Cardiopoietic programming of embryonic stem cells for tumor-free heart repair
Bibliographic record
Abstract
Embryonic stem cells have the distinct potential for tissue regeneration, including cardiac repair. Their propensity for multilineage differentiation carries, however, the liability of neoplastic growth, impeding therapeutic application. Here, the tumorigenic threat associated with embryonic stem cell transplantation was suppressed by cardiac-restricted transgenic expression of the reprogramming cytokine TNF-alpha, enhancing the cardiogenic competence of recipient heart. The in vivo aptitude of TNF-alpha to promote cardiac differentiation was recapitulated in embryoid bodies in vitro. The procardiogenic action required an intact endoderm and was mediated by secreted cardio-inductive signals. Resolved TNF-alpha-induced endoderm-derived factors, combined in a cocktail, secured guided differentiation of embryonic stem cells in monolayers produce cardiac progenitors termed cardiopoietic cells. Characterized by a down-regulation of oncogenic markers, up-regulation, and nuclear translocation of cardiac transcription factors, this predetermined population yielded functional cardiomyocyte progeny. Recruited cardiopoietic cells delivered in infarcted hearts generated cardiomyocytes that proliferated into scar tissue, integrating with host myocardium for tumor-free repair. Thus, cardiopoietic programming establishes a strategy to hone stem cell pluripotency, offering a tumor-resistant approach for regeneration.
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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".