What Molecular Recognition Systems Do Mesenchymal Stem Cells/Medicinal Signaling Cells (MSC) Use to Facilitate Cell-Cell and Cell Matrix Interactions? A Review of Evidence and Options
Bibliographic record
Abstract
Mesenchymal stem cells, also called medicinal signaling cells (MSC), have been studied regarding their potential to facilitate tissue repair for >30 years. Such cells, derived from multiple tissues and species, are capable of differentiation to a number of lineages (chondrocytes, adipocytes, bone cells). However, MSC are believed to be quite heterogeneous with regard to several characteristics, and the large number of studies performed thus far have met with limited or restricted success. Thus, there is more to understand about these cells, including the molecular recognition systems that are used by these cells to perform their functions, to enhance the realization of their potential to effect tissue repair. This perspective article reviews what is known regarding the recognition systems available to MSC, the possible systems that could be looked for, and alternatives to enhance their localization to specific injury sites and increase their subsequent facilitation of tissue repair. MSC are reported to express recognition molecules of the integrin family. However, there are a number of other recognition molecules that also could be involved such as lectins, inducible lectins, or even a MSC-specific family of molecules unique to these cells. Finally, it may be possible to engineer expression of recognition molecules on the surface of MSC to enhance their function in vivo artificially. Thus, improved understanding of recognition molecules on MSC could further their success in fostering 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".