Ultrasound-targeted microRNA-26a Therapy for Abdominal Aortic Aneurysms
Bibliographic record
Abstract
Abdominal aortic aneurysms (AAA) affect ~5% of adults and are characterized by vessel inflammation, aortic smooth muscle cell (haSMC) depletion and extracellular matrix (ECM) degradation. microRNA-26a (miR-26a) regulates haSMC function but is reduced in human AAA. We induced targeted delivery of miR-26a or scrambled-control miR to the infrarenal aorta of a clinically relevant rat AAA model using ultrasound-targeted microbubble destruction (UTMD). An additional control group received no therapy. In-vitro, haSMCs were stimulated with the inflammatory interleukin-1β (IL-1β; 20ng/ml) and transfected with miR-26a, antimiR-26a and scrambled-control miR. In-vivo, UTMD of miR-26a downregulated inflammatory cytokines (IL-1β, IL-6 and TGF-β) and elastolytic enzymes (MMP-2 and MMP-9), leading to reduced ECM degradation and AAA dilation. In-vitro, miR-26a downregulated TGF-β signaling (SMAD1 and SMAD4) and differentiation markers (ACTA2 and TAGLN) but enhanced haSMC proliferation, which alludes to a phenotypic switch towards greater haSMC survival. Thus, this study demonstrates a promising miRNA-based UTMD therapy for AAA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".