Force Microscopy Studies of Fibronectin Adsorption and Subsequent Cellular Adhesion to Substrates with Well-Defined Surface Chemistries
Bibliographic record
Abstract
Molecular force spectroscopy was used to study the mechanical behavior of plasma fibronectin (FN) on mica, gold, poly(ethylene glycol), and -CH(3), -OH, and -COOH terminated alkanethiol self-assembled monolayers. Proteins were examined at two concentrations, one resulting in a saturated surface with multiple intermolecular interactions referred to as the aggregate state and another resulting in a semiaggregate state where the proteins were neither completely isolated nor completely aggregated. Modeling of the force-extension data using two different theories resulted in similar trends for the fitted thermodynamic parameters from which insight into the protein's binding state could be obtained. Aggregated proteins adsorbed on hydrophobic surfaces adopted more rigid conformations apparently as a result of increased surface denaturation and tighter binding while looser conformations were observed on more hydrophilic surfaces. Studies of FN in a semiaggregate state showed heterogeneity in the model's thermodynamic parameters suggesting that, in the early stages of nonspecific adsorption, multiple protein conformations exist, each having bound irreversibly to the substrate. Proteins in this state all demonstrated a more rigid conformation than in the corresponding aggregate studies due to the greater number of substrate contacts available to the protein. Finally, the force spectroscopy experiments were examined for any biocompatibility correlation by seeding substrates with human umbilical vascular endothelial cells. As predicted from the models used in this work, surfaces with aggregated FN promoted cellular deposition while surfaces with FN in a semiaggregate state appeared to hinder cellular deposition and growth. The atomic force microscope's use as a means for projecting surface biocompatibility, although requiring additional testing, does look promising.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".