Three-dimensional printing of polycaprolactone/hydroxyapatite bone tissue engineering scaffolds mechanical properties and biological behavior
Bibliographic record
Abstract
Controlled pore size and desirable internal architecture of bone scaffolds play a significant role in bone regeneration efficiency. In addition to choosing appropriate materials, the manufacturing method is another significant factor in fabricating the ideal scaffold. In this study, scaffolds were designed and fabricated by the fused filament fabrication (FFF) technique. Polycaprolactone (PCL) and composites films with various percentages of hydroxyapatite (HA) (up to 20%wt) were used to fabricate filaments. The influence of (HA) addition on the mechanical properties of filaments and scaffolds was investigated. in vitro biological evaluation was examined as well as the apatite formation in simulated body fluid (SBF). The addition of HA particles increased the compressive strength and Young's modulus of filaments and consequently the scaffolds. Compared to PCL, Young's modulus of PCL/HA20% filament and three-dimensional (3D) printed scaffold has increased by 30% and 50%, respectively. Also, Young's modulus for all scaffolds was in the range of 30-70 MPa, which is appropriate to use in spongy bone. Besides, the MTT assay was utilized to evaluate cell viability on the scaffolds. All the samples had qualified cytocompatibility, and it would be anticipated that addition of HA particles raise the biocompatibility in vivo. Alkaline phosphatase (ALP) evaluation shows that the addition of HA caused higher ALP activity in the PCL/HA scaffolds than PCL. Furthermore, calcium deposition in the PCL/HA specimens is higher than control. In conclusion, the addition of HA particles into the PCL matrix, as well as utilizing an inexpensive commercial FFF device, lead to the fabrication of scaffolds with proper mechanical and biological properties for bone tissue engineering applications. Graphical abstract.
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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) | 0.001 | 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".