Critical Role of the Axonal Guidance Cue EphrinB2 in Lung Growth, Angiogenesis, and Repair
Bibliographic record
Abstract
RATIONALE: Lung diseases characterized by alveolar damage currently lack efficient treatments. The mechanisms contributing to normal and impaired alveolar growth and repair are incompletely understood. Axonal guidance cues (AGC) are molecules that guide the outgrowth of axons to their targets. Among these AGCs, members of the Ephrin family also promote angiogenesis, cell migration, and organogenesis outside the nervous system. The role of Ephrins during alveolar growth and repair is unknown. OBJECTIVES: We hypothesized that EphrinB2 promotes alveolar development and repair. METHODS: We used in vitro and in vivo manipulation of EphrinB2 signaling to assess the role of this AGC during normal and impaired lung development. MEASUREMENTS AND MAIN RESULTS: In vivo EphrinB2 knockdown using intranasal siRNA during the postnatal stage of alveolar development in rats arrested alveolar and vascular growth. In a model of O(2)-induced arrested alveolar growth in newborn rats, air space enlargement, loss of lung capillaries, and pulmonary hypertension were associated with decreased lung EphrinB2 and receptor EphB4 expression. In vitro, EphrinB2 preserved alveolar epithelial cell viability in O(2), decreased O(2)-induced alveolar epithelial cell apoptosis, and accelerated alveolar epithelial cell wound healing, maintained lung microvascular endothelial cell viability, and proliferation and vascular network formation. In vivo, treatment with intranasal EphrinB2 decreased alveolar epithelial and endothelial cell apoptosis, preserved alveolar and vascular growth in hyperoxic rats, and attenuated pulmonary hypertension. CONCLUSION: The AGC EphrinB2 may be a new therapeutic target for lung repair and pulmonary hypertension.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".