Effect of packing density of hollow fibers on solute removal performances of dialyzers
Bibliographic record
Abstract
Abstract Solute removal performances of dialyzers are dependent not only on the solute permeabilities of the membrane but also on the module design. We have investigated how the packing density of hollow fiber (PDF) affects the solute removal performances. A series of 4 polyester polymer alloy membrane test dialyzers were assembled with varying PDFs of 29.6%, 35.3%, 44.1%, and 53.1%. Clearances (CL) were measured in vitro for creatinine (MW113), vitamin B12 (MW1355), and chymotrypsin (MW25300) with various QB=100 to 400 and QD=350 to 650 mL/min in the absence of net ultrafiltration. When QB was ≤300 mL/min, no significant changes were found in creatinine CL with the increase of PDF up to 35.3%. A slightly greater increase was found in CL when QB=400 mL/min. Clearances for vitamin B12, however, increased with the increase of PDF in the range of 29.6% to 35.3%. The effects of PDF on CL were greater with larger QB. More importantly, an abrupt increase of CL was found when PDF was increased from 44.1% to 53.1%. According to a rigorous mathematical model, this may be caused by the internal filtration, which is reverse ultrafiltration occurring in a dialyzer at any given time. No significant increase was found in chymotrypsin CL when the PDF was ≤35.3%, which suggested that CL for large molecules was strongly dependent on the solute permeability rather than the conditions of flow patterns. A very steep increase was also found in CL for chymotrypsin when the PDF was >44.1%, which was also considered to be due to the internal filtration. Packing density of hollow fiber can be optimized in terms of solute removal performances when the target solute and therapeutic conditions are specified.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".