Function of KvLQT1 potassium channels in a mouse model of bleomycin-induced acute lung injury
Bibliographic record
Abstract
Acute respiratory distress syndrome (ARDS) is characterized by an exacerbated inflammatory response, severe damage to the alveolar-capillary barrier and a secondary infiltration of protein-rich fluid into the airspaces, ultimately leading to respiratory failure. Resolution of ARDS depends on the ability of the alveolar epithelium to reabsorb lung fluid through active transepithelial ion transport, to control the inflammatory response, and to restore a cohesive and functional epithelium through effective repair processes. Interestingly, several lines of evidence have demonstrated the important role of potassium (K + ) channels in the regulation of epithelial repair processes. Furthermore, these channels have previously been shown to be involved in sodium/fluid absorption across alveolar epithelial cells, and we have recently demonstrated the contribution of KvLQT1 channels to the resolution of thiourea-induced pulmonary edema in vivo . The aim of our study was to investigate the role of the KCNQ1 pore-forming subunit of KvLQT1 channels in the outcome of ARDS parameters in a model of acute lung injury (ALI). We used a molecular approach with KvLQT1-KO mice challenged with bleomycin, a well-established ALI model that mimics the key features of the exudative phase of ARDS on day 7. Our data showed that KvLQT1 deletion exacerbated the negative outcome of bleomycin on lung function (resistance, elastance and compliance). An alteration in the profile of infiltrating immune cells was also observed in KvLQT1-KO mice while histological analysis showed less interstitial and/or alveolar inflammatory response induced by bleomycin in KvLQT1-KO mice. Finally, a reduced repair rate of KvLQT1-KO alveolar cells after injury was observed. This work highlights the complex contribution of KvLQT1 in the development and resolution of ARDS parameters in a model of ALI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".