STUDY OF UREMIC TOXIN FLUXES ACROSS NANOFABRICATED HEMODIALYSIS MEMBRANES USING IRREVERSIBLE THERMODYNAMICS
Bibliographic record
Abstract
INTRODUCTION: The flux of uremic toxin middle molecules through currently used hemodialysis membranes is suboptimal, mainly because of the membranes' pore architecture. AIM: Identifying the modifiable sieving parameters that can be improved by nanotechnology to enhance fluxes of uremic toxins across the walls of dialyzers' capillaries. METHODS: We determined the maximal dimensions of endothelin, cystatin C, and interleukin - 6 using the macromolecular modeling software, COOT. We also applied the expanded Nernst-Plank equation to calculate the changes in the overall flux as a function of increased electro-migration and pH of the respective molecules. RESULTS: In a high flux hemodialyzer, the effective diffusivities of endothelin, cystatin C, and interleukin - 6 are 15.00 x 10(-10) cm(2)/s, 7.7 x 10(-10) cm(2)/s, and 5.4 x 10(-10) cm(2)/s, respectively, through the capillaries' walls. In a nanofabricated membrane, the effective diffusivities of endothelin, cystatin C, and interleukin - 6 are 13.87 x 10(-7) cm(2)/s, 5.73 x 10(-7) cm(2)/s, and 3.45 x 10(-7) cm(2)/s, respectively, through a nanofabricated membrane. Theoretical modeling showed that a 96% reduction in the membrane's thickness and the application of an electric potential of 10 mV across the membrane could enhance the flux of endothelin, cystatin C, and interleukin - 6 by a factor of 25. A ΔpH of 0.07 altered the fluxes minimally. CONCLUSIONS: Nanofabricated hemodialysis membranes with a reduced thickness and an applied electric potential can enhance the effective diffusivity and electro-migration flux of the respective uremic toxins by 3 orders of magnitude as compared to those passing through the high flux hemodialyzer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".