Microparticle Formation in Peritoneal Dialysis
Bibliographic record
Abstract
BACKGROUND: Injury to the mesothelial layer of the peritoneal membrane during peritoneal dialysis (PD) is implicated in loss of ultrafiltration capacity, but there are no validated biomarkers for mesothelial cell injury. Microparticles (MPs) are 0.1 to 1.0 µm membrane vesicles shed from the cell surface following injury and are sensitive markers of tissue damage. Formation of MPs in the peritoneal cavity during PD has not been reported to date. METHODS: We designed a single-center, proof of concept study to assess whether peritoneal solution exposure induces formation of mesothelial MPs suggestive of PD membrane injury. We examined MP levels in PD effluents by electron microscopy, nanoparticle tracking analysis (NTA), flow cytometry, procoagulant activity, and Western blot. RESULTS: NTA identified particles in the size range of 30 to 900 nm, with a mean of 240 (SE: 10 nm). MP levels increased in a progressive manner during a 4-hour PD dwell. Electron microscopy confirmed size and morphology of vesicles consistent with characteristics of MPs as well as the presence of mesothelin on the surface. Western blot analysis of the MP fraction also identified the presence of mesothelin after 4 hours, suggesting that MPs found in PD effluents may arise from mesothelial cells. CONCLUSIONS: Our results suggest that MPs are formed and accumulate in the peritoneal cavity during PD, possibly as a stress response. Assessing levels of MPs in PD effluents may be useful as a biomarker for peritoneal membrane damage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".