Conjugation of arginine–glycine–aspartic acid peptides to thermoreversible <i>N</i>‐isopropylacrylamide polymers
Bibliographic record
Abstract
Abstract Thermoreversible polymeric biomaterials are finding increased acceptance in tissue engineering applications. One drawback of the polymers is their synthetic nature, which does not allow direct interaction of mammalian cells with the polymers. This limitation may be alleviated by grafting arginine–glycine–aspartic acid (RGD) containing peptides onto the polymer backbone to facilitate interactions with cell‐surface integrins. Toward this goal, N‐isopropylacrylamide (NiPAM)‐based thermoreversible polymers containing amine‐reactive N‐acryloxysuccinimide (NASI) groups were synthesized. Conjugation of RGD‐containing peptides to polymers was demonstrated with 1H NMR spectroscopy and reverse‐phase high‐pressure liquid chromatography. The conjugation reaction was optimal at 4 °C and pH of 8.0, and increased with the increasing NASI content of polymers. With a peptide grafting ratio of 0.25 mol %, there was no significant change in the lower critical solution temperature of the polymers. Finally, the NASI‐containing polymers, cast as films, on tissue culture polystyrene, were shown to conjugate to RGD‐containing peptides and support C2C12 cell attachment. We conclude that NASI‐containing thermoreversible polymers are amenable for grafting biomimetic peptides to impart cell adhesiveness to the polymers. © 2003 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 41: 3989–4000, 2003
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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 | 0.000 | 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".