Fabrication and Osteogenesis of a Porous Nanohydroxyapatite/Polyamide Scaffold with an Anisotropic Architecture
Bibliographic record
Abstract
Scaffolds are used in bone tissue engineering to provide a temporary structural template for cell seeding and extracellular matrix formation. However, tissue formation on scaffold outer edges after implantation due to insufficient interconnectivity may restrict cell infiltration and mass transfer to/from the scaffold center, leading to bone regeneration failure. To address this problem, we prepared nanohydroxyapatite/polyamide66 (n-HA/PA66) anisotropic scaffolds with axially aligned channels (300 μm) with the aim to enhance pore interconnectivity and subsequent cell and tissue infiltration throughout the scaffold. Anisotropic scaffolds with axially aligned channels had better mechanical properties and a higher porosity (86.37%) than isotropic scaffolds produced by thermally induced phase separation (TIPS). The channels in the anisotropic scaffolds provided cells with passageways to the scaffold center and thus facilitated cell attachment and proliferation inside the scaffolds. In vivo studies showed that the anisotropic scaffolds could better facilitate new bone ingrowth into the inner pores of the scaffold compared to the isotropic scaffolds. The anisotropic scaffolds also had improved vascular invasion into their inner parts, increasing the supply of oxygen and nutrients to the cells and thus facilitating revascularization and bone ingrowth. Enhanced cell and tissue penetration to the scaffold center was observed in the anisotropic scaffolds both in vitro and in vivo, indicating the axially aligned channels positively influenced cell and tissue infiltration. Thus, such scaffolds have great potential for applications in bone tissue engineering.
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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".