Chloride transport-driven alveolar fluid secretion is a major contributor to cardiogenic lung edema
Bibliographic record
Abstract
Alveolar fluid clearance driven by active epithelial Na(+) and secondary Cl(-) absorption counteracts edema formation in the intact lung. Recently, we showed that impairment of alveolar fluid clearance because of inhibition of epithelial Na(+) channels (ENaCs) promotes cardiogenic lung edema. Concomitantly, we observed a reversal of alveolar fluid clearance, suggesting that reversed transepithelial ion transport may promote lung edema by driving active alveolar fluid secretion. We, therefore, hypothesized that alveolar ion and fluid secretion may constitute a pathomechanism in lung edema and aimed to identify underlying molecular pathways. In isolated perfused lungs, alveolar fluid clearance and secretion were determined by a double-indicator dilution technique. Transepithelial Cl(-) secretion and alveolar Cl(-) influx were quantified by radionuclide tracing and alveolar Cl(-) imaging, respectively. Elevated hydrostatic pressure induced ouabain-sensitive alveolar fluid secretion that coincided with transepithelial Cl(-) secretion and alveolar Cl(-) influx. Inhibition of either cystic fibrosis transmembrane conductance regulator (CFTR) or Na(+)-K(+)-Cl(-) cotransporters (NKCC) blocked alveolar fluid secretion, and lungs of CFTR(-/-) mice were protected from hydrostatic edema. Inhibition of ENaC by amiloride reproduced alveolar fluid and Cl(-) secretion that were again CFTR-, NKCC-, and Na(+)-K(+)-ATPase-dependent. Our findings show a reversal of transepithelial Cl(-) and fluid flux from absorptive to secretory mode at hydrostatic stress. Alveolar Cl(-) and fluid secretion are triggered by ENaC inhibition and mediated by NKCC and CFTR. Our results characterize an innovative mechanism of cardiogenic edema formation and identify NKCC1 as a unique therapeutic target in cardiogenic lung edema.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".