Ibuprofen Modulates NF-ĸB Activity but Not IL-8 Production in Cystic Fibrosis Respiratory Epithelial Cells
Bibliographic record
Abstract
BACKGROUND: High-dose ibuprofen is clinically effective in cystic fibrosis (CF); however, its molecular mechanisms are poorly understood. OBJECTIVE: To test the hypothesis that clinically relevant concentrations of ibuprofen suppress activation of nuclear factor (NF)-kappaB and thus down-regulate stimulated interleukin (IL)-8 production in CF respiratory epithelial cells. METHODS: The majority of experiments were conducted in CFTE29o- cells (F508del-mutated CF transmembrane regulator, CFTR). Key experiments were confirmed in CFBE41o- cells (F508del-mutated CFTR) and 1HAEo- cells (wild-type CFTR). NF-kappaB and IL-8 were stimulated with tumour necrosis factor (TNF)-alpha or IL-1beta. NF-kappaB and IL-8 suppression by ibuprofen (480 microM) was compared to dexamethasone (5 nM). RESULTS: Both TNF-alpha and IL-1beta activated NF-kappaB and stimulated IL-8 production. Both ibuprofen and dexamethasone demonstrated comparably modest suppression of NF-kappaB transcriptional activity. However, ibuprofen had no effect on stimulated IL-8 mRNA and protein. By contrast, dexamethasone significantly down-regulated stimulated IL-8 mRNA and protein. CONCLUSIONS: The present data do not support the hypothesis that ibuprofen down-regulates IL-8 production in response to TNF-alpha and IL-1beta in CF respiratory epithelium. Suppression of NF-kappaB transcriptional activity does not discriminate between anti-inflammatory drugs with or without effects on IL-8 production. We speculate that NF-kappaB-independent mechanisms may be responsible for anti-IL-8 effects of dexamethasone.
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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 | 0.000 |
| 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".