Differential Expression of Matrix Metalloproteinases and Tissue Inhibitors and Extracellular Matrix Remodeling in Aortic Regurgitant Hearts
Bibliographic record
Abstract
OBJECTIVES: Myocardial fibrosis in experimental aortic regurgitation (AR) features abnormal fibronectin with normal collagen content, but the relevant degradative processes have not been assessed. METHODS: To elucidate these degradative processes, mRNA (Northern) and protein levels (Western) of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs), as well as MMP activity (zymography), were measured in cardiac fibroblasts (CF) from New Zealand white rabbits with experimental AR paired with normals (NL). Collagen and fibronectin were quantified by immunohistochemical staining. RESULTS: In AR CF versus NL CF, MMP-2 and -14 mRNA and protein were increased (both p < 0.005), while TIMPs 1-3 were slightly decreased (p < 0.05-0.005; TIMP-4 undetectable). Gelatinase activity in AR CF was 1.7 times that in NL CF (p < 0.005); fibronectinase activity was unaffected. The Jun N-terminal kinase (JNK) inhibitor SP600125 suppressed MMP-2 protein (0.4-fold, p < 0.05) and mRNA (0.7-fold, p < 0.005) in AR CF; MMP-2 levels in NL CF were unaffected. AR MMP-9 mRNA, protein and activity were low and indistinguishable from NL. In left ventricular tissue, fibronectin was increased 1.9-fold (AR vs. NL, p < 0.05). Total AR collagen was indistinguishable from NL, but the collagen III to collagen I isoform ratio decreased (0.4-fold, p < 0.05). CONCLUSIONS: Collagen is relatively deficient in AR fibrosis, due at least in part to upregulated MMPs and downregulated TIMPs; fibronectinase is unaltered. JNK-dependent regulation may stimulate both MMP-2 and fibronectin expression in AR, providing a potential therapeutic target.
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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) | 0.001 | 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".