Wnt Signaling Mediates Pathological Vascular Growth in Proliferative Retinopathy
Bibliographic record
Abstract
BACKGROUND: Ischemic proliferative retinopathy, characterized by pathological retinal neovascularization, is a major cause of blindness in working-age adults and children. Defining the molecular pathways distinguishing pathological neovascularization from normal vessels is critical to controlling these blinding diseases with targeted therapy. Because mutations in Wnt signaling cause defective retinal vasculature in humans with some characteristics of the pathological vessels in retinopathy, we investigated the potential role of Wnt signaling in pathological retinal vascular growth in proliferative retinopathy. METHODS AND RESULTS: In this study, we show that Wnt receptors (Frizzled4 and low-density lipoprotein receptor-related protein5 [Lrp5]) and activity are significantly increased in pathological neovascularization in a mouse model of oxygen-induced proliferative retinopathy. Loss of Wnt coreceptor Lrp5 and downstream signaling molecule dishevelled2 significantly decreases the formation of pathological retinal neovascularization in retinopathy. Loss of Lrp5 also affects retinal angiogenesis during development and formation of the blood-retinal barrier, which is linked to significant downregulation of tight junction protein claudin5 in Lrp5(-/-) vessels. Blocking claudin5 significantly suppresses Wnt pathway-driven endothelial cell sprouting in vitro and developmental and pathological vascular growth in retinopathy in vivo. CONCLUSIONS: These results demonstrate an important role of Wnt signaling in pathological vascular development in retinopathy and show a novel function of Cln5 in promoting angiogenesis.
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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".