Validation of the vitronectin knockout mouse as a model for studying myocardial infarction: Vitronectin appears to influence left ventricular remodelling following myocardial infarction.
Bibliographic record
Abstract
BACKGROUND: Vitronectin (VN) is an abundant acute-phase plasma protein that regulates cell adhesion and migration as well as interactions with components of the plasminogen activator/plasmin system, specifically plasminogen activator inhibitor type 1. This system plays a major role in tissue remodelling regulating wound healing after myocardial infarction. OBJECTIVES: To investigate the feasibility of using VN knockout mice (VN(-/-)) to study the role of VN on ventricular remodelling following myocardial infarction. METHODS: Specifically bred VN(-/-) mice and normal wild-type (VN(+/+)) mice underwent coronary artery ligation and were assessed 28 days postligation using echocardiography and morphometric histology. RESULTS: No difference was observed between VN(-/-) mice and VN(+/+) mice with respect to gross phenotype, weight, coronary anatomy or echocardiographically measured ejection fraction (56%). Following myocardial infarction, VN(-/-) mice exhibited less ventricular dilation and less impairment in echocardiographic ejection fraction compared with VN(+/+) mice (48% versus 41%; P=0.01). VN(-/-) mice also exhibited smaller infarcts on morphometric analysis. CONCLUSIONS: The results of the present study confirmed the feasibility of using coronary artery ligation in VN knockout mice to investigate the role of VN in post-myocardial infarction remodelling. The absence of VN appears to result in favourable effects on wound healing. These data suggest that this model may offer novel insights into the role of VN in the regulation of myocardial remodelling.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".