Effects of Ethylene Oxide and Steam Sterilization on Dialysis‐Induced Cytokine Release by Cuprophan Membrane
Bibliographic record
Abstract
The effects of sterilization modalities on dialysis-induced cytokine release are still unknown. To investigate these effects, 8 patients on chronic hemodialysis were enrolled for evaluating at different intervals interleukin-1beta (IL-1beta) and tumor necrosis factor-alpha (TNF-alpha) production (pg/ml/106). They were using a 1.3 m2 ethylene oxide (E3) or steam (E3S) sterilized Cuprophan membrane. The patients underwent a basal test with E3 (A1) and 2 following tests after 1 (B1) and 2 (B2) months of E3S treatment, respectively. Finally, the last test was performed 1 month after the switch to E3 (A2). Il-1beta predialysis release by mononuclear cells was 162 +/- 114 pg/ml/106 in A1, 185 +/- 129 pg/ml/106 in B1, and 226 +/- 138 pg/ml/106 in B2, then decreased to 123 +/- 134 in A2 (p < 0.07). Il-1beta postdialysis levels were 234 +/- 238 pg/ml/106 in A1, 429 +/- 285 pg/ml/106 (B1), and 438 +/- 473 pg/ml/106 (B2) with the steam membrane, decreasing to 204 +/- 134 pg/ml/106 in A2 (p < 0.01). TNF-alpha predialysis basal release (A1) was 826 +/- 817 pg/ml/106, 720 +/- 496 in B1, and 1079 +/- 515 pg/ml/106 in B2, and finally 680 +/- 588 pg/ml/106 in A2 (p < 0.03). In postdialysis TNF-alpha levels were 963 +/- 542 pg/ml/106 in A1, 1,226 +/- 541 pg/ml/106, and 1,183 +/- 776 in B1 and B2 respectively, and 388 +/- 297 pg/ml/106 in A2 (p < 0.003). Steam sterilization seems to induce a higher cytokine release by mononuclear cells when a Cuprophan membrane is used. This finding may be related to a less physiologic action of the steam in the case of Cuprophan membranes. Further studies are needed to clarify this hypothesis.
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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".