Enhanced Myocardial Angiogenesis by Gene Transfer With Transplanted Cells
Bibliographic record
Abstract
UNLABELLED: BACKGROUND The combination of myocardial cell transplantation and angiogenic gene transfer may improve postinfarction left ventricular (LV) perfusion. We evaluated the angiogenic effect of heart cells transfected with vascular endothelial growth factor (VEGF) and transplanted into a myocardial scar. METHODS AND RESULTS: Donor rat heart cells were transfected with plasmids encoding VEGF(165) and green fluorescence protein. Syngeneic adult rats underwent LV cryoinjury to create a transmural scar. Three weeks later, 4x10(6) transfected heart cells (n=14), untransfected heart cells (n=13), or culture medium (n=16) were transplanted into the center of the scar. After 5 weeks, LV function, quantitative histology, and regional blood flow were evaluated. Plates of heart cells transfected with VEGF(165) produced 6.1 times more intracellular VEGF than nontransfected cells. Capillary density (mean+/-SEM) per high-power field in the center of the myocardial scar was 1.1+/-0.02 in control rats, 3.9+/-0.11 in untransfected rats, and 6.3+/-0.11 in transfected rats (P=0.0002). Capillary density in the border zone around the scar was 1.9+/-0.03 in control rats, 6.4+/-0.10 in untransfected rats, and 8.7+/-0.16 in transfected rats (P=0.004). Regional blood flow within the scar was 8.8+/-0.8% of normalized flow in control hearts, 10.4+/-0.7% in hearts transplanted with untransfected cells, but 17.6+/-1.2% in hearts transplanted with transfected cells (P=0.03 versus control, P=0.07 versus nontransfected). There was no difference in LV function attributable to transplantation with transfected cells at the time point studied. CONCLUSIONS: Transplantation of heart cells transfected with VEGF induced greater angiogenesis than transplantation of unmodified cells. Combined gene transfer and cell transplantation strategies may improve postinfarction LV perfusion and function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".