Effects of Peritoneal Dialysis Solutions on the Murine Peritoneal Microcirculation
Bibliographic record
Abstract
Peritoneal dialysis is often used as a treatment for patients with end‐stage renal disease, however it is limited by the development of progressive fibrosis. This study uses intravital microscopy to examine the acute impact of clinically available dialysis solutions on the peritoneal inflammatory response. To test whether dialysis fluids elicit, or inhibit an inflammatory response, mice (C57Bl/6) were given either saline or TNF intraperitoneally (IP). Three hours later, the test solution was administered IP. After a 60‐minute dwell, the peritoneal microvasculature was examined. This study compares dextrose‐based, lactate buffered Dianeal 1.5%, 2.5%, and 4.25%, as well as the glucose‐based, bicarbonate‐buffered Physioneal 3.86% to Ringer's Lactate. In non‐TNF stimulated mice, Dianeal 4.25% was associated with a 5‐fold increase in leukocyte rolling, compared to Ringer's Lactate (9.1± 3.7 vs 49.1± 11.2, p<0.0001). This effect was not seen with Physioneal 3.86%. In the TNF‐stimulated mice, reduced leukocyte rolling was observed in all groups, except for Physioneal 3.86%. The cytokine‐induced increase in leukocyte adhesion within the peritoneal venules was not altered by the presence of TNF, except when treated with the Physioneal 3.86%. The acute presence of lactate‐buffered high glucose peritoneal dialysis fluid is associated with a marked increase in leukocyte rolling, but not adhesion, in the peritoneal microcirculation. This effect is not exacerbated in the presence of the cytokine TNF. The use of a bicarbonate buffer alleviates the pro‐inflammatory properties of high glucose and TNF stimulation.
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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 | 0.001 |
| 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".