Mechanical Ventilation Induces Desensitization of Lung Axl Tyrosine Kinase Receptors
Bibliographic record
Abstract
BACKGROUND: Lower tidal volumes are increasingly used in acute respiratory distress syndrome, but mortality has changed little in the last 20 yr. Therefore, in addition to ventilator settings, it is important to target molecular mediators of injury. Sepsis and other inflammatory states increase circulating concentrations of Gas6, a ligand for the antiinflammatory receptor Axl, and of a soluble decoy form of Axl. We investigated the effects of lung stretch on Axl signaling. METHODS: We used a mouse model of early injury from high tidal volume and assessed the effects of inhibiting Axl on in vivo lung injury (using an antagonist R428, n = 4/group). We further determined the effects of stretch on Axl activation using in vitro lung endothelial cells. RESULTS: High tidal volume caused mild injury (compliance decreased 6%) as intended, and shedding of the Axl receptor (soluble Axl in bronchoalveolar fluid increased 77%). The Axl antagonist R428 blocked the principal downstream Axl target (suppressor of cytokine signaling 3 [SOCS3]) but did not worsen lung physiology or inflammation. Cyclic stretch in vitro caused Axl to become insensitive to activation by its agonist, Gas6. Finally, in vitro Axl responses were rescued by blocking stretch-activated calcium channels (using guanidinium chloride [GdCl3]), and the calcium ionophore ionomycin replicated the effect of stretch. CONCLUSIONS: These data suggest that lung endothelial cell overdistention activates ion channels, and the resultant influx of Ca inactivates Axl. Downstream inactivation of Axl by stretch was not anticipated; preventing this would be required to exploit Axl receptors in reducing lung injury.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".