Albumin adsorption onto pyrolytic carbon: A molecular mechanics approach
Bibliographic record
Abstract
A number of implants of cardiac valve prosthesis, vascular prosthesis, and coronary stents present a pyrolytic carbon interface to blood. Plasma protein adsorption is essential for the hemocompatibility of the implanted devices. This work quantitatively evaluates the molecular interaction force between a biomaterial surface (pyrolytic carbon) and plasma protein (albumin) binding sites through a simplified molecular model of the interface consisting of (i) multioriented graphite microcrystallites; (ii) selected fragments of albumin; and (iii) a water environment. A number of simplifying assumptions were made in the calculation: the albumin molecule was divided into hydrophobic and hydrophilic subunits (helices); an idealized clean, nonoxidized polycrystalline graphite surface was assumed to approximate the surface of pyrolytic carbon. The interaction forces between albumin helices and pyrolytic carbon surfaces are evaluated from potential energy data. These forces are decomposed into a normal and a tangential component. The first one is calculated using a docking procedure (F( perpendicular tot MAX) = 4.16 x 10(-20) N). The second one (F( parallel)), calculated by mean of geometric models estimating the energy variation associated with the protein sliding on the material surface, varies within the range +/-9.62 x 10(-21) N. The molecular simulations were performed using the commercial software package Hyperchem 5.0 (Hyperchem, Hypercube, Canada).
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".