The cytokines interleukin-1β and tumor necrosis factor-α stimulate CFTR-mediated fluid secretion by swine airway submucosal glands
Bibliographic record
Abstract
The airway is kept sterile by an efficient innate defense mechanism. The cornerstone of airway defense is mucus containing diverse antimicrobial factors that kill or inactivate pathogens. Most of the mucus in the upper airways is secreted by airway submucosal glands. In patients with cystic fibrosis (CF), airway defense fails and the lungs are colonized by bacteria, usually Pseudomonas aeruginosa. Accumulating evidence suggests that airway submucosal glands contribute to CF pathogenesis by failing to respond appropriately to inhalation of bacteria. However, the regulation of submucosal glands by the innate immune system remains poorly understood. We studied the response of submucosal glands to the proinflammatory cytokines interleukin-1β and tumor necrosis factor-α. These are released into the airway submucosa in response to infection with the bacterium P. aeruginosa and are elevated in CF airways. Stimulation with IL-1β and TNF-α increased submucosal gland secretion in a concentration-dependent manner with a maximal secretion rate of 240 ± 20 and 190 ± 40 pl/min, respectively. The half maximal effective concentrations were 11 and 20 ng/ml, respectively. The cytokine effect was dependent on cAMP but was independent of cGMP, nitric oxide, Ca(2+), or p38 MAP kinase. Most importantly, IL-1β- and TNF-α-stimulated secretion was blocked by the CF transmembrane conductance regulator (CFTR) blocker, CFTRinh172 (100 μmol/l) but was not affected by the Ca(2+)-activated Cl(-) channel blocker, niflumic acid (1 μmol/l). The data suggest, that during bacterial infections and resulting release of proinflammatory cytokines, the glands are stimulated to secrete fluid, and this response is mediated by cAMP-activated CFTR, a process that would fail in patients with CF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".