The future of regenerating the myocardium
Bibliographic record
Abstract
PURPOSE OF REVIEW: Stem cell therapy for cardiac disease may be facing two major problems nowadays: although vasculogenesis likely occurs as a result of cell therapy, its clinical applications are limited and significant, integrated cardiomyogenesis has not demonstratively been shown to occur, even in the experimental setting, with any other source than embryonic or other pluripotent stem cells. RECENT FINDINGS: In this article, we highlight several factors that will need to be optimized if we are to achieve clinically effective cardiomyogenesis, such as the identification of optimal stem cell populations, and the ideal time and methods for cell transplantation. So far, educated attempts at achieving transplanted stem cell-induced myogenesis have largely failed outside of the embryonic stem cell realm, and we present the rationale for also considering acellular techniques, which may enhance the potential of endogenous progenitor populations. SUMMARY: In today's cardiovascular field, once a cardiomyocyte is lost it is lost for good, without any form of direct therapeutic option. For these reasons, cell therapy justifies our continued attention and efforts, and may constitute the holy grail of cardiovascular therapeutics.
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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".