Detection Of Pulmonary Tuberculosis Research Articles

GeneXpert Assay in Detection of Pulmonary Tuberculosis and Rifampicin Tuberculosis Resistant in Tehsil Hazro Population; A Cross Sectional Study

Objective: To determine the prevalence of Tuberculosis and MDR Tuberculosis by GeneXpert assay in Tehsil Hazro District Attock. Study design: cross sectional study Place and duration of study: Study was carried out at THQ hospital Hazro of District Attock from January 2019 to December 2020 Patients and Methods: Total 1460 patients of TB suspect were included in the study. First LED microscopy of all TB suspects was done. LED Microscopy positive cases along with suspects having history of HIV, close contacts of MDR TB, all TB cases of retreatment and sputum-negative cases of LED microscopy having positive clinical findings were sent for GeneXpert Assay. Results from the GeneXpert assay were categorized as GeneXpert positive, GeneXpert negative, and MDR Tuberculosis. Determinants of MDR- TB were identified using questionnaires regarding MDR –TB. Results: Out of total 205 patients on GeneXpert Assay, 43 patients were GeneXpert positive test with no rifampicin resistance, 3 patients were GeneXpert positive with rifampicin resistance and 63 patients were GeneXpert negative but sign and symptoms, X-ray findings and laboratory tests were supporting tuberculosis and according to opinion of TB specialist they were labeled as Sputum negative Tuberculosis. Tuberculosis prevalence in Tehsil Hazro was 0.073. From the results, it was concluded that an overall MDR rate was 2.7% in our study population. Conclusion: GeneXpert Assay is a rapid technique and reliable for Mycobacterium detection. The main determinants of MDR-TB were the patients who had contact h/o with MDR cases and those who did not follow the treatment regime in their previous Tuberculosis treatment. Keywords: GeneXpert Assay, LED microscopy, MDR Tuberculosis, Sputum positive TB, Sputum negative TB.

Diagnostic accuracy of truenat MTB plus for the detection of pulmonary and extrapulmonary tuberculosis

BackgroundThe diagnosis of Tuberculosis (TB) has been a challenge till the advent of rapid molecular diagnostic tests. The traditional diagnostic tests have its own limitations with regard to its performance or the turnaround time. Truenat MTB Plus assay, a battery-operated molecular assay developed in India has been introduced for its use in pulmonary TB (PTB). However, the diagnostic accuracy of the assay is not well studied in comparison with Mycobacterial culture, especially for extrapulmonary TB (EPTB). AimWe aimed at evaluating the diagnostic accuracy of Truenat MTB Plus assay for both PTB and EPTB comparing with culture for adult population. MethodsThe specimens from presumptive PTB and EPTB patients were processed for Truenat MTB Plus assay, solid or liquid culture and AFB staining. The electronic data of all the specimen reports collected retrospectively were analysed for the sensitivity and specificity. ResultsOut of the 736 samples which had valid culture reports, 364 (49.4 %) were respiratory and 372 (50.6 %) were extrapulmonary specimens. The test positivity rate for smear microscopy, Truenat MTB Plus assay and culture was 3.7 % (27), 8.2 % (60), 7.1 % (52) respectively. Of the 60 Truenat MTB Plus positive patients with TB, 33 (55 %) were PTB and 27 (45 %) were EPTB. We estimated overall sensitivity and specificity of Truenat MTB Plus as 90 % (95 % CI: 73.4–97.8) and 98. 2 (95 % CI:96–99.3) respectively for the detection of PTB. The overall sensitivity and specificity for EPTB was 81.8 % (95 % CI: 59.7–94.8) and 97.4 % (95 % CI: 95.1–98.8) respectively. ConclusionsTruenat MTB Plus assay has comparable diagnostic accuracy with other molecular assays. The Truenat MTB Plus assay can be used for the diagnosis of PTB and EPTB, especially in resource limited settings.

Detection of pulmonary tuberculosis in the republic of Tajikistan

Objective: To study the dynamics and main reasons for the significant detection of pulmonary tuberculosis with a negative sputum test result in the Republic of Tajikistan over the past 5 years. Material and methods: An analysis of the estimated data of the WHO, as well as official statistics on TB incidence using the National TB Data Registry for 2019-2023, was carried out. Definitions of clinically established cases of pulmonary TB are noted. Information is presented on the introduction of the express PCR method of bacteriological verification of TB diagnosis in all regions of the country. Results: Over the past 5 years (2019-2023), WHO estimates on TB incidence in the Republic of Tajikistan have significantly decreased, indicating improved accessibility of the population to specialized anti-TB services. At the same time, during the COVID period (2019-2020), the prognosis was more unfavorable due to deteriorating accessibility to medical and social services than in the post-COVID period (2021-2023). At the same time, official statistics on the detection of new TB cases indicate the opposite dynamics: during the COVID period, the number of TB patients sharply decreased from 5976 to 4299 cases that is, by 1677 cases (more than 28%) and began to gradually increase in subsequent years, but has not yet reached the pre-COVID level. At the same time, in proportion to the indicated data, the number of clinically established cases of pulmonary TB decreased during the COVID period from 1422 to 720 cases (2019-2021), that is, almost twofold, and in the following years (2022-2023) began to gradually increase, still not reaching the pre-COVID period. Conclusion: Thus, despite the active introduction of new innovative methods for verifying the diagnosis of TB and improving the population's access to diagnostic services, including molecular genetic methods for rapid diagnostics of both sensitive and drug-resistant forms of TB, the proportion of clinically established cases of pulmonary TB remains high (34% of all pulmonary forms of TB) and tends to increase.

Trend of pulmonary tuberculosis and rifampicin-resistance among tuberculosis presumptive patients in Central Tigray, Ethiopia; 2018 -2023: a six-year retrospective study

BackgroundTuberculosis (TB) is a major public health concern in the developing countries. Moreover, the emergence of multidrug-resistant tuberculosis is challenging. However, there are no organized data on the trends of pulmonary tuberculosis and rifampicin-resistant Mycobacterium tuberculosis in the study area.MethodsA retrospective cross-sectional study was conducted to fill the information gap in Central Tigray at St. Mary General Hospital between 2018 and 2023. Data were collected from the GeneXpert™ tuberculosis registration logbooks using standard checklists and analyzed using Statistical Package for Social Science version 22. After performing logistic regression, a p-value < 0.05 with a corresponding 95% confidence interval was considered statistically significant. Moreover, chi square test for trend was performed to assess the percentage of annual detection of pulmonary tuberculosis and rifampicin-resistant Mycobacterium tuberculosis during the study years.ResultPresumptive pulmonary tuberculosis patients with complete data (n = 3696) were included in the study. The overall prevalence of pulmonary tuberculosis was 11.7%, of which 8.1% were resistant to rifampicin. The study revealed that the incidence of pulmonary tuberculosis has been increasing, mainly in the recent four years. Likewise, an increase in rifampicin-resistant Mycobacterium tuberculosis was observed with considerable fluctuations. Age, human immunodeficiency virus infection, and presumptive rifampicin-resistant Mycobacterium tuberculosis infection were significantly associated with the presence of pulmonary tuberculosis. Moreover, pulmonary tuberculosis was more prevalent among participants in the productive-age group.ConclusionAlthough there have been fluctuations, an increasing of pulmonary tuberculosis and rifampicin-resistant Mycobacterium tuberculosis has been observed in recent years. Hence, prevention and treatment strategies for tuberculosis should be strengthened to alleviate the burden of pulmonary tuberculosis and rifampicin-resistant Mycobacterium tuberculosis in the study area.

Detection of pulmonary tuberculosis and rifampicin resistant tuberculosis from sputum samples in a tertiary care hospital, Delhi &amp; NCR, India

Detection of pulmonary tuberculosis and rifampicin resistant tuberculosis from sputum samples in a tertiary care hospital, Delhi &amp; NCR, India

A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography

In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection. We also collected 1150 abnormal CXRs and 627 normal CXRs for training abnormality detection. For external validation, we collected 250 CXRs from our hospital. We also compared the accuracy of the algorithm to five pulmonologists and radiological reports. In external validation, the AI algorithm showed areas under the curve (AUC) of 0.951 and 0.975 in validation dataset 1 and 2. The accuracy of the pulmonologists on validation dataset 2 showed AUC range of 0.936–0.995. When abnormal CXRs other than TB were added, AUC decreased in both human readers (0.843–0.888) and AI algorithm (0.828). When combine human readers with AI algorithm, the AUC further increased to 0.862–0.885. The TB CXR AI algorithm developed by using Google teachable machine in this study is effective, with the accuracy close to experienced clinical physicians, and may be helpful for detecting tuberculosis by CXR.

Pulmonary Tuberculosis Detection from Chest X-ray Using Deep Learning

Tuberculosis remains a formidable infectious disease, ranking among the top ten causes of global mortality. Timely detection is critical for effective treatment, yet current diagnostic methods face significant challenges. In this study, we propose a novel approach for automating tuberculosis detection from chest X-ray images. Our method integrates graph cut segmentation with convolutional neural network (CNN) classification, achieving an impressive accuracy of 94%, sensitivity of 96%, and specificity of 84%. This innovative approach holds promise for improving tuberculosis diagnosis, facilitating early intervention, and ultimately contributing to global tuberculosis control efforts. Key Words: Chest X-ray (CXR), Convolutional Neural Network (CNN), deep learning, graph cut, tuberculosis detection, automatic diagnosis.

The diagnostic concordance between micro real-time PCR and Lowenstein Jensen (LJ) media assays for pulmonary tuberculosis detection with associated clinical characteristics

The diagnostic concordance between micro real-time PCR and Lowenstein Jensen (LJ) media assays for pulmonary tuberculosis detection with associated clinical characteristics

Intelligent decision support system for pulmonary tuberculosis detection using bipolar fuzzy utility matrix and bipolar Mamdani fuzzy inference system

Tuberculosis (TB) stands as the second leading global infectious cause of death, following closely behind the impact of COVID-19. The standard approach to diagnose TB involves skin tests, but these tests can yield inaccurate results due to limited access to healthcare and insufficient diagnostic resources. To enhance diagnostic accuracy, this study introduces a novel approach employing a Bipolar Fuzzy Utility Matrix Inference System (BFUMIS) and a Bipolar Mamdani Fuzzy Inference System (BMFIS) to assess TB disease levels. By considering factors associated with the causation of TB, the study devises suitable membership functions for bipolar fuzzy sets (BFS) using both triangular and trapezoidal fuzzy numbers. Using a point factor scale, the study clusters the rules systematically and assesses the level of uncertainty within these grouped rules by utilizing bipolar triangular fuzzy numbers (BTFN). To handle the BTFN, this study proposes converting bipolar triangular fuzzy into bipolar crisp score (CBTFBCS) algorithm as a defuzzification method. The optimal bipolar fuzzy utility sets (BFUS) are determined from the bipolar fuzzy utility matrix to identify patients’ TB disease levels. These sets play a pivotal role in characterizing the severity of TB disease levels in patients. Additionally, rigorous validation of the utility framework is accomplished through measures of bipolar fuzzy satisfactory factors and sensitivity analyses. Furthermore, the study introduces the BMFIS, which presents a novel perspective on the conventional fuzzy inference system. This innovative system integrates the Mamdani fuzzy inference system (MFIS) into a bipolar fuzzy context, enriching the diagnostic process with enhanced insights. To demonstrate the efficacy of the proposed methods, extensive validation is carried out using actual clinical data. The performance metrics used in this validation effectively demonstrate the superiority of the proposed approach.

Deep Learning–based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning–based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854–0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912–0.965, p-value < 0.001) compared with anterior–posterior (AUC 0.782, 95% CI 0.644–0.897) or portable anterior–posterior (AUC 0.869, 95% CI 0.814–0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823–0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765–0.904) and Shenzhen (AUC 0.806, 95% CI 0.771–0.839). A deep learning–based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

ZNAČAJ KLASIČNE RADIOGRAFIJE PLUĆA U OTKRIVANJU TUBERKULOZE PLUĆA

ABSTRACT&#x0D; &#x0D; Diagnostic radiology provides a non-invasive insight into the anatomy and physiology of the human body and is one of the most useful tools in modern medicine. Accelerated technological development has led to improved image quality and the ability to obtain additional diagnostic information necessary for accurate and rapid diagnostics. As in other fields of medicine, in diagnostic radiology, all elements of risk must be balanced against the benefits arising from the use of a given diagnostic method.&#x0D; Aim: study is to determine the age and gender structure of the subjects who came for radiographs, to prove whether the X-ray has a significant role in detecting lung tuberculosis, whether the radiological record matches the findings of a pneumophysiologist or whether a microbiological analysis is needed, and to determine whether tuberculosis detected by classical radiography is also proven.&#x0D; Prospective method included 50 adult respondents from the Public Health Center of the Canton of Sarajevo, of different age and gender structures, who performed radiographic pulmonary imaging and processing of pneumophysiological findings in the period from 01.01.2018. do 30.05.2019. years. X-rays were taken from the classic Siemens Multix X-ray machine. Patients' medical records, microbiological examination of sputum on BC, pneumophysiological findings, radiographs and radiological findings from the radiological information system and the PACS system were used for the data. We used a retrospective study that included descriptive statistics, standard deviation, arithmetic mean, and correlation coefficient.&#x0D; Results: show that there is no significant statistical difference by gender, the average age of the respondents was 50, 52 years. &#x0D; X-ray imaging as a diagnostic procedure is of great importance in the detection of pulmonary tuberculosis, and 72% of the subjects had pulmonary tuberculosis first compared with the findings of pneumophysiologists, and bacteriological analysis was used in 28% subjects due to suspected pneumophysiologists in the diagnosis. By comparing the correlation of the pneumophysiologist and the bacteriological analysis, the X-ray showed 100% lung tuberculosis.

Accuracy of Nanopore Sequencing as a Diagnostic Assay for Pulmonary Tuberculosis versus Smear, Culture and Xpert MTB/RIF: A Head-to-Head Comparison.

Early diagnosis of pulmonary tuberculosis (PTB) is pivotal for achieving effective tuberculosis (TB) control. This study aimed to assess the effectiveness of nanopore sequencing of sputum, bronchoalveolar lavage fluid (BALF), and pleural fluid samples for achieving early PTB diagnosis and provided head-to-head comparisons of nanopore sequencing results versus results obtained using smear, culture, and Xpert MTB/RIF assays. Patients admitted from October 2021 to April 2023 were screened for PTB using diagnostic imaging and electronic medical records. A total of 172 patients (129 PTB, 43 non-TB patients) were included in the final analysis after the exclusion of patients who did not meet the study's inclusion criteria. PTB-positive rates were determined for each assay, and then, assay diagnostic efficacies were compared. The positive MTB-detection rates obtained using nanopore sequencing were 86.8% for all samples, 62.3% for BALF, and 84.6% for pleural fluid, all of which were significantly higher than the corresponding rates obtained using the other three assays. The overall sensitivity rates, specificity rates, and area under the curve (AUC) values obtained from smear testing were 5.4%, 95.3%, and 0.504, respectively, as compared to the respective results obtained via culture (18.6%, 100.0%, and 0.593), Xpert MTB/RIF (26.4%, 97.7%, and 0.620), and nanopore sequencing (85.3%, 95.4%, and 0.903). The diagnostic efficacy of nanopore sequencing surpassed the diagnostic efficacies of smear, culture, and Xpert MTB/RIF assays. Thus, nanopore sequencing holds promise as an alternative to Xpert MTB/RIF for early PTB detection, particularly for the testing of BALF and pleural fluid samples.

The performance of tongue swabs for detection of pulmonary tuberculosis.

Oral and/or tongue swabs have demonstrated ability to detect Mycobacterium tuberculosis (Mtb) in adults with pulmonary tuberculosis (TB). Swabs provide useful alternative specimens for diagnosis of TB using molecular assays however, the diagnostic pickup by culture requires further improvement and development. Several studies identified the presence of differentially culturable tubercle bacilli (DCTB) populations in a variety of clinical specimens. These organisms do not grow in routine laboratory media and require growth factors in the form of culture filtrate (CF) from logarithmic phase cultures of Mtb H37Rv. Herein, we compared the diagnostic performance of sputum and tongue swabs using Mycobacterial Growth Indicator Tube (MGIT) assays, Auramine smear, GeneXpert and DCTB assays supplemented with or without CF. From 89 eligible participants, 83 (93%), 66 (74%) and 79 (89%) were sputum positive by MGIT, smear and GeneXpert, respectively. The corresponding tongue swabs displayed a lower sensitivity with 39 (44%), 2 (2.0%) and 18 (20%) participants respectively for the same tests. We aimed to improve the diagnostic yield by utilizing DCTB assays. Sputum samples were associated with a higher positivity rate for CF-augmented DCTB at 82/89 (92%) relative to tongue swabs at 36/89 (40%). Similarly, sputum samples had a higher positivity rate for DCTB populations that were CF-independent at 64/89 (72%) relative to tongue swabs at 26/89 (29%). DCTB positivity increased significantly, relative to MGIT culture, for tongue swabs taken from HIV-positive participants. We next tested whether the use of an alternative smear stain, DMN-Trehalose, would improve diagnostic yield but noted no substantial increase. Collectively, our data show that while tongue swabs yield lower bacterial numbers for diagnostic testing, the use of growth supplementation may improve detection of TB particularly in HIV-positive people but this requires further interrogation in larger studies.

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

The diagnosis of pulmonary tuberculosis is a complicated process with a long wait. According to the WS 288-2017 standard, PTB can be divided into five types of imaging. To date, no relevant studies on PTB CT images based on the Yolov5 algorithm have been retrieved. To develop an improved strategy YOLOv5, for the classification of PTB lesions based on whole, CT slices were combined with three other modules. CT slices of PTB collected from hospitals were set as training, verification, and external test sets. It is compared with YOLOv5, SSD and RetinaNet neural network methods. The values of precision, recall, MAP, and F1-score of the improved strategy YOLOv5 for the external test were 0.707, 0.716, 0.715, and 0.71. In this study, based on the same dataset, the improved strategy YOLOv5 model has better results than other networks. Our method provides an effective method for the timely detection of PTB.

Evaluation of 2 Artificial Intelligence Software for Chest X-Ray Screening and Pulmonary Tuberculosis Diagnosis: Protocol for a Retrospective Case-Control Study.

According to the World Bank, Malaysia reported an estimated 97 tuberculosis cases per 100,000 people in 2021. Chest x-ray (CXR) remains the best conventional method for the early detection of pulmonary tuberculosis (PTB) infection. The intervention of artificial intelligence (AI) in PTB diagnosis could efficiently aid human interpreters and reduce health professionals' work burden. To date, no AI studies have been evaluated in Malaysia. This study aims to evaluate the performance of Putralytica and Qure.ai software for CXR screening and PTB diagnosis among the Malaysian population. We will conduct a retrospective case-control study at the Respiratory Medicine Institute, National Cancer Institute, and Sungai Buloh Health Clinic. A total of 1500 CXR images of patients who completed treatments or check-ups will be selected and categorized into three groups: (1) abnormal PTB cases, (2) abnormal non-PTB cases, and (3) normal cases. These CXR images, along with their clinical findings, will be the reference standard in this study. All patient data, including sociodemographic characteristics and clinical history, will be collected prior to screening via Putralytica and Qure.ai software and readers' interpretation, which are the index tests for this study. Interpretation from all 3 index tests will be compared with the reference standard, and significant statistical analysis will be computed. Data collection is expected to commence in August 2023. It is anticipated that 1 year will be needed to conduct the study. This study will measure the accuracy of Putralytica and Qure.ai software and whether their findings will concur with readers' interpretation and the reference standard, thus providing evidence toward the effectiveness of implementing AI in the medical setting. PRR1-10.2196/36121.

Diagnostic accuracy of the NOVA Tuberculosis Total Antibody Rapid test for detection of pulmonary tuberculosis and infection with Mycobacterium tuberculosis

BackgroundThe NOVA Tuberculosis Total Antibody Rapid Test is a commercially available lateral flow serological assay that is intended to be used as an aid in the diagnosis of tuberculosis. We conducted a study to estimate diagnostic accuracy of this assay for diagnosis of active pulmonary tuberculosis disease and for detection of M. tuberculosis infection. MethodsThis study used existing frozen plasma specimens that had been obtained previously from consenting HIV-negative adults in Cambodia, South Africa, and Vietnam whose tuberculosis status was rigorously characterized using sputum mycobacterial cultures and blood interferon gamma release assay. The investigational assay was performed in a single laboratory by laboratory staff specifically trained to conduct the assays according to the manufacturer’s procedures. In addition, intensity of the test band was subjectively assessed. ResultsPlasma specimens from 150 participants were tested. All testing attempts yielded a determinate result of either positive or negative. For diagnosis of active pulmonary tuberculosis disease, test sensitivity and specificity were 40.0 % (20/50, 95 % confidence interval [CI] 27.6 % to 53.8 %) and 85.0 % (95 % CI 76.7 % to 90.7 %), respectively. For detection of M. tuberculosis infection, test sensitivity and specificity were 28.0 % (95 % CI 20.5 % to 37.2 %) and 86.0 % (95 % CI 73.8 % to 93.0 %), respectively. Among the 35 positive tests, no statistically significant band intensity trend was found across participant groups (p = 0.17). ConclusionStudy findings do not support a role for the NOVA Tuberculosis Test in current tuberculosis diagnostic algorithms.

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.

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network.

Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples. Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined. Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model. Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

Diagnostic value of a nanopore sequencing assay of bronchoalveolar lavage fluid in pulmonary tuberculosis

BackgroundTo determine the diagnostic accuracy of a nanopore sequencing assay of PCR products from a M. tuberculosis complex-specific region for testing of bronchoalveolar lavage fluid (BALF) samples or sputum samples from suspected pulmonary tuberculosis (PTB) patients and compare the results to results obtained for MGIT and Xpert assays.MethodsCases with suspected PTB (n = 55) were diagnosed from January 2019 to December 2021 based on results of nanopore sequencing, MGIT culture, and Xpert MTB/RIF testing of BALF and sputum samples collected during hospitalization. Diagnostic accuracies of assays were compared.ResultsUltimately, data from 29 PTB patients and 26 non-PTB cases were analyzed. PTB diagnostic sensitivities of MGIT, Xpert MTB/RIF, and nanopore sequencing assays were 48.28%, 41.38%, and 75.86%, respectively, thus demonstrating that nanopore sequencing provided greater sensitivity than was provided by MGIT culture and Xpert assays (P < 0.05). PTB diagnostic specificities of the respective assays were 65.38%, 100%, and 80.77%, which corresponded with kappa coefficient (κ) values of 0.14, 0.40, and 0.56, respectively. These results indicate that nanopore sequencing provided superior overall performance as compared to Xpert and MGIT culture assays and provided significantly greater PTB diagnostic accuracy than Xpert and sensitivity comparable to that of the MGIT culture assay.ConclusionOur findings suggest that improved detection of PTB in suspected cases was achieved using nanopore sequencing-based testing of BALF or sputum samples than was achieved using Xpert and MGIT culture-based assays, and nanopore sequencing results alone cannot be used to rule out PTB.

Detection of Pulmonary Tuberculosis by Gene Xpert Method among HIV Patient in ART Center, Gadarif State, Sudan

Background: Tuberculosis (TB) is one of the most common and deadly infectious diseases in humans. Due to widespread poverty, inequity, and conflict, suboptimal health services in many countries, and the impact of the HIV/AIDS pandemic, TB/HIV co-infected patients frequently test negative for TB with direct microscopy, posing diagnostic challenges. Traditional HIV-associated TB diagnosis is complex, expensive, time-consuming, and technically demanding because it relies on conventional culture and drug susceptibility testing. The lengthy wait for results has disastrous consequences for patients who go undiagnosed or are diagnosed too late.&#x0D; Objective: To detect the accuracy of gene X pert® MTB/RIF as a novel automated molecular tool to diagnose Mycobacterium tuberculosis in HIV patients.&#x0D; Methods: A cross sectional study was conducted on 70 HIV patients suspected to have Mycobacterium tuberculosis and who were referred to the ART and Tuberculosis Centers at Gadarif state, from June 2022 to December 2022. Sputum samples were tested by two methods involved the Gene X pert (Cepheid, California, United States of America) and direct smear microscopy (SM) stained with ZN stain for detection of acid fast bacilli. Out of 70 sputum samples analyzed for acid fast bacilli, 50(71%) cases were positive using GeneXpert whereas the DM was positive only for 28 (4o %) of the cases. GeneXpert detected another 22 AFB positive cases. Therefore GeneXpert is more accurate. Sensitivity of the GeneXpert was calculated as 93% and the specificity was 43%. The majority of the patients who contributed to (SM) were found to be co-infected with HIV.&#x0D; Conclusion: GeneXpert demonstrated two-time case detection rate compared to the sputum smear microscopy so the direct smear showed poor performance tool for evaluation of TB-HIV co-infection. Also The Gene X pert’s sensitivity was found to be high while the specificity was low. Although this was the case, the GeneXpert as compared to DM could greatly reduce false negatives and the wait on treatment initiation can be significantly shortened, minimising premature death and continued transmission.