Influence of hydration shell of hemodialysis clinical membranes on surrogate biomarkers activation in uremic serum of dialysis patients
Bibliographic record
Abstract
End-stage renal disease (ESRD) patients depend on hemodialysis (HD) as a life-sustaining treatment. Cytokines are essential mediators of immune response and inflammatory reactions. Our goal is to study the impact of known properties of commercially available clinical HD membranes on the activation of inflammatory biomarkers. Two common clinical HD membranes of cellulose triacetate and polyarylethersulfone were thoroughly characterized. Surrogate biomarkers were studied at different time points after in vitro exposure of normal and uremic serum to hydrated and unhydrated membranes. In vitro adsorption of fibrinogen (FB) by hydrated and unhydrated CTA and PAES membranes was also compared at similar clinical practices. The inflammatory biomarkers released in HD patients after 30 min of dialysis were also compared to those induced in uremic samples incubated with HD membranes for 30 min to determine the influence of shear rate and membrane roughness. Samples from male and female patients were collected to assess the influence of patient sex on inflammatory and thrombotic responses. The results obtained indicate CTA membranes have a smoother surface and are more hemocompatible, while PAES membranes had a rougher surface and greater hydrophilicity, which resulted in a significant increase in red blood cell (RBC) rupture and promoted protein adsorption and higher cytokines levels. Unhydrated PAES membranes adsorbed more protein than all other membranes tested, and unhydrated CTA and PASE membranes induced more cytokines compared to hydrated counterparts. Overall, hydrating the membrane materials was determined to alter their adsorptive behavior.
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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) | 0.000 | 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) | 0.001 | 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".