Preparation of a Functionally Flexible, Three-Dimensional, Biomimetic Poly(L-Lactic Acid) Scaffold with Improved Cell Adhesion
Bibliographic record
Abstract
Poly(L-lactic acid) (PLLA) is widely used in tissue-engineering applications because of its degradation characteristics and mechanical properties, but it possesses an inert nature, affecting cell-matrix interactions. It is desirable to modify the surface of PLLA to create biomimetic scaffolds that will enhance tissue regeneration. We prepared a functionally flexible, biomimetic scaffold by derivatizing the surface of PLLA foams into primary amines, activated pyridylthiols, or sulfhydryl groups, allowing a wide variety of modifications. Poly(L-lysine) (polyK) was physically entrapped uniformly throughout the scaffold surface and in a controllable fashion by soaking the foams in an acetone-water mixture and later in a polyK solution in dimethylsulfoxide. Arginine-glycine-aspartic acid-cysteine (RGDC) adhesion peptide was linked to the polyK via creating disulfide bonds introduced through the use of the linker N-succinimidyl-3-(2-pyridylthiol)-propionate. Presence of RGDC on the surface of PLLA 2-dimensional (2-D) disks and 3-D scaffolds increased cell surface area and the number of adherent mesenchymal stem cells. We have proposed a methodology for creating biomimetic scaffolds that is easy to execute, flexible, and nondestructive.
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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) | 0.000 | 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".