Fabrication and In Situ Cross-Linking of Carboxylic-Acid-Functionalized Poly(Ester Amide) Scaffolds for Tissue Engineering
Bibliographic record
Abstract
Three-dimensional (3D) scaffolds are important tools for tissue engineering and should ideally provide both biochemical cues and biomechanical support for cells. Poly(ester amide)s (PEAs) have emerged as promising materials for the preparation of tissue engineering scaffolds, and the pendant side chains of residues, such as l -lysine and l -aspartic acid, can provide sites for the conjugation of biochemical signals. However, it has been challenging to combine scaffold morphological stability with the presentation of reactive groups on PEA scaffolds. We describe here a new approach involving the functionalization of a l -lysine-containing PEA with maleic anhydride to simultaneously introduce cross-linkable alkenes and carboxylic acid conjugation sites. Maleic-acid-functionalized PEA was processed to form 3D scaffolds using a salt leaching method and the scaffolds were cross-linked in situ using a poly(ethylene glycol) dimethacrylate cross-linking agent by thermal free radical curing. Microcomputed tomography analysis indicated that the cross-linked scaffolds had higher polymer volume fraction, lower porosity, and smaller pore size than the non-cross-linked scaffolds, but both scaffolds exhibited high morphological stability and negligible mass loss upon incubation in phosphate-buffered saline for 5 days. The Young’s moduli of the cross-linked and non-cross-linked scaffolds were 28 and 9 kPa, respectively. Fluorescein-labeled bovine serum albumin was successfully conjugated to the scaffolds using a carbodiimide-based coupling. Finally, it was shown that the scaffolds supported the attachment and proliferation of mouse embryonic mesenchymal multipotent cells, showing their promise as platforms for tissue engineering applications.
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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".