Development of novel hybrid poly(l-lactide)/chitosan scaffolds using the rapid freeze prototyping technique
Bibliographic record
Abstract
Engineered scaffolds have been shown to be critical to various tissue engineering applications. This paper presents the development of a novel three-dimensional scaffold made from a mixture of chitosan microspheres (CMs) and poly(L-lactide) by means of the rapid freeze prototyping (RFP) technique. The CMs were used to encapsulate bovine serum albumin (BSA) and improve the scaffold mechanical properties. Experiments to examine the BSA release were carried out; the BSA release could be controlled by adjusting the crosslink degree of the CMs and prolonged after the CMs were embedded into the PLLA scaffolds, while the examination of the mechanical properties of the scaffolds illustrates that they depend on the ratio of CMs to PLLA in the scaffolds as well as the cryogenic temperature used in the RFP fabrication process. The chemical characteristics of the PLLA/chitosan scaffolds were evaluated by Fourier transform infrared (FTIR) spectroscopy. The morphological and pore structure of the scaffolds were also examined by scanning electron microscopy and micro-tomography. The results obtained show that the scaffolds have higher porosity and enhanced pore size distribution compared to those fabricated by the dispensing-based rapid prototyping technique. This study demonstrates that the novel scaffolds have not only enhanced porous structure and mechanical properties but also showed the potential to preserve the bioactivities of the biomolecules and to control the biomolecule distribution and release rate.
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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".