Harnessing the secretome of adipose-derived stem cells in the treatment of ischemic heart diseases
Bibliographic record
Abstract
Adipose-derived stem cells (ASCs) are promising therapeutic cells for ischemic heart diseases, due to the ease and efficiency of acquisition, the potential of myocardial lineage differentiation, and the paracrine effects. Recently, many researchers have claimed that the ASC-based myocardial repair is mainly attributed to its paracrine effects, including the anti-apoptosis, pro-angiogenesis, anti-inflammation effects, and the inhibition of fibrosis, rather than the direct differentiation into cardiovascular lineage cells. However, the usage of ASCs comes with the problems of low cardiac retention and survival after transplantation, like other stem cells, which compromises the effectiveness of the therapy. To overcome these drawbacks, researchers have proposed various strategies for improving survival rate and ensuring sustained paracrine secretion. They also investigated the safety and efficacy of phase I and II clinical trials of ASC-based therapy for cardiovascular diseases. In this review, we will discuss the characterization and paracrine effects of ASCs on myocardial repair, followed by the strategies for stimulating the paracrine secretion of ASCs, and finally their clinical usage.
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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".