Engineering cardiac healing using embryonic stem cell-derived cardiac cell seeded constructs
Bibliographic record
Abstract
Myocardial infarction is one of the leading causes of death in industrialized nations. Recent advances in tissue engineering and cell biology have changed our understanding of regenerative activities in the infarcted heart and have raised considerable hopes for novel therapeutic approaches to treat patients. Studies have shown that cell transplantation results in small improvements in the infarct area and while these therapies hold promise, significant challenges remain in order to increase both cellular engraftment efficiencies and transplanted cell function. Robust cardiac healing will require appropriate revascularization of the infarct site, mechanical recovery of damaged tissue and electrophysiological coupling with native tissue. Embryonic stem cells, uniquely, have the potential to generate bonafide cardiomyocytes and other derivatives which should contribute toward these multifaceted requirements. Guiding embryonic stem derived cells to support healing and regeneration of heart tissue using tissue engineered constructs may provide advantages over direct cell transplantation, including replacement of damaged infrastructure, temporary support for transplanted cells, and control of size, shape, strength and composition of the graft.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".