Corticosteroid-resistant inflammatory signalling in<i>Pseudomonas</i>-infected bronchial cells
Bibliographic record
Abstract
Decreasing the inflammatory response that leads to tissue damage during cystic fibrosis (CF) lung disease has been a long-standing goal of CF therapy. While corticosteroids are widely used anti-inflammatory drugs, their efficacy in CF lung disease remains debated. The complex interaction between the colonising bacteria and the host environment may impact corticosteroid responsiveness. In this study, sputum samples from adult CF patients were collected at baseline and during pulmonary exacerbation episodes. Lung function measurements and sputum microbiological analyses were performed. In parallel, the inflammatory response and corticosteroid sensitivity of airway epithelial cells toPseudomonas-derived exoproducts was investigated. We report that adult CF patients colonised with mucoidPseudomonas aeruginosahave higher levels of baseline inflammation, more frequent exacerbations and worse lung function compared with patients colonised with nonmucoidP. aeruginosa. Moreover, mucoidP. aeruginosaactivates NF-κBviaToll-like receptor (TLR) 2, which acts in an additive manner to TLR5 to drive inflammation in airway epithelial cells. Furthermore, TLR2-mediated intracellular signalling is more resistant to the anti-inflammatory effects of corticosteroid when compared with other TLR signalling pathways. Overall, these results suggest that airway inflammation triggered by mucoidP. aeruginosais less responsive to the anti-inflammatory action of corticosteroids. Whether this translates into a diminished response of CF patients to corticosteroid therapy should be examined in future clinical studies.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".