Development and Characterization of Functional Polylactic Acid/Chitosan Porous Scaffolds for Bone Tissue Engineering
Bibliographic record
Abstract
In this study, we developed and characterized various open-cell composite scaffolds for bone regeneration. These scaffolds were made from Polylactic acid (PLA) as the scaffold matrix biopolymeric phase, and chitosan (CS) and chitosan-grafted-PLA (CS-g-PLA) copolymer as the dispersed biopolymeric phase. As a first step, successful grafting of PLA onto CS backbone was executed and confirmed by both FTIR and XPS. Mechanical characterization confirmed that adding CS or CS-g-PLA to the intrinsically rigid PLA made their corresponding PLA/CS and PLA/CS-g-PLA composite scaffolds more flexible under compression. This flexibility was higher for the latter due to the improved compatibility between PLA and CS-g-PLA copolymer. The hydrolytic stability of both PLA/CS and PLA/CS-g-PLA composite scaffolds inside phosphate-buffered saline (PBS) solution, as well as MG-63 osteoblast cell adhesion and proliferation inside both scaffolds, were characterized. The corresponding results revealed that PLA/CS composite scaffolds showed hydrolytic degradation due to the cationic properties of CS. However, modified PLA/CS-g-PLA scaffolds were hydrolytically stable due to the improved interfacial adhesion between the PLA matrix and CS-g-PLA copolymer. Finally, biological characterization was done for both PLA/CS and PLA/CS-g-PLA composite scaffolds. Contrarily to what was observed for uncompatibilized PLA/CS scaffolds, compatibilized PLA/CS-g-PLA scaffolds showed a high MG-63 osteoblast cell proliferation after three and five days of cell culture. Moreover, it was observed that cell proliferation increased with CS-g-PLA content. This suggests that the PLA/CS-g-PLA composite scaffolds could be a potential solution for bone regeneration.
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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 | 0.001 | 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".