Noncoding RNAs regulate NF-κB signaling to modulate blood vessel inflammation
Bibliographic record
Abstract
Cardiovascular diseases such as atherosclerosis are one of the leading causes of morbidity and mortality worldwide. The clinical manifestations of atherosclerosis, which include heart attack and stroke, occur several decades after initiation of the disease and become more severe with age. Inflammation of blood vessels plays a prominent role in atherogenesis. Activation of the endothelium by inflammatory mediators leads to the recruitment of circulating inflammatory cells, which drives atherosclerotic plaque formation and progression. Inflammatory signaling within the endothelium is driven predominantly by the pro-inflammatory transcription factor, NF-κB. Interestingly, activation of NF-κB is enhanced during the normal aging process and this may contribute to the development of cardiovascular disease. Importantly, studies utilizing mouse models of vascular inflammation and atherosclerosis are uncovering a network of noncoding RNAs, particularly microRNAs, which impinge on the NF-κB signaling pathway. Here we summarize the literature regarding the control of vascular inflammation by microRNAs, and provide insight into how these microRNA-based pathways might be harnessed for therapeutic treatment of disease. We also discuss emerging areas of endothelial cell biology, including the involvement of long noncoding RNAs and circulating microRNAs in the control of vascular inflammation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".