Perspective: Is It Time to Rename MSC (Mesenchymal Stem Cells/Medicinal Signaling Cells) with a Name that Reflects Their Combined <i> In Vivo </i> Functions and Their <i> In Vitro </i>Abilities?—Possibly “Pluripotent Mesenchymal Regulatory Cells (PMRC)”
Bibliographic record
Abstract
Over 30 years ago, it was reported by Caplan that cells could be found in various adult tissues and fluids of a variety of species that could be induced in vitro to progress towards lineages such as chondrogenesis, osteogenesis and adipogenesis with different “cocktails” of reagents. These cells were called Mesenchymal Stem Cells (MSC) to reflect this pluripotency. After 30 years of intense research effort to directly use such cells for the repair or regeneration of damaged or injured tissues, the effort has met with limited in vivo success, but their use for in vitro tissue engineering has met with some success. This failure to live up to expectations for in vivo differentiation success has led Caplan to recently rename these cells Medicinal Signaling Cells (MSC) to reflect other abilities of these cells to secrete mediators and release exosomes containing biologically active molecules that can influence their neighboring cells in a paracrine manner. However, neither of these names completely captures the combined apparent in vivo functioning of MSC and their in vitro abilities to exhibit pluripotent behavior. Thus, it is suggested, based on the attributes of these cells and their tissue and clonal heterogeneity, that an alternative name be applied to these cells and they be described as Pluripotent Mesenchymal Regulatory Cells (PMRC). This name reflects their regulatory function as pericytes in tissues, as well as their well-known immunoregulatory activity when injected into the intra-articular space and their influence on activities such as wound healing. It also reflects their ability to differentiate along several different lineages to facilitate tissue engineering approaches for tissue repair.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".