Dual surface modification with PEG and corn trypsin inhibitor: Effect of PEG:CTI ratio on protein resistance and anticoagulant properties
Bibliographic record
Abstract
The objective of this study was to investigate the bioactivity and protein-resistant properties of dual functioning surfaces modified with PEG for protein resistance and corn trypsin inhibitor (CTI) for anticoagulant effect. Surfaces on gold substrate were prepared with varying ratios of free PEG to CTI-conjugated PEG. Two methods designated, respectively, "sequential" and "direct" were used. For sequential surfaces, PEG was first immobilized on gold and the surfaces were incubated with CTI at varying concentration. For direct surfaces, a PEG-CTI conjugate was synthesized and gold surfaces were modified using solutions of the conjugate of varying concentration. The CTI density on these surfaces was measured using radiolabeled CTI. Water contact angles were measured and the thickness of PEG-CTI layers was determined by ellipsometry. Fibrinogen adsorption from buffer and human plasma, and adsorption from binary solutions of fibrinogen and α-lactalbumin were investigated using radiolabeling methods. Bioactivity of the surfaces was evaluated via their effects on FXIIa inhibition and plasma clotting time. It was found that as the ratio of CTI-conjugated PEG to free PEG increased, bioactivity increased but protein resistance was relatively constant. It is concluded that on these surfaces conjugation of PEG to CTI does not greatly compromise the protein resistance of the PEG but results in improved interactions between the CTI and the "target" protein FXIIa. At the same CTI density, sequential surfaces were more effective in terms of inhibiting FXIIa and prolonging clotting time.
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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) | 0.000 | 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 | 0.001 | 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".