Comprehensive cell surface protein profiling of human mesenchymal stromal cells from peritoneal dialysis effluent and comparison with those from human bone marrow and adipose tissue
Bibliographic record
Abstract
Peritoneal mesenchymal stromal cells (pMSCs) are isolated from peritoneal dialysis (PD) effluent, and treatment with the pMSCs reduces peritoneal membrane injury in rat model of PD. This study was designed to verify the identity of the pMSCs. pMSCs were grown in plastic dishes for 4-7 passages, and their cell surface phenotype was examined by staining with a panel of 242 antibodies. The positive stain of each target protein was determined by an increase in fluorescence intensity as compared with isotype controls in flow cytometrical analysis. Here, we showed that pMSCs predominantly expressed CD9, CD26, CD29, CD42a, CD44, CD46, CD47, CD49b, CD49c, CD49e, CD54, CD55, CD57, CD59, CD63, CD71, CD73, CD81, CD90, CD98, CD147, CD151, CD200, CD201, β2-micoglobulin, epithelial growth factor receptor, human leukocyte antigen (HLA) class 1, and, to a lesser extent, CD31, CD45RO, CD49a, CD49f, CD50, CD58, CD61, CD105, CD164, and CD166. These cells lacked expression of most hematopoietic markers such as CD11b, CD14, CD19, CD34, CD40, CD80, CD79, CD86, and HLA-DR. There was 38.55% difference in the expression of 83 surface proteins between bone marrow (BM)-derived MSCs and pMSCs, and 14.1% in the expression of 242 proteins between adipose tissue (AT)-derived MSCs and pMSCs. The BM-MSCs but not both AT-MSCs and pMSCs express cytokine receptors (IFNγR, TNFI/IIR, IL-1R, IL-4R, IL-6R, and IL-7R). In conclusion, pMSCs exhibited a typical cell surface phenotype of MSCs, which was not the same as on BM-MSCs or AT-MSCs, suggesting that the pMSCs may represent a different MSC lineage from peritoneal cavity.
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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".