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PROVIDER: PRJEB54732 | ENA |
REPOSITORIES: ENA
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Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:Respiratory microbiota in children with severe lower respiratory tract infections.
| PRJNA669463 | ENA
Project description:Lower respiratory tract microbiome in critically ill patients
| PRJEB64676 | ENA
Project description:This series includes 278 microarrays used to detect respiratory viruses in a set of nasopharyngeal lavage specimens from children with respiratory tract infections Objective: To assess the utility of a pan-viral DNA microarray platform (Virochip) in the detection of viruses associated with pediatric respiratory tract infections. Study Design: The Virochip was compared to conventional clinical direct fluorescent antibody (DFA) and PCR-based testing for the detection of respiratory viruses in 278 consecutive nasopharyngeal aspirate samples from 222 children. Results: The Virochip was superior in performance to DFA, showing a 19% increase in the detection of 7 respiratory viruses included in standard DFA panels, and was similar to virus-specific PCR (sensitivity 85-90%, specificity 99%, PPV 94-96%, NPV 97-98%) in the detection of respiratory syncytial virus, influenza A, and rhino-/enteroviruses. The Virochip also detected viruses not routinely tested for or missed by DFA and PCR, as well as double infections and infections in critically ill patients that DFA failed to detect. Conclusions: Given its favorable sensitivity and specificity profile and greatly expanded spectrum of detection, microarray-based viral testing holds promise for clinical diagnosis of pediatric respiratory tract infections. Keywords: viral detection The series includes 278 clinical specimens
2010-05-26 | E-GEOD-10294 | biostudies-arrayexpress
Project description:Bovine respiratory epithelial cells have different susceptibility to bovine
respiratory syncytial virus infection. The cells derived from the lower
respiratory tract were significantly more susceptible to the virus than those
derived from the upper respiratory tract. Pre-infection with virus of lower
respiratory tract with increased adherence of P. multocida; this was not the
case for upper tract. However, the molecular mechanisms of enhanced
bacterial adherence are not completely understood. To investigate whether
virus infection regulates the cellular adherence receptor on bovine trachea-,
bronchus- and lung-epithelial cells, we performed proteomic analyses.
2020-07-28 | PXD019509 | JPOST Repository
Project description:Human metapneumovirus (HMPV) is a primary causative agent of acute lower respiratory tract infections. We used single cell RNA-sequencing (scRNA-seq) to assess lung immune profiles in a mouse model of HMPV infection.
2024-03-18 | GSE261511 | GEO