Characterization of EGF coupling to aminated silicone rubber surfaces
Bibliographic record
Abstract
Tethering of growth factors to biomaterial substrates via a polyethylene glycol (PEG) spacer has been established as a means of controlling dosage and conformation of the protein at the material surface, while retaining biological activity. However, the extent of modification through a comparison of bound versus unbound protein has not generally been characterized. In this work, covalent tethering of epidermal growth factor (EGF) to allylamine plasma modified polydimethylsiloxane (PDMS) substrates is characterized to determine the nature of the bound growth factor and to optimize the conditions for the reaction. Tethering is achieved via conjugation of EGF with homobifunctional N-hydroxysuccinimide (NHS) ester of PEG-butanoic acid (SBA2-PEG) in solution, followed by exposure of the pegylated EGF to the aminated surfaces (solution first reaction). SDS-PAGE analysis indicates that a low ratio of EGF:PEG is required to maximize the yield of the EGF-PEG reaction; a relatively short reaction time is needed to limit hydrolysis of the NHS ester. With increasing amounts of PEG and a higher reaction time, a higher fraction of the EGF can be covalently tethered to the surfaces, as shown by binding of 125I-labeled EGF and subsequent washing with sodium dodecyl sulfate (SDS) to remove adsorbed protein. However, even under the optimal reaction conditions established by the SDS-PAGE analysis, higher molecular weight EGF-PEG complexes are observed by SDS-PAGE and matrix-assisted laser desorption/ionization (MALDI). The presence of these complexes, as well as unreacted growth factor, can lead to a surface of heterogeneous composition. While these surfaces were found to have biological activity, stimulating the adhesion and growth of corneal epithelial cells versus PDMS controls, further optimization of reaction conditions, including the use of a homobifunctional PEG linker and possibly separation of reaction species are required to achieve a uniformly active and well-defined biomaterial surface.
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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".