MicroRNA-34a attenuates VEGF-mediated retinal angiogenesis via targeting Notch1
Bibliographic record
Abstract
Pathological angiogenesis in the retina is one of the main ocular diseases closely associated with vision loss. This work investigated the roles of microRNA-34a (miR-34a) and its potential target Notch1, in retinal angiogenesis. For this we used oxygen-induced retinopathy (OIR) rats and human retinal microvascular endothelial cells (HRMECs) stimulated with vascular endothelial growth factor (VEGF). We performed hematoxylin-eosin staining, Western blot for VEGF, and immunofluorescence staining for CD31 to verify the establishment of our OIR model. We observed down-regulation of miR-34a, and up-regulation of Notch1 and Hey1 in retinas from OIR rats. We found similar results with the VEGF-stimulated HRMECs. By performing MTT assay, cell scratch assay, tube formation assay, and by detecting the expression of matrix-metalloproteinase-2 (MMP-2), MMP-9, tissue inhibitors of metalloproteinases-1 (TIMP-1), and TIMP-2, we found that transfection of miR-34a ameliorated VEGF-mediated angiogenesis of HRMECs. We further observed that siRNA-induced gene silencing of Notch1 prevented VEGF-induced angiogenesis via regulating cell proliferation, cell migration, and tube formation of HRMECs. Additionally, activation of Notch1 by transfection of Notch1 plasmid attenuated the inhibitory effects of miR-34a on tube formation, in the present of VEGF. Results from our dual-luciferase reporter gene assay suggested that miR-34a targets Notch1. In summary, our data demonstrate that miR-34a attenuates retinal angiogenesis via targeting Notch1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".