A Comparative Assessment of Human Serum Proteins Interactions with Hemodialysis Clinical Membranes using Molecular Dynamics Simulation
Bibliographic record
Abstract
Abstract End stage renal disease (ESRD) affects ≈10% of the world's population. Hemodialysis (HD) is a life‐sustaining extracorporeal blood purifying treatment for ESRD patients. Despite the advances in technology, the hemoincompatibility of the dialyzers leads to a high morbidity and mortality rate. Decreasing interactions between polymers and blood constituents will result in controlled binding of human serum proteins to the surface and consequently enhanced biocompatibility. This study aims to assess the interaction energy between common hemodialysis polymer structures and human serum proteins using molecular dynamics simulation to offer a framework for understanding the dominant interactions as a reference for material development. Molecular dynamics and molecular docking simulations are conducted for calculating polymerprotein binding energies. Common serum proteins are selected for protein models. Poly aryl ether sulfone (PAES) with and without polyvinyl pyrrolidone (PVP), polyvinylidene fluoride (PVDF), cellulose triacetate (CTA), polyacrylonitrile (PAN), and polymethyl methacrylate (PMMA) membrane structures are chosen as the different classes of dialyzers. The van der Waals interactions between polymers and proteins dominate the binding energies sequence. Molecular docking results of the affinity between protein receptors‐ligands are aligned with the MD binding interaction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".