Mesenchymal stem cells overexpressing Akt dramatically repair infarcted myocardium and improve cardiac function despite infrequent cellular fusion or differentiation
Bibliographic record
Abstract
We previously reported that intramyocardial injection of bone marrow-derived mesenchymal stem cells overexpressing Akt (MSC-Akt) efficiently repaired infarcted rat myocardium and improved cardiac function. Controversy still exists over the mechanisms by which MSC contribute to tissue repair. Herein, we tested if cellular fusion of MSC plays a determinant role in cardiac repair. We injected MSC expressing Cre recombinase, with or without Akt, into Cre reporter mice. In these mice, LacZ is expressed only after Cre-mediated excision of a loxP-flanked stop signal and is indicative of fusion. MSC engraftment within infarcted myocardium was transient but significantly enhanced by Akt. MSC fusion with cardiomyocytes was observed as early as 3 days, but was infrequent, and we found a low rate of differentiation of MSC into cardiomyocytes. MSC-Akt decreased infarct size at 3 days and restored early cardiac function. In conclusion, MSC-Akt improved early repair despite transient engraftment, low levels of cellular fusion, and differentiation. These new observations further confirm our recently reported data that early paracrine mechanisms mediated by MSC are responsible for enhancing the survival of existing myocytes and that Akt could alter the secretion of various cytokines and growth factors.
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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) | 0.001 | 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".