<i>Pseudomonas aeruginosa</i> triggers CFTR-mediated airway surface liquid secretion in swine trachea
Bibliographic record
Abstract
Cystic fibrosis (CF) is an autosomal recessive genetic disorder caused by mutations in the gene encoding for the anion channel cystic fibrosis transmembrane conductance regulator (CFTR). Several organs are affected in CF, but most of the morbidity and mortality comes from lung disease. Recent data show that the initial consequence of CFTR mutation is the failure to eradicate bacteria before the development of inflammation and airway remodeling. Bacterial clearance depends on a layer of airway surface liquid (ASL) consisting of both a mucus layer that traps, kills, and inactivates bacteria and a periciliary liquid layer that keeps the mucus at an optimum distance from the underlying epithelia, to maximize ciliary motility and clearance of bacteria. The airways in CF patients and animal models of CF demonstrate abnormal ASL secretion and reduced antimicrobial properties. Thus, it has been proposed that abnormal ASL secretion in response to bacteria may facilitate the development of the infection and inflammation that characterize CF airway disease. Whether the inhalation of bacteria triggers ASL secretion, and the role of CFTR, have never been tested, however. We developed a synchrotron-based imaging technique to visualize the ASL layer and measure the effect of bacteria on ASL secretion. We show that the introduction of Pseudomonas aeruginosa and other bacteria into the lumen of intact isolated swine tracheas triggers CFTR-dependent ASL secretion by the submucosal glands. This response requires expression of the bacterial protein flagellin. In patients with CF, the inhalation of bacteria would fail to trigger ASL secretion, leading to infection and inflammation.
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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 | 0.001 |
| 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 | 0.001 | 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".