Printing tissue-engineered scaffolds made of polycaprolactone and nano-hydroxyapatite with mechanical properties appropriate for trabecular bone substitutes
Bibliographic record
Abstract
BACKGROUND: Bone tissue engineering, based on three-dimensional (3D) printing technology, has emerged as a promising approach to treat bone defects using scaffolds. The objective of this study was to investigate the influence of porosity and internal structure on the mechanical properties of scaffolds. METHODS: We fabricated composite scaffolds (which aimed to replicate trabecular bone) from polycaprolactone (PCL) reinforced with 30% (wt.) nano-hydroxyapatite (nHAp) by extrusion printing. Scaffolds with various porosities were designed and fabricated with and without an interlayer offset, termed as staggered and lattice structure, respectively. Mechanical compressive testing was performed to determine scaffold elastic modulus and yield strength. Linear regression was used to evaluate mechanical properties as a function of scaffold porosity. RESULTS: Different relationships between mechanical properties and porosities were noted for the staggered and lattice structures. For elastic moduli, the two relationships intersected (porosity = 55%) such that the lattice structure exhibited higher moduli with porosity values greater than the intersection point; vice versa for the staggered structure. The lattice structure exhibited higher yield strength at all porosities. Mechanical testing results also indicated elastic moduli and yield strength properties comparable to trabecular bone (elastic moduli: 14-165 MPa; yield strength: 0.9-10 MPa). CONCLUSIONS: Taken together, this study demonstrates that scaffolds printed from PCL/30% (wt.) nHAp with lattice and staggered structure offer promise for treating trabecular bone defects. This study identified the effect of porosity and internal structure on scaffold mechanical properties and provided suggestions for developing scaffolds with mechanical properties for substituting trabecular bone.
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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) | 0.000 | 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".