Does Very Low Dialysis Endtoxin Level Influence the Plasma Pentosidine Levels in Dialysis Patients?
Bibliographic record
Abstract
In order to confirm whether or not very low endtoxin level in dialysate influences the formation of advanced glycation end products (AGEs) in dialysis patients, plasma pentosidine were determined in the dialysis patients before and after the switch to new water-supply system. Method: Plasma pentosidine were measured by high-performance liquid chromatography in 84 patients on long-term hemodialysis before 3 month, 6 months after the switch of dialysate endtoxin level from 0.020–0.025 to 0.001 EU/ml. Endtoxin measurement was done by the Wellreader SK603 (Seikagaku Kogyo Co. Tokyo) by which detection limit is less than 0.001 EU/ml. The plasma pentosidine levels fell from 1.55 ± 0.61 nmol/ml to 1.38 ± 0.52 nmol/ml (3 month after, p < 0.0001) and 1.31 ± 0.50 nmol/ml (6 month after, p < 0.0001). The fall in plasma pentosidine levels were equally observed in patients given dialysis with high-flux polysulfone, PMMA, and cellulose acetate membranes. Unexpectedly, the plasma triglycerides levels fell from 150 ± 116 mg/dl to 124 ± 79 mg/dl (3 month after, p < 0.01) and 119 ± 75 mg/dl (6 month after, P < 0.01). The levels of total cholesterol, c-reactive protein, β 2-microglobulin did not change during this study. Conclusion: Water purity of dialysis fluid has an impact on AGE formation and improve a status of lipid metabolism in hemodialysis patients even if endtoxin level was very low.
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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 | 0.001 |
| 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 | 0.001 | 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".