micro<scp>RNA</scp>‐181a inhibits ocular neovascularization by interfering with vascular endothelial growth factor expression
Bibliographic record
Abstract
AIM: Excess angiogenesis or neovascularization plays a key role in the pathophysiology of several ocular diseases such as retinopathy of prematurity, diabetic retinopathy, and exudative age-related macular degeneration. microRNA-181a (miR-181a) was found highly expressed in retina and choroidal tissues. This study intends to investigate the role of miR-181a in the regulation of ocular neovascularization in different pathophysiological conditions. METHOD: We performed the RNA sequence to identify the microRNAs components of anti-angiogenic lymphocyte-derived microparticles (LMPs). The effect of miR-181a on human retinal endothelial cells proliferation was assessed in vitro. The impact of miR-181a on angiogenesis was confirmed using in vitro angiogenesis assay, ex vivo choroidal explant, and in vivo retinal neovascularization. The expression of major angiogenic factors was assessed by real-time qPCR. RESULTS: RNA sequence revealed that miR-181a is selectively enriched in LMPs. Importantly, the inhibition of miR-181a significantly abrogated the effect of LMPs on endothelial viability, but overexpression of miR-181a reduced endothelial cell viability in a dose-dependent manner. miR-181a strongly inhibited in vitro angiogenesis and ex vivo choroidal neovascularization. The strong anti-angiogenic effect of miR-181a was also displayed on the retinal neovascularization of the in vivo mouse model of oxygen-induced retinopathy. In keeping with its effect, several angiogenesis-related genes were dysregulated in the miR-181a overexpressed endothelial cells. CONCLUSION: These data may open unexpected avenues for the development of miR-181a as a novel therapeutic strategy that would be particularly useful and relevant for the treatment of neovascular diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".