Cellular mechanisms underlying cardiac engraftment of stem cells
Bibliographic record
Abstract
INTRODUCTION: Over the past decade, it has become clear that long-term engraftment of any ex vivo expanded cell product transplanted into injured myocardium is modest and all therapeutic regeneration is mediated by stimulation of endogenous repair rather than differentiation of transplanted cells into working myocardium. Given that increasing the retention of transplanted cells boosts myocardial function, focus on the fundamental mechanisms limiting retention and survival of transplanted cells may enable strategies to help to restore normal cardiac function. Areas covered: This review outlines the challenges confronting cardiac engraftment of ex vivo expanded cells and explores means of enhancing cell-mediated repair of injured myocardium. Expert opinion: Stem cell therapy has already come a long way in terms of regenerating damaged hearts though the poor retention of transplanted cells limits the full potential of truly cardiotrophic cell products. Multifaceted strategies directed towards fundamental mechanisms limiting the long-term survival of transplanted cells will be needed to enhance transplanted cell retention and cell-mediated repair of damaged myocardium for cardiac cell therapy to reach its full potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".