Effects of vitamin E‐coated membrane dialyzer on reduction of inflammation
Bibliographic record
Abstract
Objective: Blood‐membrane interaction during hemodialysis may contribute to inflammatory process, which accelerates the development of atherosclerosis in maintenance hemodialysis patients (MHD). Vitamin E has been widely used against oxidative stress in MHD. One of the strategies for the utilization of vitamin E in MHD patients is the usage of vitamin E‐coated membrane dialyzer. We investigated the effects of vitamin E‐coated membrane dialyzer on serum C‐reactive protein and interleukin‐6, the biomarker of inflammation, compared to polysulfone membrane dialyzer. Methods: Vitamin E‐coated membrane dialyzer (1.5‐m2 surface area) and synthetic polysulfone dialyzer (1.5‐m2 surface area) were manipulated in a crossover clinical study for 24 weeks in 10 non‐diabetic MHD patients. Run‐in and wash‐out periods (Cellulose tri‐acetate) were performed for 4 weeks before the treatment. Pre‐ and post‐dialysis blood samples were taken at the begining and the end of each dialyzer period (12 weeks). High‐sensitivity C‐reactive protein (hs‐CRP) and interleukin‐6 (IL‐6) were examined. Results: Mean age of the patients was 54.9 years old. CRP and IL‐6 levels were similarly increased after dialysis in both groups (4.8 ± 0.7 and 37.2 ± 9.4, respectively). The CRP and IL‐6 level in vitamin E‐coated membrane dialyzer treatment were lower than in polysulfone treatment (5.0 ± 1.2, p < 0.008 and 67.2 ± 12.4, p < 0.04, respectively). Serum albumin, hemoglobin level, and white blood cell count were not affected by types of dialyzer membrane. Conclusions: In our study, hemodialysis stimulated the inflammation as the previous study. Vitamin E‐coated membrane dialyzer may diminish the inflammatory process in MHD patients and may also prevent further atherosclerosis.
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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".