Fibronectin Displacement at Polymer Surfaces
Bibliographic record
Abstract
The interactions of fibronectin with thin polymer films are studied in displacement experiments using human serum albumin. Fibronectin adsorption and exchange on two different maleic anhydride copolymer surfaces differing in hydrophobicity and surface charge density have been analyzed by quartz crystal microbalance and laser scanning microscopy with respect to adsorbed amounts, viscoelastic properties, and conformation. Fibronectin is concluded to become attached onto hydrophilic surfaces as a "softer", less rigid protein layer, in contrast to the more rigid, densely packed layer on hydrophobic surfaces. As a result, the fibronectin conformation is more distorted on the hydrophobic substrates together with remarkably different displacement characteristics in dependence on the adsorbed fibronectin surface concentration and the displacing albumin solution concentration. While the displacement kinetic remains constant for the strongly interacting surface, an acceleration in fibronectin exchange is observed for the weakly interacting surface with increasing fibronectin coverage. For displaced amounts, no change is determined for the hydrophobic substrate, in contrast to the hydrophilic substrate with a decrease of fibronectin exchange with decreasing coverage leading finally to a constant nondisplaceable amount of adsorbed proteins. Furthermore, the variation of the albumin exchange concentration reveals a stronger dependence of the kinetic for the weakly interacting substrate with higher rates at higher albumin concentrations.
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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) | 0.000 | 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 | 0.001 |
| 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".