A high-density PEG interfacial layer alters the response to an EGF tethered polydimethylsiloxane surface
Bibliographic record
Abstract
Previously, epidermal growth factor (EGF)-modified surfaces have shown promise in supporting cellular growth and adhesion on synthetic polymeric substrates. Surfaces prepared using a novel modification technique were investigated in the current work for their ability to support corneal epithelialization, important to the integration of a synthetic artificial cornea. EGF could be tethered to PDMS surfaces via a high-density, hetero-bifunctional PEG-NSC linking layer with a tunable surface concentration of up to 300 ng/cm(2). Only a small fraction of the EGF on these surfaces could be removed with SDS rinsing, indicative of covalent tethering. Studies with human corneal epithelial cells suggest a relatively linear increase in the number of corneal epithelial cells with increasing EGF concentration at all times. However, confluence was not achieved at any time point. It is believed that the presence of the non-adsorbent PEG layer, useful for preventing non-specific adsorption of proteins, may limit the cellular response by minimizing the adsorption of adhesion molecules. The effects of the EGF alone are clearly not sufficient to result in epithelialization of an artificial cornea surface. Altering both the adhesion and growth of corneal epithelial cells in a controlled manner may be necessary for epithelialization of an artificial cornea.
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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".