Effects of mussel‐inspired co‐deposition of 2‐hydroxymethyl methacrylate and poly (2‐methoxyethyl acrylate) on the hydrophilicity and binding tendency of common hemodialysis membranes: Molecular dynamics simulations and molecular docking studies
Bibliographic record
Abstract
Despite advances in the field, hemoincompatibility remains a critical issue for hemodialysis (HD) as interactions between various human blood constituents and the polymeric structure of HD membranes results in complications such as activation of immune system cascades. Adding hydrophilic polymer structures to the membranes is one modification approach that can decrease the extent of protein adsorption. This study conducted molecular dynamics (MD) simulations to understand the interactions between three human serum proteins (fibrinogen [FB], human serum albumin, and transferrin) and common HD membranes in untreated and modified forms. Poly(aryl ether sulfone) (PAES) and cellulose triacetate were used as the common dialyzer polymers, and membrane modifications were performed with 2-hydroxymethyl methacrylate (HEMA) and poly (2-methoxyethyl acrylate) (PMEA), using polydopamine-assisted co-deposition. The MD simulations were used as the framework for binding energy simulations, and molecular docking simulations were also performed to conduct molecular-level investigations between the two modifying polymers (HEMA and PMEA) and FB. Each of the three proteins acted differently with the membranes due to their unique nature and surface chemistry. The simulations show PMEA binds less intensively to FB with a higher number of hydrogen bonds, which reflects PMEA's superior performance compared to HEMA. The simulations suggest PAES membranes could be used in modified forms for blood-contact applications as they reflect the lowest binding energy to blood proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".