CircRNA_000203 enhances the expression of fibrosis-associated genes by derepressing targets of miR-26b-5p, Col1a2 and CTGF, in cardiac fibroblasts
Bibliographic record
Abstract
Circular RNAs (circRNAs) participate in regulating gene expression in diverse biological and pathological processes. The present study aimed to investigate the mechanism underlying the modulation of circRNA_000203 on expressions of fibrosis-associated genes in cardiac fibroblasts. CircRNA_000203 was shown upregulated in the diabetic mouse myocardium and in Ang-II-induced mouse cardiac fibroblasts. Enforced-expression of circRNA_000203 could increase expressions of Col1a2, Col3a1 and α-SMA in mouse cardiac fibroblasts. RNA pull-down and RT-qPCR assay indicated that circRNA_000203 could specifically sponge miR-26b-5p. Dual luciferase reporter assay revealed that miR-26b-5p interacted with 3'UTRs of Col1a2 and CTGF, and circ_000203 could block the interactions of miR-26b-5p and 3'UTRs of Col1a2 and CTGF. Transfection of miR-26b-5p could post-transcriptionaly inhibit expressions of Col1a2 and CTGF, accompanied with the suppressions of Col3a1 and α-SMA in cardiac fibroblasts. Additionally, over-expression of circRNA_000203 could eliminate the anti-fibrosis effect of miR-26b-5p in cardiac fibroblasts. Together, our results reveal that suppressing the function of miR-26b-5p contributes to the pro-fibrosis effect of circRNA_000203 in cardiac fibroblasts.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".