Lung microbiome signatures and explainable predictive modeling of glucocorticoid response in severe community acquired pneumonia
Collectively, our results demonstrate that SG therapy imposes reproducible ecological pressures on the lung microbiome and that a two-feature microbial fingerprint can forecast treatment success with single-sample resolution. These findings show that SG therapy actively reshapes the respiratory ecosystem and that lightweight microbiome-aware machine learning can stratify treatment response, offering a tractable path toward precision corticosteroid stewardship.
Authors
- Tzong-Yi Lee (ORCID: https://orcid.org/0009-0002-0283-7712)
- Yun‐Long Tang (ORCID: https://orcid.org/0000-0001-6555-3296)
- Yeong-Nan Cheng (ORCID: https://orcid.org/0000-0003-0827-9985)
- Pin‐Kuei Fu (ORCID: https://orcid.org/0000-0002-9416-4094)
- Guan-Ting Chen (ORCID: https://orcid.org/0000-0002-0414-1412)
- Yen-Peng Chiu
- Wei‐Chih Huang (ORCID: https://orcid.org/0009-0006-3698-6993)
Institutions
- National Yang Ming Chiao Tung University (TW)
- National Chung Hsing University (TW)
- Taichung Veterans General Hospital (TW)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2025-11-28
- DOI
- https://doi.org/10.3389/fmicb.2025.1706432
- Primary Topic
- Gut microbiota and health
- Type
- article
- Field-Weighted Citation Impact
- 0.00