Building new cardiac vasculature and myocardium: where are we at?
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review describes the latest advances in cell therapy, biomaterials and 3D bioprinting for the treatment of cardiovascular disease. RECENT FINDINGS: Cell therapies offer the greatest benefit for patients suffering from chronic ischemic and nonischemic cardiomyopathy. Rather than replacing lost cardiomyocytes, the effects of most cell therapies are mediated by paracrine signalling, mainly through the induction of angiogenesis and immunomodulation. Cell preconditioning, or genetic modifications are being studied to improve the outcomes. Biomaterials offer stand-alone benefits such as bioactive cues for cell survival, proliferation and differentiation, induction of vascularization or prevention of further cardiomyocyte death. They also provide mechanical support or electroconductivity, and can be used to deliver cells, growth factors or drugs to the injured site. Apart from classical biomaterial manufacturing techniques, 3D bioprinting offers greater spatial control over biomaterial deposition and higher resolution of the details, including hollow vessel-like structures. SUMMARY: Cell therapy induces mainly angiogenesis and immunomodulation. The ability to induce direct cardiomyocyte regeneration to replace the lost cardiomyocytes is, however, still missing until embryonic or induced pluripotent stem cell use becomes available. Cell therapy would benefit from combinatorial use with biomaterials, as these can prolong cell retention and survival, offer additional mechanical support and provide inherent bioactive cues. Biomaterials can also be used to deliver growth factors, drugs, and other molecules. 3D bioprinting is a high-resolution technique that has great potential in cardiac therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".