Abstract 15379: Regulation of Long Non-Coding RNA Fingerprint by Cyclic Mechanical Stretch in Vascular Smooth Muscle Cells: Implications for Aortic Aneurysms
Bibliographic record
Abstract
Introduction: Emerging evidence suggests that long noncoding RNAs (lncRNAs) may be a cellular hub for the coordination of cellular processes involved in health and disease. Alterations in the characteristics of vascular smooth muscle cells (VSMCs), triggered largely by changes in mechanical stress, play a critical role in the pathophysiology of vascular diseases like aneurysms and hypertension. Hypothesis: LncRNAs are involved in mechanical stretch-induced changes in HASMCs. Methods: Total RNA was extracted with TRIzol. LncRNAs and mRNAs were profiled with the Arraystar Human LncRNA Microarray V3.0. Aneurysmal and non-aneurysmal samples were collected from patients undergoing aortic arch repair and the aortas of ApoE -/- mice infused for 4 weeks with angiotensin II or saline. Gene expression was quantified via qRT-PCR. For knockdown studies, HASMCs were transfected with 10 nM siLincRNA-p21 or 5 nM scrambled control (nM). Apoptosis was assessed using Annexin V/PI double staining. Results: Of the 30,586 human lncRNAs screened in HASMCs, 580 were differentially expressed (P -/- mice (N = 3, P Bax , Puma , Noxa , and Mdm2 under conditions of stretch (N = 3, P Conclusions: We describe the first transcriptome profile of stretch-induced changes in HASMCs. The data implicate lincRNA-p21 as a mechanoresponsive regulator of aneurysm formation in mice and humans, and provide novel insights into the regulatory switches governing aberrant VSMC remodeling, which may have importance in the pathogenesis of aneurysms and hypertension.
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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) | 0.004 | 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".