Inhibiting Matrix Metalloproteinase by Cell-Based <i>Timp-3</i> Gene Transfer Effectively Treats Acute and Chronic Ischemic Cardiomyopathy
Bibliographic record
Abstract
After a myocardial infarction (MI), an increase in the cardiac ratio of matrix metalloproteinases (MMPs) relative to their inhibitors (TIMPs) causes extracellular matrix modulation that leads to ventricular dilatation and congestive heart failure. Cell therapy can mitigate these effects. In this study, we tested whether increasing MMP inhibition via cell-based gene transfer of Timp-3 further preserved ventricular morphometry and cardiac function in a rat model of MI. We also measured the effect of treatment timing. We generated MI (coronary artery ligation) in adult rats. Three or 14 days later, we implanted medium (control) or vascular smooth muscle cells transfected with empty vector (VSMCs) or Timp-3 (C-TIMP-3) into the peri-infarct region (n = 15-24/group). We assessed MMP-2 and -9 expression and activity, TIMP-3, and TNF-α expression, cell apoptosis, infarct size and thickness, ventricular morphometry, and cardiac function (by echocardiography). Relative to medium, VSMCs delivered at either time point significantly reduced cardiac expression and activity of MMP-2 and -9, reduced expression of TNF-α, and increased expression of TIMP-3. Cell therapy also reduced apoptosis and scar area, increased infarct thickness, preserved ventricular structure, and reduced functional loss. All these effects were augmented by C-TIMP-3 treatment. Survival and cardiac function were significantly greater when VSMCs or C-TIMP-3 were delivered at 3 (vs. 14) days after MI. Upregulating post-MI cardiac TIMP-3 expression via cell-based gene therapy contributed additional regulation of MMP, TIMP, and TNF-α levels, thereby boosting the structural and functional effects of VSMCs transplanted at 3 or 14 days after an MI in rats. Early treatment may be superior to late, though both are effective.
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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 | 0.001 |
| 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".