Vx-809/Vx-770 treatment reduces inflammatory response to <i>Pseudomonas aeruginosa</i> in primary differentiated cystic fibrosis bronchial epithelial cells
Bibliographic record
Abstract
Cystic fibrosis patients exhibit chronic Pseudomonas aeruginosa respiratory infections and sustained proinflammatory state favoring lung tissue damage and remodeling, ultimately leading to respiratory failure. Loss of cystic fibrosis transmembrane conductance regulator (CFTR) function is associated with MAPK hyperactivation and increased cytokines expression, such as interleukin-8 [chemoattractant chemokine (C-X-C motif) ligand 8 (CXCL8)]. Recently, new therapeutic strategies directly targeting the basic CFTR defect have been developed, and ORKAMBI (Vx-809/Vx-770 combination) is the only Food and Drug Administration-approved treatment for CF patients homozygous for the F508del mutation. Here we aimed to determine the effect of the Vx-809/Vx-770 combination on the induction of the inflammatory response by fully differentiated primary bronchial epithelial cell cultures from CF patients carrying F508del mutations, following exposure to P. aeruginosa exoproducts. Our data unveiled that CFTR functional rescue with Vx-809/Vx-770 drastically reduces CXCL8 (as well as CXCL1 and CXCL2) transcripts and p38 MAPK phosphorylation in response to P. aeruginosa exposure through a CFTR-dependent mechanism. These results suggest that ORKAMBI has anti-inflammatory properties that could decrease lung inflammation and contribute to the observed beneficial impact of this treatment in CF patients.
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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 | 0.001 |
| 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".