Bibliographic record
Abstract
Stem cell treatments are a desirable therapeutic option to regenerate myocardium and improve cardiac function after myocardial infarction. Several different types of cells have been explored, each with their own benefits and limitations. Induced pluripotent stem cells possess an embryonic-like state and therefore have a high proliferative capacity, but they also pose a risk of teratoma formation. Mesenchymal stem cells have been investigated from both bone marrow and adipose tissue. Their immunomodulatory characteristics may permit the use of allogeneic cells as universal donor cells in the future. Lastly, studies have consistently shown that cardiac stem cells are better able to express markers of cardiogenesis compared to other cell types, as well improve cardiac function. The ideal source of stem cells depends on multiple factors such as the ease of extraction/isolation, effectiveness of engraftment, ability to differentiate into cardiac lineages and effect on cardiac function. Although multiple studies highlight the benefits and limitations of each cell type and reinforce the successful potential use of these cells to regenerate damaged myocardium, more studies are needed to directly compare cells from various sources. It is interesting to note that research using stem cell therapies is also expanding to treat other cardiovascular diseases including non-ischemic cardiomyopathies.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".