Bioactive Polymer Fibers to Direct Endothelial Cell Growth in a Three-Dimensional Environment
Bibliographic record
Abstract
This study reports the fabrication of bioactive polymer fibers onto which signaling molecules can control and direct cell responses. To encourage and control directional biological responses, GRGDS peptides were immobilized onto the surface of 100 microm diameter poly(ethylene terephtalate) (PET) fibers (monofilaments). PET fiber surfaces were first coated with a thin polymeric interfacial bonding layer bearing amine groups by plasma polymerization. Carboxy-methyl-dextran (CMD) was covalently grafted onto the surface amine groups using water-soluble carbodiimide chemistry. GRGDS were covalently immobilized onto CMD-coated fiber surfaces. X-ray photoelectron spectroscopy (XPS) analyses enabled characterization of the multilayer fabrication steps. Human umbilical vein endothelial cells were seeded and grown on fibers to investigate cell patterning behavior (i.e., adhesion, spreading, cytoskeleton organization, and cell orientation). Cell adhesion was reduced on CMD-coated fibers, whereas amine- and GRGDS-coated fibers promoted cell adhesion and spreading. Cell adhesion was enhanced as the GRGDS concentration increased. Epifluorescence microscopic visualization of cells on RGD-coated substrates showed well-defined stress fibers and sharp spots of vinculin, typical of focal adhesions. In comparison to plasticware commonly used in cell cultures, fiber curvature promoted cell orientation along the fiber axis.
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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".