Surface immobilization of elastin‐like polypeptides using fluorinated surface modifying additives
Bibliographic record
Abstract
Elastin-like polypeptide (ELP) surface modification represents a valuable approach for the development of biomaterials in a wide range of medical applications. In this study, ELP surface modification has been achieved through the use of elastin cross-linking peptide (ECP) bioactive fluorinated surface modifiers (ECP-BFSMs). The synthesis of low molecular weight fluorinated additives was described and their subsequent blending with a base polycarbonate urethane (PCNU) was shown to successfully enrich the surface to allow for ELP surface cross-linking via lysine moieties on the peptide segments of the ECP-BFSMs. The kinetics for the surface migration of fluorescent ECP-BFSMs was studied over a 2-week period by two-photon confocal microscopy. A decrease in advancing contact angle from 87.9° to 75.3° was observed for ECP-BFSM modified PCNU and was associated with the presence of ECP peptides on the surface. X-ray photoelectron spectroscopy demonstrated an increase in surface atomic percent of fluorine (from 0.2 to 7.2%) and nitrogen (from 1.0 to 3.0%) associated with the surface localization of fluoro groups and amide groups associated with the peptides in the ECP-BFSMs. A further increase in surface atomic percent of nitrogen (from 3.0 to 8.3%) was observed after ELP surface cross-linking. These ELP-modified surfaces were shown to promote increased smooth muscle cell adhesion, spreading and retention over a 7-day culture period relative to their non-ELP4 analogs. This novel surface modifying additive approach may be used for various biomimetic applications since it generates a stable ECM-like surface retained onto a relatively inert fluorinated background.
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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) | 0.000 | 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 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".