ENDOTHELIAL NO-SYNTHASE GENE TRANSFER RESTORES REGENERATIVE CAPACITY OF ENDOTHELIAL PROGENITOR CELLS FROM PATIENTS WITH CORONARY ARTERY DISEASE
Bibliographic record
Abstract
Background: Endothelial progenitor cells (EPCs) from patients with coronary arterydisease (CAD) or CAD risk factors exhibit greatly reduced regenerativecapacity, which likely contributes to the relatively modest nature of the benefit seen in recent clinical trials of autologous cell therapy postmyocardial infarction. We hypothesized that eNOS overexpression will improve the functional capacity of EPCs from these patients. Methods and Results: EPCs were isolated from the peripheral blood ofpatients with high Framingham risk scores (FRS > 15%) and were transducedusing lentiviral vectors containing either eNOS or GFP (sham). We observed that eNOS-transduction significantly improved migration toward chemotactic factors(VEGF and SDF-1) compared to sham-transduced cells. EPCs were co-cultured witha mature endothelial cell (EC) line on Matrigel to measure their pro-angiogenicfunction in vitro. eNOS-transduced EPCs induced longer angiogenic tubes withmore branch points compared to sham-transduced cells, and exhibited higherassociation with EC tubes. We did not observe a significant difference in the adhesion of EPCs to an EC layer pre-activated with TNF-?, suggesting that the association to angiogenictubes is likely through a different mechanism. In immunodeficient mice, eNOS-transduced EPCs resulted in significant improvement in ischemic hindlimbperfusion compared to sham-transduced cells. PCR arrays revealed changes in angiogenic and pro-survivalgene expression in response to eNOS overexpression, providing preliminaryinsight into the mechanisms underlying its beneficial actions. Conclusions: The present data show that reduced regenerative activity of EPCsisolated from CAD patients can be significantly improved by the overexpressionof eNOS. The combination of cell and gene therapy may improve the efficacy ofautologous cell therapies for cardiovascular disease.
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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".