Effect of Haluronan-Supplemented Dialysate on in vitro Function of Human Peritoneal Mesothelial Cells
Bibliographic record
Abstract
BACKGROUND: Addition of hyaluronan (HA) to the dialysis solution has been suggested as a means to protect the peritoneum from injury during peritoneal dialysis (PD). METHODS: Concentrations of inflammatory mediators were determined in dialysate samples obtained from PD patients after 6-hour dwells with glucose-based (13.6 g/l) solution containing 0.1 and 0.5 g/l of exogenous high-molecular-weight HA. We additionally evaluated the effect of HA-supplemented dialysate, drained after dwell in PD patients, on function of human peritoneal mesothelial cells (MC) in in vitro culture. RESULTS: Concentration of nitrites was significantly higher in HA 0.5 g/l supplemented dialysate (+43%, p < 0.05) as compared to control. Levels of monocyte chemoattractant protein (MCP-1), soluble intercellular adhesive molecule (s-ICAM), vascular endothelial growth factor (VEGF) and fibronectin were comparable in all the studied groups. However, when MC were exposed in in vitro conditions for 24 h to the studied dialysates, we observed that HA containing fluids inhibited the synthesis of MCP-1, s-ICAM, VEGF and fibronectin in these cells. HA-supplemented dialysate accelerated growth rate of in vitro proliferating MC. CONCLUSION: High-molecular-weight HA added to the dialysis fluid exerts anti-inflammatory and antifibrotic actions on the in vitro cultured MC and accelerates their growth rate what may be important for peritoneal healing during PD.
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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".