Hypoxia-Inducible Factors Promote Alveolar Development and Regeneration
Bibliographic record
Abstract
Understanding how alveoli and the underlying capillary network develop and how these mechanisms are disrupted in disease states is critical for developing effective therapies for lung regeneration. Recent evidence suggests that lung angiogenesis promotes lung development and repair. Vascular endothelial growth factor (VEGF) preserves lung angiogenesis and alveolarization in experimental O2-induced arrested alveolar growth in newborn rats, but combined VEGF+angiopoietin 1 treatment is necessary to correct VEGF-induced vessel leakiness. Hypoxia-inducible factors (HIFs) are transcription factors that activate multiple O2-sensitive genes, including those encoding for angiogenic growth factors, but their role during postnatal lung growth is incompletely understood. By inducing the expression of a range of angiogenic factors in a coordinated fashion, HIF may orchestrate efficient and safe angiogenesis superior to VEGF. We hypothesized that HIF inhibition impairs alveolarization and that HIF activation regenerates irreversible O2-induced arrested alveolar growth. HIF inhibition by intratracheal dominant-negative adenovirus (dnHIF-1α)-mediated gene transfer or chetomin decreased lung HIF-1α, HIF-2α, and VEGF expression and led to air space enlargement and arrested lung vascular growth. In experimental O2-induced arrested alveolar growth in newborn rats, the characteristic features of air space enlargement and loss of lung capillaries were associated with decreased lung HIF-1α and HIF-2α expression. Intratracheal administration of Ad.HIF-1α restored HIF-1α, endothelial nitric oxide synthase, VEGF, VEGFR2, and Tie2 expression and preserved and rescued alveolar growth and lung capillary formation in this model. HIFs promote normal alveolar development and may be useful targets for alveolar regeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".