Airway submucosal glands from cystic fibrosis swine suffer from abnormal ion transport across the serous acini, collecting duct, and ciliated duct
Bibliographic record
Abstract
The human airway is protected by an efficient innate defense mechanism that requires healthy secretion of airway surface liquid (ASL) to clear pathogens from the lungs. Most of the ASL in the upper airway is secreted by submucosal glands. In cystic fibrosis (CF), the function of airway submucosal glands is abnormal, and these abnormalities are attributed to anomalies in ion transport across the epithelia lining the different sections of the glands that function coordinately to produce the ASL. However, the ion transport properties of most of the anatomical regions of the gland have never been measured, and there is controversy regarding which segments express CFTR. This makes it difficult to determine the glandular abnormalities that may contribute to CF lung disease. Using a noninvasive, extracellular self-referencing ion-selective electrode technique, we characterized ion transport properties in all four segments of submucosal glands from wild-type and CFTR−/−swine. In wild-type airways, the serous acini, mucus tubules, and collecting ducts secrete Cl−and Na+into the lumen in response to carbachol and forskolin stimulation. The ciliated duct also transports Cl−and Na+but in the opposite direction, i.e., reabsorption from the ASL, which may contribute to lowering Na+and Cl−activities in the secreted fluid. In CFTR−/−airways, the serous acini, collecting ducts, and ciliated ducts fail to transport ions after forskolin stimulation, resulting in the production of smaller volumes of ASL with normal Cl−, Na+, and K+concentration.
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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 | 0.001 |
| 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".