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 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) | 0.001 | 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) | 0.003 | 0.001 |
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".