Long noncoding RNA MALAT1 participates in the pathological angiogenesis of diabetic retinopathy in an oxygen-induced retinopathy mouse model by sponging miR-203a-3p
Bibliographic record
Abstract
Diabetic retinopathy (DR) is a devastating complication of diabetes. The aim of the present study is to investigate the exact role and mechanism of long noncoding RNA MALAT1 (MALAT1) in the progress of DR. An oxygen-induced retinopathy (OIR) mouse model and high glucose (HG) stimulated human retinal microvascular endothelial cells (HRMECs) were employed to mimic the pathological statues of DR. Quantitative real-time PCR (qRT-PCR) and Western blot results showed that MALAT1, VEGFA, and HIF-1α levels were increased in DR retinal tissues and HG-stimulated HRMECs, whereas the expression of miR-203a-3p was decreased. Knockdown of MALAT1 or upregulation of miR-203a-3p both suppressed HG-induced proliferation, migration, and tube formation of HRMECs. A dual-luciferase reporter assay showed that miR-203a-3p could bind to the predicted seed regions of MALAT1 as evidenced by the reduced luciferase activity. Furthermore, enforced downregulation of miR-203a-3p abolished the suppressive effect of MALAT1 silencing on HRMEC cell migration and tube formation. In conclusion, these data demonstrated that MALAT1 may affect angiogenesis by sponging miR-203a-3p in DR, suggesting that MALAT1 may act as a novel therapeutic target for the treatment of DR.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".