Vascular Endothelial Growth Factor Gene Therapy Increases Survival, Promotes Lung Angiogenesis, and Prevents Alveolar Damage in Hyperoxia-Induced Lung Injury
Bibliographic record
Abstract
BACKGROUND: Bronchopulmonary dysplasia (BPD) and pulmonary emphysema, both significant global health problems, are characterized by a loss of alveoli. Vascular endothelial growth factor (VEGF) is a trophic factor required for endothelial cell survival and is abundantly expressed in the lung. METHODS AND RESULTS: We report that VEGF blockade decreases lung VEGF and VEGF receptor 2 (VEGFR-2) expression in newborn rats and impairs alveolar development, leading to alveolar simplification and loss of lung capillaries, mimicking BPD. In hyperoxia-induced BPD in newborn rats, air space enlargement and loss of lung capillaries are associated with decreased lung VEGF and VEGFR-2 expression. Postnatal intratracheal adenovirus-mediated VEGF gene therapy improves survival, promotes lung capillary formation, and preserves alveolar development in this model of irreversible lung injury. Combined VEGF and angiopoietin-1 gene transfer matures the new vasculature, reducing the vascular leakage seen in VEGF-induced capillaries. CONCLUSIONS: These findings underscore the importance of the vasculature in what is traditionally thought of as an airway disease and open new therapeutic avenues for lung diseases characterized by irreversible loss of alveoli through the modulation of angiogenic growth factors.
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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".