Ghrelin Modulates Physiologic and Pathologic Retinal Angiogenesis through GHSR-1a
Bibliographic record
Abstract
PURPOSE: Vascular degeneration and the ensuing abnormal vascular proliferation are central to proliferative retinopathies. Given the metabolic discordance associated with these diseases, the authors explored the role of ghrelin and its growth hormone secretagogue receptor 1a (GHSR-1a) in proliferative retinopathy. METHODS: In a rat model of oxygen-induced retinopathy (OIR), the contribution of ghrelin and GHSR-1a was investigated using the stable ghrelin analogs [Dap3]-ghrelin and GHRP6 and the GSHR-1a antagonists JMV-2959 and [D-Lys3]-GHRP-6. Plasma and retinal levels of ghrelin were analyzed by ELISA, whereas retinal expression and localization of GHSR-1a were examined by immunohistochemistry and Western blot analysis. The angiogenic and vasoprotective properties of ghrelin and its receptor were further confirmed in aortic explants and in models of vaso-obliteration. RESULTS: Ghrelin is produced locally in the retina, whereas GHSR-1a is abundantly expressed in retinal endothelial cells. Ghrelin levels decrease during the vaso-obliterative phase and rise during the proliferative phase of OIR. Intravitreal delivery of [Dap3]-ghrelin during OIR significantly reduces retinal vessel loss when administered during the hyperoxic phase. Conversely, during the neovascular phase, ghrelin promotes pathologic angiogenesis through the activation of GHSR-1a. These angiogenic effects were confirmed ex vivo in aortic explants. CONCLUSIONS: New roles were disclosed for the ghrelin-GHSR-1a pathway in the preservation of retinal vasculature during the vaso-obliterative phase of OIR and during the angiogenic phase of OIR. These findings suggest that the ghrelin-GHSR-1a pathway can exert opposing effects on retinal vasculature, depending on the phase of retinopathy, and thus holds therapeutic potential for proliferative retinopathies.
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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".