Na <sup>+</sup> transport by small airway surface epithelia
Bibliographic record
Abstract
Background Maintaining an adequate level of Airway Surface Liquid (ASL) is crucial for the normal functioning of the immune system of the lungs. A balance between the secretion and the absorption of ions and fluid by the airways surface epithelia (ASE) ensures proper amount of ASL for mucociliar clearance. ASE expresses Epithelial Na + Channel (ENaC) and the Cl ‐ channel Cystic Fibrosis Transmembrane conductance Regulator (CFTR). Currently, there are two opposing hypotheses to explain how the interplay between CFTR and ENaC activity regulate ASL height. One theory proposes that each surface epithelial cell expresses both CFTR and ENaC and is able to secrete or reabsorb fluid and ions. A recent hypothesis proposes that CFTR and ENaC are expressed in different groups of cells. Cells within the pleats of ASE express CFTR and are mainly secretory while the cells of the folds express ENaC and are principally absorptive. Question Are the cells of ASE able to transport ions in both directions? Methods To detect transepithelial Na + flux we used Scanning Ion Selective Techniquethe, which measures ion concentration gradient generated by the epithelia using ion selective microelectrodes. We investigated the ion transport in the folds of the swine distal airways under normal Kreb's solution and in the presence of CFTR and ENaC modulators. Results ASE showed spontaneous basal Na + transport from the lumen into the tissue. Adding the sodium azide blocked the basal secretion. Stimulation with forskolin or thapsigargin triggered rapid and reversal Na + transport and was blocked by treatment with the CFTR blocker. Conclusions The epithelial cells of the fold regions of ASE are able to secret and absorb Na + . Our results allow us to reject the hypothesis of the monodirectional Na + transportation in the surface epithelia.
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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 | 0.001 | 0.001 |
| 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".