Banking Mesenchymal Stromal Cells from Umbilical Cord Tissue: Large Sample Size Analysis Reveals Consistency Between Donors
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) have emerged as candidate cells with therapeutic potential to treat different pathologies. The underlying mechanism is paracrine signaling. The cells secrete proteins that can impact inflammation, apoptosis, angiogenesis, and cell proliferation. All are important in wound healing and tissue regeneration. Although the bone marrow has been the most widely used source of MSCs, umbilical cord tissue (CT) presents a source that is just starting to be used in the clinic, yet can be obtained with more ease and easily stored. Here, we characterize CT-MSCs obtained from multiple donors by analyzing cell surface proteins, differentiation capacity, and proteome profile. Analysis of low, medium, and high passage cells indicates that the morphology and proliferation rate stay constant and with the exception of cluster of differentiation (CD) 105 at late passage, there are no changes in the cell surface protein characteristics, indicating the population does not change with passage. TNF-stimulated gene 6 protein was measured in a subset of samples and variable expression was observed, but this did not impact the ability of the cells to enhance skin regeneration. In conclusion, CT-MSC represents a consistent, easily accessible source of cells for cell therapy. Stem Cells Translational Medicine 2019;8:1041-1054.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".