ArticleFrontiers in microbiology2025
Proteomic characteristics of bronchoalveolar lavage fluid in children with mild and severe
Article in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Label-free Raman spectroscopic imaging enables high-content molecular phenotyping of respiratory diseases from bronchoalveolar lavage fluid.Journal of translational medicine · 2026Article
- Machine learning-driven risk assessment of severeFrontiers in medicine · 2026Article
- Immune dysregulation inFrontiers in immunology · 2026Review
- A fever-based therapeutic window for bronchoscopy to prevent bronchiolitis obliterans in children with Mycoplasma pneumoniae pneumonia.Frontiers in pediatrics · 2026Article
- From genes to clinical application: a circulating four-gene signature for early diagnosis model of refractoryFrontiers in cellular and infection microbiology · 2026Article
- Building a diagnostic prediction model for severeFrontiers in public health · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
Objectives: Methods: In this study, bronchoalveolar lavage fluid (BALF) samples were collected from 30 children, including 15 with mild and 15 with severe MPP, for quantitative proteomic analysis. The two groups were compared and differentially expressed proteins (DEPs) were identified. Core proteins associated with MPP severity were identified using least absolute shrinkage and selection operator (LASSO) analysis. Logistic regression analysis was used to develop a predictive model. Results: A total of 154 DEPs were identified, of which 57 were upregulated in the severe group. Upregulated signaling was found to be mainly involved in the immune response and inflammatory signaling. Thirteen proteins were selected as core proteins associated with MPP severity. CD209, CHM, PBRM1, and SCAMP1 were the most influential predictors and a predictive model using these four proteins predicted MPP severity. Conclusion: A predictive model was developed to assess the potential of using the identified biomarkers to predict disease severity. This model provides insights into the pathogenesis of Importance: Differences in the proteomic characteristics of bronchoalveolar lavage fluid in children with mild and severe
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Registered trials
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