<i>In Vitro</i>Expansion and Characterization of Mesenchymal Stromal Cells from Peritoneal Dialysis Effluent in a Human Protein Medium
Bibliographic record
Abstract
The therapeutic potential of mesenchymal stromal cells (MSCs) from various tissue origins have extensively been explored in both experimental and clinical studies, and peritoneal dialysis effluent-derived MSC (pMSC) may be an easily obtainable MSC source for clinical applications. In this study, we expanded and characterized the pMSCs after expansion in a human protein culture medium. The pMSCs were expanded in plastic dishes with the human protein medium. MSC marker expression was examined by flow cytometry. Spherical formation was tested by hanging drop method, and osteogenic, adipogenic, and chondrogenic differentiation capacities were confirmed by positive staining with Alizarin red, Oil red O, and Alcian blue, respectively. Here, we showed that after four passages of culturing in plastic dishes, pMSCs in the human protein medium displayed a homogeneous pattern of classical MSC markers (positive: CD29, CD44, CD73, CD90, and CD166; negative: CD14, CD34, CD45, CD79a, CD105, CD146, CD271, HLA-DR, SSEA-4, and Stro-1), while in the standard medium, pMSCs from some donors were CD45 or HLA-DR positive. For nonclassical MSC markers, pMSCs were CD200 positive from all the donors, negative for CD163, CD271, CD36, and CD248, and either positive or negative for CD274 and CD140b. Further, pMSCs from the human protein medium had the spherical formation capacity and multipotent differentiation capacityin vitro. In conclusion, upon expansion in a human protein medium, pMSCs showed a differential MSC marker expression profile from those of bone marrow or adipose tissue-derived MSCs and could maintain the multipotency. The therapeutic potential of the pMSCs requires further investigation.
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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".