WCN23-0020 IMPACT OF HYDRATION SHELL OF HEMODIALYSIS MEMBRANES ON ACTIVATION OF SURROGATE BIOMARKERS IN THE 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. The goal of this study was to achieve an in-depth understanding of the factors affecting the induction of inflammatory biomarkers in a uremic HD patient’s serum. The study aimed to: i) characterize the morphology and hydrophilicity of membrane modules commonly used in Canadian hospitals; ii) examine the influence of incubation time on the initiation of complement and coagulation cascades in both uremic patients and controls; iii) evaluate the influence of hydrating CTA and PAES membranes on inflammatory biomarkers and cytokines and on surface charge; iv) evaluate the adsorptive behaviour of fibrinogen (FB) to membrane fibers under different in vitro flow conditions on unhydrated and hydrated membranes; and v) evaluate the influence of membrane roughness on protein-mediated inflammatory and thrombotic responses under the hydrodynamic conditions in HD treatment compared to samples incubated for the same duration.
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 | 0.000 | 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) | 0.004 | 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".