Homogeneity and heterogeneity of biological characteristics in mesenchymal stem cells from human umbilical cords and exfoliated deciduous teeth
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) have proven powerful potential for cell-based therapy both in regenerative medicine and disease treatment. Human umbilical cords and exfoliated deciduous teeth are the main sources of MSCs with no donor injury or ethical issues. The goal of this study was to investigate the differences in the biological characteristics of human umbilical cord mesenchymal stem cells (UCMSCs) and stem cells from human exfoliated deciduous teeth (SHEDs). UCMSCs and SHEDs were identified by flow cytometry. The proliferation, differentiation, migration, chemotaxis, paracrine, immunomodulatory, neurite growth-promoting capabilities, and acetaldehyde dehydrogenase (ALDH) activity were comparatively studied between these two MSCs in vitro. The results showed that both SHEDs and UCMSCs expressed cell surface markers characteristic of MSCs. Furthermore, SHEDs exhibited better capacity for proliferation, migration, promotion of neurite growth, and chondrogenic differentiation. Meanwhile, UCMSCs showed more outstanding adipogenic differentiation and chemotaxy. Additionally, there were no significant differences in osteogenic differentiation, immunomodulatory capacity, and the proportion of ALDHBright compartment. Our findings indicate that although both UCMSCs and SHEDs are mesenchymal stem cells and presented some similar biological characteristics, they also have differences in many aspects, which might be helpful for developing future clinical cellular therapies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".