Paracrine Engineering of Human Cardiac Stem Cells With Insulin‐Like Growth Factor 1 Enhances Myocardial Repair
Bibliographic record
Abstract
BACKGROUND: Insulin-like growth factor 1 (IGF-1) activates prosurvival pathways and improves postischemic cardiac function, but this key cytokine is not robustly expressed by cultured human cardiac stem cells. We explored the influence of an enhanced IGF-1 paracrine signature on explant-derived cardiac stem cell-mediated cardiac repair. METHODS AND RESULTS: Receptor profiling demonstrated that IGF-1 receptor expression was increased in the infarct border zones of experimentally infarcted mice by 1 week after myocardial infarction. Human explant-derived cells underwent somatic gene transfer to overexpress human IGF-1 or the green fluorescent protein reporter alone. After culture in hypoxic reduced-serum media, overexpression of IGF-1 enhanced proliferation and expression of prosurvival transcripts and prosurvival proteins and decreased expression of apoptotic markers in both explant-derived cells and cocultured neonatal rat ventricular cardiomyocytes. Transplant of explant-derived cells genetically engineered to overexpress IGF-1 into immunodeficient mice 1 week after infarction boosted IGF-1 content within infarcted tissue and long-term engraftment of transplanted cells while reducing apoptosis and long-term myocardial scarring. CONCLUSIONS: Paracrine engineering of explant-derived cells to overexpress IGF-1 provided a targeted means of improving cardiac stem cell-mediated repair by enhancing the long-term survival of transplanted cells and surrounding myocardium.
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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) | 0.002 | 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".