Nebulized hypertonic saline triggers nervous system-mediated active liquid secretion in cystic fibrosis swine trachea
Bibliographic record
Abstract
Abstract Inhaled hypertonic saline (HTS) treatment is used to improve lung health in patients with cystic fibrosis (CF). The current consensus is that the treatment generates an osmotic gradient that draws water into the airways and increases airway surface liquid (ASL) volume. However, there is evidence that HTS may also stimulate active secretion of ASL by airway epithelia through the activation of sensory neurons. We tested the contribution of the nervous system and airway epithelia on HTS-stimulated ASL height increase in CF and wild-type swine airway. We used synchrotron-based imaging to investigate whether airway neurons and epithelia are involved in HTS treatment-triggered ASL secretion in CFTR−/− and wild-type swine. We showed that blocking parasympathetic and sensory neurons in airway resulted in ~50% reduction of the effect of HTS treatment on ASL volume in vivo. Incubating tracheal preparations with inhibitors of epithelial ion transport across airway decreased secretory responses to HTS treatment. CFTR−/− swine ex-vivo tracheal preparations showed substantially decreased secretory response to HTS treatment after blockage of neuronal activity. Our results indicated that HTS-triggered ASL secretion is partially mediated by the stimulation of airway neurons and the subsequent activation of active epithelia secretion; osmosis accounts for only ~50% of the effect.
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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".