Long non-coding RNA SNHG16 regulates E2F1 expression by sponging miR-20a-5p and aggravating proliferative diabetic retinopathy
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) were reported to be related to microvascular dysfunction in diabetic retinopathy (DR), but the potential mechanism remains unknown. This study was designed to elucidate the effects of lncRNA small nucleolar RNA host gene 1 (SNHG16) in proliferative DR progression. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the levels of SNHG16 and miR-20a-5p from peripheral blood samples of different participants. Pearson's correlation analysis was applied to the plasma data to detect correlations between SNHG16 and miR-20a-5p. Finally, the interactions of miR-20a-5p and SNHG16 or E2F1 were assessed by luciferase reporter assays. SNHG16 and E2F1 were increased and miR-20a-5p was decreased in proliferative DR both in vivo and in vitro when compared with control or non-proliferative DR. E2F1 was identified as the target of miR-20a-5p. The miR-20a-5p interacted with SNHG16 and E2F1 and was controlled by SNHG16. The regulation of SNHG16 on E2F1 was mediated by miR-20a-5p. Cells transfected with SNHG16 overexpression plasmid markedly increased cell apoptosis and vessel-like formation, whereas the miR-20a-5p mimic partially reversed these effects. Transfection with gene silencing E2F1 plasmid rescued SNHG16 overexpression-aggravated proliferative DR. This study indicated that SNHG16 regulated E2F1 expression by sponging miR-20a-5p and aggravating proliferative 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) | 0.000 | 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".