Targeting VEGF‐encapsulated immunoliposomes to MI heart improves vascularity and cardiac function
Bibliographic record
Abstract
Recent attempts at rebuilding the myocardium using stem cells have yielded disappointing results. The lack of a supporting vasculature may, in part, explain these disappointing findings. However, concerns over possible side effects have hampered attempts at revascularizing the infarcted myocardium using systemic delivery of proangiogenic compounds. In this study, we develop the technology to enhance the morphology and function of postinfarct neovasculature. Previously, we have shown that the up-regulated expression of endothelial cell adhesion molecules in the myocardial infarction (MI) region provides a potential avenue for selectively targeting drugs to infarcted tissue. After treatment with anti-P-selectin-conjugated liposomes containing vascular endothelial growth factor (VEGF), changes in cardiac function and vasculature post-MI were quantified in a rat MI model. Targeted delivery of VEGF to post-MI tissue resulted in significant increase in fractional shortening and improved systolic function. These functional improvements were accompanied by a 21% increase in the number of anatomical vessels and a 74% increase in the number of perfused vessels in the MI region of treated animals. No significant improvements in cardiac function were observed in untreated, systemic VEGF-treated, nontargeted liposome-treated, or blank immunoliposome-treated animals. Targeted delivery of low doses of proangiogenic compounds to post-MI tissue results in significant improvements in cardiac function and vascular structure.
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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".