Method For Detection Of Tuberculosis Research Articles

FDG-PET-CT as an early detection method for tuberculosis: a systematic review and meta-analysis

Tuberculosis (TB) causes major public health problems worldwide. Fighting TB requires sustained efforts in health prevention, diagnosis and treatment. Previous literature has shown that conventional diagnostic methods like X-ray and sputum microscopy often miss early or extrapulmonary TB due to their limited sensitivity. Blood tests, while useful, lack the anatomical detail needed for precise localization of TB lesions. A possible step forward in the fight against TB could be the use of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and Computed Tomography (CT). This meta-analysis discusses the current literature, including the methods, results and implications of using FDG-PET-CT in the early diagnosis of TB. Analysis of the studies showed that the sensitivity of FDG-PET-CT as a potential method for early detection of TB was 82.6%.

Breathomics for diagnosing tuberculosis in diabetes mellitus patients.

Individuals with diabetes mellitus (DM) are at an increased risk of Mycobacterium tuberculosis (Mtb) infection and progressing from latent tuberculosis (TB) infection to active tuberculosis disease. TB in the DM population is more likely to go undiagnosed due to smear-negative results. Exhaled breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry. An eXtreme Gradient Boosting (XGBoost) model was utilized for breathomics analysis and TB detection. XGBoost model achieved a sensitivity of 88.5%, specificity of 100%, accuracy of 90.2%, and an area under the curve (AUC) of 98.8%. The most significant feature across the entire set was m106, which demonstrated a sensitivity of 93%, specificity of 100%, and an AUC of 99.7%. The breathomics-based TB detection method utilizing m106 exhibited high sensitivity and specificity potentially beneficial for clinical TB screening and diagnosis in individuals with diabetes.

Developing a method to detect lipoarabinomannan in pleural fluid and assessing its diagnostic efficacy for tuberculous pleural effusion

ObjectivesThe diagnosis of tuberculosis pleural effusion (TPE) remains challenging, traditional diagnostic tests have limited diagnostic efficacy. This study aimed to assess the diagnostic performance of pleural fluid (PF) lipoarabinomannan (LAM) in TPE. MethodsA diagnostic method for PF LAM (LAM-PF) was established using LDEBIO's AIMLAM kit. The diagnostic performance of LAM-PF was evaluated in 162 HIV-negative patients with suspected TPE. ResultsThe LAM-PF method established in this study exhibited good linearity and recovery rate, with a limit of detection (LOD) of 2.90 pg/mL. Using a cut-off value of 5.33 pg/mL, the sensitivity and specificity of LAM-PF in diagnosing TPE (n = 128) were 47.7% and 100.0%, respectively. The sensitivity in patients with probable TPE (n = 29) and definite TPE (n = 99) were 41.4% and 49.5%, respectively. LAM-PF displayed a significantly higher sensitivity in probable TPE compared to other tuberculosis detection methods. Combined testing of adenosine deaminase (ADA)and LAM increased the detection sensitivity of TPE to 68.0%, and the area under the curve was 0.84 (0.77–0.89). ConclusionThis study successfully established a method for detecting LAM in PF, which exhibited favorable diagnostic performance for TPE, particularly in challenging cases of probable TPE. Combined detection of LAM and ADA in PF significantly improves TPE diagnostic efficiency.

T-BACCO SCORE: A predictive scoring tool for tuberculosis (TB) loss to follow-up among TB smokers.

Loss to follow-up (LTFU) and smoking during TB treatment are major challenges for TB control programs. Smoking increases the severity and prolongs TB treatment duration, which lead to a higher rate of LTFU. We aim to develop a prognostic scoring tool to predict LTFU among TB patients who smoke to improve successful TB treatment outcomes. The development of the prognostic model utilized prospectively collected longitudinal data of adult TB patients who smoked in the state of Selangor between 2013 until 2017, which were obtained from the Malaysian Tuberculosis Information System (MyTB) database. Data were randomly split into development and internal validation cohorts. A simple prognostic score (T-BACCO SCORE) was constructed based on the regression coefficients of predictors in the final logistic model of the development cohort. Estimated missing data was 2.8% from the development cohort and was completely at random. Model discrimination was determined using c-statistics (AUCs), and calibration was based on the Hosmer and Lemeshow goodness of fit test and calibration plot. The model highlights several variables with different T-BACCO SCORE values as predictors for LTFU among TB patients who smoke (e.g., age group, ethnicity, locality, nationality, educational level, monthly income level, employment status, TB case category, TB detection methods, X-ray categories, HIV status, and sputum status). The prognostic scores were categorized into three groups that predict the risk for LTFU: low-risk (<15 points), medium-risk (15 to 25 points) and high-risk (> 25 points). The model exhibited fair discrimination with a c-statistic of 0.681 (95% CI 0.627-0.710) and good calibration with a nonsignificant chi-square Hosmer‒Lemeshow's goodness of fit test χ2 = 4.893 and accompanying p value of 0.769. Predicting LTFU among TB patients who smoke in the early phase of TB treatment is achievable using this simple T-BACCO SCORE. The applicability of the tool in clinical settings helps health care professionals manage TB smokers based on their risk scores. Further external validation should be carried out prior to use.

Development of a new cellular immunological detection method for tuberculosis based on HupB protein induced IL-6 release test.

As a virulence factor, HupB plays important roles in the survival of MTB after infection and modulates the host immune response. In the current study, we aim to explore a new cellular immunological detection method for tuberculosis infection detection based on HupB protein. HupB was used to stimulate PBMCs extracted from pulmonary tuberculosis (PTB) patients, and secreted cytokines was examined. Then, we constructed a single center and a multi-center clinical trials to collect PBMCs from PTB patients, nPTB patients, or healthy volunteers to verify our findings. Cytokine's screening illustrated that IL-6 was the only cytokine released after HupB stimulation. Single-center and multi-center clinical trials showed that HupB stimulation significantly increased the level of IL-6 in the supernatant of PBMCs from PTB patients. Then we compared the specificity and sensitivity of HupB induced IL-6 release assay with ESAT-6 and CFP10 induced interferon γ release assay (IGRA), and found in smear positive PTB patients, the specificity and sensitivity of HupB induced IL-6 release assay was better than IGRA, and in smear negative PTB patients, the sensitivity was better. Combination of both assays provided an improved specificity and sensitivity for tuberculosis diagnosis. This study explored an immunological detection method for tuberculosis infection cells based on HupB protein-induced IL-6 release test, which can be used to enhance the diagnosis diagnostic accuracy of TB.

A cross-sectional study: a breathomics based pulmonary tuberculosis detection method

BackgroundDiagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection.MethodExhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients.ResultsThe breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961.ConclusionsThe simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

Recent progress in the genotyping of bovine tuberculosis and its rapid diagnosis via nanoparticle-based electrochemical biosensors.

Bovine tuberculosis (bTB) is considered a worldwide infectious zoonotic disease. Mycobacterium bovis causes bTB disease. It is one of the Mycobacterium tuberculosis complex (MTBC) members. MTBC is a clonal complex of close relatives with approximately 99.95% similarity. M. bovis is a spillover pathogen that can transmit from animals to humans and rarely from humans to animals with contact. Genotyping techniques are important to discriminate and differentiate between MTBC species. Spoligotyping and mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) are widely used but they have some limitations. As an alternative, whole genome sequencing approaches have been utilized due to their high-resolution power. They are employed in typing M. bovis and explain the evolutionary and phylogenetic relationships between isolates. The control of bTB disease has attracted a large amount of attention. Rapid and proper diagnosis is necessary for monitoring the disease as an initial step for its control and treatment. Nanotechnology has a potential impact on the rapid diagnosis and treatment of bTB through the use of nanocarrier and metal nanoparticles (NPs). Special attention has been paid to voltammetric and impedimetric electrochemical strategies as facile, sensitive, and selective methods for the efficient detection of tuberculosis. The efficacy of these sensors is enhanced in the presence of NPs, which act as recognition and/or redox probes. Gold, silver, copper, cobalt, graphene, and magnetic NPs, as well as polypyrrole nanowires and multiwalled carbon nanotubes have been employed for detecting tuberculosis. Overall, NP-based electrochemical sensors represent a promising tool for the diagnosis of bTB.

THE MOLECULAR STUDY OF LncRNA “NEAT2” FOR EARLY DIAGNOSIS OF TB IN POPULATION OF MADHYA PRADESH

One of the deadliest infectious diseases in the world is tuberculosis (TB), a chronic disease brought on by the bacteria Mycobacterium tuberculosis. There is still no viable method for early screening, despite substantial advancements in tuberculosis detection methods and therapeutic strategies. Different cellular processes can hinder the growth of TB due to dysregulation of gene expression patterns connected to particular substances. Long non-coding RNAs (lncRNAs) are molecules that have been found to control the expression of protein-coding genes and take part in processes like cell cycle control, gene silencing, and cellular differentiation. It has been discovered that dysregulation of lncRNAs is linked to a variety of illnesses, including cancer and infectious diseases. Therefore, it is encouraging that lncRNAs have been identified as potential molecular indicators and therapeutic targets for tuberculosis. In present study. Human blood samples from the TB patients and healthy individuals from Sidhi District of Madhya Pradesh were collected. Total RNA were extracted from blood samples of using TRIzol Reagent (Takara) according to the manufacturer’s protocol. cDNA were prepared using synthesis kit (Biorad), In the present investigation gene expression of LncRNA “NEAT2” and IFNG were assessed in WBCs of TB Patients and Healthy individuals, using Real Time PCR technique. This study reported Lnc RNA NEAT2 up-regulation, which reflect significant involvement of this LncRNA in pathogenesis of TB and it may be one of the potential marker for the early diagnosis of TB.

Deep learning based pulmonary nodule detection method research

In the development of modern social and economic construction, the level of scientific research and technology in various countries is getting higher and higher. The medical field began to use intelligent algorithms for detection and analysis, and achieved excellent results in practice. It is known that lung cancer is one of the most common diseases affecting human life and health, among which it ranks the first among male malignant tumor diseases and the second among female malignant tumor diseases. On the basis of understanding the research status of intelligent algorithms in the new era, this paper mainly studies the principle of tuberculosis detection methods with deep learning as the core according to the tuberculosis detection methods proposed by researchers in recent years. In the end, deep learning could help medical researchers detect more TB, improving the accuracy and sensitivity of actual tests.

К ВОПРОСУ О ВЫЯВЛЕНИИ ТУБЕРКУЛЕЗА ОРГАНОВ ДЫХАНИЯ У ПОДРОСТКОВ ПРИ ПРОВЕДЕНИИ ПЛАНОВОЙ ФЛЮОРОГРАФИИ: ВЗГЛЯД ФТИЗИАТРА

The scheduled, routine fluorography does not allow detecting of the primary small forms of tuberculosis (TB) and the limited prevalence processes without bacterial excretion. The purpose of the study was to evaluate the contribution and the informative content of the scheduled fluorography as a screening method for the early detection of TB in adolescents. Materials and methods: a single-center retrospective cohort study of 142 disease history cases of adolescents admitted for treatment at the Children's and Adolescents Department with the Central Research Institute of Tuberculosis (Moscow, Russia) at the age of 15 to 17 years old, of which 74 (52.1%) were girls, and 68 (47.9%) boys, with respiratory TB. The continuous sample from 2015 to 2020, 34 (24.0%) of which were identified with the scheduled, routine fluorography. The data for inclusion in the study was obtained from medical records submitted upon admission. Results: according to the routine fluorography results, the infiltrative TB of the respiratory organs was diagnosed statistically significantly more often compared to the results of skin immunological tests: in 76.5% and 20.5% of cases, respectively, р˂0.001; the lung tissue decay in 58.8% and 15.4% of cases, respectively, p˂0.001; and the bacterial excretion in 58.8% and 5.1% of cases, respectively, p˂0.001. This indicates a high epidemic risk of patients. Conclusion: the routine fluorography in adolescents does not correspond to the concept of screening for TB infection, the essence of which is the early detection of the disease. From the authors’ point of view, the expediency of fluorography in this age group should be considered primarily as the first - and not the only - stage for diagnostic approach in the acute period of a pulmonary disease.

Prevalence of Rifampicin-Resistant Mycobacterium Tuberculosis by CBNAAT in a Tertiary Care Hospital of West Bengal, India

Background: Tuberculosis (TB) remains one of the commonest infectious diseases in India, where control is difficult due to the emergence of multi-drug-resistant TB (MDR-TB). The rifampicin resistance (RR) is an important surrogate marker of MDR-TB. Aims and Objectives: The aim of this article is to determine the prevalence of RR-TB among all symptomatic presumptive TB patients by cartridge-based nucleic acid amplification test (CBNAAT) in our institution. Materials and Methods: A retrospective cross-sectional study was conducted from January 2018 to December 2020 in the Directly Observed Treatment (DOT) center of a tertiary care hospital. All presumptive cases of pulmonary TB patients coming to the DOT center were subjected to CBNAAT by GeneXpert Mycobacterium tuberculosis rifampicin (MTB/RIF) (Cepheid). Data were collected from a data-extraction sheet from registration books and analyzed using SPSS version 20. Fisher’s exact test was applied to examine the association between categorical variables and P-value less than 0.05 was considered significant. Results: A total of 6171 presumptive TB patients were included in the study. The overall prevalence of TB was 1590 (25.77%) in all age groups. The prevalence of RR-TB among all confirmed TB cases was 10.75% (171/1590) with the majority being males (130 cases). The prevalence of RR cases among new and previously treated was 3.1% and 36.34%, respectively, which were statistically significant (P &lt; 0.05). Conclusion: We found that RR-TB is still much prevalent in our area, and it was significantly higher in previously treated cases. The CBNAAT is a good molecular assay method for rapid detection of TB and also for the detection of RR-TB.

A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features

Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.

RAPID AND SPESIFIC DETECTION OF MYCOBACTERIUM TUBERCULOSIS USING POLYMERASE CHAIN REACTION

Background: Tuberculosis (TB) is one of the major causes of health burden worldwide, especially in lower middle-income countries. TB is caused by Mycobacterium tuberculosis (MTB) and characterized by severe condition incuding coughing and fever. Purpose: To review the current methods for detection of TB using Polymerase Chain Reaction (PCR). Review: several studies have been done to give valuable insight into TB transmission, diagnosis, and treatment, however research is constantly needed to decrease the incidence of eradicate TB. This infectious disease still give big health problem in all over the world by being second in causing high mortality rates after HIV/AIDS. A specific, sensitive, rapid and cheap method for TB and other mycobacteria diagnosis in clinical specimen is a desperate needed in the laboratory diagnosis and hence management of tuberculosis. PCR as one of nucleic acid amplification assays have revolutionized MTB detection. Since it was first invented in fifteen years ago, it’s been through many developments. Conclusion: PCR is one of the most specific and sensitive method currently available for TB diagnosis that can also detect in in all types of specimens obtained from TB patients.

Polyamic acid (PAA) immobilized on glassy carbon electrode (GCE) as an electrochemical platform for the sensing of tuberculosis (TB) antibodies and hydrogen peroxide determination

Tuberculosis (TB) is a chronic infectious disease that usually affects the pulmonary region of human beings. It remains critically important to develop cost effective methods for TB detection, especially since impoverished communities are often the target population. Immunological assays are commonly used for the detection of TB and are typically based on selected antibody (Ab) antigen (Ag) interactions as measured by the enzyme-linked immunosorbent assay (ELISA). It is also common to have tagged antibody/antigen binding pairs where the signaling is produced by the tag as is the case with the indirect tuberculosis IgG ELISA Kit, where a horseradish peroxidase (HRP) tag on the antibody is responsible for the analytical signal. Mycobacterium tuberculosis (MTB), recombinant protein antigen 85B (Ag85B), is the most abundant protein exposed by Mycobacterium tuberculosis acting as a potent immuno-protective antigen. In this work, both Ag85B and antibody-HRP (Ab-HRP) were immobilized on a semi-conductive polyamic acid (PAA) electrochemical interface towards the design of new electrochemical sensors. In a first approach, the antigen, Ag85B, was employed as the bioreceptor to afford label-free TB signaling through antibody detection and the obtained limit of detection was 8 μg/mL of antibody. The response was evaluated by voltammetric methods and electron impedance spectroscopy (EIS). Cyclic voltammetry (CV) and EIS were used to characterize the charge transfer and capacitive behavior of the label-free sensor. In a second approach, the HRP tagged antibody was used for the design of a stable hydrogen peroxide (H2O2) sensor with very good analytical performance with a limit of detection equal to 1.3 μM. Even though we developed an ultrasensitive H2O2 sensor, no further development of TB antigen detection was possible in this work.

Trends in Diagnosis for Active Tuberculosis Using Nanomaterials.

Tuberculosis (TB), one of the leading causes of death worldwide, is difficult to diagnose based only on signs and symptoms. Methods for TB detection are continuously being researched to design novel effective clinical tools for the diagnosis of TB. This article reviews the methods to diagnose TB at the latent and active stages and to recognize prospective TB diagnostic methods based on nanomaterials. The current methods for TB diagnosis were reviewed by evaluating their advantages and disadvantages. Furthermore, the trends in TB detection using nanomaterials were discussed regarding their performance capacity for clinical diagnostic applications. Current methods such as microscopy, culture, and tuberculin skin test are still being employed to diagnose TB, however, a highly sensitive point of care tool without false results is still needed. The utilization of nanomaterials to detect the specific TB biomarkers with high sensitivity and specificity can provide a possible strategy to rapidly diagnose TB. Although it is challenging for nanodiagnostic platforms to be assessed in clinical trials, active TB diagnosis using nanomaterials is highly expected to achieve clinical significance for regular application. In addition, aspects and future directions in developing the high-efficiency tools to diagnose active TB using advanced nanomaterials are expounded. This review suggests that nanomaterials have high potential as rapid, costeffective tools to enhance the diagnostic sensitivity and specificity for the accurate diagnosis, treatment, and prevention of TB. Hence, portable nanobiosensors can be alternative effective tests to be exploited globally after clinical trial execution.

A Clinical TB Detection Method Based on Molecular Typing Technique with Quality Control.

The gold standard for diagnosing pulmonary Mycobacterium tuberculosis (TB) is the detection of tubercle bacillus in patient sputum samples. However, current methods either require long waiting times to culture the bacteria or have a risk of getting false-positive results due to cross-contamination. In this study, a method to detect tubercle bacillus based on the molecular typing technique is presented. This method can detect genetic units, variable number of tandem repeat (VNTR), which are the characteristic of tuberculosis (TB), and performs quality control using a mathematical model, ensuring the reliability of the results. Compared to other methods, the proposed method was able to process and diagnose a large volume of samples in a run time of six hours, with high sensitivity and specificity. Our method is also in the pipeline for implementation in clinical testing. Reliable and confirmed results are stored into a database, and these data are used to further refine the model. As the volume of data processed from reliable samples increases, the diagnostic power of the model improves. In addition to improving the quality control scheme, the collected data can be also used to support other TB research, such as that regarding the evolution of the tubercle bacillus.

Identification of mycolic acid forms using surface-enhanced Raman scattering as a fast detection method for tuberculosis.

BackgroundTuberculosis (TB) is the ninth leading cause of death worldwide and the leading cause from a single infectious agent, based on the WHO Global Tuberculosis Report in 2017. TB causes massive health care burdens in many parts of the world, specifically in the resource constrained developing world. Most deaths from TB could be prevented with cost effective early diagnosis and appropriate treatment.PurposeConventional TB detection methods are either too slow as it takes a few weeks for diagnosis or they lack the specificity and accuracy. Thus the objective of this study was to develop a fast and efficient detection for TB using surface enhanced Raman scattering (SERS) technique.MethodsSERS spectra for different forms of mycolic acids (MAs) that are both synthetic origin and actual extracts from the mycobacteria species were obtained by label-free direct detection mode. Similarly, we collected SERS spectra for γ-irradiated whole bacteria (WB). Measurements were done using silver (Ag) coated silicon nanopillar (Ag SNP) as SERS substrate.ResultsWe report the SERS based detection of MA, which is a biomarker for mycobacteria species including Mycobacterium tuberculosis. For the first time, we also establish the SERS spectral characterization of the three major forms of MA – αMA, methoxy-MA, and keto-MA, in bacterial extracts and also in γ-irradiated WB. We validated our findings by mass spectrometry. SERS detection of these three forms of MA could be useful in differentiating pathogenic and nonpathogenic Mycobacterium spp.ConclusionsWe have demonstrated the direct detection of three major forms of MA – αMA, methoxy-MA, and keto-MA, in two different types of MA extracts from MTB bacteria, namely delipidated MA and undelipidated MA and finally in γ-irradiated WB. In the near future, this study could pave the way for a fast and efficient detection method for TB, which is of high clinical significance.

ХАРАКТЕРИСТИКА БОЛЬНЫХ, ЗАБОЛЕВШИХ ТУБЕРКУЛЕЗОМ, В Г. КИШИНЭУ В 2015 Г.

The objective of the study: to characterize patients who developed tuberculosis in Chisinau in 2015. Subjects and methods. The retrospective descriptive study was conducted aimed to analyze data of 668 tuberculosis patients diagnosed in Chisinau in 2015. Results. The social characteristics demonstrated that in 2015 in Chisinau a tuberculosis patient was most often a young man belonging to socially marginalized population with no medical insurance. The analysis of tuberculosis detection methods showed that only 22.3% of patients were detected through regular screening in risk groups. Among tuberculosis patients, new cases made 60.3%, relapses – 21.7%, those after treatment interruption – 10.3%, and those after treatment failure – 5.7%. In 14 (2.0%) patients, tuberculosis was detected post-mortem. Out of 674 patients with life time diagnosed tuberculosis, pulmonary tuberculosis prevailed – 638 (94.7%), of them, 193 (30.3%) had a positive result of sputum microscopy for acid-fast bacilli, and 187 (29.3%) patients had a positive result of sputum culture by Lowenstein-Jensen or Bactec 960, and the result of Xpert MTB/Rif was positive in 259 (40.6%) patients. Multiple drug resistance was diagnosed in 118/187 (63.1%) patients. In 12 months, treatment outcomes were assessed in 647 patients: treatment was successful in 393 (60.7%) patients; 80 (12.4%) patients died, and 174 (26.9%) were still on treatment.

Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.

Tuberculosis (TB) is a leading global public health problem. To achieve the end TB strategy, non-invasive markers for diagnosis and treatment monitoring of TB disease are urgently needed, especially in high-endemic countries such as China. Interferon-gamma release assays (IGRAs) and tuberculin skin test (TST), frequently used immunological methods for TB detection, are intrinsically unable to discriminate active tuberculosis (ATB) from latent tuberculosis infection (LTBI). Thus, the specificity of these methods in the diagnosis of ATB is dependent upon the local prevalence of LTBI. The pathogen-detecting methods such as acid-fast staining and culture, all have limitations in clinical application. ImmunoScore (IS) is a new promising prognostic tool which was commonly used in tumor. However, the importance of host immunity has also been demonstrated in TB pathogenesis, which implies the possibility of using IS model for ATB diagnosis and therapy monitoring. In the present study, we focused on the performance of IS model in the differentiation between ATB and LTBI and in treatment monitoring of TB disease. We have totally screened five immunological markers (four non-specific markers and one TB-specific marker) and successfully established IS model by using Lasso logistic regression analysis. As expected, the IS model can effectively distinguish ATB from LTBI (with a sensitivity of 95.7% and a specificity of 92.1%) and also has potential value in the treatment monitoring of TB disease.

Accurate and effective multidrug-resistant Mycobacterium tuberculosis detection method using gap-filling ligation coupled with high-resolution capillary electrophoresis-based single strand conformation polymorphism

Tuberculosis (TB) has severely threatened public health via emerging multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis (MTB) strains. For effective TB treatment, rapid, accurate, and multiplex detection of drug resistance is extremely important. However, conventional methods for TB diagnosis are time consuming and have a limited effect on treatment. Nucleic acid-based molecular detection methods have been developed as an effective MDR/XDR-TB diagnosis technology. Among the nucleic acid-based methods, ligation-dependent methods are attractive as MDR/XDR-MTB detection technologies, but multiplex analysis is limited by the detection method. Although an electrophoresis-based method is considered for multiple target detection because it is free from the errors pertaining to hybridization-based systems, the procedure of multiplex analysis is quite complicated owing to the DNA size-based separation system. In this study, we report an accurate, rapid, and simple multiple MDR/XDR-MTB detection technology using gap-filling ligation reaction coupled with high-resolution capillary electrophoresis-based single-strand conformation polymorphism. Using this system, rapid and accurate MDR/XDR-MTB detection is feasible via similar length probes without the complicated step of probe design. We found that this method could accurately and effectively detect highly polymorphic regions in specific codons associated with drug resistance.