Computational Analysis of Amine Functionalization in Zwitterionized Polyether Sulfone Dialysis Membranes
Bibliographic record
Abstract
Hemodialysis is a critical treatment for patients with end-stage renal disease (ESRD) who lack kidney transplant options. The compatibility of hemodialysis membranes is vital, as incompatibility can trigger inflammation, coagulation, and immune responses, potentially increasing morbidity and mortality among patients with ESRD. This study employed molecular dynamics simulation (MDS) and molecular docking to assess the hemocompatible properties of Polyether Sulfone (PES) membranes modified via two distinct amine functionalization techniques. The molecular docking results demonstrated that side amine functionalization exhibited a lower affinity energy (−7.6) for fibrinogen compared to the middle amine functionalization (−8.2), suggesting enhanced antifouling properties and superior hemocompatibility. Additionally, side amine functionalization formed hydrogen bonds with four amino acids, enhancing its resistance to protein adhesion compared to three amino acids in the middle amine structure. Furthermore, the molecular dynamics simulations revealed differences in water mobility, with the side amine functionalized membranes showing a lower mobility value (9.74 × 10−7) than those treated with the middle amine method (9.85 × 10−7), indicating higher water stability and potentially better patient outcomes. This study’s findings contribute to the design of more efficient and safer hemodialysis treatments by optimizing membrane materials.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".