Early Activation of Growth Pathways in Mitral Leaflets Exposed to Aortic Regurgitation: New Insights from an Animal Model.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: Mitral leaflet enlargement in patients with chronic aortic regurgitation (AR) has been identified as an adaptive mechanism potentially able to prevent functional mitral regurgitation (FMR) in response to left ventricular (LV) dilatation. The timing of valve enlargement is not known, and the related mechanisms are largely unexplored. METHODS: AR was induced in 58 rats, and another 54 were used as sham controls. Animals were euthanized at different time points after AR creation (48 h, one week, and three months), and AR severity, FMR and LV dilatation were assessed using echocardiography. Mitral valves were harvested to document the reactivation of embryonic growth pathways. RESULTS: AR animals had increased LV dimensions and mitral annulus size. No animal developed FMR. No change in leaflet length or thickness was seen at 48 h; however, anterior mitral leaflets were longer and thicker in AR animals at one week and three months. Molecular changes were present early (at 48 h and at one week), with positive staining for transforming growth factor-b1 (TGF-b1), Alpha-smooth muscle actin (α-SMA) and matrix metalloproteinase-2 (MMP-2), which suggested active matrix remodeling. Increased gene expression for collagen 1, TGF-β1, α-SMA and MMP-2 was found in the mitral valve at 48 h and at one week, but after three months their expression had returned to normal. CONCLUSIONS: This model of AR induces active expansion and thickening of the mitral leaflets. Growth signals are expressed acutely, but not at three months, which suggests that most of this enlargement occurs at an early stage. The stimulation of valvular growth could represent a new strategy for the prevention of FMR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".