Nonsteroidal anti-inflammatory drugs upregulate function of wild-type and mutant CFTR
Bibliographic record
Abstract
Small-scale clinical trials show that treatment of cystic fibrosis (CF) patients with ibuprofen, a nonsteroidal anti-inflammatory drug, improves the symptoms of CF and slows down the decline of lung function. Paradoxically, ibuprofen inhibits ligand-stimulated CF transmembrance conductance regulator (CFTR) activity. The aim of the present study was to investigate the effects of ibuprofen on CFTR function under different conditions. Patch-clamp recordings were performed in two lines of human airway epithelial cells: IB3-8-3-7 cells, which express wild-type CFTR; and IB3-1 cells, which express the variant CFTR with deletion of phenylalanine 580 (DeltaF580CFTR). Addition of ibuprofen to the extracellular solution caused a rapid inhibition of CFTR activity in IB3-8-3-7 cells in the presence of a high intracellular concentration of cAMP, whereas ibuprofen enhanced the CFTR conductance at low levels of cAMP. Introducing ibuprofen into the interior of cells occluded the enhancing effect of ibuprofen. Notably, the variant CFTR-mediated conductance was detected in IB3-1 cells treated with myoinositol and was enhanced by ibuprofen at endogenous levels of cAMP. In summary, nonsteroidal anti-inflammatory drugs increase the function of both wild-type cystic fibrosis transmembrane conductance regulator and the phenylalanine 580 deletion in cultured human airway epithelial cells at endogenous levels of cAMP.
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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) | 0.002 | 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".