Blocking endothelial apoptosis revascularizes the retina in a model of ischemic retinopathy
Bibliographic record
Abstract
Aberrant, neovascular retinal blood vessel growth is a vision-threatening complication in ischemic retinal diseases. It is driven by retinal hypoxia frequently caused by capillary nonperfusion and endothelial cell (EC) loss. We investigated the role of EC apoptosis in this process using a mouse model of ischemic retinopathy, in which vessel closure and EC apoptosis cause capillary regression and retinal ischemia followed by neovascularization. Protecting ECs from apoptosis in this model did not prevent capillary closure or retinal ischemia. Nonetheless, it prevented the clearance of ECs from closed capillaries, delaying vessel regression and allowing ECs to persist in clusters throughout the ischemic zone. In response to hypoxia, these preserved ECs underwent a vessel sprouting response and rapidly reassembled into a functional vascular network. This alleviated retinal hypoxia, preventing subsequent pathogenic neovascularization. Vessel reassembly was not limited by VEGFA neutralization, suggesting it was not dependent on the excess VEGFA produced by the ischemic retina. Neutralization of ANG2 did not prevent vessel reassembly, but did impair subsequent angiogenic expansion of the reassembled vessels. Blockade of EC apoptosis may promote ischemic tissue revascularization by preserving ECs within ischemic tissue that retain the capacity to reassemble a functional network and rapidly restore blood supply.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".