Maximizing Ventricular Function With Multimodal Cell-Based Gene Therapy
Bibliographic record
Abstract
BACKGROUND: Angiogenesis is enhanced after transplantation of vascular endothelial growth factor (VEGF)-expressing cells into a myocardial scar. Insulin-like growth factor I (IGF-I) may induce hypertrophy and inhibit apoptosis. We evaluated the effect of cell-based IGF-I and VEGF multigene therapy on left ventricular (LV) function, cell survival, and apoptosis after bone marrow cell (BMC) transplantation. METHODS AND RESULTS: Female Lewis rats underwent left anterior descending ligation 3 weeks before transplantation with male donor BMC, BMC transfected with VEGF (BMC+VEGF), IGF-I (BMC+IGF-I), VEGF and IGF-I (BMC+VEGF+IGF-I), or medium without cells (control) (n=4 per group x 5 groups x 4 time points). Three days and 1, 2, and 4 weeks after transplantation, VEGF and IGF-I expression was quantitated by real-time polymerase chain reaction, cell survival by polymerase chain reaction for sry2, apoptosis by TUNEL staining, LV function by echocardiography and myosin heavy chain, and light chain and troponin I by Western blot. One week after transplantation, IGF-I expression in the scar and border zone was greatest in BMC+IGF-I and BMC+VEGF+IGF-I rats (P<0.05). VEGF expression in the scar and border zone was greatest in BMC+VEGF and BMC+VEGF+IGF-I hearts (P<0.05). Transplanted cell survival was lowest in BMC, intermediate in BMC+VEGF and BMC+IGF-I, and greatest in BMC+VEGF+IGF-I (P<0.05). Apoptotic indices were significantly reduced in BMC+VEGF+IGF-I, BMC+VEGF, and BMC+IGF-I (P<0.05). Two and 4 weeks after transplantation, LV ejection fraction was lowest in control, intermediate in BMC, BMC+VEGF, and BMC+IGF-I, and greatest in BMC+VEGF+IGF-I (P<0.05). CONCLUSIONS: Transplantation of VEGF- and IGF-I-expressing BMC reduced apoptosis, maximized transplanted cell survival, and enhanced LV function. Multimodal cell-based gene therapy may maximize the benefits of cell transplantation.
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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".