The Use of MSCs, iPSCs, and EVs in the Repair of Human MSK Tissues: Is Ultimate Success Dependent on Developing Excellent Implant Materials as Well as Creating an Optimal Environment for Implantation? What Is the Rationale for These Choices?
Bibliographic record
Abstract
It has been >35 years since the cells described as mesenchymal stem cells (MSCs) were reported to have multi-lineage potential, which opened the possibility that they could be used to repair injured or diseased musculoskeletal tissues. Since that time, similar cells have been isolated from many tissues, again raising expectations that they could be used to repair or regenerate many types of tissues. While some progress in using these cells, as well as induced pluripotent stem cells (iPSCs), to facilitate the repair of tissues has been achieved, an emerging body of literature would suggest that the cells in question facilitate repair via released extracellular vesicles (EVs) that contain a cargo of molecules which induce endogenous cells to do the actual repair. How the "stemness" of the cells is involved in such processes remains to be elucidated. While progress in the repair of compromised tissues has been obtained, from some perspectives, the progress has been challenging and successful translation to patients has been slow. In part, this has been due to considerable emphasis being placed on the cells or EVs, and not as much on the environments in which they are implanted. However, successful outcomes likely depend on both the development of optimized materials to be implanted and an environment that is conducive to success after implantation. This perspective article reviews some of the options regarding the implantable materials and the variables or factors that could impact the local environment's suitability for success following implantation. In addition, attempts are made to reconcile the designation of endogenous cells labeled MSCs and their potential roles as regulators of tissue integrity in vivo.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".