Circ_RUSC2 upregulates the expression of miR-661 target gene <i>SYK</i> and regulates the function of vascular smooth muscle cells
Bibliographic record
Abstract
Many studies have identified circRNA as a prospective direction in the field of cardiovascular research. Detection of circRNA expression in different vascular smooth muscle cell (VSMC) phenotypes revealed that circ_RUSC2 is upregulated in proliferative VSMCs. Sequence analysis of circ_RUSC2 showed that there are multiple binding sites of miR-661 on circ_RUSC2, and that SYK is an important target gene of miR-661. MiR-661 expression is downregulated in proliferative VSMCs, whereas the expression of SYK is upregulated. Circ_RUSC2 and miR-661 do not affect each other’s expression levels, but circ_RUSC2 can promote the expression of SYK and inhibit the expression of SM22-alpha, whereas miR-661 has the opposite effect. At the same time, VSMC proliferation and migration can be promoted by SYK or circ_RUSC2, but the linear sequence of circ_RUSC2 can not. MiR-661 and circ_RUSC2 siRNAs inhibit VSMC proliferation and migration, and promote cell apoptosis. When an miR-661 mimic or SYK siRNAs were co-transfected with circ_RUSC2 overexpression vector, VSMC proliferation, apoptosis, and migration were not significantly altered. Accordingly, circ_RUSC2 can promote the expression of SYK, a target gene of miR-661, and regulate VSMC proliferation, apoptosis, phenotypic modulation, and migration. These findings will supply a theoretical basis for studying circRNA function in VSMCs, and new ideas for the diagnosis and treatment of cardiovascular 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) | 0.002 | 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".