Short-chain fatty acids affect cystic fibrosis airway inflammation and bacterial growth
Bibliographic record
Abstract
The hypoxic environment of cystic fibrosis airways allows the persistence of facultative anaerobic bacteria, which can produce short-chain fatty acids (SCFAs) through fermentation. However, the relevance of SCFAs in cystic fibrosis lung disease is unknown. We show that SCFAs are present in sputum samples from cystic fibrosis patients in millimolar concentrations (mean±sem 1.99±0.36 mM).SCFAs positively correlated with sputum neutrophil count and higher SCFAs were predictive for impaired nitric oxide production. We studied the effects of the SCFAs acetate, propionate and butyrate on airway inflammatory responses using epithelial cell lines and primary cell cultures. SCFAs in concentrations present in cystic fibrosis airways (0.5-2.5 mM) affected the release of granulocyte-macrophage colony-stimulating factor, granulocyte colony-stimulating factor and interleukin (IL)-6. SCFAs also resulted in higher IL-8 release from stimulated cystic fibrosis transmembrane conductance regulator (CFTR) F508del-mutant compared to wild-type CFTR-corrected bronchial epithelial cells. At 25 mM propionate reduced IL-8 release in control but not primary cystic fibrosis epithelial cells. Low (0.5-2.5 mM) SCFA concentrations increased, while high (25-50 mM) concentrations decreased inducible nitric oxide synthase expression. In addition, SCFAs affected the growth of Pseudomonas aeruginosa in a concentration- and pH-dependent manner.Thus, our data suggest that SCFAs contribute to cystic fibrosis-specific alterations of responses to airway infection and inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".