Cell-based Angiopoietin-1 Gene Therapy for Acute Lung Injury
Bibliographic record
Abstract
RATIONALE: The acute respiratory distress syndrome is a significant cause of morbidity and mortality in critically ill patients. Angiopoietin-1 (Ang-1), a ligand for the endothelial Tie2 receptor, is an endothelial survival and vascular stabilization factor that reduces endothelial permeability and inhibits leukocyte-endothelium interactions. OBJECTIVES: We hypothesized that Ang-1 counteracts vascular inflammation and pulmonary vascular leak in experimental acute lung injury. METHODS: We used cell-based gene therapy in a rat model of ALI. Transgenic mice overexpressing Ang-1 or deficient in the Tie2 receptor were also studied to better elucidate the mechanisms of protection. MEASUREMENTS AND MAIN RESULTS: The present report provides data that support a strong protective role for the Ang-1/Tie2 system in two experimental models of LPS-induced acute lung injury. In a rat model, cell-based Ang-1 gene transfer improved morphological, biochemical, and molecular indices of lung injury and inflammation. These findings were confirmed in a gain-of-function conditional, targeted transgenic mouse model, in which Ang-1 reduced endothelial cell activation and the expression of adhesion molecules, associated with a marked improvement in airspace inflammation and intraalveolar septal thickening. Moreover, heterozygous Tie2-deficient mice demonstrated enhanced evidence of lung injury and increased early mortality. CONCLUSIONS: These results support a critical role for the Ang-1/Tie2 axis in modulating the pulmonary vascular response to lung injury and suggest that Ang-1 therapy may represent a potential new strategy for the treatment and/or prevention of acute respiratory distress syndrome in critically ill patients.
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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 | 0.001 |
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".