MicroRNA-145 Restores Contractile Vascular Smooth Muscle Phenotype and Coronary Collateral Growth in the Metabolic Syndrome
Bibliographic record
Abstract
OBJECTIVE: Transient, repetitive occlusion stimulates coronary collateral growth (CCG) in normal animals. Vascular smooth muscle cells (VSMCs) switch to synthetic phenotype early in CCG, then return to contractile phenotype. CCG is impaired in the metabolic syndrome. We determined whether impaired CCG was attributable to aberrant VSMC phenotypic modulation by miR-145-mediated mechanisms, and whether restoration of physiological miR-145 levels in metabolic syndrome (JCR rat) improved CCG. APPROACH AND RESULTS: CCG was stimulated by transient, repetitive left anterior descending artery occlusion and evaluated after 9 days by coronary blood flow measurements (microspheres). miR-145 was delivered to JCR VSMCs via adenoviral vector (miR-145-Adv). In JCR rats, miR-145 was decreased late in CCG (≈ 2-fold day 6; ≈ 4-fold day 9 versus SD), which correlated with decreased expression of smooth muscle-specific contractile proteins (≈ 5-fold day 6; ≈ 10-fold day 9 versus SD), indicative of VSMCs' failure to return to the contractile phenotype late in CCG. miR-145 expression in JCR rats (miR-145-Adv) on days 6 to 9 of CCG completely restored VSMCs contractile phenotype and CCG (collateral/normal zone flow ratio was 0.93 ± 0.09 JCR+miR-145-Adv versus 0.12 ± 0.02 JCR versus 0.87 ± 0.02 SD). CONCLUSIONS: Restoration of VSMC contractile phenotype through miR-145 delivery is a highly promising intervention for restoration of CCG in the metabolic syndrome.
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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) | 0.000 | 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 | 0.000 |
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".