ArticleFrontiers in cellular and infection microbiology2024
Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.
Article in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Epidemiological characteristics of influenza A virus, influenza B virus, respiratory syncytial virus, human rhinovirus, adenovirus and Mycoplasma pneumoniae infections in Henan, China.BMC infectious diseases · 2026Article
- The emerging role of non-Gastroenterology report · 2026Review
- Phage characterization analysis in respiratory samples from infected patients based on metagenomic next-generation sequencing.Frontiers in cellular and infection microbiology · 2026Article
- Analysis of bacterial pathogen spectrum and epidemiological characteristics of pediatric lower respiratory tract infections: a large sample study based on bronchoalveolar lavage.BMC infectious diseases · 2025Article
- High co-infection burden and ICU-specific pathogen profiles in pediatric Candida albicans respiratory infections: a large-scale tNGS analysis.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Article
- Airway microbiota and immunity associated with chronic obstructive pulmonary disease severity.Journal of translational medicine · 2025Article
- Lung microbiome signatures and explainable predictive modeling of glucocorticoid response in severe community acquired pneumonia.Frontiers in microbiology · 2025Article
- Microbiome and its role in bronchiectasis.Therapeutic advances in respiratory diseaseReview
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Authors and funding
8 authors.
Funding
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Abstract
Background: Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Leveraging the advancements in metagenomic next-generation sequencing (mNGS) technology alongside the emergence of machine learning, it is now viable to compare the attributes of lower respiratory tract microbial communities among patients across diverse age groups, diseases, and infection types. Method: We collected bronchoalveolar lavage fluid samples from 138 patients diagnosed with lower respiratory tract infections and conducted mNGS to characterize the lung microbiota. Employing various machine learning algorithms, we investigated the correlation of key bacteria in patients with concurrent bronchiectasis and developed a predictive model for hospitalization duration based on these identified key bacteria. Result: We observed variations in microbial communities across different age groups, diseases, and infection types. In the elderly group, Conclusion: Our study provided a comprehensive description of the microbial characteristics among patients with lower respiratory tract infections, offering insights from various perspectives. Additionally, we investigated the advanced predictive capability of microbial community features in determining the hospitalization duration of these patients.
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