Effect of sterilization on solute transport performances of super high‐flux dialyzers
Bibliographic record
Abstract
The effect of sterilization was quantitatively evaluated for modern super high-flux dialyzers in terms of solute transport performances for autoclave sterilization (AC), gamma ray sterilization (G-ray), combination of these two and no sterilization (NS) as reference. A commercial polysulfone dialyzer (Kawasumi Laboratory Co., Tokyo, Japan) was chosen for investigation with six different sterilization processes, i.e., sterilization with AC one time and two times, that with G-ray using either no additives or an additive, that with AC one time followed by G-ray using no additive and that with NS. In vitro dialysis and ultrafiltration experiments were performed with aqueous test solution as pseudo blood, varying Q(B) , Q(D) , and Q(F) . Creatinine (MW113), vitamin B(12) (MW1355), and α-chymotrypsin (MW25000) were chosen as test solutes for dialysis experiments. Clearances (C(L) ) calculated from dialysis experiments and the sieving coefficient for albumin (MW66000) from ultrafiltration experiments were compared among six models. A dialyzer with NS showed much lower clearances for all three solutes than those with sterile. Therefore, the sterilization increases the solute transfer performances. Although no significant changes in clearances for creatinine and vitamin B(12) were found among five sterile models, much higher clearances for α-chymotrypsin were found in AC sterile models. Then, the effect of sterilization may become greater with the increase of molecular weight of solutes. According to the results of the sieving coefficient for albumin, AC may have enlarged the pore size of the membrane that could increase clearances for large solutes without changing those for relatively small solutes. The sterilization increases the solute clearance even in so-called super high-flux dialyzers and the effect of sterilization may be greater in larger solutes.
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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) | 0.001 | 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".