: Hepatitis B virus, HCV: Hepatitis C virus, HPV: Human Papillomavirus, HSV: Herpes Simplex Virus, Mycobact.: Mycobacterium, RPP: Respiratory Pathogen Panel. Pathogen detection rates varied significantly by gender (P < 0.05), with males showing higher positivity (15.7%) than females (10.2%). Detection also differed markedly across sample types (P < 0.01), with the highest rates in urine (22.7%), blood (21.0%), and swabs (15.6%), moderate rates in sputum (12.5%) and pus (10.5%), and lowest in body fluids (8.6%) and tissue (4.7%). Among pathogens, respiratory pathogen panel (RPP) exhibited the highest detection rate (76.9%), followed by HBV (28.5%), HCV (28.0%), and CMV (26.1%). HPV testing was negative across all 36 samples, HSV showed low positivity (5.3%), and Mycobacteria had the lowest rate (6.4% from 519 samples) (Table 3 ). Table 3 Association between patient characteristics, sample types, pathogens, and RT-PCR positivity rates (n = 786). Characteristics Tests results by RT-PCR P value Age group Detected (%) Not detected (%) Newborn to adolescent (0-17.99 years) 10 (09.8) 92 (90.2) 0.333 Adult (18-59.99 years) 56 (12.1) 405 (87.9) Old (≥ 60 years) 34 (15.2) 189 (84.8) Gender 0.022 Male 57 (15.7) 307 (84.3) Female 43 (10.2) 379 (89.8) Clinical samples for pathogen detection 0.001 Sputum 06 (12.5) 42 (87.5) Pus 04 (10.5) 34 (89.5) Urine 05 (22.7) 17 (77.3) Swab 10 (15.6) 54 (84.4) Body fluid 13 (08.6) 139 (91.4) Blood 52 (21.0) 196 (79.0) Tissue 10 (04.7) 204 (95.3) Pathogens detected by RT-PCR 0.001 CMV 06 (26.1) 17 (73.9) HBV 43 (28.5) 108 (71.5) HCV 07 (28.0) 18 (72.0) HPV 00 (00.0) 36 (100.0) HSV 01 (05.3) 18 (94.7) Mycobacterium 33 (06.4) 486 (93.6) RPP 10 (76.9) 03 (23.1) Sources of patients 0.078 IPD 20 (17.9) 92 (82.1) OPD 80 (11.9) 594 (88.1) Male patients exhibited higher odds of test positivity than females (aOR 1.64, 95% CI 1.07–2.50; P < 0.05). Compared to blood samples, body fluid and tissue samples showed 65% (aOR 0.35, 95% CI 0.18–0.67; P < 0.05) and 82% (aOR 0.18, 95% CI 0.09–0.37; P < 0.01) lower odds of positivity respectively. Combined HPV/HSV testing had 95% lower odds (aOR 0.05, 95% CI 0.01–0.47; P < 0.05), and Mycobacterial testing showed 81% lower odds (aOR 0.19, 95% CI 0.07–0.52; P < 0.01) versus CMV. Conversely, respiratory pathogen panel (RPP) detection had 9.4-fold higher odds (aOR 9.44, 95% CI 1.92–46.35; P < 0.05) than CMV (Table 4 ). Table 4 Binary logistic regression analysis of gender, sample types, and target pathogens associated with test positivity (n = 786) Indicators aOR (95% CI) p-value Gender Female Ref. Male 1.64 (1.07, 2.5) 0.023 Sample group Blood Ref. Sputum 0.54 (0.22, 1.34) 0.182 Pus 0.44 (0.15, 1.31) 0.140 Urine 1.11 (0.39, 3.15) 0.846 Swab 0.70 (0.33, 1.46) 0.341 Body fluid 0.35 (0.18, 0.67) 0.002 Tissue 0.18 (0.09, 0.37) 0.000 Tests for CMV Ref. HBV 1.13 (0.42, 3.05) 0.812 HCV 1.10 (0.31, 3.95) 0.882 HPV/HSV 0.05 (0.01, 0.47) 0.008 Mycobacteria 0.19 (0.07, 0.52) 0.001 RPP 9.44 (1.92, 46.35) 0.006 Analysis of samples for Mycobacterial detection revealed both Mycobacterium tuberculosis (MTB) and Nontuberculous mycobacteria (NTM). Notably, NTM exhibited a higher detection rate (3.5%) than MTB (2.9%) (Fig. 2 ). The pie chart illustrates the proportions of Mycobacterium tuberculosis (MTB; 2.9%, orange), nontuberculous mycobacteria (NTM; 3.5%, blue), and mycobacterium-negative samples (93.6%, gray). However, MTB and NTM positivity rates in clinical samples were comparatively low in our private hospital setting (Fig. 2 ). Discussion In this study, infectious disease prevalence was highest among the elderly (≥ 60 years). Older adults generally experience elevated rates of both communicable and non-communicable diseases. A study in Pakistan similarly reported that ~ 39% of communicable disease mortality among older age groups was attributable to tuberculosis, diarrhea, cholera, and hepatitis [ 19 ]. In this study, patients aged ≥ 60 years accounted for 28.4% of samples submitted for RT-PCR testing, underscoring the substantial infectious disease burden in this group. These findings align with Pakistani data reporting high communicable disease mortality among older adults. Outpatient department (OPD) samples comprised 85.8% of submissions, indicating a strong patient preference for ambulatory care over inpatient (IPD) services for infectious diseases. This pattern aligns with typical trends across Bangladeshi public and private facilities. A nationwide analysis (2017–2021) similarly showed OPD visits consistently exceeding IPD admissions, reflecting broader reliance on outpatient management [ 20 ]. Blood, tissue, body fluids, swabs, sputum, and urine were the predominant clinical samples submitted for RT-PCR testing, consistent with findings from prior reviews documenting similar specimen types for molecular diagnostics [ 21 ]. No stool samples were analyzed, likely reflecting low demand in this corporate hospital setting and availability of alternative diagnostics. HBV and Mycobacterial testing predominated (85.2% combined), consistent with patterns reported in prior studies [ 22 , 23 ]. In our study, the overall pathogen detection rate by RT-PCR was 12.7%. This rate likely reflects multiple interdependent factors, including clinical presentation, physician expertise, sample collection/processing quality, laboratory detection proficiency, and result interpretation. Beyond facility reputation and diagnostic standards, cost-effectiveness considerations critically influence optimal detection rates, balancing institutional efficiency with patient access to care. A pathogen detection rate > 10% is generally considered optimal for clinical diagnostics, indicating effective screening and reliable protocols. Our finding aligns with a study reporting an 11% detection rate across 11 common pathogens using RT-PCR [ 24 ]. This study targeted six specific pathogens and one pathogen panel across seven sample types. Mycobacteria predominated (66.0% of samples), followed by HBV (19.2%). Prior research similarly emphasizes frequent RT-PCR use for HBV and MTB detection, reflecting consistent priorities in molecular diagnostics [ 25 – 28 ]. In this study, females provided more samples, yet males showed a higher pathogen detection rate. This may suggest initial clinical assessments underestimated symptoms in females. Interestingly, other studies report the opposite trend, with higher positivity rates in females [ 29 ]. These discrepancies in pathogen detection rates between genders arise from multiple factors. Immune responses differ, with variations in antibody production or inflammation patterns. Health-seeking behaviors also play a role, as females often seek care earlier despite milder symptoms. Physician suspicion may be lower for certain infections in females due to atypical presentations. Biological factors, including hormonal influences on pathogen replication or clearance, further contribute to this variability. These elements underscore the need for gender-specific diagnostic approaches in infectious disease management. In this study, most samples originated from body fluids, blood, and tissue, with blood exhibiting the highest pathogen detection rate. Less common samples like urine and swabs also showed elevated detection rates relative to sputum and pus. While blood, urine, and swabs may appear preferable for broad pathogen detection, specimen choice remains highly pathogen-specific due to varying microbial habitats in the body. RT-PCR showed strong performance for respiratory pathogen panels, HBV, HCV, and CMV detection. Success likely stemmed from precise symptom evaluation, suitable sample choices, and meticulous processing. Despite Bangladesh's focus on cervical cancer prevention, zero HPV detections highlight a training gap for physicians in recognizing HPV signs and making timely referrals to boost early diagnosis. In this study on Mycobacteria, NTM detection rates unexpectedly surpassed those of MTB. This trend signals shifting epidemiology and enhanced diagnostics, including greater clinician awareness and RT-PCR's improved NTM sensitivity. Contributing factors likely include rising environmental exposures, an aging population, and more immunocompromised patients, all amplifying NTM prevalence [ 30 ]. NTMs are increasingly significant pathogens worldwide, particularly in regions with successful TB control and rising NTM cases. National Tuberculosis Programs rely on Xpert MTB/RIF, culture, imaging, and related tests to detect MTB, NTM, and resistance. However, NTPs often underdetect and fail to report NTM cases to WHO, masking true prevalence [ 10 ]. A Chinese study of 717 low-bacterial-load patients reported 24% MTB and 2% NTM detections. It highlighted the GeneXpert's limited 68% sensitivity for rifampicin resistance detection. Previously treated cases faced elevated false-positive risks, underscoring diagnostic challenges in such scenarios [ 31 ]. RT-PCR may offer advantages over GeneXpert in certain contexts, particularly for nuanced NTM detection. Both Bangladesh's NTP and WHO often neglect NTM documentation and reporting, underestimating its burden. Although MTB spreads airborne while NTM is primarily waterborne, their mimicking symptoms may cause misdiagnosis and suboptimal treatment [ 32 ]. NTM infections demand distinct treatment regimens compared to MTB, involving specific antibiotics like macrolides and ethambutol. Accurate differentiation is vital to avert ineffective therapy, drug toxicity, and poor outcomes. Strengthening disease surveillance, physician education on modern diagnostics and protocols, follow-up monitoring, and systematic reporting might enhance patient care and reduce morbidity [ 33 – 35 ]. This study relied on secondary data from a single hospital, limiting comparative analyses. It precluded evaluation of socio-demographic associations and constrained pathogen diversity. Consequently, results lack generalizability and warrant cautious interpretation within these constraints. Despite its advantages, RT-PCR carries risks of lab errors in sample collection, nucleic acid extraction, and result interpretation, potentially undermining accuracy. Rigorous quality control, staff training, and strict adherence to standard operating procedures (SOPs) are essential to maximize its benefits in clinical settings. The Clinical and Laboratory Standard Institute (CLSI) guidelines emphasize sample integrity through uncontaminated collection, proper blood techniques, accurate labeling, and integration of physician-assessed clinical signs, all critical for reliable RT-PCR performance [ 36 , 37 ]. Despite its limitations, this study effectively distinguished multiple pathogens across diverse sample types using RT-PCR. These results highlight the importance of selecting appropriate specimens and employing thorough diagnostic strategies to improve detection accuracy and inform clinical decisions in comparable hospital environments. Conclusions RT-PCR is a reliable and versatile diagnostic tool for identifying infectious pathogens, aiding timely and evidence-based treatment decisions. Selecting appropriate specimen types and aligning clinical skills with underlying diseases can enhance positivity rates while reducing diagnostic costs. RT-PCR holds substantial untapped potential for optimization in Bangladesh, particularly in infectious disease diagnostics. The National Tuberculosis Program (NTP) should prioritize efforts to enhance detection of missed NTM cases, thereby mitigating misdiagnosis risks, preventing inappropriate treatment, and improving national public health outcomes. Furthermore, the WHO Global Tuberculosis Report should address NTM to heighten global policy awareness. Highlighting NTM would guide policymakers toward implementing comprehensive strategies for its detection and management. Declarations Author Contributions Md. Abu Sayem and Md. Akram Hossain conceptualized the study. Md. Abu Sayem acquired, cleaned, coded, and analyzed the data; drafted the original manuscript. All authors (Md. Abu Sayem, Md. Akram Hossain, Md. Abdus Salam, Ayesha Ahmed Khan, Sourav Nath, Mohammad Amir Hossain, Md. Ibrahim Adham, and Md. Ahsanul Haque) reviewed and edited the manuscript, including methods and data analysis. Md. Abu Sayem revised and finalized the final version. Acknowledgement: We gratefully acknowledge the hospital authorities for providing support and necessary approvals. Funding: There was no funding from any source for this study. Consent for publication : All authors have approved the final version of the manuscript and consented to its submission and publication. Conflicts of interest: The authors declare that they have no conflict of interest. Data availability: Data are available within the manuscript and with corresponding author. Ethics approval and consent to participate The research utilized fully anonymized, publicly available secondary data from Apollo Imperial Hospital Ltd., Chattogram, Bangladesh in accordance with Helsinki declaration. 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Park J, Kwak N, Chae JC, Yoon EJ, Jeong SH. A Two-Step Real-Time PCR Method To Identify Mycobacterium tuberculosis Infections and Six Dominant Nontuberculous Mycobacterial Infections from Clinical Specimens. Microbiol Spectr. 2023;11:e0160623. https://doi.org/10.1128/spectrum.01606-23 . Kumar S, Wang L, Fan J, Kraft A, Bose ME, Tiwari S, et al. Detection of 11 common viral and bacterial pathogens causing community-acquired pneumonia or sepsis in asymptomatic patients by using a multiplex reverse transcription-PCR assay with manual (enzyme hybridization) or automated (electronic microarray) detection. J Clin Microbiol. 2008;46:3063–72. https://doi.org/10.1128/JCM.00625-08 . Tajik Z, Keyvani H, Bokharaei-Salim F, Zolfaghari MR, Fakhim S, Keshvari M, Alavian SM. Detection of Hepatitis B Virus Covalently Closed Circular DNA in the Plasma of Iranian HBeAg-Negative Patients With Chronic Hepatitis B. Hepat Mon. 2015;15:e30790. https://doi.org/10.5812/hepatmon.30790 . Alkhateeb ZD, Maleek MI, Faraj A. Applying RT-PCR standard detection procedures of HBV and HCV in Wasit blood bank. Int J Health Sci. 2022;6:1668–79. https://doi.org/10.53730/ijhs.v6nS5.9676 . Parwati I, Chaidir L, Yunus M, Montain MM, Budhiarko D, Selasih SF, et al. Evaluation of a real-time PCR assay performance to detect Mycobacterium tuberculosis , rifampicin, and isoniazid resistance in sputum specimens: a multicenter study in two major cities of Indonesia. Front Microbiol. 2024;15:1372647. https://doi.org/10.3389/fmicb.2024.1372647 . Ahmadi Ghezeldasht S, Mosavat A, Soleimanpour S, Rezaee SA, Derakhshan M. Screening of tuberculosis suspected subjects using real-time PCR, TaqMan method; Northeastern Iran. J Infect Public Health. 2025;18:102932. https://doi.org/10.1016/j.jiph.2025.102932 . Shin S. Epidemiologic Characteristics of 1.4 Million Multiplex PCR Tests for 12 Urogenital and Sexually Transmitted Infection Pathogens in Korea (2021–2024). Pathogens 2025;14: 1073. https://doi.org/10.3390/pathogens14111073 Pereira AC, Ramos B, Reis AC, Cunha MV. Non-Tuberculous Mycobacteria: Molecular and Physiological Bases of Virulence and Adaptation to Ecological Niches. Microorganisms. 2020;8:1380. https://doi.org/10.3390/microorganisms8091380 . Zeng X, Hu P, Tan X, et al. Evaluation of Xpert MTB/ RIF assay accuracy for diagnosing TB and detecting rifampin resistance in patients with very low bacterial loads results: a retrospective study in Hunan, China. BMC Infect Dis. 2026. https://doi.org/10.1186/s12879-026-12933-3 . Ahasan HAMN, Reza IB, Nobi MA. Nontuberculous Mycobacterial Infection: An Achilles heel for Clinician. Bangladesh J Med. 2025;36:15–8. https://doi.org/10.3329/bjm.v36i1.76721 . Maleki MR, Moaddab SR. The growing impact of nontuberculous mycobacteria: A multidisciplinary review of ecology, pathogenesis, diagnosis, and treatment. Infect Med (Beijing). 2025;4:100203. https://doi.org/10.1016/j.imj.2025.100203 . Loebinger MR, van der Laan R, Obradovic M, van Ingen J. Global survey of physician testing practices for nontuberculous mycobacteria. ERJ Open Res. 2023;9:00737–2022. https://doi.org/10.1183/23120541.00737-2022 . Mejia-Chew C, Chavez MA, Lian M, McKee A, Garrett L, Bailey TC, Spec A, Agarwal M, Turabelidze G. Spatial Epidemiologic Analysis and Risk Factors for Nontuberculous Mycobacteria Infections, Missouri, USA, 2008–2019. Emerg Infect Dis. 2023;29:1540–6. https://doi.org/10.3201/eid2908.230378 . Almotairi WA, Alrashidi MN, Alqarni MA, Alsubaie RI, Alshahrani AM, Almuaybid RA, et al. Analyzing the impact of blood collection errors on patient safety and clinical outcomes⁠. Int J Com Med Public Health. 2025;12:1123–7. https://doi.org/10.18203/2394-6040.ijcmph20250220 . Yurttaş TT, Dikme Ö, Akdağ B, Kızıldağ F, Dikme Ö. Concordance between Patient and Physician Predictions of PCR Results and Predictive Capacity of Presenting Complaints in Suspected Infectious Diseases. Istanbul Med J. 2023;24:340–4. https://doi.org/10.4274/imj.galenos.2023.03604 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 02 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9251620","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619188969,"identity":"29cf9888-d8cf-4d5f-b41c-96b935c81446","order_by":0,"name":"Md. Abu Sayem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYNACg/9ybMzsBx8wMBwgVksFszE/e0+yAQlazjAnzuw5YCZBlBb59jOGj262sTFuuJGQVs1Tc0eOn4H54aMbeLQYnMkxNs5t42E2uJF47DbPsWfGkg1sxsY5+LQw5JhJ57ZJsBkAbbnNw3Y4ccMBHjZpfFrk+9+Y/85tM+ABajEr5vlHhBaGGzlmzDlnEiQkgd5n5m0jQovBjWfF0jkVBwxAgSw5t++wsWQzAb/I9ydv/JxjcKC+DRiVH958OyzHz9788DFehzFwGMCZTDwgkhmvchBgfwBnMv4gqHoUjIJRMApGIgAAq99Q277Mtl0AAAAASUVORK5CYII=","orcid":"","institution":"University of Rajshahi","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Abu","lastName":"Sayem","suffix":""},{"id":619188970,"identity":"0d45b3e0-2e35-4213-b097-01675ad90ced","order_by":1,"name":"Md. Akram Hossain","email":"","orcid":"","institution":"Apollo Imperial Hospitals Ltd","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Akram","lastName":"Hossain","suffix":""},{"id":619188971,"identity":"018dbb6d-d83d-4a02-abac-d525800de51a","order_by":2,"name":"Md. Abdus Salam","email":"","orcid":"","institution":"Rajshahi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Abdus","lastName":"Salam","suffix":""},{"id":619188972,"identity":"fd4d607b-f3a5-4402-b5d0-afe41be67966","order_by":3,"name":"Ayesha Ahmed Khan","email":"","orcid":"","institution":"Apollo Imperial Hospitals Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"Ahmed","lastName":"Khan","suffix":""},{"id":619188973,"identity":"2e66ad45-f0a5-4cfe-a538-f2dabc85ca36","order_by":4,"name":"Sourav Nath","email":"","orcid":"","institution":"Apollo Imperial Hospitals Ltd","correspondingAuthor":false,"prefix":"","firstName":"Sourav","middleName":"","lastName":"Nath","suffix":""},{"id":619188974,"identity":"861979c5-7419-4d15-b1aa-2edb2f0ebd26","order_by":5,"name":"Mohammad Amir Hossain","email":"","orcid":"","institution":"Evercare Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Amir","lastName":"Hossain","suffix":""},{"id":619188975,"identity":"282ea115-edb2-4e28-833c-d01d36dec56f","order_by":6,"name":"Md. Ibrahim Adham","email":"","orcid":"","institution":"National Heart Foundation Hospital \u0026 Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Ibrahim","lastName":"Adham","suffix":""},{"id":619188976,"identity":"919b154e-884b-4e58-8e95-8b7c2f1d657e","order_by":7,"name":"Md. Ahshanul Haque","email":"","orcid":"","institution":"International Centre for Diarrhoeal Disease Research","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Ahshanul","lastName":"Haque","suffix":""}],"badges":[],"createdAt":"2026-03-28 09:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9251620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9251620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106535094,"identity":"b55c2342-ba4c-4570-9fa2-0e39644ca360","added_by":"auto","created_at":"2026-04-09 15:07:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35132,"visible":true,"origin":"","legend":"\u003cp\u003ePathogens detection rate by RT-PCR at Apollo Imperial Hospitals Ltd.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9251620/v1/2e023e09b42275c3c4563471.png"},{"id":106535106,"identity":"fc46743c-6e0f-4948-86be-416f8e70fbff","added_by":"auto","created_at":"2026-04-09 15:07:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65433,"visible":true,"origin":"","legend":"\u003cp\u003eDetection rate of MTB and NTM at Apollo Imperial Hospitals Ltd. (n=519).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9251620/v1/b31ae5e3004a9205ea2b1353.png"},{"id":106535131,"identity":"1d0d40a8-3725-415c-8f66-4fa90f94e04e","added_by":"auto","created_at":"2026-04-09 15:07:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":945497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9251620/v1/0283b461-f002-40e2-a3c7-770f21409474.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"RT-PCR-based diagnosis of infectious diseases with special reference to non- tuberculous mycobacteria in a corporate hospital in Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, infectious diseases remain a leading cause of morbidity and mortality, responsible for approximately one-quarter of annual deaths. Recent years have seen resurgences of respiratory infections (e.g., pneumonia), the COVID-19 pandemic, and outbreaks of vector-borne diseases like dengue [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Enteric infections causing diarrheal diseases are major contributors to childhood mortality, with rotavirus, \u003cem\u003eShigella\u003c/em\u003e, and \u003cem\u003eCampylobacter\u003c/em\u003e ranking among the leading pathogens. In 2019, rotavirus accounted for 19% of global diarrheal deaths in children, followed by shigellosis and campylobacteriosis at 12% each [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Bloodstream infections caused nearly 3\u0026nbsp;million deaths globally, with ~\u0026thinsp;26% attributable to gram-negative bacteria [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Infectious diseases are more prevalent in low- and middle-income countries (LMICs) due to prevailing conducive factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Bangladesh as one of the LMICs is facing a complex, evolving infectious disease burden, dominated by recurrent dengue outbreaks, acute respiratory infections, and diarrheal diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In 2025, Bangladesh reported over 102,000 dengue cases and 412 deaths, marking a severe outbreak that extended beyond urban areas and overwhelmed healthcare resources [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Bangladesh rank 7th among 30 high tuberculosis burden countries globally [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nontuberculous mycobacteria (NTM) pose an additional critical challenge worldwide, including in Bangladesh [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Tropical climate of Bangladesh also favors the persistence of malaria, lymphatic filariasis, helminthiases, viral hepatitis, diarrheal diseases, and cholera [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Accurate and timely identification of infections is crucial for reducing morbidity, costs, enabling prompt treatment, and informing public health responses.\u003c/p\u003e \u003cp\u003eMolecular diagnostics have transformed infectious disease detection worldwide, with real-time reverse transcription polymerase chain reaction (RT-PCR) established as the gold standard for pathogen identification in clinical settings [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Bangladesh, both public and private hospitals expanded molecular laboratory capacities during large-scale outbreaks, notably the COVID-19 pandemic. RT-PCR offers superior sensitivity, specificity, and precision, making it indispensable for confirming infections across diverse pathogens and informing clinical decision-making and patient management [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, RT-PCR yields consistent, reliable results, enabling more accurate, efficient, and cost-effective detection and management of infectious diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite advances in molecular diagnostics, most Bangladeshi healthcare facilities continue to rely on conventional microbiological and serological methods, which suffer from low sensitivity, prolonged turnaround times, and limited ability to detect emerging pathogens [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. RT-PCR adoption has markedly enhanced diagnostic precision by detecting specific genetic sequences at low concentrations, proving invaluable for early or atypical infections. It identifies diverse agents including viruses, fungi, \u003cem\u003eMycoplasma\u003c/em\u003e, \u003cem\u003eChlamydia\u003c/em\u003e, atypical bacteria, and enteric pathogens such as \u003cem\u003eSalmonella typhi\u003c/em\u003e and \u003cem\u003eSalmonella paratyphi\u003c/em\u003e A [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. RT-PCR plays a crucial role in detecting diverse infectious diseases, including tuberculosis, HIV/AIDS, and pathogens responsible for sporadic, endemic, epidemic, and pandemic outbreaks.\u003c/p\u003e \u003cp\u003eApollo Imperial Hospitals Ltd., a leading private healthcare facility in Chattogram, Bangladesh, has been utilizing RT-PCR since its inception in 2022. The hospital maintains comprehensive records of patient socio-demographic, clinical, and laboratory data to monitor treatment outcomes and laboratory performance. This study analyzed these records to correlate patient characteristics with RT-PCR-confirmed infectious diseases, identify key challenges, and provide policy recommendations for optimization.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy settings\u003c/h2\u003e \u003cp\u003eThis retrospective study utilized secondary data from Apollo Imperial Hospitals Ltd., a 120-bed facility in Chattogram, Bangladesh, equipped with inpatient (IPD), outpatient (OPD), and emergency departments. The molecular biology laboratory, a key diagnostic unit, provided RT-PCR data for infectious diseases. Duty physicians referred IPD and OPD patients for laboratory workups, including RT-PCR when indicated. Test results were delivered to patients and routinely documented in the hospital's medical records system. Data collection was approved by the appropriate authorities. Notably, diarrheal pathogen panels, encephalitis/meningitis panels, genitourinary panels, dengue, malaria, and other common pathogens were not assessed by RT-PCR due to low demand and availability of alternatives such as culture or rapid diagnostic tests (RDTs).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSamples\u003c/h3\u003e\n\u003cp\u003eData on molecular diagnostics for infectious diseases from June 2019 to July 2024 were retrospectively collected from the hospital's molecular biology laboratory. A total of 810 anonymized patient records were extracted; after excluding of incomplete entries, 786 samples data were used for analysis. Samples were categorized as follows: sputum, pus, urine; the cervical swab, wound, nasal, nasopharyngeal, and penile swabs were grouped as \"swab\" (n\u0026thinsp;=\u0026thinsp;64); plasma, serum, and whole blood as \"blood\" (n\u0026thinsp;=\u0026thinsp;248); pericardial, peritoneal, synovial, cerebrospinal, pleural, eye, gastric aspirate, mouth discharge, tracheal, bronchial, and other fluids as \"body fluid\" (n\u0026thinsp;=\u0026thinsp;152); and granuloma, colon, breast, terminal ileum, excised block, cervical lymph node, skin scraping, neck tissue, fallopian tube, endotracheal, alveolar, endometrial tissues, fine-needle aspirate cytology (FNAC) material, bone marrow, and other tissues as \"tissue\" (n\u0026thinsp;=\u0026thinsp;214).\u003c/p\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eAll RT-PCR-requested samples by duty physicians from inpatient (IPD) or outpatient (OPD) departments, regardless of patient age or gender, were included. Samples requested for conventional tests were excluded. The patients from emergency department usually being transferred to IPD immediately after managing critical condition. Hence, there was no sample from emergency department.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData were stored in Microsoft Excel and imported into SPSS (version 25.0) and Stata (version 14.0) for analysis. Entries were verified, cleaned, coded, and analyzed. Independent variables included age group, gender, patient source (IPD or OPD), sample type, and targeted infectious agent. The primary outcome was RT-PCR positivity. Associations were assessed using chi-square tests, and multivariable binary logistic regression model evaluated the independent factors influencing the outcome. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 786 patients, 53.7% were female with adults (18\u0026ndash;59.99 years) comprising the majority (58.7%), followed by elderly (\u0026ge;\u0026thinsp;60 years; 28.4%) and newborns to adolescents (13.0%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most samples originated from outpatient department (OPD) (85.8%). Blood (31.6%) and tissue (27.2%) were the most common sample types, followed by body fluids (19.3%), swabs (8.1%), sputum (6.1%), pus (4.8%), and urine (2.8%). Mycobacterial testing predominated (66.0%), followed by HBV (19.2%), with fewer tests for other pathogens: tissue (HCV 3.2%), HPV (4.6%), HSV (2.4%), CMV (2.9%), and RPP (1.7%) (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\u003eSocio-demographic characteristics of patients and details of clinical samples tested by RT-PCR (n\u0026thinsp;=\u0026thinsp;786)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0-17.99 years (Newborn to adolescents group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18-59.99 years (Adult age group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 years (Old age group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.3\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\u003e422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of patient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInpatient department (IPD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient department (OPD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical samples\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e06.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e04.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e08.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fluid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSamples tested for\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytomegalovirus (CMV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis B virus (HBV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis C virus (HCV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Papillomavirus (HPV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e04.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerpes Simplex Virus (HSV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycobacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory Pathogen Panel (RPP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01.7\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 \u003cp\u003eThe overall cumulative pathogen detection rate by RT-PCR was 12.7% across 786 clinical samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This low positivity likely reflects interlinked factors, including sample quality, physician-assessed clinical signs and symptoms, personnel training, and other factors.\u003c/p\u003e \u003cp\u003eMycobacteria dominated across most sample types, comprising 100% of sputum (n\u0026thinsp;=\u0026thinsp;48), pus (n\u0026thinsp;=\u0026thinsp;38), and tissue (n\u0026thinsp;=\u0026thinsp;214) detections. Mycobacteria was tested among 87.5% of body fluids (n\u0026thinsp;=\u0026thinsp;133), 36.4% of urine (n\u0026thinsp;=\u0026thinsp;08), 25.4% of blood (n\u0026thinsp;=\u0026thinsp;63), and 23.4% of swabs (n\u0026thinsp;=\u0026thinsp;15) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HBV was prominent in blood (60.9%), while swabs showed diverse detections: HPV (56.3%), RPP (20.3%), and mycobacteria (23.4%). CMV and HCV were detected primarily in urine, body fluid and blood respectively; HSV mainly in body fluids. Overall, mycobacteria accounted for 66.0% of all detections (519/786), followed by HBV (19.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency distribution of pathogens detected by RT-PCR across clinical sample types (n\u0026thinsp;=\u0026thinsp;786); values represent counts (row percentages)\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMycobact.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRPP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e08 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fluid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e02 (01.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e133 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e07 (02.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e02 (00.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e214 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (02.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (03.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36 (04.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19 (02.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e519 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (01.7)\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\u003eN.B. CMV: Cytomegalovirus, HBV: Hepatitis B virus, HCV: Hepatitis C virus, HPV: Human Papillomavirus, HSV: Herpes Simplex Virus, Mycobact.: Mycobacterium, RPP: Respiratory Pathogen Panel.\u003c/p\u003e \u003cp\u003ePathogen detection rates varied significantly by gender (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with males showing higher positivity (15.7%) than females (10.2%). Detection also differed markedly across sample types (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the highest rates in urine (22.7%), blood (21.0%), and swabs (15.6%), moderate rates in sputum (12.5%) and pus (10.5%), and lowest in body fluids (8.6%) and tissue (4.7%). Among pathogens, respiratory pathogen panel (RPP) exhibited the highest detection rate (76.9%), followed by HBV (28.5%), HCV (28.0%), and CMV (26.1%). HPV testing was negative across all 36 samples, HSV showed low positivity (5.3%), and Mycobacteria had the lowest rate (6.4% from 519 samples) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between patient characteristics, sample types, pathogens, and RT-PCR positivity rates (n\u0026thinsp;=\u0026thinsp;786).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTests results by RT-PCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetected (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot detected (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNewborn to adolescent (0-17.99 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (09.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdult (18-59.99 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405 (87.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOld (\u0026ge;\u0026thinsp;60 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (84.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307 (84.3)\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\u003e43 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379 (89.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical samples for pathogen detection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e06 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (87.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e04 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (89.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e05 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (77.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (84.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fluid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (08.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (91.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (79.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (04.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (95.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathogens detected by RT-PCR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e06 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (73.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (71.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e07 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (72.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e00 (00.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01 (05.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (94.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycobacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (06.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e486 (93.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03 (23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSources of patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (82.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e594 (88.1)\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\u003eMale patients exhibited higher odds of test positivity than females (aOR 1.64, 95% CI 1.07\u0026ndash;2.50; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to blood samples, body fluid and tissue samples showed 65% (aOR 0.35, 95% CI 0.18\u0026ndash;0.67; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and 82% (aOR 0.18, 95% CI 0.09\u0026ndash;0.37; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) lower odds of positivity respectively. Combined HPV/HSV testing had 95% lower odds (aOR 0.05, 95% CI 0.01\u0026ndash;0.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Mycobacterial testing showed 81% lower odds (aOR 0.19, 95% CI 0.07\u0026ndash;0.52; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) versus CMV. Conversely, respiratory pathogen panel (RPP) detection had 9.4-fold higher odds (aOR 9.44, 95% CI 1.92\u0026ndash;46.35; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than CMV (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logistic regression analysis of gender, sample types, and target pathogens associated with test positivity (n\u0026thinsp;=\u0026thinsp;786)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 (1.07, 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.22, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.15, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.39, 3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.33, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fluid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35 (0.18, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.09, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTests for\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (0.42, 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.31, 3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV/HSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.01, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.07, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.44 (1.92, 46.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\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\u003eAnalysis of samples for Mycobacterial detection revealed both \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) and Nontuberculous mycobacteria (NTM). Notably, NTM exhibited a higher detection rate (3.5%) than MTB (2.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe pie chart illustrates the proportions of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB; 2.9%, orange), nontuberculous mycobacteria (NTM; 3.5%, blue), and mycobacterium-negative samples (93.6%, gray). However, MTB and NTM positivity rates in clinical samples were comparatively low in our private hospital setting (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, infectious disease prevalence was highest among the elderly (\u0026ge;\u0026thinsp;60 years). Older adults generally experience elevated rates of both communicable and non-communicable diseases. A study in Pakistan similarly reported that ~\u0026thinsp;39% of communicable disease mortality among older age groups was attributable to tuberculosis, diarrhea, cholera, and hepatitis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this study, patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years accounted for 28.4% of samples submitted for RT-PCR testing, underscoring the substantial infectious disease burden in this group. These findings align with Pakistani data reporting high communicable disease mortality among older adults. Outpatient department (OPD) samples comprised 85.8% of submissions, indicating a strong patient preference for ambulatory care over inpatient (IPD) services for infectious diseases. This pattern aligns with typical trends across Bangladeshi public and private facilities. A nationwide analysis (2017\u0026ndash;2021) similarly showed OPD visits consistently exceeding IPD admissions, reflecting broader reliance on outpatient management [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Blood, tissue, body fluids, swabs, sputum, and urine were the predominant clinical samples submitted for RT-PCR testing, consistent with findings from prior reviews documenting similar specimen types for molecular diagnostics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. No stool samples were analyzed, likely reflecting low demand in this corporate hospital setting and availability of alternative diagnostics. HBV and Mycobacterial testing predominated (85.2% combined), consistent with patterns reported in prior studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the overall pathogen detection rate by RT-PCR was 12.7%. This rate likely reflects multiple interdependent factors, including clinical presentation, physician expertise, sample collection/processing quality, laboratory detection proficiency, and result interpretation. Beyond facility reputation and diagnostic standards, cost-effectiveness considerations critically influence optimal detection rates, balancing institutional efficiency with patient access to care. A pathogen detection rate\u0026thinsp;\u0026gt;\u0026thinsp;10% is generally considered optimal for clinical diagnostics, indicating effective screening and reliable protocols. Our finding aligns with a study reporting an 11% detection rate across 11 common pathogens using RT-PCR [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study targeted six specific pathogens and one pathogen panel across seven sample types. Mycobacteria predominated (66.0% of samples), followed by HBV (19.2%). Prior research similarly emphasizes frequent RT-PCR use for HBV and MTB detection, reflecting consistent priorities in molecular diagnostics [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, females provided more samples, yet males showed a higher pathogen detection rate. This may suggest initial clinical assessments underestimated symptoms in females. Interestingly, other studies report the opposite trend, with higher positivity rates in females [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These discrepancies in pathogen detection rates between genders arise from multiple factors. Immune responses differ, with variations in antibody production or inflammation patterns. Health-seeking behaviors also play a role, as females often seek care earlier despite milder symptoms. Physician suspicion may be lower for certain infections in females due to atypical presentations. Biological factors, including hormonal influences on pathogen replication or clearance, further contribute to this variability. These elements underscore the need for gender-specific diagnostic approaches in infectious disease management. In this study, most samples originated from body fluids, blood, and tissue, with blood exhibiting the highest pathogen detection rate. Less common samples like urine and swabs also showed elevated detection rates relative to sputum and pus. While blood, urine, and swabs may appear preferable for broad pathogen detection, specimen choice remains highly pathogen-specific due to varying microbial habitats in the body. RT-PCR showed strong performance for respiratory pathogen panels, HBV, HCV, and CMV detection. Success likely stemmed from precise symptom evaluation, suitable sample choices, and meticulous processing. Despite Bangladesh's focus on cervical cancer prevention, zero HPV detections highlight a training gap for physicians in recognizing HPV signs and making timely referrals to boost early diagnosis. In this study on Mycobacteria, NTM detection rates unexpectedly surpassed those of MTB. This trend signals shifting epidemiology and enhanced diagnostics, including greater clinician awareness and RT-PCR's improved NTM sensitivity. Contributing factors likely include rising environmental exposures, an aging population, and more immunocompromised patients, all amplifying NTM prevalence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. NTMs are increasingly significant pathogens worldwide, particularly in regions with successful TB control and rising NTM cases. National Tuberculosis Programs rely on Xpert MTB/RIF, culture, imaging, and related tests to detect MTB, NTM, and resistance. However, NTPs often underdetect and fail to report NTM cases to WHO, masking true prevalence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A Chinese study of 717 low-bacterial-load patients reported 24% MTB and 2% NTM detections. It highlighted the GeneXpert's limited 68% sensitivity for rifampicin resistance detection. Previously treated cases faced elevated false-positive risks, underscoring diagnostic challenges in such scenarios [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. RT-PCR may offer advantages over GeneXpert in certain contexts, particularly for nuanced NTM detection. Both Bangladesh's NTP and WHO often neglect NTM documentation and reporting, underestimating its burden. Although MTB spreads airborne while NTM is primarily waterborne, their mimicking symptoms may cause misdiagnosis and suboptimal treatment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. NTM infections demand distinct treatment regimens compared to MTB, involving specific antibiotics like macrolides and ethambutol. Accurate differentiation is vital to avert ineffective therapy, drug toxicity, and poor outcomes. Strengthening disease surveillance, physician education on modern diagnostics and protocols, follow-up monitoring, and systematic reporting might enhance patient care and reduce morbidity [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study relied on secondary data from a single hospital, limiting comparative analyses. It precluded evaluation of socio-demographic associations and constrained pathogen diversity. Consequently, results lack generalizability and warrant cautious interpretation within these constraints. Despite its advantages, RT-PCR carries risks of lab errors in sample collection, nucleic acid extraction, and result interpretation, potentially undermining accuracy. Rigorous quality control, staff training, and strict adherence to standard operating procedures (SOPs) are essential to maximize its benefits in clinical settings. The Clinical and Laboratory Standard Institute (CLSI) guidelines emphasize sample integrity through uncontaminated collection, proper blood techniques, accurate labeling, and integration of physician-assessed clinical signs, all critical for reliable RT-PCR performance [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Despite its limitations, this study effectively distinguished multiple pathogens across diverse sample types using RT-PCR. These results highlight the importance of selecting appropriate specimens and employing thorough diagnostic strategies to improve detection accuracy and inform clinical decisions in comparable hospital environments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eRT-PCR is a reliable and versatile diagnostic tool for identifying infectious pathogens, aiding timely and evidence-based treatment decisions. Selecting appropriate specimen types and aligning clinical skills with underlying diseases can enhance positivity rates while reducing diagnostic costs. RT-PCR holds substantial untapped potential for optimization in Bangladesh, particularly in infectious disease diagnostics. The National Tuberculosis Program (NTP) should prioritize efforts to enhance detection of missed NTM cases, thereby mitigating misdiagnosis risks, preventing inappropriate treatment, and improving national public health outcomes. Furthermore, the WHO Global Tuberculosis Report should address NTM to heighten global policy awareness. Highlighting NTM would guide policymakers toward implementing comprehensive strategies for its detection and management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMd. Abu Sayem and Md. Akram Hossain conceptualized the study. Md. Abu Sayem acquired, cleaned, coded, and analyzed the data; drafted the original manuscript. All authors (Md. Abu Sayem, Md. Akram Hossain, Md. Abdus Salam, Ayesha Ahmed Khan, Sourav Nath, Mohammad Amir Hossain, Md. Ibrahim Adham, and Md. Ahsanul Haque) reviewed and edited the manuscript, including methods and data analysis. Md. Abu Sayem revised and finalized the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eWe gratefully acknowledge the hospital authorities for providing support and necessary approvals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e There was no funding from any source for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: All authors have approved the final version of the manuscript and consented to its submission and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData are available within the manuscript and with corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research utilized fully anonymized, publicly available secondary data from Apollo Imperial Hospital Ltd., Chattogram, Bangladesh in accordance with Helsinki declaration. As no primary data collection or identifiable human subjects were involved, informed consent was not required. The study was approved by the \u0026ldquo;Institutional Review Board (IRB)\u0026rdquo; of the Apollo Imperial Hospitals Ltd., Chattogram (Ref.: AIHL/IRB/20250605, dated 18th June 2025). Data processing maintained strict confidentiality, complying with data protection standards including anonymization protocols to prevent re-identification. No biological materials or clinical interventions were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDai Y, Sheng K, Hu L. Diagnostic efficacy of targeted high-throughput sequencing for lower respiratory infection in preterm infants. 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Istanbul Med J. 2023;24:340\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4274/imj.galenos.2023.03604\u003c/span\u003e\u003cspan address=\"10.4274/imj.galenos.2023.03604\" 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-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"RT-PCR, Infectious disease diagnostics, Clinical samples, Nontuberculous mycobacteria, Mycobacterium tuberculosis, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-9251620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9251620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTimely detection, prevention, and response to infectious diseases are essential for enhancing quality of life, societal well-being, and reducing healthcare costs. Advanced diagnostics like reverse transcription polymerase chain reaction (RT-PCR) improve accuracy, mitigate suffering, limit transmission, and decrease mortality. This study evaluated utility of RT-PCR for detecting infectious agents, including Nontuberculous mycobacteria (NTM), to guide clinical decision-making.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed infectious disease data from Apollo Imperial Hospitals Ltd., Chattogram, Bangladesh from June 2019 to July 2024. Of 810 anonymized records extracted from the molecular biology laboratory, 786 samples with complete diagnostic information undergoing RT-PCR testing were included after excluding incomplete entries. As a hospital-based analysis, no eligible cases were overlooked beyond incomplete records.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 786 patients (mean age 38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3 years), 53.7% were female, and 87.1% were adults or elderly. Samples comprised blood (31.6%), tissue (27.2%), body fluids (19.3%), swabs (8.1%), sputum (6.1%), pus (4.8%), and urine (2.8%). Mycobacteria were detected in 6.4% of samples, including \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB; 2.9%) and nontuberculous mycobacteria (NTM; 3.5%). Overall RT-PCR positivity was 12.7%. Associations between positivity and socio-demographic/clinical factors (e.g., gender, sample type, pathogen) were analyzed, revealing higher NTM than MTB rates, a concern for policymakers, in particular National Tuberculosis Program (NTP).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRapid identification and treatment of infectious diseases are essential. NTM warrants particular attention due to symptom overlapping with tuberculosis. The NTP should prioritize accurate diagnostics and management to avert misdiagnosis and inappropriate therapy. Annual NTP and WHO reports should highlight NTM prevalence and challenges to inform policy.\u003c/p\u003e","manuscriptTitle":"RT-PCR-based diagnosis of infectious diseases with special reference to non- tuberculous mycobacteria in a corporate hospital in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 15:07:44","doi":"10.21203/rs.3.rs-9251620/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T13:50:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T09:44:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T07:14:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171513472095980549702276347616147291163","date":"2026-04-07T20:31:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301121174711799194944477539498364861608","date":"2026-04-05T05:27:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T16:20:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T08:37:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T12:39:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T12:38:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-03-28T09:49:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5981c12a-3ced-424e-a5a3-8a1f90800f97","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T13:50:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T09:44:41+00:00","index":30,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T16:23:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 15:07:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9251620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9251620","identity":"rs-9251620","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}