Protection of the Peritoneal Membrane by Peritoneal Dialysis Effluent-Derived Mesenchymal Stromal Cells in a Rat Model of Chronic Peritoneal Dialysis
Bibliographic record
Abstract
Peritoneal dialysis (PD) is a renal replacement option for patients with end-stage renal disease. However, a long-term exposure to hypertonic PD solutions leads to peritoneal membrane (PM) injury, resulting in ultrafiltration (UF) failure. This study was designed to primarily evaluate efficacy of PD effluent-derived mesenchymal stromal cells (pMSCs) in the prevention of PM injury in rats. The pMSCs were isolated from PD effluent. Male Wistar rats received daily intraperitoneal (IP) injection of 10 mL of Dianeal (4.25% dextrose) and were treated with pMSCs ( 1.2‐1.5×106/rat/wk , IP). UF was determined by IP injection of 30 mL of Dianeal (4.25% dextrose) with dwell time of 1.5 h, and PM injury was examined by histology. Apoptosis was quantitated by using flow cytometric analysis, and gene expression by using the PCR array and Western blot. Here, we showed that as compared to naive control, daily IP injection of the Dianeal PD solution for 6 weeks without pMSC treatment significantly reduced UF, which was associated with an increase in both PM thickness and blood vessel, while pMSC treatment prevented the UF loss and reduced PM injury and blood vessels.In vitroincubation with pMSC-conditioned medium prevented cell death in cultured human peritoneal mesothelial cells (HPMCs) and downregulated proinflammatory (i.e., CXCL6, NOS2, IL1RN, CCL5, and NR3C1) while upregulated anti-inflammatory (i.e., CCR1, CCR4, IL9, and IL-10) gene expression in activated THP1 cells. In conclusion, pMSCs prevent bioincompatible PD solution-induced PM injury and UF decline, suggesting that infusing backex vivo-expanded pMSCs intraperitoneally may have therapeutic potential for reduction of UF failure in PD patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".