Novel insights for improving the therapeutic safety and efficiency of mesenchymal stromal cells
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) have attracted great interest in the field of regenerative medicine.They can home to damaged tissue, where they can exert pro-regenerative and anti-inflammatory properties.These therapeutic effects involve the secretion of growth factors, cytokines, and chemokines.Moreover, the functions of MSCs could be mediated by extracellular vesicles (EVs) that shuttle various signaling messengers.Although preclinical studies and clinical trials have demonstrated promising therapeutic results, the efficiency and the safety of MSCs need to be improved.After transplantation, MSCs face harsh environmental conditions, which likely dampen their therapeutic efficacy.A possible strategy aiming to improve the survival and therapeutic functions of MSCs needs to be developed.The preconditioning of MSCs ex vivo would strength their capacities by preparing them to survive and to better function in this hostile environment.In this review, we will discuss several preconditioning approaches that may improve the therapeutic capacity of MSCs.As stated above, EVs can recapitulate the beneficial effects of MSCs and may help avoid many risks associated with cell transplantation.As a result, this novel type of cell-free therapy may be safer and more efficient than the whole cell product.We will, therefore, also discuss current knowledge regarding the therapeutic properties of MSC-derived EVs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".