IL-4 and IL-10 as a diagnostic and predictive factor for the severity of Mycobacterium Tuberculosis Infection

IL-4 and IL-10 as a diagnostic and predictive factor for the severity of Mycobacterium Tuberculosis Infection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IL-4 and IL-10 as a diagnostic and predictive factor for the severity of Mycobacterium Tuberculosis Infection Rasha Eid Mohamed Dosoky, Ahmed Gad Taha, Mayar Mosaad Tawfik, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9168542/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Tuberculosis (TB) is a global health problem that claims millions of lives each year. Unfortunately, the current diagnostic markers and therapeutic options are not satisfactory. Understanding the immune pathogenesis provides insights into more reliable diagnostic and therapeutic options. T helper 2 cells' cytokines are known to inhibit cell-mediated immune response and adversely affect the progression of mycobacterial infections. Objectives This research aims to elucidate the ability of IL-4 and IL-10 serum levels to differentiate between latent and active TB infections and to serve as an indicator for the disease severity. Methods The study population consisted of eighty TB patients. Patients were classified into active TB infection group (40) and latent group (40) based on the presence of clinical symptoms, tuberculin test, sputum acid-fast bacilli and GeneXpert MTB/RIF testing. All patients were subjected to clinical examination, CT chest evaluation, IL-4 and IL-10 serum level measurement. Results IL-4 and IL-10 levels were significantly higher in the active TB than in latent TB group. ROC curve analysis, showed cut-off values of > 88 pg/mL for IL-4 and > 119 pg/mL for IL-10 in differentiating active from latent TB, with 100% sensitivity, specificity, positive predictive value, and negative predictive value. Serum levels of IL-4 and IL-10 correlated with bacterial load and disease severity. IL-4 levels were statistically significantly higher in patients with rifampicin-sensitive strains; however, the IL-10 levels were not. Conclusion IL-4 and IL-10 serum levels can differentiate between latent and active TB infections and perfectly correlate with the severity of the disease. IL-4 IL-10 Mycobacterium Tuberculosis T helper 2 TB Figures Figure 1 Figure 2 Introduction Tuberculosis (TB) is a global health problem that causes more than nine million new cases and a mortality of about two million each year. According to the World Health Organization (WHO), nearly 2 billion people or nearly one-third of the global population is latently infected with Mycobacterium tuberculosis , the etiological agent of TB [ 1 ]. Current Management for TB has challenges in diagnosis and management, including the challenge of distinguishing active TB from latent infection, the lack of prognostic markers and not only the need for lengthy and multiple antibiotics for treatment but also the risk of the emerging drug-resistant variants of Mycobacterium tuberculosis. In fact, almost all countries, irrespective of their socioeconomic status, are now under threat from multiple drug-resistant and extensively drug-resistant strains of M. tuberculosis [ 2 , 3 ]. Investigation of the immune pathogenesis of TB may provide a closer insight and probably the keys to improving the current management of TB. Immunity against Mycobacterium tuberculosis requires a balance between different components of adaptive immune responses to constrain bacterial replication and prevent the potentially damaging immune activation. Th cells include Th1 cells secrete interleukin-2 (IL-2), interferon-gamma (IFN-y) and lymphotoxins, whereas Th2 cells produce IL-4, IL-10, and IL-13 [ 4 ]. Th2 cells producing IL-4 and IL-10 are detrimental in the control of intracellular M. tuberculosis infection. Th2 cytokines suppress IFN-γ production by Th 1 cells thus inhibiting IFN-γ-mediated effects, including M1 macrophage activation. They also inhibit Th 1 -induced autophagy, reducing the intracellular degradation of Mycobacterium tuberculosis bacteria, antagonise the host defense and lead to the induction of fibrosis and cavitation, which compromise lung function in TB patients [ 5 ]. The present study aims to elucidate the ability of IL-4 and IL-10 serum levels to differentiate between latent and active TB infections and to serve as an indicator for the disease severity. Patients, methods and materials Study Population: This cross-sectional study included eighty TB patients. Patients were further classified into an active TB infection group (forty participants) and a latent group (forty participants) based on the presence of clinical symptoms, Mantoux test (tuberculin test), the presence of acid-fast bacilli, and GeneXpert MTB/RIF testing. Inclusion criteria According to the Tuberculosis Control Guidelines by the Egyptian Ministry of Health and Population, National Tuberculosis Control Program [6]. For active cases: In a participant aged> 18 years, an active case (including pulmonary and extra-pulmonary) was defined as at least one recent sputum specimen positive for acid-fast bacilli on microscopic examination and clinical symptoms . For latent cases: In a participant aged > 18 years, a latent case was defined as a state of persistent immune response as shown by having a tuberculin test of induration more than 15 mm without any clinical symptoms, radiological abnormality, or microbiological evidence . Exclusion criteria: We excluded patients with upper respiratory tract infections within the past 6 weeks, those who received antimicrobial therapy in the past week, or those who received corticosteroids, chemotherapy, or other immunosuppressants in the past 3 months. Patients with HIV, chronic liver diseases, chronic renal diseases, autoimmune diseases and known allergic diseases were also excluded from the study. All subjects in the study were subjected to the following: Clinical examination: Complete history taking and clinical examination were conducted for all participants. In the case of pulmonary TB, a patient can have crepitations and bronchial breath sounds, especially over the upper lobes or affected area, indicating cavity or consolidation. Signs of extrapulmonary TB are varied and can include Lymphadenopathy, Cutaneous lesions, Pleural effusion, Neurological deficit, Confusion, Coma, Chorioretinitis, and Vertebral collapse [7]. Detection of the TB bacilli in the smear: Ziehl-Neelsen staining was performed on the collected samples. Smears were graded on the scale; negative for Mycobacterium tuberculosis , scanty, + 1, +2, and + 3 [6]. The Mantoux Test (Tuberculin skin testing): The Mantoux test is a two-part test consisting of an intradermal injection of 0.1ml purified protein derivative and observing for induration 48-72 hours. The patient’s risk of exposure is taken into consideration when interpreting the result [8]. Because the BCG vaccine is compulsory in Egypt, a tuberculin test is considered positive in the latent group if only the induration exceeds 15mm after 48-72 hours [6]. GeneXpert MTB/RIF diagnostic system (Cepheid, Sunnyvale, CA, USA): One ml of unprocessed specimens was required for the assay. Then the sample reagent was added in a 2:1 ratio to the unprocessed specimen in a Falcon tube, then the tube was vortexed twice and incubated for 15-min room temperature. Subsequently, 2ml of the inactivated sample was transferred to the test cartridge by a sterile disposable pipette. The cartridge contains the reagents needed for DNA extraction and PCR amplification, and then the fluorescent probes automatically lead the assay. GeneXpert MTB/RIF is a semiquantitative test that reports the Mycobacterial Tuberculosis load depending on the cycle threshold (Ct). Therefore, four categories can be identified: high bacterial load when the Ct is 28. The specimen is considered negative when the bacteria cannot be detected [9]. Chest x-ray and CT grading Chest x-ray was done for all patients, those with abnormal findings where further evaluated and graded by Computed tomography (CT) Grade 1(Minimal): Mildly enlarged lymph nodes, Small nodular opacities in the upper lobes or superior segments of lower lobes, and no cavitation. Grade 2 (Moderate): Moderately enlarged lymph nodes with central low attenuation (caseation), Multiple nodular opacities, Minimal to moderate pleural effusion, and early bronchogenic spread Grade 3(Severe): Massively enlarged lymph nodes with caseation, Extensive nodular opacities with or without cavitation, Large pleural effusion, and significant bronchogenic spread [10]. IL-4 and IL-10 measurement: These interleukins were assayed in the serum samples of the active and latent patient groups using a human ELISA Kit (R&D Systems, USA), according to the manufacturer's guidelines. The assay range for IL-4 is 15.6 - 1,000 pg/mL, and the sensitivity is 8 pg/mL. The assay range for IL-10 is 0.8 – 50 pg/mL, and the sensitivity is 0.17 pg/mL Statistical Analysis: The collected data were revised, coded, and tabulated using Statistical Package for the Social Science (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.). Data were presented and suitable analysis was done according to the type of data obtained for each parameter. Results The mean age of the study participants was 45.15 ± 17.26 years, ranging from 20 to 74 years. All active patients report night sweats, night fever, and weight loss. All active TB patients (n=40) tested positive on smear microscopy, with a mean acid-fast bacilli (AFB) smear load of 21.45 ± 26.5, ranging from 5 to 95. Gene expert results showed that 55% (n=22) had high bacterial load, 37.5% (n=15) had moderate, and 7.5% (n=3) had low. The mean tuberculin skin test size was 15.43 ± 6.61 mm (range: 3–28). Rifampicin resistance was identified in 45% (n=18) of the cases, while 55% (n=22) were sensitive (table 1). Table 1: Diagnostic criteria for active group only. Active group (n= 40) N (%) Mean ± SD Range Smear Negative 0 (0%) Positive 40 (100%) Acid-Fast Bacilli (AFB) in smear 21.45 ± 26.5 (5 - 95) Gene expert Low 3 (7.5%) Moderate 15 (37.5%) High 22 (55%) Tuberculin test (mm) 15.43 ± 6.61 (3 - 28) Rifampicin Resistance Sensitive 22 (55%) Resist 18 (45%) The latent group of patients (n=40) were selected from individuals who were in close contact to proven patients and showed a positive tuberculin test of more than 15 mm induration with a wheal size of clinical symptoms, radiological abnormality, or microbiological evidence of TB infection Among the active group, 75% (n=30) had abnormal chest X-rays. CT grading showed 26.67% had minimal lesions, 40% moderate, and 33.33% severe. Pulmonary involvement was seen in 75%, while 25% had extrapulmonary manifestations where lesions were located in varied areas such as abdominal lymph nodes, brain, spine, breast and joints. (table 2). Table 2: Radiological data assessment for the active group. Active group (n= 40) N (%) Chest X-Ray No 10 (25%) Yes 30 (75%) CT Grading Minimal 8 (26.67%) Moderate 12 (40%) Severe 10 (33.33%) Pulmonary Involvement Pulmonary 30 (75%) Extrapulmonary 10 (25%) IL-4 and IL-10 levels were much higher in active TB with mean± SD (212.8 ± 78.27 and 307.6 ± 91.42 pg/mL) respectively, versus latent TB (74 ± 7.94 and 103.45 ± 8.13 pg/mL) respectively, with the difference between both cytokines shows highly significant p-value <0.001 (table 3). Table 3: IL-4 and IL-10 serum level difference between the two groups. Group Test of significance t - test Active Latent Mean ± SD Mean ± SD p-value Sig. IL4 (pg/ml) 212.8 ± 78.27 74 ± 7.94 <0.001** HS IL10 (pg/ml) 307.6 ± 91.42 103.45 ± 8.13 <0.001** HS **: highly significant, HS: significant There was a statistically significant and very strong positive correlation between IL-4 and IL-10 across both groups. The correlation was stronger in the active group (r=0.869),than the latent group (r=0.846), with p-values <0.001 (table 4, figure1). Table 4: Correlation between IL-4 and IL-10 in both groups Active group Latent group Spearman’s rho 0.869 0.846 p-value <0.001 <0.001 Sig. HS HS HS: highly significant Both IL-4 and IL-10 demonstrated perfect diagnostic performance with an area under the curve (AUC) of 1.00 (95% CI: 1.00–1.00, p-value 88 pg/mL for IL-4 and >119 pg/mL for IL-10, both markers achieved 100% sensitivity, specificity, positive predictive value (+PV), and negative predictive value (–PV) in differentiating active from latent TB (table 5, figure 2). Table 5: Diagnostic performance of IL-4 and IL-10. AUC 95% CI P value Sig. Cut-off value Sensitivity Specificity +PV -PV IL4 (pg/ml) 1.00 1.00 - 1.00 88 100% 100% 100% 100% IL10 (pg/ml) 1.00 1.00 - 1.00 119 100% 100% 100% 100% AUC: area under the curve, HS: significance, +PV: positive predictive value, - PV: negative predictive value. When correlating IL-4 and IL-10 levels with the gene expert grades. IL-4 was lowest in the low-grade group mean±SD (90.33 ± 1.53 pg/mL), peaking in the moderate group (246.2 ± 71.78), and slightly lower in the high-grade group (206.73 ± 70.47). IL-10 followed a similar trend: 156.67 ± 8.14 in low, 335.67 ± 78.92 in moderate, and 309.05 ± 86.85 in high-grade groups (p-values= 0.004 and 0.005), respectively Post-hoc analysis showed significance between low vs. moderate/high groups, but there was no statistically significant difference between moderate and high loaded (table 6). Table 6: Comparison between different grades of gene expert and IL-4 & 10. Active group (n= 40) Gene expert One Way ANOVA Low (n= 3) Moderate (n= 15) High (n= 22) Mean ± SD Mean ± SD Mean ± SD p-value Sig. IL4 (pg/ml) 90.33 ± 1.53 246.2 ± 71.78 206.73 ± 70.47 0.004* S IL10 (pg/ml) 156.67 ± 8.14 335.67 ± 78.92 309.05 ± 86.85 0.005* S *Post-hoc LSD test was significant between Low Vs. (Moderate & High groups), S: significant There was a highly significant increase in IL-4 and IL-10 levels across CT severity grades (both p-values <0.001). IL-4 rose from 105.13 ± 12.61 pg/mL in minimal grade to 201.58 ± 16.89 in moderate and 310.9 ± 30.28 in severe cases. Similarly, IL-10 increased from 166.25 ± 11.9 to 328.67 ± 41.06 and 396 ± 25.71. Post-hoc tests confirmed significant differences between all groups, suggesting strong correlation with disease severity (table 7). Table 7: Comparison between different grades of CT and IL-4 & 10. Active group (n= 30) CT Grading One Way ANOVA Minimal (n= 8) Moderate (n= 12) Severe (n= 10) Mean ± SD Mean ± SD Mean ± SD p-value Sig. IL4 (pg/ml) 105.13 ± 12.61 201.58 ± 16.89 310.9 ± 30.28 <0.001* HS IL10 (pg/ml) 166.25 ± 11.9 328.67 ± 41.06 396 ± 25.71 <0.001* HS *Post-hoc LSD test was significant between all groups, HS: highly significant IL-4 levels were statistically significantly higher in patients with rifampicin-sensitive strains (238.18 ± 83.76 pg/mL) compared to those with rifampicin-resistant strains (181.78 ± 59.46 pg/mL), with a statistically significant p-value of 0.021. However, IL-10 levels were not significantly different between the two subgroups; the sensitive group had a mean IL-10 of 323 ± 91.72 pg/mL, while the resistant group had 288.78 ± 89.99 pg/mL (p-value= 0.244) (table 8). Table 8: Relation between Rifampicin resistance and IL-4 & 10. Active group (n= 40) Rifampicin resistance Student t-test Sensitive (n= 22) Resist (n= 18) Mean ± SD Mean ± SD p-value Sig. IL4 (pg/ml) 238.18 ± 83.76 181.78 ± 59.46 0.021 S IL10 (pg/ml) 323 ± 91.72 288.78 ± 89.99 0.244 NS S: significant, NS: non-significant Discussion In this study, all individuals in the active TB group presented with classical symptoms, including night sweats, night fever, and weight loss, while none of the latent TB participants exhibited these features. Previous studies concluded that symptomatology remains one of the most distinguishing clinical criteria separating active from latent TB, particularly when diagnostic biomarkers are unavailable or inconclusive [ 11 , 12 ]. However, others noted that symptoms such as weight loss and fever are not universal in active TB, especially in elderly or immunocompromised patients, suggesting that relying solely on symptom presence may underestimate atypical cases [ 13 ]. In the present study, all active TB patients (100%) demonstrated positive smear results with a mean AFB smear count of 21.45 ± 26.5. This finding strongly confirms the high diagnostic yield of direct smear microscopy in bacteriologically active TB, especially when bacillary load is high. This aligns with Jeong et al., 2024 who concluded that smear microscopy, though limited in sensitivity compared to molecular tools, maintains excellent specificity and remains pivotal in resource-limited settings when bacterial burden is high [ 14 ]. Additionally, Krivošová et al., 2024 correlate with disease severity and bacterial burden with AFB smear count [ 15 ]. On the other hand, others reported lower smear positivity among pediatric TB cases, highlighting the diagnostic challenge in smear-negative but culture-positive patients, particularly in extrapulmonary TB or HIV co-infection. This discrepancy emphasizes the need for adjunct diagnostic tools in certain populations [ 11 ]. The GeneXpert MTB/RIF assay performed in all cases, revealed 55% with high bacterial load, 37.5% moderate, and 7.5% low bacterial load, none of our active patients showed very low count or presented with undetectable bacilli. GeneXpert has been endorsed as a frontline diagnostic tool for its rapid turnaround and capacity to detect rifampicin resistance simultaneously [ 16 , 17 , 18 ]. In disagreement, Khan et al., 2024 criticized GeneXpert for its limited sensitivity in detecting low bacterial loads, particularly in extrapulmonary samples. They suggest combining it with culture or histopathology in such contexts [ 19 ]. Likewise, Massou et al., 2021 reported variable sensitivity based on sample timing (morning vs. spot specimens), raising practical concerns regarding sample collection [ 20 ]. In this study, the Tuberculin Skin Test (TST) in the active group, showed a mean induration size of 15.43 ± 6.61 mm, reflecting robust immune reactivity among patients. This agrees with other findings where larger TST reactions were common in confirmed active TB cases due to heightened Th1 responses. However, the TST’s specificity is limited due to cross-reactivity with BCG vaccination and environmental mycobacteria. This is particularly relevant in countries like Egypt with high BCG coverage, as noted by Awad et al., 2022. Thus, while our results confirm active infection, they must be cautiously interpreted in populations with prior vaccination or latent TB [ 6 , 21 ]. A notable finding in our data is the 45% rifampicin resistance detected via GeneXpert. This indicates a substantial burden of drug-resistant TB (DR-TB) in the study population, which agrees with other researchers who highlighted the increasing trend of rifampicin resistance globally, emphasizing the need for early molecular testing to inform treatment strategies [ 22 , 23 ]. Additionally, Peng et al., 2020 identified high AFB smear loads and drug resistance as predictors of treatment failure and relapse, consistent with the elevated smear counts in our cohort [ 24 ]. Our study revealed that among the active TB group, 75% showed abnormal chest X-rays, with findings ranging from trivial inactive lesions to gross very extensive bilateral disease. This radiographic heterogeneity aligns with the well-documented spectrum of pulmonary involvement in active TB, where the extent of lung pathology reflects disease severity and immune response. Others reported that moderate to extensive lung lesions with more aggressive TB phenotypes, confirming the utility of radiology as a surrogate for disease burden and emphasized that radiological findings, particularly cavitary and extensive lung lesions, enhance diagnostic discrimination between active and latent TB [ 14 , 25 ]. On the other hand, some studies argue that radiological findings alone are insufficient to predict TB activity or immune status stressing the importance of combining imaging with immunological assays like CXCL5, IFN-γ, and IL-10 to better distinguish active versus latent TB, pointing out that patients with normal imaging can still be infectious [ 11 , 22 , 26 ]. Most notably, our data showed significantly higher levels of IL-4 and IL-10 in active TB (212.8 and 307.6 pg/mL, respectively) compared to latent TB (74 and 103.45 pg/mL, p < 0.001 for both). These findings are strongly in agreement with several studies. A meta-analysis confirmed increased IL-4 expression in active TB, linking it to immune evasion by promoting Th2 responses that impair effective mycobacterial clearance. Similarly, Wei et al., 2020 and Druszczynska et al., 2021 identified IL-10 as a key immunosuppressive cytokine elevated in active TB, inhibiting macrophage and T-cell function and contributing to pathogen persistence [ 11 , 13 , 26 ]. In contrast to latent TB, which is associated with Th1 dominance (e.g., IFN-γ). Further demonstrated the utility of IL-4 as a marker of TB severity in lymphadenitis cases [ 12 , 27 ]. On the other hand, Jafrasteh et al., 2021 suggested that IL-2 might be a more specific differentiator between latent and active TB than IL-10 or IL-4, especially among household contacts of TB patients. This raises the possibility that reliance on IL-4 and IL-10 alone may not provide sufficient diagnostic precision in all epidemiological settings [ 28 ]. In the current study demonstrated a statistically significant and very strong positive correlation between IL-4 and IL-10 levels across all participant groups, with the highest correlation observed in the active TB group (r = 0.869), followed by the latent group (r = 0.846), with p < 0.001. These findings suggest a parallel increase in Th2 cytokines during different stages of Mycobacterium tuberculosis infection. This finding is well correlated with He et al., 2023 who show that IL-10, when produced early during infection, contributes to immune evasion by promoting vascular-associated CD4 + T cells that fail to control M.tb infection. These observations support the notion that simultaneous IL-4 and IL-10 elevation reflects a host immune regulatory response, potentially dampening the pro-inflammatory milieu to reduce tissue damage [ 5 , 15 , 18 ]. In our study, both IL-4 and IL-10 demonstrated perfect diagnostic performance in distinguishing active from latent tuberculosis (TB), with an AUC of 1.00 (95% CI: 1.00–1.00, p 88 pg/mL for IL-4 and > 119 pg/mL for IL-10), both cytokines achieved 100% sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). These findings align with several previous reports that highlight the immunodiagnostic potential of Th2 cytokines in TB [ 5 , 15 , 27 ]. Our study demonstrates a statistically significant association between bacillary load (as determined by GeneXpert MTB/RIF grading) and the levels of Th2 cytokines IL-4 and IL-10. Both cytokines were markedly elevated in the moderate and high GeneXpert groups compared to the low-load group, with no significant difference between moderate and high grades. These findings agree with previous works that reported that serum IL-4 levels correlate with bacterial burden and treatment progression, proposing IL-4 as a sensitive marker of disease activity in TB patients [ 15 , 27 ]. Regarding IL-10, the present data agree with other researchers who observed that elevated IL-10 is associated with increased bacterial loads and disease severity [ 15 , 27 ]. Additionally, Shey et al. 2023 highlighted that IL-10 was notably absent or low in healthcare workers who resisted TB infection despite high exposure, reinforcing its role in facilitating bacterial persistence in active cases [ 30 ]. In contrast, Sa’ad et al., 2024 observed no consistent pattern of IL-4 or IL-10 elevation across different TB severity levels in their Nigerian cohort. They instead emphasized IL-6 as a more reliable inflammatory marker for TB activity, potentially due to population heterogeneity or differences in assay sensitivity [ 31 ]. Moreover, some studies suggest that IL-10 elevations may occur independently of bacterial load and instead reflect a compensatory mechanism to limit host tissue damage, as proposed by Tiwari and Martineau, 2023 [ 25 ]. Our study demonstrated a highly significant increase in IL-4 and IL-10 levels across different grades of chest CT severity in patients with active pulmonary tuberculosis (p < 0.001 for both cytokines). IL-4 levels escalated from 105.13 ± 12.61 pg/mL in minimal disease to 310.9 ± 30.28 pg/mL in severe cases, and IL-10 followed a similar pattern, rising from 166.25 ± 11.9 to 396 ± 25.71 pg/mL. These results suggest that both Th2 cytokines are strongly associated with disease burden as reflected radiologically. These findings agree with other studies that reported an upsurge of IL-4 and IL-10 among patients with drug-resistant TB [ 5 , 13 , 29 ]. This aligns well with our findings and post-hoc LSD test results that confirmed significant cytokine level differences between minimal, moderate, and severe CT groups. In our study, IL-4 levels were significantly higher in rifampicin-sensitive TB patients than in those with rifampicin-resistant strains, whereas IL-10 levels did not differ significantly between the two groups. These findings shed light on potential immunological differences between drug-sensitive and drug-resistant tuberculosis (TB) infections and agree with the work by Sampath et al., 2023 demonstrated that patients with drug-resistant TB had lower Th2 cytokine activity, in favor of a hyperinflammatory signature dominated by pro-inflammatory cytokines like TNF-α and IFN-γ, which may suppress IL-4 responses [ 29 ]. However, some studies have reported elevated IL-4 levels in drug-resistant TB, challenging our findings. For example, Ferreira et al., 2021 suggested that early IL-4 production may drive immune tolerance and facilitate persistence of drug-resistant TB, highlighting a complex immunoregulatory role. Similarly, Ahmad et al., 2022 emphasized that alternating Th1/Th2 dynamics in drug-resistant TB can vary based on host factors and disease chronicity, and IL-4 elevation may be seen even in MDR-TB, especially during relapse or cavitary disease stages [ 5 , 32 ]. The non-significant difference in IL-10 between rifampicin-sensitive and resistant groups is supported by others [ 21 , 33 ]. Wu and colleagues, 2023 described IL-10 as a double-edged sword, while it may suppress protective immunity, it is also variably upregulated during immune exhaustion, not strictly differentiating sensitive vs. resistant TB phenotypes [ 22 , 34 ]. Despite the promising performance observed in our study, this study is limited by the diagnostic reliability of individual cytokines. Cytokine release may show a different pattern related to differences in individuals’ genetic makeup [ 30 ], different mycobacterial strains [ 35 ], the presence or absence of other disease conditions such as HIV, autoimmune diseases, major organ failure, or pregnancy [ 36 ]. Further studies classifying patients according to several parameters with a larger sample size are recommended. We also recommend studying other aspects of pathogenesis to get a comprehensive insight into the natural course of the disease and thus offer better choices for diagnosis and management of Mycobacterial tuberculosis infection. Conclusion Both IL-4 and IL-10 cytokine levels are statistically correlated to the disease severity, bacillary load, pulmonary and extrapulmonary manifestations, and chest X-ray findings. This research elucidated the potential of using these markers as a diagnostic and prognostic tool for identifying individuals at higher risk for severe or disseminated diseases. Additionally, IL-4 can differentiate rifampicin-sensitive from rifampicin-resistant strains. Hopefully, these cytokines may be a suitable therapeutic candidate. Further studies with a larger sample size, including patients with different co-morbid conditions, are needed. Abbreviations AFB: Acid-fast bacilli AUC: Area under the curve BCG: Bacillus Calmette and Guerin CT: Computed Tomography DR-TB: Drug-resistant TB GeneXpert MTB/RIF: Automated molecular test that simultaneously detects Mycobacterium tuberculosis (MTB) complex and rifampicin resistance HIV: Human Immunodeficiency Virus INF-γ: Interferon gamma IL-4: Interleukin -4 IL-10: Interleukin -10 MDR-TB: Multi drug resistant TB. M.tb: Mycobacterium TB NPV: Negative predictive value PPV: Positive predictive value ROC curve: Receiver Operating Characteristic curve TB: Tuberculosis bacilli TNF-α: Tumor necrosis factor alpha TST: Tuberculin Skin Test Declarations Ethics approval and consent to participate: Ethical review and approval were obtained from Benha Chest Hospital Institutional Review Boards (IRB) and the Military Medical Academy Health and Preventive Institute (Approval no: 18-2024). Consent from all subjects included in the study was obtained according to the Declaration of Helsinki. and the Principles of Good Clinical Practice (ICH 1996). Consent to participate Informed consent was obtained from the participants. Consent for Publication: All authors have reviewed and approved the manuscript for publication. Consent for the publication of images or personal or clinical details of participants in this study is “Not Applicable”. Funding The authors declare that no grants, funds, or any other support were received during the preparation of this manuscript.” Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions All authors contributed to the research conception and design. R. M., M. N., R.A., M. T., and A. G., performed material preparation, data collection, and analysis. The first draft of the manuscript was written by Prof R.M., Prof. M.N., they also supervised the project. All authors read and approved the final manuscript.” Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgements We acknowledge all individuals for their participation in this study. References Bagcchi S. WHO's Global Tuberculosis Report 2022. Lancet Microbe. 2023; 4(1): e20. 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DOI: 10.3389/fmicb.2023.1115295 Jeong JH, Shim SR, Han S, Hwang I et al. Diagnostic performance of biomarkers for differentiating active tuberculosis from latent tuberculosis: a systematic review and Bayesian network meta-analysis. Front. Microbiol. 2014;15:1506127.doi: 10.3389/fmicb.2024.1506127 Krivošová M, Dohál M, Mäsiarová S, et al. Exploring cytokine dynamics in tuberculosis: A comparative analysis of patients and controls with insights from three-week antituberculosis intervention. PLoS One. 2024;19(8):e0305158. doi: 10.1371/journal.pone.0305158. World Health Organization. Global Tuberculosis Report 2022. World Health Organization; Geneva, Switzerland: 2022. [accessed on 1 February 2023)]. Licence: CC BY-NC-SA 3.0 IGO. Elbrolosy AM, El Helbawy RH, Mansour OM, et al. Diagnostic utility of GeneXpert MTB/RIF assay versus conventional methods for diagnosis of pulmonary and extra-pulmonary tuberculosis. BMC Microbiol. 2021;21(1):144. doi: 10.1186/s12866-021-02210-5. Ramachandran S, Pajanivel R. The Imperative Role of Xpert® Mycobacterium Tuberculosis Complex/Resistance to Rifampin (MTB/RIF) in Rapid Diagnosis of Pulmonary and Extrapulmonary Tuberculosis. Cureus. 2024;16(12):e76706. DOI: 10.7759/cureus.76706. Khan A, Shah K, Abdeljawad T, et al. Fractal fractional model for tuberculosis: existence and numerical solutions. Sci Rep. 2024; 14: 12211. https://doi.org/10.1038/s41598-024-62386-4 Massou F, Fandohan M, Wachinou AP, et al. Spot specimen testing with GeneXpert MTB/RIF results compared to morning specimen in a programmatic setting in Cotonou, Benin. BMC Infect Dis. 2021; 21: 979. https://doi.org/10.1186/s12879-021-06676-6 Awad SF, Dargham SR, Omori R, et al. Analytical Exploration of Potential Pathways by which Diabetes Mellitus Impacts Tuberculosis Epidemiology. Sci Rep. 2019;9:8494. https://doi.org/10.1038/s41598-019-44916-7 Razbek J, Daken M, Chen Y, et al. Association Studies of Serum Levels of TNF- α, IL-10, IFN-γ and CXCL 5 with Latent Tuberculosis Infection in Close Contacts. Infect Drug Resist. 2024;17:899-910. doi: 10.2147/IDR.S442682. Khan AS, Phelan JE, Khan MT, et al. Characterization of rifampicin-resistant Mycobacterium tuberculosis in Khyber Pakhtunkhwa, Pakistan. Sci Rep. 2021;11:14194. https://doi.org/10.1038/s41598-021-93501-4 Peng Y, Meng L, Hu et al., Tuberculosis in Patients with Primary Myelofibrosis During Ruxolitinib Therapy: Case Series and Literature Review. Infection and drug resistance. 2020:13 3309-3316. DOI: 10.2147/IDR.S267997. Tiwari D, Martineau AR. Inflammation-mediated tissue damage in pulmonary tuberculosis and host-directed therapeutic strategies. Semin Immunol. 2023;65:101672. DOI: 10.1016/j.smim.2022.101672. Wei Z, Li Y, Wei C. et al. The meta-analysis for ideal cytokines to distinguish the latent and active TB infection. BMC Pulm Med. 2020;20:248. https://doi.org/10.1186/s12890-020-01280-x Lubis HML, Lubis MND, Delyuzar D. Interleukin-4 Cytokine as an Indicator of the Severity of Tuberculous Lymphadenitis. Maced J Med Sci. 2021;9(A):82-6. Available from: https://oamjms.eu/index.php/mjms/article/view/5667 Jafrasteh A , Karimi A , Hoseinialfatemi SH, et al., Evaluation of Interleukin-2 to Detect Active and Latent Tuberculosis among Household Contacts of Pulmonary Tuberculosis Cases. Arch Pediatr Infect Dis. 2021; 9(2):e109398. DOI: 10.5812/pedinfect.109398. Sampath P, Rajamanickam A, Thiruvengadam K, et al. Cytokine upsurge among drug-resistant tuberculosis endorse the signatures of hyper inflammation and disease severity. Sci Rep. 2023;13(1):785. doi: 10.1038/s41598-023-27895-8. Shey MS, Balfour A, Masina N, et al. Mycobacterial-specific secretion of cytokines and chemokines in healthcare workers with apparent resistance to infection with Mycobacterium tuberculosis. Front Immunol. 2023;14:1176615. doi: 10.3389/fimmu.2023.1176615. Sa’ad M, Abba AA, Musa BOP, et al. Assessment of interleukin 6 (IL-6) as a marker of inflammation among adult patients with pulmonary tuberculosis in Zaria, Nigeria. Egypt J Bronchol. 2024;18:8. https://doi.org/10.1186/s43168-024-00263-4 Ahmed M, Tezera LB, Elkington PT, Leslie AJ. The paradox of immune checkpoint inhibition re-activating tuberculosis. Eur Respir J. 2022;60(5):2102512. doi: 10.1183/13993003.02512-2021. Elbrolosy AM, El Helbawy RH, Mansour OM et al. Diagnostic utility of GeneXpert MTB/RIF assay versus conventional methods for diagnosis of pulmonary and extra-pulmonary tuberculosis. BMC Microbiol 2021;21,144. https://doi.org/10.1186/s12866-021-02210-5 Wu J, Xiao P, Zhang Y et al., Evaluation of the Effectiveness of Global Tuberculosis Control Strategies at Different Stages and Analysis of Risk Factors: Findings From the Global Burden of Disease 2021, Archivos de ronconeumología, https://doi.org/10.1016/j.arbres.2024.11.017 Ranaivomanana P, Ratovoson R, Razafimahatratra C, et al. Longitudinal Variations of M. tuberculosis-Induced IFN-g Responses in HIV-Negative Pregnant Women Exposed to Tuberculosis. Front. Immunol. 2021;12:805157. doi: 10.3389/fimmu.2021.805157 Donniacuo A, Mauro A, Cardamone C, Basile A, Manzo P, Dimitrov J, Cammarota AL, Marzullo L, Triggiani M, Turco MC, et al. Comprehensive Profiling of Cytokines and Growth Factors: Pathogenic Roles and Clinical Applications in Autoimmune Diseases. International Journal of Molecular Medicine 2025; 26(18):8921. https://doi.org/10.3390/ijms26188921 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9168542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633540591,"identity":"79ad9b36-4875-4f59-87da-af75b8394cbc","order_by":0,"name":"Rasha Eid Mohamed Dosoky","email":"","orcid":"","institution":"Health \u0026 Epidemiological Institute, Military Medical Academy","correspondingAuthor":false,"prefix":"","firstName":"Rasha","middleName":"Eid Mohamed","lastName":"Dosoky","suffix":""},{"id":633540592,"identity":"0d4c4725-3442-4434-bd18-fea83d73657d","order_by":1,"name":"Ahmed Gad Taha","email":"","orcid":"","institution":"Health \u0026 Epidemiological Institute, Military Medical Academy","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Gad","lastName":"Taha","suffix":""},{"id":633540593,"identity":"5d7db55b-0c44-49dc-bb32-226ab8b8df51","order_by":2,"name":"Mayar Mosaad Tawfik","email":"","orcid":"","institution":"Modern university for technology and information","correspondingAuthor":false,"prefix":"","firstName":"Mayar","middleName":"Mosaad","lastName":"Tawfik","suffix":""},{"id":633540594,"identity":"7b4060d8-fd4c-40c6-8699-3726fc9b7eee","order_by":3,"name":"Mostafa Mahmoud Mohamed Elnakib","email":"","orcid":"","institution":"Health \u0026 Epidemiological Institute, Military Medical Academy","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"Mahmoud Mohamed","lastName":"Elnakib","suffix":""},{"id":633540595,"identity":"7eac5751-4355-4419-a350-a4a8d5e67690","order_by":4,"name":"Reham M. El Shabrawy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFACHjApwyDBwAZm8TNAGYS08MC1SDaQrMXgAAEt5u29Bz/dYLDhkZ/d/OxxRc3hxM03kp89+FDBIM8vdgCrFpkz55KlcxjSeAzuHDM3PHPscOK2G2nmhjPOMBjOnJ2AVYuERI4BUMthHgOJBDPJBrbbQC0JZtK8bQwJBrdxaJF/Y/w7h+E/j/yM9G+SDf9uJ24GMvBrkeAxA9pygIfhRo6ZZGPb7cQNEjkEbOHJMbPOYUjmMbiRU27Y2PffeMaZN2WSM85I4PYL+xnj2zkMdnJAh2172PAtTba/PX2bxIcKG3l+aexawIDxH4Lt2CAAVimBWzk6sGfgP0C86lEwCkbBKBgRAABJ+Fu8+9uGVAAAAABJRU5ErkJggg==","orcid":"","institution":"Faculty of Medicine, Zagazig University","correspondingAuthor":true,"prefix":"","firstName":"Reham","middleName":"M. El","lastName":"Shabrawy","suffix":""}],"badges":[],"createdAt":"2026-03-19 10:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9168542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9168542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108734960,"identity":"0f07868c-d017-42ae-8434-a4ee2b4389df","added_by":"auto","created_at":"2026-05-07 19:58:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between IL-4 \u0026amp; 10 among the studied group.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9168542/v1/b2b8ad667f864e6362d36f1c.png"},{"id":108807061,"identity":"8258b81c-aa41-4ac4-a1f6-2c19541be398","added_by":"auto","created_at":"2026-05-08 15:30:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShows ROC curve analysis to assess the diagnostic performance of IL-4 \u0026amp; 10 to predict active and Latent T.B.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9168542/v1/b0150fd19a49c6d5aea5b22d.png"},{"id":108809850,"identity":"59de8ab8-d211-45b9-96cf-f92430ade9c3","added_by":"auto","created_at":"2026-05-08 15:55:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":394071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9168542/v1/86d78339-ccb6-4e38-ab9a-0ae40e948b69.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIL-4 and IL-10 as a diagnostic and predictive factor for the severity of Mycobacterium Tuberculosis Infection\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) is a global health problem that causes more than nine million new cases and a mortality of about two million each year. According to the World Health Organization (WHO), nearly 2\u0026nbsp;billion people or nearly one-third of the global population is latently infected with \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, the etiological agent of TB [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent Management for TB has challenges in diagnosis and management, including the challenge of distinguishing active TB from latent infection, the lack of prognostic markers and not only the need for lengthy and multiple antibiotics for treatment but also the risk of the emerging drug-resistant variants of \u003cem\u003eMycobacterium tuberculosis.\u003c/em\u003e In fact, almost all countries, irrespective of their socioeconomic status, are now under threat from multiple drug-resistant and extensively drug-resistant strains of M. tuberculosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInvestigation of the immune pathogenesis of TB may provide a closer insight and probably the keys to improving the current management of TB. Immunity against \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e requires a balance between different components of adaptive immune responses to constrain bacterial replication and prevent the potentially damaging immune activation. Th cells include Th1 cells secrete interleukin-2 (IL-2), interferon-gamma (IFN-y) and lymphotoxins, whereas Th2 cells produce IL-4, IL-10, and IL-13 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTh2 cells producing IL-4 and IL-10 are detrimental in the control of intracellular M. tuberculosis infection. Th2 cytokines suppress IFN-γ production by Th\u003csub\u003e1\u003c/sub\u003e cells thus inhibiting IFN-γ-mediated effects, including M1 macrophage activation. They also inhibit Th\u003csub\u003e1\u003c/sub\u003e-induced autophagy, reducing the intracellular degradation of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e bacteria, antagonise the host defense and lead to the induction of fibrosis and cavitation, which compromise lung function in TB patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study aims to elucidate the ability of IL-4 and IL-10 serum levels to differentiate between latent and active TB infections and to serve as an indicator for the disease severity.\u003c/p\u003e"},{"header":"Patients, methods and materials","content":"\u003cp\u003e\u003cstrong\u003eStudy Population:\u0026nbsp;\u003c/strong\u003eThis cross-sectional study included eighty TB patients. Patients were further classified into an active TB infection group (forty participants) and a latent group (forty participants) based on the presence of clinical symptoms, Mantoux test (tuberculin test), the presence of acid-fast bacilli, and GeneXpert MTB/RIF testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the Tuberculosis Control Guidelines by the \u0026nbsp;Egyptian Ministry of Health and Population, National Tuberculosis Control Program [6].\u003c/p\u003e\n\u003col style=\"list-style-type: upper-roman;\"\u003e\n \u003cli\u003e\u003cstrong\u003eFor active cases:\u0026nbsp;\u003c/strong\u003eIn a participant aged\u0026gt; 18 years, an active case (including pulmonary and extra-pulmonary) was defined as at least one recent sputum specimen positive for acid-fast bacilli on microscopic examination and clinical symptoms\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFor latent cases:\u0026nbsp;\u003c/strong\u003eIn a participant aged\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026gt; 18 years, a latent case was defined as a state of persistent immune response as shown by having a tuberculin test of induration more than 15 mm without any clinical symptoms, radiological abnormality, or microbiological evidence\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria:\u0026nbsp;\u003c/strong\u003eWe excluded patients with upper respiratory tract infections within the past 6 weeks, those who received antimicrobial therapy in the past week, or those who received corticosteroids, chemotherapy, or other immunosuppressants in the past 3 months. Patients with HIV, chronic liver diseases, chronic renal diseases, autoimmune diseases and known allergic diseases were also excluded from the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll subjects in the study were subjected to the following:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical examination:\u0026nbsp;\u003c/strong\u003eComplete history taking and clinical examination were conducted for all participants. In the case of pulmonary TB, a patient can have crepitations and bronchial breath sounds, especially over the upper lobes or affected area, indicating cavity or consolidation. \u0026nbsp;Signs of extrapulmonary TB are varied and can include Lymphadenopathy, Cutaneous lesions, Pleural effusion, Neurological deficit, Confusion, Coma, Chorioretinitis, and Vertebral collapse [7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of the TB bacilli in the smear:\u0026nbsp;\u003c/strong\u003eZiehl-Neelsen staining was performed on the collected samples. Smears were graded on the scale; negative for \u0026nbsp;\u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, scanty, +\u0026thinsp;1, +2, and +\u0026thinsp;3\u0026nbsp;[6].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Mantoux Test (Tuberculin skin testing):\u0026nbsp;\u003c/strong\u003eThe Mantoux test is a two-part test consisting of an intradermal injection of 0.1ml purified protein derivative and observing for induration 48-72 hours. The patient\u0026rsquo;s risk of exposure is taken into consideration when interpreting the result [8]. Because the BCG vaccine is compulsory in Egypt, a tuberculin test is considered positive in the latent group if only the induration exceeds 15mm after 48-72 hours [6].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneXpert MTB/RIF diagnostic system (Cepheid, Sunnyvale, CA, USA):\u0026nbsp;\u003c/strong\u003eOne ml of unprocessed specimens was required for the assay. Then the sample reagent was added in a 2:1 ratio to the unprocessed specimen in a Falcon tube, then the tube was vortexed twice and incubated for 15-min room temperature. Subsequently, 2ml of the inactivated sample was transferred to the test cartridge by a sterile disposable pipette. The cartridge contains the reagents needed for DNA extraction and PCR amplification, and then the fluorescent probes automatically lead the assay. GeneXpert MTB/RIF is a semiquantitative test that reports the \u003cem\u003eMycobacterial Tuberculosis\u0026nbsp;\u003c/em\u003eload depending on the cycle threshold (Ct). Therefore, four categories can be identified: high bacterial load when the Ct is \u0026lt;16, medium load with a Ct between 16 and 22. Specimens with low bacterial load are those with Ct between 22 and 28, very low loads have Ct \u0026gt;28. The specimen is considered negative when the bacteria cannot be detected [9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChest x-ray and CT grading\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChest x-ray was done for all patients, those with abnormal findings where further evaluated and graded by Computed tomography (CT)\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGrade 1(Minimal): Mildly enlarged lymph nodes, Small nodular opacities in the upper lobes or superior segments of lower lobes, and no cavitation.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGrade 2 (Moderate): Moderately enlarged lymph nodes with central low attenuation (caseation), Multiple nodular opacities, Minimal to moderate pleural effusion, and early bronchogenic spread\u003c/li\u003e\n \u003cli\u003eGrade 3(Severe): Massively enlarged lymph nodes with caseation, Extensive nodular opacities with or without cavitation, Large pleural effusion, and significant bronchogenic spread [10].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eIL-4 and IL-10 measurement:\u003c/strong\u003e These interleukins were assayed in the serum samples of the active and latent patient groups using a human ELISA Kit (R\u0026amp;D Systems, USA), according to the manufacturer\u0026apos;s guidelines. The assay range for IL-4 is 15.6 - 1,000 pg/mL, and the sensitivity is 8 pg/mL. The assay range for IL-10 is\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e0.8 \u0026ndash; 50 pg/mL, and the sensitivity is 0.17 pg/mL\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis:\u0026nbsp;\u003c/strong\u003eThe collected data were revised, coded, and tabulated using Statistical Package for the Social Science (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.). Data were presented and suitable analysis was done according to the type of data obtained for each parameter.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age of the study participants was 45.15 \u0026plusmn; 17.26 years, ranging from 20 to 74 years.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll active patients report night sweats, night fever, and weight loss. All active TB patients (n=40) tested positive on smear microscopy, with a mean acid-fast bacilli (AFB) smear load of 21.45 \u0026plusmn; 26.5, ranging from 5 to 95. Gene expert results showed that 55% (n=22) had high bacterial load, 37.5% (n=15) had moderate, and 7.5% (n=3) had low. The mean tuberculin skin test size was 15.43 \u0026plusmn; 6.61 mm (range: 3\u0026ndash;28). Rifampicin resistance was identified in 45% (n=18) of the cases, while 55% (n=22) were sensitive (table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Diagnostic criteria for active group only.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Active group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n= 40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003cbr\u003e\u0026nbsp;Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e40 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcid-Fast Bacilli (AFB) in smear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e21.45 \u0026plusmn; 26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e(5 - 95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene expert\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e15 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e22 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 294px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculin test (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e15.43 \u0026plusmn; 6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e(3 - 28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRifampicin Resistance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSensitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e22 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eResist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e18 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe latent group of patients (n=40) were selected from individuals who were in close contact to proven patients and showed a positive tuberculin test of more than 15 mm induration with a wheal size of clinical symptoms, radiological abnormality, or microbiological evidence of TB infection\u003c/p\u003e\n\u003cp\u003eAmong the active group, 75% (n=30) had abnormal chest X-rays. CT grading showed 26.67% had minimal lesions, 40% moderate, and 33.33% severe. \u0026nbsp;Pulmonary involvement was seen in 75%, while 25% had extrapulmonary manifestations where lesions were located in varied areas such as abdominal lymph nodes, brain, spine, breast and joints. (table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Radiological data assessment for the active group.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 529px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Active group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n= 40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChest X-Ray\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e30 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT Grading\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eMinimal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8 (26.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulmonary Involvement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003ePulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e30 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 394px;\"\u003e\n \u003cp\u003eExtrapulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIL-4 and IL-10 levels were much higher in active TB with mean\u0026plusmn; SD (212.8 \u0026plusmn; 78.27 and 307.6 \u0026plusmn; 91.42 pg/mL) respectively, \u0026nbsp;versus latent TB (74 \u0026plusmn; 7.94 and 103.45 \u0026plusmn; 8.13 pg/mL) respectively, with the difference between both cytokines shows highly significant p-value \u0026lt;0.001 (table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: IL-4 and IL-10 serum level difference between the two groups.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of significance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003et - test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eActive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eLatent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL4 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e212.8 \u0026plusmn; 78.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e74 \u0026plusmn; 7.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL10 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e307.6 \u0026plusmn; 91.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e103.45 \u0026plusmn; 8.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e**: highly significant, HS: significant\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There was a statistically significant and very strong positive correlation between IL-4 and IL-10 across both groups. The correlation was stronger in the active group (r=0.869),than the latent group (r=0.846), with p-values \u0026lt;0.001 (table 4, figure1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Correlation between IL-4 and IL-10 in both groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"448\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActive group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatent group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpearman\u0026rsquo;s rho\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHS: highly significant\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth IL-4 and IL-10 demonstrated perfect diagnostic performance with an area under the curve (AUC) of 1.00 (95% CI: 1.00\u0026ndash;1.00, p-value \u0026lt;0.001). At cut-off values of \u0026gt;88 pg/mL for IL-4 and \u0026gt;119 pg/mL for IL-10, both markers achieved 100% sensitivity, specificity, positive predictive value (+PV), and negative predictive value (\u0026ndash;PV) in differentiating active from latent TB (table 5, figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Diagnostic performance of IL-4 and IL-10.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"584\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e+PV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-PV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL4 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.00 - 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026gt;88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL10 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.00 - 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026gt;119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC: area under the curve, HS: significance, +PV: positive predictive value, - PV: negative predictive value.\u003c/p\u003e\n\u003cp\u003eWhen correlating IL-4 and IL-10 levels with the gene expert grades. IL-4 was lowest in the low-grade group mean\u0026plusmn;SD (90.33 \u0026plusmn; 1.53 pg/mL), peaking in the moderate group (246.2 \u0026plusmn; 71.78), and slightly lower in the high-grade group (206.73 \u0026plusmn; 70.47). IL-10 followed a similar trend: 156.67 \u0026plusmn; 8.14 in low, 335.67 \u0026plusmn; 78.92 in moderate, and 309.05 \u0026plusmn; 86.85 in high-grade groups (p-values= 0.004 and 0.005), respectively Post-hoc analysis showed significance between low vs. moderate/high groups, but there was no statistically significant difference between moderate and high loaded (table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Comparison between different grades of gene expert and IL-4 \u0026amp; 10.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"634\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Active group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n= 40)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 368px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene expert\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOne Way ANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003e(n= 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003e(n= 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003e(n= 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL4 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e90.33 \u0026plusmn; 1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e246.2 \u0026plusmn; 71.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e206.73 \u0026plusmn; 70.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL10 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e156.67 \u0026plusmn; 8.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e335.67 \u0026plusmn; 78.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e309.05 \u0026plusmn; 86.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Post-hoc LSD test was significant between Low Vs. (Moderate \u0026amp; High groups), S: significant\u003c/p\u003e\n\u003cp\u003eThere was a highly significant increase in IL-4 and IL-10 levels across CT severity grades (both p-values \u0026lt;0.001). IL-4 rose from 105.13 \u0026plusmn; 12.61 pg/mL in minimal grade to 201.58 \u0026plusmn; 16.89 in moderate and 310.9 \u0026plusmn; 30.28 in severe cases. Similarly, IL-10 increased from 166.25 \u0026plusmn; 11.9 to 328.67 \u0026plusmn; 41.06 and 396 \u0026plusmn; 25.71. Post-hoc tests confirmed significant differences between all groups, suggesting strong correlation with disease severity (table 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Comparison between different grades of CT and IL-4 \u0026amp; 10.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Active group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n= 30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT Grading\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOne Way ANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMinimal\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n= 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003cp\u003e(n= 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003e(n= 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL4 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e105.13 \u0026plusmn; 12.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e201.58 \u0026plusmn; 16.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e310.9 \u0026plusmn; 30.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL10 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e166.25 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e328.67 \u0026plusmn; 41.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e396 \u0026plusmn; 25.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Post-hoc LSD test was significant between all groups, HS: highly significant\u003c/p\u003e\n\u003cp\u003eIL-4 levels were statistically significantly higher in patients with rifampicin-sensitive strains (238.18 \u0026plusmn; 83.76 pg/mL) compared to those with rifampicin-resistant strains (181.78 \u0026plusmn; 59.46 pg/mL), with a statistically significant p-value of 0.021. However, IL-10 levels were not significantly different between the two subgroups; the sensitive group had a mean IL-10 of 323 \u0026plusmn; 91.72 pg/mL, while the resistant group had 288.78 \u0026plusmn; 89.99 pg/mL (p-value= 0.244) (table 8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Relation between Rifampicin resistance and IL-4 \u0026amp; 10.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Active group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n= 40)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRifampicin resistance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudent t-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eSensitive\u003c/p\u003e\n \u003cp\u003e(n= 22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eResist\u003c/p\u003e\n \u003cp\u003e(n= 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL4 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e238.18 \u0026plusmn; 83.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e181.78 \u0026plusmn; 59.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL10 (pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e323 \u0026plusmn; 91.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e288.78 \u0026plusmn; 89.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eS: significant, NS: non-significant\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, all individuals in the active TB group presented with classical symptoms, including night sweats, night fever, and weight loss, while none of the latent TB participants exhibited these features. Previous studies concluded that symptomatology remains one of the most distinguishing clinical criteria separating active from latent TB, particularly when diagnostic biomarkers are unavailable or inconclusive [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, others noted that symptoms such as weight loss and fever are not universal in active TB, especially in elderly or immunocompromised patients, suggesting that relying solely on symptom presence may underestimate atypical cases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, all active TB patients (100%) demonstrated positive smear results with a mean AFB smear count of 21.45\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5. This finding strongly confirms the high diagnostic yield of direct smear microscopy in bacteriologically active TB, especially when bacillary load is high. This aligns with Jeong et al., 2024 who concluded that smear microscopy, though limited in sensitivity compared to molecular tools, maintains excellent specificity and remains pivotal in resource-limited settings when bacterial burden is high [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, Krivošov\u0026aacute; et al., 2024 correlate with disease severity and bacterial burden with AFB smear count [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, others reported lower smear positivity among pediatric TB cases, highlighting the diagnostic challenge in smear-negative but culture-positive patients, particularly in extrapulmonary TB or HIV co-infection. This discrepancy emphasizes the need for adjunct diagnostic tools in certain populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GeneXpert MTB/RIF assay performed in all cases, revealed 55% with high bacterial load, 37.5% moderate, and 7.5% low bacterial load, none of our active patients showed very low count or presented with undetectable bacilli. GeneXpert has been endorsed as a frontline diagnostic tool for its rapid turnaround and capacity to detect rifampicin resistance simultaneously [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn disagreement, Khan et al., 2024 criticized GeneXpert for its limited sensitivity in detecting low bacterial loads, particularly in extrapulmonary samples. They suggest combining it with culture or histopathology in such contexts [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Likewise, Massou et al., 2021 reported variable sensitivity based on sample timing (morning vs. spot specimens), raising practical concerns regarding sample collection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the Tuberculin Skin Test (TST) in the active group, showed a mean induration size of 15.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61 mm, reflecting robust immune reactivity among patients. This agrees with other findings where larger TST reactions were common in confirmed active TB cases due to heightened Th1 responses. However, the TST\u0026rsquo;s specificity is limited due to cross-reactivity with BCG vaccination and environmental mycobacteria. This is particularly relevant in countries like Egypt with high BCG coverage, as noted by Awad et al., 2022. Thus, while our results confirm active infection, they must be cautiously interpreted in populations with prior vaccination or latent TB [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA notable finding in our data is the 45% rifampicin resistance detected via GeneXpert. This indicates a substantial burden of drug-resistant TB (DR-TB) in the study population, which agrees with other researchers who highlighted the increasing trend of rifampicin resistance globally, emphasizing the need for early molecular testing to inform treatment strategies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, Peng et al., 2020 identified high AFB smear loads and drug resistance as predictors of treatment failure and relapse, consistent with the elevated smear counts in our cohort [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study revealed that among the active TB group, 75% showed abnormal chest X-rays, with findings ranging from trivial inactive lesions to gross very extensive bilateral disease. This radiographic heterogeneity aligns with the well-documented spectrum of pulmonary involvement in active TB, where the extent of lung pathology reflects disease severity and immune response. Others reported that moderate to extensive lung lesions with more aggressive TB phenotypes, confirming the utility of radiology as a surrogate for disease burden and emphasized that radiological findings, particularly cavitary and extensive lung lesions, enhance diagnostic discrimination between active and latent TB [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, some studies argue that radiological findings alone are insufficient to predict TB activity or immune status stressing the importance of combining imaging with immunological assays like CXCL5, IFN-γ, and IL-10 to better distinguish active versus latent TB, pointing out that patients with normal imaging can still be infectious [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost notably, our data showed significantly higher levels of IL-4 and IL-10 in active TB (212.8 and 307.6 pg/mL, respectively) compared to latent TB (74 and 103.45 pg/mL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). These findings are strongly in agreement with several studies. A meta-analysis confirmed increased IL-4 expression in active TB, linking it to immune evasion by promoting Th2 responses that impair effective mycobacterial clearance. Similarly, Wei et al., 2020 and Druszczynska et al., 2021 identified IL-10 as a key immunosuppressive cytokine elevated in active TB, inhibiting macrophage and T-cell function and contributing to pathogen persistence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In contrast to latent TB, which is associated with Th1 dominance (e.g., IFN-γ). Further demonstrated the utility of IL-4 as a marker of TB severity in lymphadenitis cases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, Jafrasteh et al., 2021 suggested that IL-2 might be a more specific differentiator between latent and active TB than IL-10 or IL-4, especially among household contacts of TB patients. This raises the possibility that reliance on IL-4 and IL-10 alone may not provide sufficient diagnostic precision in all epidemiological settings [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study demonstrated a statistically significant and very strong positive correlation between IL-4 and IL-10 levels across all participant groups, with the highest correlation observed in the active TB group (r\u0026thinsp;=\u0026thinsp;0.869), followed by the latent group (r\u0026thinsp;=\u0026thinsp;0.846), with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. These findings suggest a parallel increase in Th2 cytokines during different stages of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e infection. This finding is well correlated with He et al., 2023 who show that IL-10, when produced early during infection, contributes to immune evasion by promoting vascular-associated CD4\u0026thinsp;+\u0026thinsp;T cells that fail to control M.tb infection. These observations support the notion that simultaneous IL-4 and IL-10 elevation reflects a host immune regulatory response, potentially dampening the pro-inflammatory milieu to reduce tissue damage [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, both IL-4 and IL-10 demonstrated perfect diagnostic performance in distinguishing active from latent tuberculosis (TB), with an AUC of 1.00 (95% CI: 1.00\u0026ndash;1.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At optimal cut-off values (\u0026gt;\u0026thinsp;88 pg/mL for IL-4 and \u0026gt;\u0026thinsp;119 pg/mL for IL-10), both cytokines achieved 100% sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). These findings align with several previous reports that highlight the immunodiagnostic potential of Th2 cytokines in TB [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study demonstrates a statistically significant association between bacillary load (as determined by GeneXpert MTB/RIF grading) and the levels of Th2 cytokines IL-4 and IL-10. Both cytokines were markedly elevated in the moderate and high GeneXpert groups compared to the low-load group, with no significant difference between moderate and high grades. These findings agree with previous works that reported that serum IL-4 levels correlate with bacterial burden and treatment progression, proposing IL-4 as a sensitive marker of disease activity in TB patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding IL-10, the present data agree with other researchers who observed that elevated IL-10 is associated with increased bacterial loads and disease severity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, Shey et al. 2023 highlighted that IL-10 was notably absent or low in healthcare workers who resisted TB infection despite high exposure, reinforcing its role in facilitating bacterial persistence in active cases [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, Sa\u0026rsquo;ad et al., 2024 observed no consistent pattern of IL-4 or IL-10 elevation across different TB severity levels in their Nigerian cohort. They instead emphasized IL-6 as a more reliable inflammatory marker for TB activity, potentially due to population heterogeneity or differences in assay sensitivity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, some studies suggest that IL-10 elevations may occur independently of bacterial load and instead reflect a compensatory mechanism to limit host tissue damage, as proposed by Tiwari and Martineau, 2023 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study demonstrated a highly significant increase in IL-4 and IL-10 levels across different grades of chest CT severity in patients with active pulmonary tuberculosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both cytokines). IL-4 levels escalated from 105.13\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61 pg/mL in minimal disease to 310.9\u0026thinsp;\u0026plusmn;\u0026thinsp;30.28 pg/mL in severe cases, and IL-10 followed a similar pattern, rising from 166.25\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9 to 396\u0026thinsp;\u0026plusmn;\u0026thinsp;25.71 pg/mL. These results suggest that both Th2 cytokines are strongly associated with disease burden as reflected radiologically. These findings agree with other studies that reported an upsurge of IL-4 and IL-10 among patients with drug-resistant TB [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This aligns well with our findings and post-hoc LSD test results that confirmed significant cytokine level differences between minimal, moderate, and severe CT groups.\u003c/p\u003e \u003cp\u003eIn our study, IL-4 levels were significantly higher in rifampicin-sensitive TB patients than in those with rifampicin-resistant strains, whereas IL-10 levels did not differ significantly between the two groups. These findings shed light on potential immunological differences between drug-sensitive and drug-resistant tuberculosis (TB) infections and agree with the work by Sampath et al., 2023 demonstrated that patients with drug-resistant TB had lower Th2 cytokine activity, in favor of a hyperinflammatory signature dominated by pro-inflammatory cytokines like TNF-α and IFN-γ, which may suppress IL-4 responses [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, some studies have reported elevated IL-4 levels in drug-resistant TB, challenging our findings. For example, Ferreira et al., 2021 suggested that early IL-4 production may drive immune tolerance and facilitate persistence of drug-resistant TB, highlighting a complex immunoregulatory role. Similarly, Ahmad et al., 2022 emphasized that alternating Th1/Th2 dynamics in drug-resistant TB can vary based on host factors and disease chronicity, and IL-4 elevation may be seen even in MDR-TB, especially during relapse or cavitary disease stages [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe non-significant difference in IL-10 between rifampicin-sensitive and resistant groups is supported by others [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Wu and colleagues, 2023 described IL-10 as a double-edged sword, while it may suppress protective immunity, it is also variably upregulated during immune exhaustion, not strictly differentiating sensitive vs. resistant TB phenotypes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the promising performance observed in our study, this study is limited by the diagnostic reliability of individual cytokines. Cytokine release may show a different pattern related to differences in individuals\u0026rsquo; genetic makeup [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], different mycobacterial strains [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the presence or absence of other disease conditions such as HIV, autoimmune diseases, major organ failure, or pregnancy [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Further studies classifying patients according to several parameters with a larger sample size are recommended. We also recommend studying other aspects of pathogenesis to get a comprehensive insight into the natural course of the disease and thus offer better choices for diagnosis and management of \u003cem\u003eMycobacterial tuberculosis\u003c/em\u003e infection.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBoth IL-4 and IL-10 cytokine levels are statistically correlated to the disease severity, bacillary load, pulmonary and extrapulmonary manifestations, and chest X-ray findings. This research elucidated the potential of using these markers as a diagnostic and prognostic tool for identifying individuals at higher risk for severe or disseminated diseases. Additionally, IL-4 can differentiate rifampicin-sensitive from rifampicin-resistant strains. Hopefully, these cytokines may be a suitable therapeutic candidate. Further studies with a larger sample size, including patients with different co-morbid conditions, are needed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAFB: Acid-fast bacilli\u003c/p\u003e\n\u003cp\u003eAUC: Area under the curve\u003c/p\u003e\n\u003cp\u003eBCG: Bacillus Calmette and Guerin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT: Computed Tomography\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;DR-TB: Drug-resistant TB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGeneXpert MTB/RIF: Automated molecular test that simultaneously detects Mycobacterium tuberculosis (MTB) complex and rifampicin resistance\u003c/p\u003e\n\u003cp\u003eHIV: Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eINF-\u0026gamma;: Interferon gamma\u003c/p\u003e\n\u003cp\u003eIL-4: Interleukin -4\u003c/p\u003e\n\u003cp\u003eIL-10: Interleukin -10\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;MDR-TB: Multi drug resistant TB.\u003c/p\u003e\n\u003cp\u003eM.tb: Mycobacterium TB\u003c/p\u003e\n\u003cp\u003eNPV: Negative predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPV: Positive predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC curve: Receiver Operating Characteristic curve\u003c/p\u003e\n\u003cp\u003eTB: Tuberculosis bacilli\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha;: Tumor necrosis factor alpha\u003c/p\u003e\n\u003cp\u003eTST: Tuberculin Skin Test\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical review and approval were obtained from Benha Chest Hospital Institutional Review Boards (IRB) and the Military Medical Academy Health and Preventive Institute (Approval no: 18-2024). \u0026nbsp; Consent from all subjects included in the study was obtained according to the Declaration of Helsinki. and the Principles of Good Clinical Practice (ICH 1996).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Informed consent was obtained from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed and approved the manuscript for publication. Consent for the publication of images or personal or clinical details of participants in this study is \u0026ldquo;Not Applicable\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no grants, funds, or any other support were received during the preparation of this manuscript.\u0026rdquo;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the research conception and design. R. M., M. N., R.A., M. T., and A. G., performed material preparation, data collection, and analysis. The first draft of the manuscript was written by Prof R.M., Prof. M.N., they also supervised the project. All authors read and approved the final manuscript.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge all individuals for their participation in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBagcchi S. WHO\u0026apos;s Global Tuberculosis Report 2022. Lancet Microbe. 2023; 4(1): e20. DOI: 10.1016/S2666-5247(22)00359-7 \u003c/li\u003e\n\u003cli\u003eBartolomeu-Gon\u0026ccedil;alves G, Souza JM, Fernandes BT, et al. Tuberculosis Diagnosis: Current, Ongoing, and Future Approaches. 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Diagnostics (Basel). 2020;10(9):716. doi: 10.3390/diagnostics10090716. \u003c/li\u003e\n\u003cli\u003eHe W, Tan Y, Song Z, et al. Endogenous relapse and exogenous reinfection in recurrent pulmonary tuberculosis: A retrospective study revealed by whole genome sequencing. Front. Microbiol. 2023;14:1115295. DOI: 10.3389/fmicb.2023.1115295\u003c/li\u003e\n\u003cli\u003eJeong JH, Shim SR, Han S, Hwang I et al. Diagnostic performance of biomarkers for differentiating active tuberculosis from latent tuberculosis: a systematic review and Bayesian network meta-analysis. Front. Microbiol. 2014;15:1506127.doi: 10.3389/fmicb.2024.1506127\u003c/li\u003e\n\u003cli\u003eKrivo\u0026scaron;ov\u0026aacute; M, Doh\u0026aacute;l M, M\u0026auml;siarov\u0026aacute; S, et al. Exploring cytokine dynamics in tuberculosis: A comparative analysis of patients and controls with insights from three-week antituberculosis intervention. PLoS One. 2024;19(8):e0305158. doi: 10.1371/journal.pone.0305158. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Tuberculosis Report 2022. World Health Organization; Geneva, Switzerland: 2022. [accessed on 1 February 2023)]. Licence: CC BY-NC-SA 3.0 IGO. \u003c/li\u003e\n\u003cli\u003eElbrolosy AM, El Helbawy RH, Mansour OM, et al. Diagnostic utility of GeneXpert MTB/RIF assay versus conventional methods for diagnosis of pulmonary and extra-pulmonary tuberculosis. BMC Microbiol. 2021;21(1):144. doi: 10.1186/s12866-021-02210-5. \u003c/li\u003e\n\u003cli\u003eRamachandran S, Pajanivel R. The Imperative Role of Xpert\u0026reg; Mycobacterium Tuberculosis Complex/Resistance to Rifampin (MTB/RIF) in Rapid Diagnosis of Pulmonary and Extrapulmonary Tuberculosis. Cureus. 2024;16(12):e76706. DOI: 10.7759/cureus.76706. \u003c/li\u003e\n\u003cli\u003eKhan A, Shah K, Abdeljawad T, et al. Fractal fractional model for tuberculosis: existence and numerical solutions. Sci Rep. 2024; 14: 12211. https://doi.org/10.1038/s41598-024-62386-4\u003c/li\u003e\n\u003cli\u003eMassou F, Fandohan M, Wachinou AP, et al. Spot specimen testing with GeneXpert MTB/RIF results compared to morning specimen in a programmatic setting in Cotonou, Benin. BMC Infect Dis. 2021; 21: 979. https://doi.org/10.1186/s12879-021-06676-6\u003c/li\u003e\n\u003cli\u003eAwad SF, Dargham SR, Omori R, et al. Analytical Exploration of Potential Pathways by which Diabetes Mellitus Impacts Tuberculosis Epidemiology. Sci Rep. 2019;9:8494. https://doi.org/10.1038/s41598-019-44916-7 \u003c/li\u003e\n\u003cli\u003eRazbek J, Daken M, Chen Y, et al. Association Studies of Serum Levels of TNF- \u0026alpha;, IL-10, IFN-\u0026gamma; and CXCL 5 with Latent Tuberculosis Infection in Close Contacts. Infect Drug Resist. 2024;17:899-910. doi: 10.2147/IDR.S442682. \u003c/li\u003e\n\u003cli\u003eKhan AS, Phelan JE, Khan MT, et al. Characterization of rifampicin-resistant Mycobacterium tuberculosis in Khyber Pakhtunkhwa, Pakistan. Sci Rep. 2021;11:14194. https://doi.org/10.1038/s41598-021-93501-4\u003c/li\u003e\n\u003cli\u003ePeng Y, Meng L, Hu et al., Tuberculosis in Patients with Primary Myelofibrosis During Ruxolitinib Therapy: Case Series and Literature Review. Infection and drug resistance. 2020:13 3309-3316. DOI: 10.2147/IDR.S267997.\u003c/li\u003e\n\u003cli\u003eTiwari D, Martineau AR. Inflammation-mediated tissue damage in pulmonary tuberculosis and host-directed therapeutic strategies. Semin Immunol. 2023;65:101672. DOI: 10.1016/j.smim.2022.101672. \u003c/li\u003e\n\u003cli\u003eWei Z, Li Y, Wei C. et al. The meta-analysis for ideal cytokines to distinguish the latent and active TB infection. BMC Pulm Med. 2020;20:248. https://doi.org/10.1186/s12890-020-01280-x\u003c/li\u003e\n\u003cli\u003eLubis HML, Lubis MND, Delyuzar D. Interleukin-4 Cytokine as an Indicator of the Severity of Tuberculous Lymphadenitis. Maced J Med Sci. 2021;9(A):82-6. Available from: https://oamjms.eu/index.php/mjms/article/view/5667\u003c/li\u003e\n\u003cli\u003eJafrasteh A , Karimi A , Hoseinialfatemi SH, et al., Evaluation of Interleukin-2 to Detect Active and Latent Tuberculosis among Household Contacts of Pulmonary Tuberculosis Cases. Arch Pediatr Infect Dis. 2021; 9(2):e109398. DOI: 10.5812/pedinfect.109398.\u003c/li\u003e\n\u003cli\u003eSampath P, Rajamanickam A, Thiruvengadam K, et al. Cytokine upsurge among drug-resistant tuberculosis endorse the signatures of hyper inflammation and disease severity. Sci Rep. 2023;13(1):785. doi: 10.1038/s41598-023-27895-8. \u003c/li\u003e\n\u003cli\u003eShey MS, Balfour A, Masina N, et al. Mycobacterial-specific secretion of cytokines and chemokines in healthcare workers with apparent resistance to infection with Mycobacterium tuberculosis. Front Immunol. 2023;14:1176615. doi: 10.3389/fimmu.2023.1176615.\u003c/li\u003e\n\u003cli\u003eSa\u0026rsquo;ad M, Abba AA, Musa BOP, et al. Assessment of interleukin 6 (IL-6) as a marker of inflammation among adult patients with pulmonary tuberculosis in Zaria, Nigeria. Egypt J Bronchol. 2024;18:8. https://doi.org/10.1186/s43168-024-00263-4\u003c/li\u003e\n\u003cli\u003eAhmed M, Tezera LB, Elkington PT, Leslie AJ. The paradox of immune checkpoint inhibition re-activating tuberculosis. Eur Respir J. 2022;60(5):2102512. doi: 10.1183/13993003.02512-2021. \u003c/li\u003e\n\u003cli\u003eElbrolosy AM, El Helbawy RH, Mansour OM et al. Diagnostic utility of GeneXpert MTB/RIF assay versus conventional methods for diagnosis of pulmonary and extra-pulmonary tuberculosis. BMC Microbiol 2021;21,144. https://doi.org/10.1186/s12866-021-02210-5\u003c/li\u003e\n\u003cli\u003eWu J, Xiao P, Zhang Y et al., Evaluation of the Effectiveness of Global Tuberculosis Control Strategies at Different Stages and Analysis of Risk Factors: Findings From the Global Burden of Disease 2021, Archivos de ronconeumolog\u0026iacute;a, https://doi.org/10.1016/j.arbres.2024.11.017\u003c/li\u003e\n\u003cli\u003eRanaivomanana P, Ratovoson R, Razafimahatratra C, et al. Longitudinal Variations of M. tuberculosis-Induced IFN-g Responses in HIV-Negative Pregnant Women Exposed to Tuberculosis. Front. Immunol. 2021;12:805157. doi: 10.3389/fimmu.2021.805157\u003c/li\u003e\n\u003cli\u003eDonniacuo A, Mauro A, Cardamone C, Basile A, Manzo P, Dimitrov J, Cammarota AL, Marzullo L, Triggiani M, Turco MC, et al. Comprehensive Profiling of Cytokines and Growth Factors: Pathogenic Roles and Clinical Applications in Autoimmune Diseases. International Journal of Molecular Medicine 2025; 26(18):8921. https://doi.org/10.3390/ijms26188921\u003c/li\u003e\n\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-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"IL-4, IL-10, Mycobacterium Tuberculosis, T helper 2, TB","lastPublishedDoi":"10.21203/rs.3.rs-9168542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9168542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis (TB) is a global health problem that claims millions of lives each year. Unfortunately, the current diagnostic markers and therapeutic options are not satisfactory. Understanding the immune pathogenesis provides insights into more reliable diagnostic and therapeutic options. T helper 2 cells' cytokines are known to inhibit cell-mediated immune response and adversely affect the progression of mycobacterial infections.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis research aims to elucidate the ability of IL-4 and IL-10 serum levels to differentiate between latent and active TB infections and to serve as an indicator for the disease severity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study population consisted of eighty TB patients. Patients were classified into active TB infection group (40) and latent group (40) based on the presence of clinical symptoms, tuberculin test, sputum acid-fast bacilli and GeneXpert MTB/RIF testing. All patients were subjected to clinical examination, CT chest evaluation, IL-4 and IL-10 serum level measurement.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIL-4 and IL-10 levels were significantly higher in the active TB than in latent TB group. ROC curve analysis, showed cut-off values of \u0026gt;\u0026thinsp;88 pg/mL for IL-4 and \u0026gt;\u0026thinsp;119 pg/mL for IL-10 in differentiating active from latent TB, with 100% sensitivity, specificity, positive predictive value, and negative predictive value. Serum levels of IL-4 and IL-10 correlated with bacterial load and disease severity. IL-4 levels were statistically significantly higher in patients with rifampicin-sensitive strains; however, the IL-10 levels were not.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIL-4 and IL-10 serum levels can differentiate between latent and active TB infections and perfectly correlate with the severity of the disease.\u003c/p\u003e","manuscriptTitle":"IL-4 and IL-10 as a diagnostic and predictive factor for the severity of Mycobacterium Tuberculosis Infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 19:58:34","doi":"10.21203/rs.3.rs-9168542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:30:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221109198924638377538274532607986800897","date":"2026-04-25T13:17:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282780707027412334251860208070171601465","date":"2026-04-25T07:37:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T12:02:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124768594638703720569801052470528906452","date":"2026-04-23T11:31:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T10:14:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126298678456779182144975237778297690537","date":"2026-04-23T09:48:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225193867367373031381713487871229244138","date":"2026-04-23T08:26:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T07:16:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T13:19:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T11:05:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T12:53:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-03-31T12:41:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54e8a941-c978-4562-938b-6ce6a7760b9c","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T14:30:41+00:00","index":53,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T19:58:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 19:58:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9168542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9168542","identity":"rs-9168542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}