Application of injectable hydrogels for cardiac stem cell therapy and tissue engineering
Bibliographic record
Abstract
Cardiovascular diseases are responsible for approximately one-third of deaths around the world. Among cardiovascular diseases, the largest single cause of death is ischemic heart disease. Ischemic heart disease typically manifests as progressive constriction of the coronary arteries, which obstructs blood flow to the heart and can ultimately lead to myocardial infarction. This adversely affects the structure and function of the heart. Conventional treatments lack the ability to treat the myocardium lost during an acute myocardial infarction. Stem cell therapy offers an excellent solution for myocardial regeneration. Stem cell sources such as adult stem cells, embryonic and induced pluripotent stem cells have been the focal point of research in cardiac tissue engineering. However, cell survival and engraftment post-transplantation are major limitations that must be addressed prior to widespread use of this technology. Recently, biomaterials have been introduced as 3D vehicles to facilitate stem cell transplantation into infarct sites. This has shown significant promise with improved cell survival after transplantation. In this review, we discuss the various injectable hydrogels that have been tried in cardiac tissue engineering. Exploring and optimizing these cell-material interactions will guide cardiac tissue engineering towards developing stem cell based functional 3D constructs for cardiac regeneration.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".