Dramatic differences in activity of purines metabolizing ecto-enzymes between mesenchymal stem cells isolated from human umbilical cord blood and umbilical cord tissue
Bibliographic record
Abstract
The high quality human mesenchymal stem cells (MSCs) with remarkable expansion potential in culture are demonstrated to possess multifold clinical applications. However, their isolation and characterization are difficult and sometimes ambiguous. We exploited nucleotide metabolizing ecto-enzymes for more complete characterization of MSCs. Using standard methods of cell culturing and analyses, we detected significant differences in the activity of ecto-nucleotidases on the surface of MSCs isolated from umbilical cord tissue and MSC-like cells derived from umbilical cord blood. Interestingly, the proliferation rate and the immunophenotypic characteristics of mesenchymal stem cells also correspond to the activities of these enzymes. Compared with the CD90-, CD105-, and CD73-deficient and slowly proliferating UCB-MSC-like cells that had relatively higher ecto-NTPDases activity, the CD90-, CD105-, and CD73-positive and rapidly proliferating UC-MSCs rather had ecto-5'-nucleotidase activity and presented neither ecto-nucleotidases nor adenylate kinase activities. In summary, our results demonstrate for the first time that activity of purine nucleotide metabolizing ecto-enzymes differs significantly between mesenchymal stem cells drawn from different neonatal sources, corresponding with a distinct proliferative potential.
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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 | 0.000 | 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".