CircNMD3 relieves endothelial cell injury induced by oxidatively modified low-density lipoprotein through regulating miR-498/ BMP and activin membrane-bound inhibitor (BAMBI) axis
Bibliographic record
Abstract
Atherosclerosis (AS) is one of the most common vascular diseases. The endothelial injury theory indicates that atherosclerotic plaque is the result of endothelial cell injury. Recent studies have revealed that circRNAs are abnormally expressed in AS cell models, which are implicated in the regulation of various cell behaviors. In this study, we showed the downregulation of circNMD3 in AS, and studied its role in the model of endothelial cell injury by proliferation and apoptosis assay, caspase 3 activity assay, and ELISA. We also identified and validated its downstream targets by luciferase reporter assay, RNA pull-down experiment, Western blot, and RT-qPCR. CircNMD3 overexpression or miR-498 knockdown could inhibit the ox-LDL (oxidatively modified low-density lipoprotein)-induced injury in HUVEC (human umbilical vein endothelial cells), while the co-transfection of miR-498 mimic or siRNA targeting BAMBI (BMP and activin membrane bound inhibitor) attenuated the protective effect of circNMD3 overexpression. Overall, our data suggest that circNMD3 regulates the miR-498/BAMBI axis in endothelial cells to protect ox-LDL-induced damages. As a molecular sponge of miR-498, circNMD3 regulates the level of miR-498, which in turn modulates BAMBI expression and suppresses ox-LDL-induced injury in HUVECs.
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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) | 0.002 | 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".