Truncated netrin-1 contributes to pathological vascular permeability in diabetic retinopathy
Bibliographic record
Abstract
Diabetic retinopathy (DR) is a major complication of diabetes and a leading cause of blindness in the working-age population. Impaired blood-retinal barrier function leads to macular edema that is closely associated with the deterioration of central vision. We previously demonstrated that the neuronal guidance cue netrin-1 activates a program of reparative angiogenesis in microglia within the ischemic retina. Here, we provide evidence in both vitreous humor of diabetic patients and in retina of a murine model of diabetes that netrin-1 is metabolized into a bioactive fragment corresponding to domains VI and V of the full-length molecule. In contrast to the protective effects of full-length netrin-1 on retinal microvasculature, the VI-V fragment promoted vascular permeability through the uncoordinated 5B (UNC5B) receptor. The collagenase matrix metalloprotease 9 (MMP-9), which is increased in patients with diabetic macular edema, was capable of cleaving netrin-1 into the VI-V fragment. Thus, MMP-9 may release netrin-1 fragments from the extracellular matrix and facilitate diffusion. Nonspecific inhibition of collagenases or selective inhibition of MMP-9 decreased pathological vascular permeability in a murine model of diabetic retinal edema. This study reveals that netrin-1 degradation products are capable of modulating vascular permeability, suggesting that these fragments are of potential therapeutic interest for the treatment of DR.
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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".