Enhanced smooth muscle cell adhesion and proliferation on protein‐modified polycaprolactone‐based copolymers
Bibliographic record
Abstract
Smooth muscle cells (SMC) were cultured for up to 6 days on copolymer films fabricated from a PCL-PEG-PCL block copolymer or P(epsilon-CL-co-D,L-LA)-PEG-P(epsilon-CL-co-D,L-LA), named P(100/0) and P(70/30), respectively. The films were modified by aminolysis using 1,6-hexanediamine, and fibronectin, fibrinogen, or fibrin layers were subsequently immobilized by physisorption or by covalent coupling using imidoester chemistry. Immobilization of all the tested proteins resulted in significantly enhanced cell adhesion on these polymers. Moreover, we found that covalently immobilized proteins supported significantly greater cell proliferation than physisorbed proteins over 6 days. SMC cultured on P(100/0) films modified by covalently attached fibronectin or fibrin layers proliferated at a rate comparable to that observed on control tissue culture polystyrene. The proposed surface modification schemes were much less efficient in improving cell attachment and proliferation on P(70/30) films. However, prewetting P(70/30) with a phosphate buffer prior to aminolysis significantly improved cell numbers following immobilization of fibronectin. Immunostaining of smooth muscle-specific alpha-actin of SMC grown on protein-modified P(100/0) 8 h and 48 h after cell seeding, confirmed preserved SMC phenotype on all modified surfaces.
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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".