Bioactivation of porous polyurethane scaffolds using fluorinated RGD surface modifiers
Bibliographic record
Abstract
Biomaterial scaffolds for tissue engineering require appropriate cell adhesion, proliferation, and infiltration into their three-dimensional (3D) porous structures. Surface modification techniques have the potential to enhance cell infiltration into synthetic scaffolds while retaining bulk material properties intact. The objective of this work was to assess the potential of achieving a uniform surface modification in 3D porous constructs through the blending of surface-modifying additives known as bioactive fluorinated surface modifiers (BFSMs) with a base polyurethane material. By coupling RGD peptides to the fluorinated surface modifiers to form RGD-BFSMs, the BFSMs can act as a vehicle for the delivery of RGD moieties to the surface without direct covalent attachment to the polymer substrate. Fluorescent RGD-BFSMs were shown to migrate to the polymer-air interfaces within the porous scaffolds by two-photon confocal microscopy. A-10 rat aortic smooth muscle cells were cultured for 4 weeks on nonmodified and RGD-BFSM-modified porous scaffolds, and cell adhesion, proliferation, and viability were quantified at different depths. RGD-BFSM-modified scaffolds showed significantly greater cell numbers within deeper regions of the scaffolds, and this difference became more pronounced over time. This study demonstrates an effective approach to promote cell adhesion and infiltration within thick (approximately 0.5 cm) porous synthetic scaffolds by providing a uniform distribution of adhesive peptide throughout the scaffolds without the use of covalent surface reaction chemistry.
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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".