Genetic Modification of Embryonic Stem Cells with VEGF Enhances Cell Survival and Improves Cardiac Function
Bibliographic record
Abstract
Cardiac stem cell therapy remains hampered by acute donor cell death posttransplantation and the lack of reliable methods for tracking cell survival in vivo. We hypothesize that cells transfected with inducible vascular endothelial growth factor 165 (VEGF(165)) can improve their survival as monitored by novel molecular imaging techniques. Mouse embryonic stem (ES) cells were transfected with an inducible, bidirectional tetracycline (Bi-Tet) promoter driving VEGF(165) and renilla luciferase (Rluc). Addition of doxycycline induced Bi-Tet expression of VEGF(165) and Rluc significantly compared to baseline (p<0.05). Expression of VEGF(165) enhanced ES cell proliferation and inhibited apoptosis as determined by Annexin-V staining. For noninvasive imaging, ES cells were transduced with a double fusion (DF) reporter gene consisting of firefly luciferase and enhanced green fluorescence protein (Fluc-eGFP). There was a robust correlation between cell number and Fluc activity (R(2)=0.99). Analysis by immunostaining, histology, and RT-PCR confirmed that expression of Bi-Tet and DF systems did not affect ES cell self-renewal or pluripotency. ES cells were differentiated into beating embryoid bodies expressing cardiac markers such as troponin, Nkx2.5, and beta-MHC. Afterward, 5 x 10(5) cells obtained from these beating embryoid bodies or saline were injected into the myocardium of SV129 mice (n=36) following ligation of the left anterior descending (LAD) artery. Bioluminescence imaging (BLI) and echocardiography showed that VEGF(165) induction led to significant improvements in both transplanted cell survival and cardiac function (p<0.05). This is the first study to demonstrate imaging of embryonic stem cell-mediated gene therapy targeting cardiovascular disease. With further validation, this platform may have broad applications for current basic research and further clinical studies.
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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".