<scp>eNOS</scp> Overexpressing Bone Marrow Cells are Safe and Effective in a Porcine Model of Myocardial Regeneration Following Acute Myocardial Infarction
Bibliographic record
Abstract
AIM: Cell therapy has been shown to be effective in improving LV function postmyocardial infarction (MI). We hypothesized that eNOS-transfected bone marrow cells (BMCs) are safe in a swine model of myocardial infarction (MI). We also hypothesized that endothelial nitric oxide synthase (eNOS) transfection would enhance cell function, as assessed by myocardial functional recovery post-MI. METHODS: Fifteen female Yorkshire pigs underwent bone marrow aspiration and creation of MI. Bone marrow cells were cultured for 7 days, and each pig received either autologous BMCs transiently transfected with eNOS plasmid (eNOS-BMC, n = 5), nontransfected BMCs (nt-BMC, n = 4), or phosphate-buffered saline (PBS) control (n = 6). Cardiac MRI was performed at baseline (1 week post-MI) and 6 weeks post-MI. RESULTS: There was no difference in safety outcomes between groups. Absolute left ventricular ejection fraction (LVEF) at 6 weeks showed a trend toward improvement in both cell therapy groups compared with baseline but worsened in the PBS control group. The absolute improvement in LVEF was significantly greater in both cell therapy groups compared with PBS control. Infarct mass was significantly lower in the eNOS-BMC group between baseline and 6 weeks, but the absolute change in infarct mass was not different between groups. Finally, there was a trend toward reduced LV mass in the eNOS-BMC group. CONCLUSIONS: Bone marrow cell delivery, with and without eNOS overexpression, is safe and leads to improvement in LVEF when administered in the coronary circulation 7 days following acute MI in swine. Transfection of healthy BMCs with eNOS resulted in some improvement in left ventricular remodeling. Further study is warranted in a preclinical model that approximates the impact of cardiovascular risk factors on BMC function.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".