Impact of Dialysis Clinical Operating Conditions on Human Serum Protein-Mediated Inflammatory Biomarkers Released in Patients Using Polyarylethersulfone Membranes
Bibliographic record
Abstract
Hemodialysis (HD) is a life-sustaining treatment of crucial importance in managing end-stage renal disease (ESRD). However, this membrane-based therapy is associated with acute side-effects due to bioincompatibility issues and limitations on the removal of uremic toxins. The present study assessed the influence of hydrodynamic conditions applied during HD treatment on protein-mediated inflammatory and thrombotic responses. The membrane modules considered are commonly used in Canadian hospitals and are comprised of a polymer blend of polyarylether sulfone-polyvinylpyrrolidone (PAES). The membranes morphology and hydrophilicity were assessed using SEM, AFM, BET, and zeta potential. An in vitro study evaluated the adsorptive behavior of fibrinogen (FB) to the membrane under different flow conditions. Lower rates of 200 mL/min promoted slower and significant FB adsorption, leading to more severe inflammatory and thrombotic responses. Hydrodynamic conditions also affected the concentration of all inflammatory biomarkers. Lower flow rates triggered more complement activation as well as coagulation, clotting, and inflammatory responses compared to higher flow rates. At the end of the dialysis session, patients treated with a Qb of 200 mL/min presented a significant increase in the concentration of C5a (232%), properdin (114%), serpin (545%), IL-1α (50%), IL-6 (450%), and vWF (212%). IL-1β and TNF-α concentrations declined by 12.5 and 35.5%, respectively. Male patients experienced more severe inflammatory responses than female patients at the operating conditions considered. Comparing the pre- and post-dialysis levels of female and male patients, female patients experienced significantly higher levels of IL-6 and properdin, while male patients presented higher levels of C5a, IL-1α, and IL-6. The results of this study will help clinical doctors evaluate the impact of HD operating conditions on blood activations before prescribing treatment and inform expectations for outcomes in female and male patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".