Pro-Reparative Effects of KvLQT1 Potassium Channel Activation in a Mouse Model of Acute Lung Injury Induced by Bleomycin
Bibliographic record
Abstract
Acute Respiratory Distress Syndrome (ARDS) is a complex and devastating form of respiratory failure, with high mortality rates, for which there is no pharmacological treatment. The acute exudative phase of ARDS is characterized by severe damage to the alveolar–capillary barrier, infiltration of protein-rich fluid into the lungs, neutrophil recruitment, and high levels of inflammatory mediators. Rapid resolution of this reversible acute phase, with efficient restoration of alveolar functional integrity, is essential before the establishment of irreversible fibrosis and respiratory failure. Several lines of in vitro and in vivo evidence support the involvement of potassium (K+) channels—particularly KvLQT1, expressed in alveolar cells—in key cellular mechanisms for ARDS resolution, by promoting alveolar fluid clearance and epithelial repair processes. The aim of our study was to investigate whether pharmacological activation of KvLQT1 channels could elicit beneficial effects on ARDS parameters in an animal model of acute lung injury. We used the well-established bleomycin model, which mimics (at day 7) the key features of the exudative phase of ARDS. Our data demonstrate that treatments with the KvLQT1 activator R-L3, delivered to the lungs, failed to improve endothelial permeability and lung edema in bleomycin mice. However, KvLQT1 activation significantly reduced neutrophil recruitment and tended to decrease levels of pro-inflammatory cytokines/chemokines in bronchoalveolar lavages after bleomycin administration. Importantly, R-L3 treatment was associated with significantly lower injury scores, higher levels of alveolar type I (HTI-56, AQP5) and II (pro-SPC) cell markers, and improved alveolar epithelial repair capacity in the presence of bleomycin. Together, these results suggest that the KvLQT1 K+ channel may be a potential target for the resolution of the acute phase of ARDS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".