Challenges and Strategies for Improving the Regenerative Effects of Mesenchymal Stromal Cell-Based Therapies
Bibliographic record
Abstract
Cell-based therapies have the potential to revolutionize current treatments for diseases with high prevalence and related economic and social burden. Unfortunately, clinical trials have made only modest improvements in restoring normal function to degenerating tissues. This limitation is due, at least in part, to the death of transplanted cells within a few hours after transplant due to a combination of mechanical, cellular, and host factors. In particular, mechanical stress during implantation, extracellular matrix loss upon delivery, nutrient and oxygen deprivation at the recipient site, and host inflammatory response are detrimental factors limiting long-term transplanted cell survival. The beneficial effect of cell therapy for regenerative medicine ultimately depends on the number of administered cells reaching the target tissue, their viability, and their promotion of tissue regeneration. Therefore, strategies aiming at improving viable cell engraftment are crucial for regenerative medicine. Here we review the major factors that hamper successful cell engraftment and the strategies that have been studied to enhance the beneficial effects of cell therapy. Moreover, we provide a perspective on whether mesenchymal stromal cell-derived extracellular vesicle delivery, as a cell-free regenerative approach, may circumvent current cell therapy limitations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".