<sup>125</sup>I-Radiolabeling, Surface Plasmon Resonance, and Quartz Crystal Microbalance with Dissipation: Three Tools to Compare Protein Adsorption on Surfaces of Different Wettability
Bibliographic record
Abstract
The extent of protein adsorption is an important consideration in the biocompatibility of biomaterials. Various experimental methods can be used to determine the quantity of protein adsorbed, but the results usually differ. In the present work, self-assembled monolayers (SAMs) were used to prepare a series of model gold surfaces varying systematically in water wettability, from hydrophilic to hydrophobic. Three commonly used methods, namely, surface plasmon resonance (SPR), quartz crystal microbalance with dissipation (QCM-D), and (125)I-radiolabeling, were employed to quantify fibrinogen (Fg) adsorption on these surfaces. This approach allows a direct comparison of the mass of Fg adsorbed using these three techniques. The results from all three methods showed that protein adsorption increases with increasing surface hydrophobicity. The increase in the mass of Fg adsorbed with increasing surface hydrophobicity in the SPR data was parallel to that from (125)I-radiolabeling, but the absolute values were different and there does not seem to be a "universally congruent" relationship between the two methods for surfaces with varying wettability. For QCM-D, the variation in protein adsorption with wettability was different from that for SPR and radiolabeling. On the more hydrophobic surfaces, QCM-D gave an adsorbed mass much higher than from the two other methods, possibly because QCM-D measures both the adsorbed Fg and its associated water. However, on the more hydrophilic surfaces, the adsorbed mass from QCM-D was slightly greater than that from SPR, and both were smaller than from (125)I-radiolabeling; this was true no matter whether the Sauerbrey equation or the Voigt model was used to convert QCM-D data to adsorbed mass.
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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 | 0.000 | 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) | 0.000 | 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".