Stem cell mediated cardiovascular repair
Bibliographic record
Abstract
Recent increase in the interest in stem and progenitor cells may be attributed to their behavioural characteristics. A consensus has been reached that embryonic or adult stem cells have therapeutic potential. As cardiovascular health issues are still the major culprits in many developed countries, stem and progenitor cell driven approaches may give the clinicians a new arsenal to tackle many significant health issues. However, stem and progenitor cell mediated cardiovascular regeneration can be achieved via complex and dynamic molecular mechanisms involving a variety of cells, growth factors, cytokines, and genes. Functional contributions of transplanted cells on target organs and their survival are still critical problems waiting to be resolved. Moreover, the regeneration of contracting myocardial tissue has controversial results in human trials. Thus, moderately favourable clinical results should be interpreted carefully. Determining the behavioural programs, genetic and transcriptional control of stem cells, mechanisms that determine cell fate, and functional characteristics are the primary targets. In addition, ensuring the long-term follow-up of cells with efficient imaging techniques in human clinical studies may provide a resurgence of the initial enthusiasm, which has faded over time. Here, we provide a brief historical perspective on stem cell driven cardiac regeneration and discuss cardiac and vascular repair in the context of translational science.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".