Ras pathway inhibition prevents neovascularization by repressing endothelial cell sprouting
Bibliographic record
Abstract
Vascular networks develop from a growing vascular front that responds to VEGF and other guidance cues. Angiogenesis is required for normal tissue function, but, under conditions of stress, inappropriate vascularization can lead to disease. Therefore, inhibition of angiogenic sprouting may prevent neovascularization in patients with blinding neovascular eye diseases, including macular degeneration. VEGF antagonists have therapeutic benefits but also can elicit off-target effects. Here, we found that the Ras pathway, which functions downstream of a wide range of cytokines including VEGF, is active in the growing vascular front of developing and pathological vascular networks. The endogenous Ras inhibitor p120RasGAP was expressed predominately in quiescent VEGF-insensitive endothelial cells and was ectopically downregulated in multiple neovascular models. MicroRNA-132 negatively regulated p120RasGAP expression. Experimental delivery of α-miR-132 to developing mouse eyes disrupted tip cell Ras activity and prevented angiogenic sprouting. This strategy prevented ocular neovascularization in multiple rodent models even more potently than the VEGF antagonist, VEGF-trap. Targeting microRNA-132 as a therapeutic strategy may prove useful for treating multiple neovascular diseases of the eye and for preventing vision loss regardless of the neovascular stimulus.
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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) | 0.001 | 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".