BMC Infectious Diseases最新文献

Background: The interferon-gamma release assays (IGRAs) are characterized by their complexity and reliance on specific instrumentation. The recombinant Mycobacterium tuberculosis (MTB) fusion protein skin test (C-TST) utilizes the same MTB fusion protein as IGRAs. If the C-TST demonstrates consistent results and greater cost-effectiveness, it could potentially replace IGRAs and be used alongside the tuberculin skin test (TST) for the screening of latent tuberculosis infection (LTBI). This study aims to assess whether the TST/C-TST screening strategy offers superior diagnostic efficacy and cost-effectiveness compared to the conventional TST/IGRAs strategy, thereby evaluating the potential application of the combined C-TST and TST approach for LTBI screening.

Methods: The study population consisted of young males, aged 18 to 40 years, who attended the outpatient clinic of a hospital in 2023. These participants underwent TST and C-TST, followed by IGRAs between October and December 2023. The sensitivity, specificity, and reliability of the screening results were assessed. Furthermore, a hybrid decision tree and Markov model were utilized to evaluate the costs and health outcomes associated with five active screening strategies-TST, C-TST, IGRAs, TST combined with C-TST, and TST combined with IGRAs-from a societal perspective over a 20-year period.

Results: In this study, valid results were obtained from 1412 participants: 202 individuals completed three tests, while 1210 completed two tests. The sensitivity for the TST, the C-TST, and IGRA (QFT-GIT) was 92.7%, 52.9%, and 58.8%, respectively, with corresponding specificities of 56.0%, 83.6%, and 83.6%. Notably, the C-TST and IGRA (QFT-GIT) exhibited high concordance, as indicated by a Kappa value of 0.869. However, through a simulation-based analysis of this single-center cohort, which consisted exclusively of male participants and included a small subgroup subjected to all three tests, and under predefined model assumptions, the combination of TST and C-TST emerged as a more effective screening strategy. This combination achieved an AUC of 0.821, achieved the optimal balance between accuracy and cost-effectiveness.

Conclusion: Within the context of this study, the novel screening strategy that integrates TST and C-TST demonstrates promising efficacy and economic advantages when compared to the traditional TST/IGRAs approach for the detection of LTBI.

The Marburg virus (MARV), responsible for severe hemorrhagic fevers with mortality rates as high as 90%, remains a significant public health threat. This study employs machine learning to identify inhibitors targeting the MARV Gene 4 Small ORF protein, crucial for the virus's replication and immune evasion. The Gene 4 Small ORF protein is pivotal in taking over the host's cellular mechanisms, facilitating unchecked viral replication and significant immune system disruption. Effective targeting of this protein holds promise for mitigating the viral lifecycle and entry, potentially curbing the severity of the disease outbreaks. A dataset from PubChem, including 301,745 compounds, was utilized to train models like Random Forest (RF), Gradient Boosting Machines (GBM), CatBoost (CB), AdaBoost (AB), and Logistic Regression (LR). The activity outcomes were classified with integers active as 1 and inactive as 0, followed by molecular descriptor generation using RDKit and PaDEL. The models were trained on an 80:20 split and validated on a novel dataset to ensure robustness, with performance metrics such as accuracy and AUC-ROC guiding evaluation. Morgan fingerprints outperformed PubChem fingerprints, achieving higher accuracy (76%), precision (80%), and ROC-AUC (84%). Among the machine learning models evaluated, RF and GBM were the best performers, with RF achieving the highest specificity (83%) and ROC-AUC (0.84). Validation on new datasets further confirmed the effectiveness of these models, with RF and GBM demonstrating strong predictive reliability for identifying potential inhibitors of the Marburg virus. A Web Application known as MARVpred was developed to predict the activity of compounds with anti-MARV properties from the ChEMBL database. MARVpred is freely accessible online (https://igmr.org/software/marvpred). This study signifies a critical step forward in the computational prediction of viral inhibitors, offering a valuable tool for accelerating the development of Marburg virus therapeutics.