Calcific pericarditis strangling the heart, an answer to unexplained heart failurediagnostic modalities.

Pericardial calcification is often found incidentally from imaging studies and may be a clue to constrictive pericarditis. Constrictive pericarditis often mimics other causes of heart failure, pulmonary, or liver disease, making it hard to diagnose. Tuberculosis is the most common infectious aetiology of Constrictive Pericarditis. Living in developing countries, such as Indonesia, should warn us of the possibility of tuberculous constrictive pericarditis as a differential diagnosis of unexplained heart failure. The presented case came with complaints of shortness of breath, especially on exertion for five years, which worsened in the last 6 months. The past history of pulmonary Tuberculosis with the Cardiac CT findings confirmed the diagnosis of Constrictive Pericarditis.

Programmatic implications of a sub-national TB prevalence survey in India.

Subnational TB estimates are crucial for making informed decisions to tailor TB control activities to local TB epidemiology.A cross-sectional survey was conducted among 143,005 individuals in Tamil Nadu, India. Participants were screened for symptoms and underwent chest X-ray (CXR). Participants with symptoms of TB and/or abnormal CXR were tested for TB using Xpert, smear, and liquid culture.The prevalence of microbiologically confirmed pulmonary TB (MCPTB) was 212 (95% CI 184-239) per 100,000 population. The prevalence-to-notification ratio (P:N) in the state was 2.05 (95% CI 1.8-2.29). Low body mass index and diabetes together had a population attributable fraction of 54.15 (95% CI 45.68-61.97). Approximately 39% of the TB cases were asymptomatic and were identified only by CXR screening. In the general population, only 26.9% sought care at a health facility among those with symptoms suggestive of TB.The programme needs to prioritise screening with CXR to potentially detect cases earlier and curtail the transmission and upscale molecular tests in the selected population to increase the yield of case finding. Innovative health education strategies must be devised to address health-seeking behaviour..

Exploring the use of exhaled breath as a diagnostic tool for pulmonary TB.

Despite its historical decline, TB remains a significant cause of infectious disease-related global deaths. The lack of reliable diagnostic tests for vulnerable groups, such as children and immunocompromised patients, remains a challenge for TB control. For decades, it has been recognised that exhaled breath has great potential as a non-invasive and universally accessible clinical alternative to sputum and invasive sampling methods. Although translation into clinical practice has not yet occurred, there has been significant progress with promising results in various applications, including diagnosis, estimation of infectiousness, and monitoring of treatment response. More recently, the COVID-19 pandemic reignited global interest in this field and technological advances have further accelerated its development. In the coming decade, breath sampling will enhance our understanding of respiratory infectious diseases and host-immune responses, which may lead to clinical applications. Here we discuss the diagnostic landscape of TB and the current state of the art of breath sampling.

Retrospective analysis of the clinical utility of multi-cytokine profiles in smear-negative pulmonary tuberculosis.

OBJECTIVES: To evaluate cytokine profiles and interferon-gamma release assay (IGRA) for their diagnostic capabilities in the differentiation of tuberculosis (TB) from non-TB conditions, as well as smear-negative pulmonary tuberculosis (SNPT) from smear-positive pulmonary tuberculosis (SPPT). METHODS: A total of 125 participants were included, 77 of whom had TB and 48 who didn't, and demographic, clinical, and laboratory data were collected, including cytokine levels and IGRA results. The TB patients were further divided into 2 subgroups: SNPT (n=42) and SPPT (n=35). RESULTS: Compared to non-TB, the TB group had lower BMI, higher WBC, neutrophils, monocytes, ESR and CRP (p<0.05). TB patients showed higher IL-2, IL-6, IFN-γ, IL-8 (p<0.001) and higher IGRA positivity (88.3% versus [vs.] 29.2%, p<0.001). Between SNPT and SPPT, moderate effect sizes were observed for IFN-α, IL-2, IL-10, IL-8 (Cohen's d 0.59-0.76), with lower IGRA positivity in SNPT (81.0% vs. 97.1%, p=0.015). ROC analysis indicated IFN-α, IL-2, IL-10, IL-8 had moderate accuracy for SNPT diagnosis (AUCs 0.668-0.734), and combining these improved accuracy (AUC 0.759, 80% sensitivity, 64.2% specificity). CONCLUSION: A multi-biomarker approach combining these cytokines demonstrates enhanced diagnostic accuracy for tuberculosis.

Comparison of microscopic and xpert MTB diagnoses of presumptive mycobacteria tuberculosis infection: retrospective analysis of routine diagnosis at Cape Coast Teaching Hospital.

INTRODUCTION: Tuberculosis is a global health problem that causes 1. 4 million deaths every year. It has been estimated that sputum smear-negative diagnosis but culture-positive pulmonary TB diagnosis contribute to 12.6% of pulmonary TB transmission. TB diagnosis by smear microscopy smear has a minimum detection limit (LOD) of 5,000 to 10,000 bacilli per milliliter (CFU/ml) of sputum result in missed cases and false positives. However, GeneXpert technology, with a LOD of 131-250 CFU/ml in sputum samples and its implementation is believe to facilitate early detection TB and drug-resistant TB case. Since 2013, Ghana health Service (GHS) introduce GeneXpert MTB/RIF diagnostic in all regional hospitals in Ghana, however no assessment of performance between microscopy and GeneXpert TB diagnosis cross the health facilities has been reported. The study compared the results of routine diagnoses of TB by microscopy and Xpert MTB from 2016 to 2020 at the Cape Coast Teaching Hospital (CCTH). METHODS: The study compared routine microscopic and GeneXpert TB diagnosis results at the Cape Coast Teaching Hospital (CCTH) from 2016 to 2020 retrospectively. Briefly, sputum specimens were collected into 20 mL sterile screw-capped containers for each case of suspected TB infection and processed within 24 h. The samples were decontaminated using the NALC-NaOH method with the final NaOH concentration of 1%. The supernatants were discarded after the centrifuge and the remaining pellets dissolved in 1-1.5 ml of phosphate buffer saline (PBS) and used for diagnosis. A fixed smears were Ziehl-Neelsen acid-fast stain and observed under microscope and the remainings were used for GeneXpert MTB/RIF diagnosis. The data were analyze using GraphPad Prism. RESULTS: 50.11% (48.48-51.38%) were females with an odd ratio (95% CI) of 1.004 (0.944-1.069) more likely to report to the TB clinic for suspected TB diagnosis. The smear-positive cases for the first sputum were 6.6% (5.98-7.25%), and the second sputum was 6.07% (5.45-6.73%). The Xpert MTB-RIF diagnosis detected 2.93% (10/341) (1.42-5.33%) in the first and 5.44% (16/294) (3.14-8.69%) in the second smear-negative TB samples. The prevalence of Xpert MTB-RIF across smear positive showed that males had 56.87% (178/313) and 56.15% (137/244) and females had 43.13% (135/313) and 43.85% (107/244) for the first and second sputum. Also, false negative smears were 0.18% (10/5607) for smear 1 and 0.31% (16/5126) for smear 2. CONCLUSION: In conclusion, the study highlights the higher sensitivity of the GeneXpert assay compared to traditional smear microscopy for detecting MTB. The GeneXpert assay identified 10 and 16 positive MTB from smear 1 and smear 2 samples which were microscopic negative.

[Auxiliary diagnostic value of T cells spot test of Mycobacterium tuberculosis infection for pulmonary and extra-pulmonary tuberculosis among the elderly].

OBJECTIVE: To evaluate the auxiliary diagnostic value of T cells spot test of Mycobacterium tuberculosis infection (T-SPOT.TB) for pulmonary and extra-pulmonary tuberculosis among the elderly. METHODS: A total of 173 elderly patients at ages of 60 years and older and with suspected tuberculosis that were admitted to People's Hospital of Xinjiang Uygur Autonomous Region during the period from October 2022 through February 2024 were enrolled, and all patients underwent T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests. The etiological tests of MTB served as a gold standard, and the diagnostic values of T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests for pulmonary and extra-pulmonary tuberculosis were compared among the elderly patients. RESULTS: Of the 173 elderly patients suspected of tuberculosis, there were 44 patients definitely diagnosed with pulmonary tuberculosis, 30 cases with extra-pulmonary tuberculosis, and 99 cases without tuberculosis. The sensitivities of T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests were 86.5%, 27.0% and 54.1% for diagnosis of tuberculosis. The sensitivities of T-SPOT.TB were 86.4% and 86.7% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with an 80.8% specificity for diagnosis of tuberculosis. The sensitivities of GeneXpert MTB/RIF were 56.8% and 50.0% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with a 100.0% specificity each, and the sensitivities of acid fast staining were 31.8% and 20.0% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with a 100.0% specificity each. In addition, the areas under the receiver operating characteristic curve were 0.836, 0.635 and 0.770 for diagnosis of tuberculosis with T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests among the elderly patients, respectively. CONCLUSIONS: T-SPOT.TB has a high auxiliary diagnostic value for both pulmonary and extra-pulmonary tuberculosis among elderly patients.

EasyNAT MTC Assay: a Rapid Method to Detect Mycobacterium Tuberculosis in Respiratory Specimens.

BACKGROUND: This study aimed to effectively evaluate the diagnostic performance of the EasyNAT Mycobacterium tuberculosis complex (MTC) assay for tuberculosis (TB) detection from sputum. METHODS: The retrospectively analyzed data was collected from September 1, 2021, to November 1, 2023, in our hospital. RESULTS: Forty EasyNAT-positive sputum specimens were simultaneously detected using the GeneXpert MTB/ rifampicin (RIF) assay. The concordance rate between the EasyNAT and GeneXpert MTB/RIF assays was 100%. CONCLUSIONS: Because of the complexity of detecting RIF resistance data information, the rapid EasyNAT system used in conjunction with GeneXpert might be a better choice for the detection of TB in hospitals.

Identification and role of differentially expressed genes/proteins between pulmonary tuberculosis patients and controls across lung tissues and blood samples.

BACKGROUND: Differentially expressed genes/proteins (DEGs/DEPs) play critical roles in pulmonary tuberculosis (PTB) diagnosis and treatment. However, there is a scarcity of reports on DEGs/DEPs in lung tissues and blood samples in PTB patients. OBJECTIVE: We aim to identify the DEGs/DEPs in lung tissues and blood samples of PTB patients and investigate their roles in PTB. MATERIALS AND METHODS: The lung granulomas and normal tissues were collected from PTB patients for proteomic and transcriptomic analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses annotated the functions of DEGs/DEPs. The GSE107994 data set was downloaded to identify the DEGs/DEPs in peripheral blood. The common DEGs and DEPs were identified. A nomogram was established. Pearson correlation analysis was conducted. RESULTS: Eighty-three DEGs/DEPs were identified. These DEGs/DEPs were mainly enriched in the movement of cell or subcellular components, regulation of cellular component biogenesis, and actin filament-based process as well as in the pathways of inositol phosphate metabolism, adherens junction, phosphatidylinositol signaling system, leukocyte transendothelial migration, regulation of actin cytoskeleton, and tight junction. There were eight common DEGs/DEPs (TYMP, LAP3, ADGRL2, SIL1, LMO7, SULF 1, ANXA3, and PACSIN3) between the lung tissues and blood samples. They were effective in predicting tuberculosis. Moreover, the activated dendritic cells, macrophages, monocytes, neutrophils, and regulatory T cells were significantly positively correlated with TYMP (r > .50), LAP3 (r > .50), SIL1 (r > .50), ANXA3 (r > .5), and PACSIN3 (r < .50), while negatively correlated with LMO7 (r < -0.50) (p < .05). ADGRL2 and SULF1 did not have a significant correlation (p > .05). LIMITATIONS: The sample size was small. CONCLUSIONS: Eight common DEGs/DEPs of lung tissues and blood samples were identified. They were correlated with immune cells and demonstrated predictive value for PTB. Our data may facilitate the diagnosis and treatment of PTB.

Diagnostic accuracy of nanopore sequencing for the rapid diagnosis of pulmonary tuberculosis: A protocol for a systematic review and meta-analysis.

BACKGROUND: Pulmonary tuberculosis (PTB) is the most common type of tuberculosis (TB). Rapid diagnosis of PTB can help in TB control. Although the use of molecular tests (such as the GeneXpert MTB/RIF) has improved the ability to rapidly diagnose PTB, there is still room for improvement. Nanopore sequencing is a novel means of rapid TB detection. The purpose of this study was to establish a systematic review and meta-analysis protocol for evaluating the accuracy of nanopore sequencing for the rapid diagnosis of PTB. METHODS: We completed this protocol according to the Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) statement and registered on the PROSPERO platform. We will screen studies related to nanopore sequencing for diagnosis of PTB by searching through PubMed, EMBASE, the Cochrane Library using English, and Wanfang database, CNKI (China National Knowledge Infrastructure) using Chinese. Eligible studies will be screened according to the inclusion and exclusion criteria established in the study protocol. We will evaluate the methodological quality of the individual included studies using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). We will use Stata (version 15.0) with the midas command and RevMan (version 5.3) for meta-analysis and forest plots and SROC curves generation. A p < 0.05 was treated as a statistically significant difference. When significant heterogeneity exists between studies, we will explore sources of heterogeneity through meta-regression analysis and subgroup analysis. CONCLUSION: To the best of our knowledge, this will be the first systematic review and meta-analysis of nanopore sequencing for the diagnosis of PTB. We hope that this study will find a new and effective tool for the early diagnosis of PTB. PROSPERO REGISTRATION NUMBER: CRD42023495593.

Simultaneous alleviation of verification and reference standard biases in a community-based tuberculosis screening study using Bayesian latent class analysis.

BACKGROUND: Estimation of prevalence and diagnostic test accuracy in tuberculosis (TB) prevalence surveys suffer from reference standard and verification biases. The former is attributed to the imperfect reference test used to bacteriologically confirm TB disease. The latter occurs when only the participants screening positive for any TB-compatible symptom or chest X-ray abnormality are selected for bacteriological testing (verification). Bayesian latent class analysis (LCA) alleviates the reference standard bias but suffers verification bias in TB prevalence surveys. This work aims to identify best-practice approaches to simultaneously alleviate the reference standard and verification biases in the estimates of pulmonary TB prevalence and diagnostic test performance in TB prevalence surveys. METHODS: We performed a secondary analysis of 9869 participants aged ≥15 years from a community-based multimorbidity screening study in a rural district of KwaZulu-Natal, South Africa (Vukuzazi study). Participants were eligible for bacteriological testing using Xpert Ultra and culture if they reported any cardinal TB symptom or had an abnormal chest X-ray finding. We conducted Bayesian LCA in five ways to handle the unverified individuals: (i) complete-case analysis, (ii) analysis assuming the unverified individuals would be negative if bacteriologically tested, (iii) analysis of multiply-imputed datasets with imputation of the missing bacteriological test results for the unverified individuals using multivariate imputation via chained equations (MICE), and simultaneous imputation of the missing bacteriological test results in the analysis model assuming the missing bacteriological test results were (iv) missing at random (MAR), and (v) missing not at random (MNAR). We compared the results of (i)-(iii) to the analysis based on a composite reference standard (CRS) of Xpert Ultra and culture. Through simulation with an overall true prevalence of 2.0%, we evaluated the ability of the models to alleviate both biases simultaneously. RESULTS: Based on simulation, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the assumption that the missing data are MAR and MNAR alleviate the reference standard and verification biases. CRS-based analysis and Bayesian LCA assuming the unverified are negative for TB alleviate the biases only when the true overall prevalence is <3.0%. Complete-case analysis produced biased estimates. In the Vukuzazi study, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the MAR and MNAR assumptions produced overall PTB prevalence of 0.9% (95% Credible Interval (CrI): 0.6-1.9) and 0.7% (95% CrI: 0.5-1.1) respectively alongside realistic estimates of overall diagnostic test sensitivity and specificity with substantially overlapping 95% CrI. The CRS-based analysis and Bayesian LCA assuming the unverified were negative for TB produced 0.7% (95% CrI: 0.5-0.9) and 0.7% (95% CrI: 0.5-1.2) overall PTB prevalence respectively with realistic estimates of overall diagnostic test sensitivity and specificity. Unlike CRS-based analysis, Bayesian LCA of multiply-imputed data using MICE mitigates both biases. CONCLUSION: The findings demonstrate the efficacy of these advanced techniques in alleviating the reference standard and verification biases, enhancing the robustness of community-based screening programs. Imputing missing values as negative for bacteriological tests is plausible under realistic assumptions.

Implication of Negative GeneXpert Mycobacterium tuberculosis/Rifampicin Results in Suspected Tuberculosis Patients: A Research Study.

OBJECTIVE: GeneXpert Mycobacterium tuberculosis/rifampicin (MTB/RIF) is a conceptually helpful tool for establishing tuberculosis (TB) disease. Negative results from the GeneXpert test do not exclude the possibility of diagnosing non-tuberculous mycobacteria lung disease (NTMLD) as a chronic pulmonary disease. When a patient is diagnosed on a clinical basis, and there is no bacteriological evidence of TB, it is necessary to consider NTM as one of the causes of disease with TB-like symptoms. The prevalence of non-tuberculous mycobacteria (NTM) disease is rising globally, but its diagnosis is still delayed and often misdiagnosed as multidrug-resistant TB (MDR-TB). This study highlights the implication of negative GeneXpert MTB/RIF results in suspected TB patients who conducted mycobacteria culture and detected the incidence of NTMLD. METHODS: In this experimental study, the performance of GeneXpert MTB/RIF-negative results with those of mycobacteria cultures and lung abnormalities among suspected TB patients in a referral hospital in Indonesia were evaluated. From January to August 2022, 100 sputum samples from suspected chronic pulmonary TB patients with GeneXpert MTB/RIF assay-negative results were cultured in Lowenstein-Jensen medium, and the implication among negative GeneXpert result MTB/RIF assay. RESULTS: 7% were confirmed to have MTB and 1% had NTM by culture assay. Moreover, 34% were diagnosed with clinical TB and treated with anti-TB drugs. CONCLUSION: For patients with negative assay results of GeneXpert MTB/RIF regarding clinically suspected chronic TB infection, further diagnostic tests to determine the causative agents of the lung abnormalities should be carried out.

Fatal Nocardia paucivorans Lower Respiratory Tract Infection in a Tuberculosis Suspect Who Worked as Gold Miner.

Nocardiosis is an opportunistic infection that affects both immunocompromised as well as immunocompetent patients. The main infections occur as soft tissue and lung infections although they might disseminate to various organs. This is a case study aimed to reflect the severity of the disease and the patient's risk factors associated with the infection. A sputum sample was collected from tuberculosis (TB) suspects for culture. Nocardia-like colonies were isolated, purified, and sent to BGI Company (Hongkong, China). Standard forward sequencing of 16S rRNA was done by ABI Genetic Analyzer (Applied Biosystems). Sequence alignment and nucleotide basic local alignment search tool (BLAST) were done using National Center for Bioinformatics (NCBI) Nucleotide BLAST. Biochemical identification to the colonies was done using an automation system (BD Phoenix™) to confirm the identification. Nocardia paucivorans was identified from the TB suspect. Risk factors were identified as extensive contact to dust, absence of primary care units with complete facilities, and old age. Since the infection of the lungs caused by Nocardia might be similar to pulmonary TB, this case report highlights the importance of accurate diagnosis and identification procedures to differentiate between the two.

Lung Lesions: Differential Diagnoses Beyond Cancer.

BACKGROUND Lung cancer is the most common malignant neoplasm diagnosed worldwide. Early diagnosis and treatment are of great importance for patient's prognosis. A wide variety of pulmonary conditions display clinical and radiological presentation similar to that of lung cancer, and the awareness of their existence can help in making correct diagnoses. CASE REPORT This article presents a description of 4 patients with an insidious type of lesions mimicking pulmonary carcinomas. The first patient was referred to Department with a tumor-like lesion in the right lung. After CT of the chest and core-needle biopsy, the lesion turned out to be an ectopic thyroid tissue. The second patient reported a dry cough and weight loss. A lung nodule mass was revealed in chest CT and the patient was diagnosed with pulmonary tuberculoma. The remaining 2 patients, despite the suspicion of lung cancer, were subsequently diagnosed with a post-traumatic pleural hematoma and diffuse large B cell lymphoma. CONCLUSIONS Low-dose computed tomography of the chest plays a significant role in the diagnosis of newly detected lesions in the lungs. However, due to the similarity of the image of cancer to that of other diseases, the ultimate diagnosis should be based on the interpretation of full imaging diagnostic tests, clinical presentation, and histopathological examination of the material obtained from the lesion. Analysis of cases enables us to expand our understanding of the diseases that need to be considered in differential diagnosis of a patient with a detected tumor-like lesion in the lungs.

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.

Identification of a Diagnostic Multiomics-Based Biomarker Cluster in a Mouse Model of Pulmonary Tuberculosis.

BACKGROUND: Tuberculosis (TB) stands as the second most prevalent infectious agent-related cause of death worldwide in 2022, trailing only COVID-19. With 1.13 million reported deaths, this figure is more than half of the mortality associated with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), which accounted for 0.63 million deaths. Diagnosing Mycobacterium tuberculosis (MTB) infection remains a formidable challenge due to the inability to isolate and detect MTB in sputum and within the human body. The absence of universally reliable diagnostic criteria for MTB infection globally poses a significant obstacle to preventing the progression of tuberculosis from the MTB infection stage. METHODS: In this study, our objective was to formulate a diagnostic biomarker cluster capable of discerning the progression of MTB infection and disease. This was achieved through a comprehensive joint multiomics analysis, encompassing transcriptome, proteome, and metabolome, conducted on lung tissue samples obtained from both normal control mice and those infected with MTB. RESULTS: A total of 1690 differentially expressed genes and 94 differentially expressed proteins were systematically screened. From this pool, 10 core genes were singled out. Additionally, eight long non-coding ribonucleic acids and eight metabolites linked to these core genes were identified to establish a cohesive cluster of biomarkers. This multiomics-based biomarker cluster demonstrated its capability to differentiate uninfected samples from MTB-infected samples effectively in both principle component analysis and the construction of a random forest model. CONCLUSION: The outcomes of our study strongly suggest that the multiomics-based biomarker cluster holds significant potential for enhancing the diagnosis of MTB infection.

Singapore tuberculosis (TB) clinical management guidelines 2024: A modified Delphi adaptation of international guidelines for drug-susceptible TB infection and pulmonary disease.

Introduction: Tuberculosis (TB) remains endemic in Singapore. Singapore's clinical practice guidelines for the management of tuberculosis were first published in 2016. Since then, there have been major new advances in the clinical management of TB, ranging from diagnostics to new drugs and treatment regimens. The National TB Programme convened a multidisciplinary panel to update guidelines for the clinical management of drug-susceptible TB infection and disease in Singapore, contextualising current evidence for local practice. Method: Following the ADAPTE framework, the panel systematically reviewed, scored and synthesised English-language national and international TB clinical guidelines published from 2016, adapting recommendations for a prioritised list of clinical decisions. For questions related to more recent advances, an additional primary literature review was conducted via a targeted search approach. A 2-round modified Delphi process was implemented to achieve consensus for each recommendation, with a final round of edits after consultation with external stakeholders. Results: Recommendations for 25 clinical questions spanning screening, diagnosis, selection of drug regimen, monitoring and follow-up of TB infection and disease were formulated. The availability of results from recent clinical trials led to the inclusion of shorter treatment regimens for TB infection and disease, as well as consensus positions on the role of newer technologies, such as computer-aided detection-artificial intelligence products for radiological screening of TB disease, next-generation sequencing for drug-susceptibility testing, and video observation of treatment. Conclusion: The panel updated recommendations on the management of drug-susceptible TB infection and disease in Singapore.

Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis.

Tuberculosis (TB) remains one of the deadliest chronic infectious diseases globally. Early diagnosis not only prevents the spread of TB but also ensures effective treatment. However, the absence of non-sputum-based diagnostic tests often leads to delayed TB diagnoses. Inflammation is a hallmark of TB, we aimed to identify biomarkers associated with TB based on immune profiling. We collected 222 plasma samples from healthy controls (HCs), disease controls (non-TB pneumonia; PN), patients with TB (TB), and cured TB cases (RxTB). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure the levels of 92 immune proteins. Based on differential analysis and the correlation with TB severity, we selected 9 biomarkers (CXCL9, PDL1, CDCP1, CCL28, CCL23, CCL19, MMP1, IFNγ and TRANCE) and explored their diagnostic capabilities through 7 machine learning methods. We identified combination of these 9 biomarkers that distinguish TB cases from controls with an area under the receiver operating characteristic curve (AUROC) of 0.89-0.99, with a sensitivity of 82-93% at a specificity of 88-92%. Moreover, the model excels in distinguishing severe TB cases, achieving AUROC exceeding 0.95, sensitivities and specificities exceeding 93.3%. In summary, utilizing targeted proteomics and machine learning, we identified a 9 plasma proteins signature that demonstrates significant potential for accurate TB diagnosis and clinical outcome prediction.

Construction and validation of a clinical differentiation model between peripheral lung cancer and solitary pulmonary tuberculosis.

OBJECTIVE: To establish and validate a clinical model for differentiating peripheral lung cancer (PLC) from solitary pulmonary tuberculosis (SP-TB) based on clinical and imaging features. MATERIALS AND METHODS: Retrospectively, 183 patients (100 PLC, 83 SP-TB) in our hospital were randomly divided into a training group and an internal validation group (ratio 7:3), and 100 patients (50 PLC, 50 SP-TB) in Sichuan Provincial People's Hospital were identified as an external validation group. The collected qualitative and quantitative variables were used to determine the independent feature variables for distinguishing between PLC and SP-TB through univariate logistic regression, multivariate logistic regression. Then, traditional logistic regression models and machine learning algorithm models (decision tree, random forest, xgboost, support vector machine, k-nearest neighbors, light gradient boosting machine) were established using the independent feature variables. The model with the highest AUC value in the internal validation group was used for subsequent analysis. The receiver operating characteristic curve (ROC), calibration curve, and decision curves analysis (DCA) were used to assess the model's discrimination, calibration, and clinical usefulness. RESULT: Age, smoking history, maximum diameter of lesion, lobulation, spiculation, calcification, and vascular convergence sign were independent characteristic variables to differentiate PLC from SP-TB. The logistic regression model had the highest AUC value of 0.878 for the internal validation group, based on which a quantitative visualization nomogram was constructed to discriminate the two diseases. The area under the ROC curve (AUC) of the model in the training, internal validation, and external validation groups were 0.915 (95 % CI: 0.866-0.965), 0.878 (95 % CI: 0.784-0.971), and 0.912 (95 % CI: 0.855-0.969), respectively, and the calibration curves fitted well. Decision curves analysis (DCA) confirmed the good clinical benefit of the model. CONCLUSION: The model constructed based on clinical and imaging features can accurately differentiate between PLC and SP-TB, providing potential value for developing reasonable clinical plans.

Operational indicators for pulmonary tuberculosis diagnosis in people living with HIV before and after Xpert MTB/RIF implementation in the state of São Paulo, Brazil.

Tuberculosis (TB) in people living with HIV (PLHIV) is usually paucibacillary and the smear microscopy has limitations and may lead to high proportions of non-confirmed pulmonary tuberculosis (NC-PTB). Despite culture being the reference method, it usually takes 6 to 8 weeks to produce the results. This study aimed to analyze the effect of a rapid molecular test (Xpert) in the confirmatory rate of PTB among PLHIV, from 2010 to 2020, in São Paulo state, Brazil. This is an ecological study with time series analysis of the trend and the NC-PTB rates before and after Xpert implementation in 21 municipalities. The use of Xpert started and gradually increased after 2014, while the rate of NC-PTB in PLHIV decreased over this time, being more significant between late 2015 and mid-2017. The city of Ribeirão Preto stands out for having the highest percentage (75.0%) of Xpert testing among PLHIV and for showing two reductions in the NC-PTB rate. The cities with low Xpert coverage had a slower and smaller decrease in the NC-PTB rate. Despite being available since 2014, a significant proportion of PLHIV suspected of PTB in the state of São Paulo did not have an Xpert ordered by the doctors. The implementation of Xpert reduced the NC-PTB rates with growing effect as the coverage increased in the municipality.

Operation-friendly and accurate naked-eye observation assay for fast zoonotic echinococcosis and pulmonary tuberculosis monitoring in clinics.

Echinococcosis and tuberculosis are two common zoonotic diseases that can cause severe pulmonary infections. Early screening and treatment monitoring are of great significance, especially in areas with limited medical resources. Herein, we designed an operation-friendly and rapid magnetic enrichment-silver acetylene chromogenic immunoassay (Me-Sacia) to monitor the antibody. The main components included secondary antibody-modified magnetic nanoparticles (MNP-Ab2) as capture nanoparticles, specific peptide (EG95 or CFP10)-modified silver nanoparticles (AgNP-PTs) as detection nanoparticles, and alkyne-modified gold nanoflowers as chromogenic nanoparticles. Based on the magnetic separation and plasma luminescence techniques, Me-Sacia could completely replace the colorimetric assay of biological enzymes. It reduced the detection time to approximately 1 h and simplified the labor-intensive and equipment-intensive processes associated with conventional ELISA. Meanwhile, the Me-Sacia showed universality for various blood samples and intuitive observation with the naked eye. Compared to conventional ELISA, Me-Sacia lowered the detection limit by approximately 96.8 %, increased the overall speed by approximately 15 times, and improved sensitivity by approximately 7.2 %, with a 100 % specificity and a coefficient of variation (CV) of less than 15 %.