Flow distribution analysis by helical scanning in polysulfone hemodialyzers: Effects of fiber structure and design on flow patterns and solute clearances
Bibliographic record
Abstract
The efficiency of a hemodialyzer is largely dependent on its ability to facilitate diffusion, as this is the main mechanism by which small solutes are removed. The diffusion process can be impaired if there is a mismatch between blood and dialysate flow distribution in the dialyzer. The objective of the paper was to study the impact of different fiber bundle configurations on blood and dialysate flow distribution and urea clearances. The Optiflux 200 NR hemodialyzer was studied and the standard F 80 A hemodialyzer was used as a control for the study. Six dialyzers of each type were studied in vitro in the radiology department utilizing a new generation of helical computed tomography (CT) scan following contrast medium injection into the blood and dialysate compartment. Dynamic sequential imaging of longitudinal sections of the dialyzer was undertaken to detect flow distribution, average and peak velocities, and calculate wall shear rates. Six patients were dialyzed with 2 different dialyzers in random consecutive sequence. In these patients, 2 consecutive dialyses were carried out with identical operational parameters (Qb = 300 mL/min, Qd = 500 mL/min). In each session, blood and dialysate side urea clearances were measured at 30 and 150 min of treatment. Macroscopic and densitometrical analysis revealed that flow distribution was most homogeneous in the dialyzer with a new bundle configuration. Significantly increased urea clearances (p < 0.001) were seen with the Optiflux dialyzer compared with the standard dialyzer. In conclusion, more homogeneous dialysate blood and dialysate flow distribution and improved small solute clearances can be achieved by modifying the configuration of the filter bundle. These effects are achieved probably as a result of reduced blood to dialysate mismatch with reduction of flow channeling. The used radiological technique allows detailed flow distribution analysis and has the potential for testing future modifications to dialyzer design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".